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https://github.com/quickwit-oss/tantivy.git
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8 Commits
quickwit-0
...
columnar-c
| Author | SHA1 | Date | |
|---|---|---|---|
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c6c1485abd | ||
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b7bfa20e38 | ||
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db8583db75 | ||
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1390834ae8 | ||
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3ac973bea4 | ||
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405e2cf4d9 | ||
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b63c6c27bc | ||
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bd5eea9852 |
@@ -59,9 +59,8 @@ columnar = { version="0.1", path="./columnar", package ="tantivy-columnar" }
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||||
sstable = { version="0.1", path="./sstable", package ="tantivy-sstable", optional = true }
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stacker = { version="0.1", path="./stacker", package ="tantivy-stacker" }
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||||
tantivy-query-grammar = { version= "0.19.0", path="./query-grammar" }
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tantivy-bitpacker = { version= "0.3", path="./bitpacker" }
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common = { version= "0.5", path = "./common/", package = "tantivy-common" }
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fastfield_codecs = { version= "0.3", path="./fastfield_codecs", default-features = false }
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tantivy-bitpacker = { version= "0.3", path="./bitpacker" }
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common = { version= "0.5", path = "./common/", package = "tantivy-common" }
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tokenizer-api = { version="0.1", path="./tokenizer-api", package="tantivy-tokenizer-api" }
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[target.'cfg(windows)'.dependencies]
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@@ -108,7 +107,7 @@ unstable = [] # useful for benches.
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quickwit = ["sstable"]
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[workspace]
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members = ["query-grammar", "bitpacker", "common", "fastfield_codecs", "ownedbytes", "stacker", "sstable", "tokenizer-api", "columnar"]
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members = ["query-grammar", "bitpacker", "common", "ownedbytes", "stacker", "sstable", "tokenizer-api", "columnar"]
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# Following the "fail" crate best practises, we isolate
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# tests that define specific behavior in fail check points
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18
TODO.txt
Normal file
18
TODO.txt
Normal file
@@ -0,0 +1,18 @@
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Make schema_builder API fluent.
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fix doc serialization and prevent compression problems
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u64 , etc. shoudl return Resutl<Option> now that we support optional missing a column is really not an error
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remove fastfield codecs
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ditch the first_or_default trick. if it is still useful, improve its implementation.
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rename FastFieldReaders::open to load
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remove fast field reader
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find a way to unify the two DateTime.
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readd type check in the filter wrapper
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add unit test on columnar list columns.
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make sure sort works
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@@ -19,7 +19,7 @@ impl BitPacker {
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}
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#[inline]
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pub fn write<TWrite: io::Write>(
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pub fn write<TWrite: io::Write + ?Sized>(
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&mut self,
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val: u64,
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num_bits: u8,
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@@ -43,7 +43,7 @@ impl BitPacker {
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Ok(())
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}
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pub fn flush<TWrite: io::Write>(&mut self, output: &mut TWrite) -> io::Result<()> {
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pub fn flush<TWrite: io::Write + ?Sized>(&mut self, output: &mut TWrite) -> io::Result<()> {
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if self.mini_buffer_written > 0 {
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let num_bytes = (self.mini_buffer_written + 7) / 8;
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let bytes = self.mini_buffer.to_le_bytes();
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@@ -54,7 +54,7 @@ impl BitPacker {
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Ok(())
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}
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pub fn close<TWrite: io::Write>(&mut self, output: &mut TWrite) -> io::Result<()> {
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pub fn close<TWrite: io::Write + ?Sized>(&mut self, output: &mut TWrite) -> io::Result<()> {
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self.flush(output)?;
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Ok(())
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}
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@@ -19,7 +19,7 @@ common = { path = "../common", package = "tantivy-common" }
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tantivy-bitpacker = { version= "0.3", path = "../bitpacker/" }
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[dev-dependencies]
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proptest = "1.0.0"
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proptest = "1"
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more-asserts = "0.3.1"
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rand = "0.8.5"
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124
columnar/benches/bench_u128.rs
Normal file
124
columnar/benches/bench_u128.rs
Normal file
@@ -0,0 +1,124 @@
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#![feature(test)]
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use std::ops::RangeInclusive;
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use std::sync::Arc;
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use common::OwnedBytes;
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use rand::rngs::StdRng;
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use rand::seq::SliceRandom;
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use rand::{random, Rng, SeedableRng};
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use tantivy_columnar::ColumnValues;
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use test::Bencher;
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extern crate test;
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// TODO does this make sense for IPv6 ?
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fn generate_random() -> Vec<u64> {
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let mut permutation: Vec<u64> = (0u64..100_000u64)
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.map(|el| el + random::<u16>() as u64)
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.collect();
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permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
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permutation
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}
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fn get_u128_column_random() -> Arc<dyn ColumnValues<u128>> {
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let permutation = generate_random();
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let permutation = permutation.iter().map(|el| *el as u128).collect::<Vec<_>>();
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get_u128_column_from_data(&permutation)
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}
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fn get_u128_column_from_data(data: &[u128]) -> Arc<dyn ColumnValues<u128>> {
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let mut out = vec![];
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tantivy_columnar::column_values::serialize_column_values_u128(&data, &mut out).unwrap();
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let out = OwnedBytes::new(out);
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tantivy_columnar::column_values::open_u128_mapped::<u128>(out).unwrap()
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}
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const FIFTY_PERCENT_RANGE: RangeInclusive<u64> = 1..=50;
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const SINGLE_ITEM: u64 = 90;
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const SINGLE_ITEM_RANGE: RangeInclusive<u64> = 90..=90;
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fn get_data_50percent_item() -> Vec<u128> {
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let mut rng = StdRng::from_seed([1u8; 32]);
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let mut data = vec![];
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for _ in 0..300_000 {
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let val = rng.gen_range(1..=100);
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data.push(val);
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}
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data.push(SINGLE_ITEM);
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data.shuffle(&mut rng);
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let data = data.iter().map(|el| *el as u128).collect::<Vec<_>>();
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data
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}
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#[bench]
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fn bench_intfastfield_getrange_u128_50percent_hit(b: &mut Bencher) {
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let data = get_data_50percent_item();
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let column = get_u128_column_from_data(&data);
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b.iter(|| {
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let mut positions = Vec::new();
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column.get_docids_for_value_range(
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*FIFTY_PERCENT_RANGE.start() as u128..=*FIFTY_PERCENT_RANGE.end() as u128,
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0..data.len() as u32,
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&mut positions,
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);
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positions
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});
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}
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#[bench]
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fn bench_intfastfield_getrange_u128_single_hit(b: &mut Bencher) {
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let data = get_data_50percent_item();
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let column = get_u128_column_from_data(&data);
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b.iter(|| {
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let mut positions = Vec::new();
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column.get_docids_for_value_range(
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*SINGLE_ITEM_RANGE.start() as u128..=*SINGLE_ITEM_RANGE.end() as u128,
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0..data.len() as u32,
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&mut positions,
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);
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positions
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});
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}
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#[bench]
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fn bench_intfastfield_getrange_u128_hit_all(b: &mut Bencher) {
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let data = get_data_50percent_item();
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let column = get_u128_column_from_data(&data);
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b.iter(|| {
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let mut positions = Vec::new();
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column.get_docids_for_value_range(0..=u128::MAX, 0..data.len() as u32, &mut positions);
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positions
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});
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}
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// U128 RANGE END
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#[bench]
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fn bench_intfastfield_scan_all_fflookup_u128(b: &mut Bencher) {
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let column = get_u128_column_random();
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b.iter(|| {
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let mut a = 0u128;
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for i in 0u64..column.num_vals() as u64 {
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a += column.get_val(i as u32);
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}
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a
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});
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}
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#[bench]
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fn bench_intfastfield_jumpy_stride5_u128(b: &mut Bencher) {
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let column = get_u128_column_random();
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b.iter(|| {
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let n = column.num_vals();
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let mut a = 0u128;
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for i in (0..n / 5).map(|val| val * 5) {
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a += column.get_val(i);
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}
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a
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});
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}
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211
columnar/benches/bench_u64.rs
Normal file
211
columnar/benches/bench_u64.rs
Normal file
@@ -0,0 +1,211 @@
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#![feature(test)]
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||||
extern crate test;
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||||
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use std::ops::RangeInclusive;
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use std::sync::Arc;
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use rand::prelude::*;
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use tantivy_columnar::column_values::{serialize_and_load_u64_based_column_values, CodecType};
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use tantivy_columnar::*;
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use test::Bencher;
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// Warning: this generates the same permutation at each call
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fn generate_permutation() -> Vec<u64> {
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let mut permutation: Vec<u64> = (0u64..100_000u64).collect();
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permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
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permutation
|
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}
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fn generate_random() -> Vec<u64> {
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let mut permutation: Vec<u64> = (0u64..100_000u64)
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.map(|el| el + random::<u16>() as u64)
|
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.collect();
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permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
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permutation
|
||||
}
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|
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// Warning: this generates the same permutation at each call
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fn generate_permutation_gcd() -> Vec<u64> {
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let mut permutation: Vec<u64> = (1u64..100_000u64).map(|el| el * 1000).collect();
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permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
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permutation
|
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}
|
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pub fn serialize_and_load(column: &[u64], codec_type: CodecType) -> Arc<dyn ColumnValues<u64>> {
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serialize_and_load_u64_based_column_values(&column, &[codec_type])
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}
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|
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#[bench]
|
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fn bench_intfastfield_jumpy_veclookup(b: &mut Bencher) {
|
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let permutation = generate_permutation();
|
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let n = permutation.len();
|
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b.iter(|| {
|
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let mut a = 0u64;
|
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for _ in 0..n {
|
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a = permutation[a as usize];
|
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}
|
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a
|
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});
|
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}
|
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|
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#[bench]
|
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fn bench_intfastfield_jumpy_fflookup_bitpacked(b: &mut Bencher) {
|
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let permutation = generate_permutation();
|
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let n = permutation.len();
|
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let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
|
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b.iter(|| {
|
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let mut a = 0u64;
|
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for _ in 0..n {
|
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a = column.get_val(a as u32);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
const FIFTY_PERCENT_RANGE: RangeInclusive<u64> = 1..=50;
|
||||
const SINGLE_ITEM: u64 = 90;
|
||||
const SINGLE_ITEM_RANGE: RangeInclusive<u64> = 90..=90;
|
||||
const ONE_PERCENT_ITEM_RANGE: RangeInclusive<u64> = 49..=49;
|
||||
fn get_data_50percent_item() -> Vec<u128> {
|
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let mut rng = StdRng::from_seed([1u8; 32]);
|
||||
|
||||
let mut data = vec![];
|
||||
for _ in 0..300_000 {
|
||||
let val = rng.gen_range(1..=100);
|
||||
data.push(val);
|
||||
}
|
||||
data.push(SINGLE_ITEM);
|
||||
|
||||
data.shuffle(&mut rng);
|
||||
let data = data.iter().map(|el| *el as u128).collect::<Vec<_>>();
|
||||
data
|
||||
}
|
||||
|
||||
// U64 RANGE START
|
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#[bench]
|
||||
fn bench_intfastfield_getrange_u64_50percent_hit(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(
|
||||
FIFTY_PERCENT_RANGE,
|
||||
0..data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
positions
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u64_1percent_hit(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(
|
||||
ONE_PERCENT_ITEM_RANGE,
|
||||
0..data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
positions
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u64_single_hit(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(SINGLE_ITEM_RANGE, 0..data.len() as u32, &mut positions);
|
||||
positions
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u64_hit_all(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&data, CodecType::Bitpacked);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(0..=u64::MAX, 0..data.len() as u32, &mut positions);
|
||||
positions
|
||||
});
|
||||
}
|
||||
// U64 RANGE END
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_stride7_vec(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
let n = permutation.len();
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for i in (0..n / 7).map(|val| val * 7) {
|
||||
a += permutation[i as usize];
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_stride7_fflookup(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
let n = permutation.len();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
|
||||
b.iter(|| {
|
||||
let mut a = 0;
|
||||
for i in (0..n / 7).map(|val| val * 7) {
|
||||
a += column.get_val(i as u32);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_scan_all_fflookup(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
let n = permutation.len();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
|
||||
let column_ref = column.as_ref();
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for i in 0u32..n as u32 {
|
||||
a += column_ref.get_val(i);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_scan_all_fflookup_gcd(b: &mut Bencher) {
|
||||
let permutation = generate_permutation_gcd();
|
||||
let n = permutation.len();
|
||||
let column: Arc<dyn ColumnValues<u64>> = serialize_and_load(&permutation, CodecType::Bitpacked);
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for i in 0..n {
|
||||
a += column.get_val(i as u32);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_scan_all_vec(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for i in 0..permutation.len() {
|
||||
a += permutation[i as usize] as u64;
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
17
columnar/columnar-cli/Cargo.toml
Normal file
17
columnar/columnar-cli/Cargo.toml
Normal file
@@ -0,0 +1,17 @@
|
||||
[package]
|
||||
name = "tantivy-columnar-cli"
|
||||
version = "0.1.0"
|
||||
edition = "2021"
|
||||
license = "MIT"
|
||||
|
||||
[dependencies]
|
||||
columnar = {path="../", package="tantivy-columnar"}
|
||||
serde_json = "1"
|
||||
serde_json_borrow = {git="https://github.com/PSeitz/serde_json_borrow/"}
|
||||
serde = "1"
|
||||
|
||||
[workspace]
|
||||
members = []
|
||||
|
||||
[profile.release]
|
||||
debug = true
|
||||
134
columnar/columnar-cli/src/main.rs
Normal file
134
columnar/columnar-cli/src/main.rs
Normal file
@@ -0,0 +1,134 @@
|
||||
use columnar::ColumnarWriter;
|
||||
use columnar::NumericalValue;
|
||||
use serde_json_borrow;
|
||||
use std::fs::File;
|
||||
use std::io;
|
||||
use std::io::BufRead;
|
||||
use std::io::BufReader;
|
||||
use std::time::Instant;
|
||||
|
||||
#[derive(Default)]
|
||||
struct JsonStack {
|
||||
path: String,
|
||||
stack: Vec<usize>,
|
||||
}
|
||||
|
||||
impl JsonStack {
|
||||
fn push(&mut self, seg: &str) {
|
||||
let len = self.path.len();
|
||||
self.stack.push(len);
|
||||
self.path.push('.');
|
||||
self.path.push_str(seg);
|
||||
}
|
||||
|
||||
fn pop(&mut self) {
|
||||
if let Some(len) = self.stack.pop() {
|
||||
self.path.truncate(len);
|
||||
}
|
||||
}
|
||||
|
||||
fn path(&self) -> &str {
|
||||
&self.path[1..]
|
||||
}
|
||||
}
|
||||
|
||||
fn append_json_to_columnar(
|
||||
doc: u32,
|
||||
json_value: &serde_json_borrow::Value,
|
||||
columnar: &mut ColumnarWriter,
|
||||
stack: &mut JsonStack,
|
||||
) -> usize {
|
||||
let mut count = 0;
|
||||
match json_value {
|
||||
serde_json_borrow::Value::Null => {}
|
||||
serde_json_borrow::Value::Bool(val) => {
|
||||
columnar.record_numerical(
|
||||
doc,
|
||||
stack.path(),
|
||||
NumericalValue::from(if *val { 1u64 } else { 0u64 }),
|
||||
);
|
||||
count += 1;
|
||||
}
|
||||
serde_json_borrow::Value::Number(num) => {
|
||||
let numerical_value: NumericalValue = if let Some(num_i64) = num.as_i64() {
|
||||
num_i64.into()
|
||||
} else if let Some(num_u64) = num.as_u64() {
|
||||
num_u64.into()
|
||||
} else if let Some(num_f64) = num.as_f64() {
|
||||
num_f64.into()
|
||||
} else {
|
||||
panic!();
|
||||
};
|
||||
count += 1;
|
||||
columnar.record_numerical(
|
||||
doc,
|
||||
stack.path(),
|
||||
numerical_value,
|
||||
);
|
||||
}
|
||||
serde_json_borrow::Value::Str(msg) => {
|
||||
columnar.record_str(
|
||||
doc,
|
||||
stack.path(),
|
||||
msg,
|
||||
);
|
||||
count += 1;
|
||||
},
|
||||
serde_json_borrow::Value::Array(vals) => {
|
||||
for val in vals {
|
||||
count += append_json_to_columnar(doc, val, columnar, stack);
|
||||
}
|
||||
},
|
||||
serde_json_borrow::Value::Object(json_map) => {
|
||||
for (child_key, child_val) in json_map {
|
||||
stack.push(child_key);
|
||||
count += append_json_to_columnar(doc, child_val, columnar, stack);
|
||||
stack.pop();
|
||||
}
|
||||
},
|
||||
}
|
||||
count
|
||||
}
|
||||
|
||||
fn main() -> io::Result<()> {
|
||||
let file = File::open("gh_small.json")?;
|
||||
let mut reader = BufReader::new(file);
|
||||
let mut line = String::with_capacity(100);
|
||||
let mut columnar = columnar::ColumnarWriter::default();
|
||||
let mut doc = 0;
|
||||
let start = Instant::now();
|
||||
let mut stack = JsonStack::default();
|
||||
let mut total_count = 0;
|
||||
|
||||
let start_build = Instant::now();
|
||||
loop {
|
||||
line.clear();
|
||||
let len = reader.read_line(&mut line)?;
|
||||
if len == 0 {
|
||||
break;
|
||||
}
|
||||
let Ok(json_value) = serde_json::from_str::<serde_json_borrow::Value>(&line) else { continue; };
|
||||
total_count += append_json_to_columnar(doc, &json_value, &mut columnar, &mut stack);
|
||||
doc += 1;
|
||||
}
|
||||
println!("Build in {:?}", start_build.elapsed());
|
||||
|
||||
println!("value count {total_count}");
|
||||
|
||||
let mut buffer = Vec::new();
|
||||
let start_serialize = Instant::now();
|
||||
columnar.serialize(doc, None, &mut buffer)?;
|
||||
println!("Serialized in {:?}", start_serialize.elapsed());
|
||||
println!("num docs: {doc}, {:?}", start.elapsed());
|
||||
println!("buffer len {} MB", buffer.len() / 1_000_000);
|
||||
let columnar = columnar::ColumnarReader::open(buffer)?;
|
||||
for (column_name, dynamic_column) in columnar.list_columns()? {
|
||||
let num_bytes = dynamic_column.num_bytes();
|
||||
let typ = dynamic_column.column_type();
|
||||
if num_bytes > 1_000_000 {
|
||||
println!("{column_name} {typ:?} {} KB", num_bytes / 1_000);
|
||||
}
|
||||
}
|
||||
println!("{} columns", columnar.num_columns());
|
||||
Ok(())
|
||||
}
|
||||
@@ -1,22 +1,22 @@
|
||||
# zero to one
|
||||
* merges
|
||||
* full still needs a num_values
|
||||
* replug u128
|
||||
* add dictionary encoded stuff
|
||||
* fix multivalued
|
||||
* find a way to make columnar work with strict types
|
||||
* plug to tantivy
|
||||
- indexing
|
||||
- aggregations
|
||||
- merge
|
||||
|
||||
* revisit line codec
|
||||
* removal of all rows of a column in the schema due to deletes
|
||||
* add columns from schema on merge
|
||||
* Plugging JSON
|
||||
* replug examples
|
||||
* move datetime to quickwit common
|
||||
* switch to nanos
|
||||
* reintroduce the gcd map.
|
||||
|
||||
# Perf and Size
|
||||
* remove alloc in `ord_to_term`
|
||||
+ multivaued range queries restrat frm the beginning all of the time.
|
||||
* re-add ZSTD compression for dictionaries
|
||||
no systematic monotonic mapping
|
||||
consider removing multilinear
|
||||
f32?
|
||||
adhoc solution for bool?
|
||||
|
||||
add metrics helper for aggregate. sum(row_id)
|
||||
review inline absence/presence
|
||||
improv perf of select using PDEP
|
||||
@@ -36,11 +36,13 @@ use the rank & select naming in unit tests branch.
|
||||
multi-linear -> blockwise
|
||||
linear codec -> simply a multiplication for the index column
|
||||
rename columnar to something more explicit, like column_dictionary or columnar_table
|
||||
rename fastfield -> column
|
||||
document changes
|
||||
rationalization FastFieldValue, HasColumnType
|
||||
isolate u128_based and uniform naming
|
||||
|
||||
# Other
|
||||
fix enhance column-cli
|
||||
|
||||
# Santa claus
|
||||
|
||||
autodetect datetime ipaddr, plug customizable tokenizer.
|
||||
|
||||
|
||||
@@ -35,27 +35,49 @@ impl BytesColumn {
|
||||
self.term_ord_column.num_rows()
|
||||
}
|
||||
|
||||
pub fn term_ords(&self, row_id: RowId) -> impl Iterator<Item = u64> + '_ {
|
||||
self.term_ord_column.values(row_id)
|
||||
}
|
||||
|
||||
/// Returns the column of ordinals
|
||||
pub fn ords(&self) -> &Column<u64> {
|
||||
&self.term_ord_column
|
||||
}
|
||||
|
||||
pub fn num_terms(&self) -> usize {
|
||||
self.dictionary.num_terms()
|
||||
}
|
||||
|
||||
pub fn dictionary(&self) -> &Dictionary<VoidSSTable> {
|
||||
self.dictionary.as_ref()
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Clone)]
|
||||
pub struct StrColumn(BytesColumn);
|
||||
|
||||
impl From<BytesColumn> for StrColumn {
|
||||
fn from(bytes_col: BytesColumn) -> Self {
|
||||
StrColumn(bytes_col)
|
||||
impl From<StrColumn> for BytesColumn {
|
||||
fn from(str_column: StrColumn) -> BytesColumn {
|
||||
str_column.0
|
||||
}
|
||||
}
|
||||
|
||||
impl StrColumn {
|
||||
pub(crate) fn wrap(bytes_column: BytesColumn) -> StrColumn {
|
||||
StrColumn(bytes_column)
|
||||
}
|
||||
|
||||
pub fn dictionary(&self) -> &Dictionary<VoidSSTable> {
|
||||
self.0.dictionary.as_ref()
|
||||
}
|
||||
|
||||
/// Fills the buffer
|
||||
pub fn ord_to_str(&self, term_ord: u64, output: &mut String) -> io::Result<bool> {
|
||||
unsafe {
|
||||
let buf = output.as_mut_vec();
|
||||
self.0.dictionary.ord_to_term(term_ord, buf)?;
|
||||
if !self.0.dictionary.ord_to_term(term_ord, buf)? {
|
||||
return Ok(false);
|
||||
}
|
||||
// TODO consider remove checks if it hurts performance.
|
||||
if std::str::from_utf8(buf.as_slice()).is_err() {
|
||||
buf.clear();
|
||||
|
||||
@@ -1,27 +1,47 @@
|
||||
mod dictionary_encoded;
|
||||
mod serialize;
|
||||
|
||||
use std::fmt::Debug;
|
||||
use std::io::Write;
|
||||
use std::ops::Deref;
|
||||
use std::sync::Arc;
|
||||
|
||||
use common::BinarySerializable;
|
||||
pub use dictionary_encoded::{BytesColumn, StrColumn};
|
||||
pub use serialize::{
|
||||
open_column_bytes, open_column_u128, open_column_u64, serialize_column_mappable_to_u128,
|
||||
serialize_column_mappable_to_u64,
|
||||
open_column_bytes, open_column_str, open_column_u128, open_column_u64,
|
||||
serialize_column_mappable_to_u128, serialize_column_mappable_to_u64,
|
||||
};
|
||||
|
||||
use crate::column_index::ColumnIndex;
|
||||
use crate::column_values::ColumnValues;
|
||||
use crate::{Cardinality, RowId};
|
||||
use crate::column_values::monotonic_mapping::StrictlyMonotonicMappingToInternal;
|
||||
use crate::column_values::{monotonic_map_column, ColumnValues};
|
||||
use crate::{Cardinality, MonotonicallyMappableToU64, RowId};
|
||||
|
||||
#[derive(Clone)]
|
||||
pub struct Column<T> {
|
||||
pub idx: ColumnIndex<'static>,
|
||||
pub struct Column<T = u64> {
|
||||
pub idx: ColumnIndex,
|
||||
pub values: Arc<dyn ColumnValues<T>>,
|
||||
}
|
||||
|
||||
impl<T: PartialOrd> Column<T> {
|
||||
impl<T: MonotonicallyMappableToU64> Column<T> {
|
||||
pub fn to_u64_monotonic(self) -> Column<u64> {
|
||||
let values = Arc::new(monotonic_map_column(
|
||||
self.values,
|
||||
StrictlyMonotonicMappingToInternal::<T>::new(),
|
||||
));
|
||||
Column {
|
||||
idx: self.idx,
|
||||
values,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<T: PartialOrd + Copy + Debug + Send + Sync + 'static> Column<T> {
|
||||
pub fn get_cardinality(&self) -> Cardinality {
|
||||
self.idx.get_cardinality()
|
||||
}
|
||||
|
||||
pub fn num_rows(&self) -> RowId {
|
||||
match &self.idx {
|
||||
ColumnIndex::Full => self.values.num_vals() as u32,
|
||||
@@ -29,7 +49,7 @@ impl<T: PartialOrd> Column<T> {
|
||||
ColumnIndex::Multivalued(col_index) => {
|
||||
// The multivalued index contains all value start row_id,
|
||||
// and one extra value at the end with the overall number of rows.
|
||||
col_index.num_vals() - 1
|
||||
col_index.num_rows()
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -37,12 +57,11 @@ impl<T: PartialOrd> Column<T> {
|
||||
pub fn min_value(&self) -> T {
|
||||
self.values.min_value()
|
||||
}
|
||||
|
||||
pub fn max_value(&self) -> T {
|
||||
self.values.max_value()
|
||||
}
|
||||
}
|
||||
|
||||
impl<T: PartialOrd + Copy + Send + Sync + 'static> Column<T> {
|
||||
pub fn first(&self, row_id: RowId) -> Option<T> {
|
||||
self.values(row_id).next()
|
||||
}
|
||||
@@ -52,6 +71,15 @@ impl<T: PartialOrd + Copy + Send + Sync + 'static> Column<T> {
|
||||
.map(|value_row_id: RowId| self.values.get_val(value_row_id))
|
||||
}
|
||||
|
||||
/// Fils the output vector with the (possibly multiple values that are associated_with
|
||||
/// `row_id`.
|
||||
///
|
||||
/// This method clears the `output` vector.
|
||||
pub fn fill_vals(&self, row_id: RowId, output: &mut Vec<T>) {
|
||||
output.clear();
|
||||
output.extend(self.values(row_id));
|
||||
}
|
||||
|
||||
pub fn first_or_default_col(self, default_value: T) -> Arc<dyn ColumnValues<T>> {
|
||||
Arc::new(FirstValueWithDefault {
|
||||
column: self,
|
||||
@@ -61,7 +89,7 @@ impl<T: PartialOrd + Copy + Send + Sync + 'static> Column<T> {
|
||||
}
|
||||
|
||||
impl<T> Deref for Column<T> {
|
||||
type Target = ColumnIndex<'static>;
|
||||
type Target = ColumnIndex;
|
||||
|
||||
fn deref(&self) -> &Self::Target {
|
||||
&self.idx
|
||||
@@ -69,7 +97,7 @@ impl<T> Deref for Column<T> {
|
||||
}
|
||||
|
||||
impl BinarySerializable for Cardinality {
|
||||
fn serialize<W: std::io::Write>(&self, writer: &mut W) -> std::io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> std::io::Result<()> {
|
||||
self.to_code().serialize(writer)
|
||||
}
|
||||
|
||||
@@ -86,7 +114,9 @@ struct FirstValueWithDefault<T: Copy> {
|
||||
default_value: T,
|
||||
}
|
||||
|
||||
impl<T: PartialOrd + Send + Sync + Copy + 'static> ColumnValues<T> for FirstValueWithDefault<T> {
|
||||
impl<T: PartialOrd + Debug + Send + Sync + Copy + 'static> ColumnValues<T>
|
||||
for FirstValueWithDefault<T>
|
||||
{
|
||||
fn get_val(&self, idx: u32) -> T {
|
||||
self.column.first(idx).unwrap_or(self.default_value)
|
||||
}
|
||||
@@ -103,7 +133,7 @@ impl<T: PartialOrd + Send + Sync + Copy + 'static> ColumnValues<T> for FirstValu
|
||||
match &self.column.idx {
|
||||
ColumnIndex::Full => self.column.values.num_vals(),
|
||||
ColumnIndex::Optional(optional_idx) => optional_idx.num_rows(),
|
||||
ColumnIndex::Multivalued(_) => todo!(),
|
||||
ColumnIndex::Multivalued(multivalue_idx) => multivalue_idx.num_rows(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -8,43 +8,31 @@ use sstable::Dictionary;
|
||||
use crate::column::{BytesColumn, Column};
|
||||
use crate::column_index::{serialize_column_index, SerializableColumnIndex};
|
||||
use crate::column_values::serialize::serialize_column_values_u128;
|
||||
use crate::column_values::{
|
||||
serialize_column_values, ColumnValues, FastFieldCodecType, MonotonicallyMappableToU128,
|
||||
MonotonicallyMappableToU64,
|
||||
};
|
||||
use crate::column_values::u64_based::{serialize_u64_based_column_values, CodecType};
|
||||
use crate::column_values::{MonotonicallyMappableToU128, MonotonicallyMappableToU64};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::StrColumn;
|
||||
|
||||
pub fn serialize_column_mappable_to_u128<
|
||||
F: Fn() -> I,
|
||||
I: Iterator<Item = T>,
|
||||
T: MonotonicallyMappableToU128,
|
||||
>(
|
||||
pub fn serialize_column_mappable_to_u128<T: MonotonicallyMappableToU128>(
|
||||
column_index: SerializableColumnIndex<'_>,
|
||||
column_values: F,
|
||||
num_vals: u32,
|
||||
iterable: &dyn Iterable<T>,
|
||||
output: &mut impl Write,
|
||||
) -> io::Result<()> {
|
||||
let column_index_num_bytes = serialize_column_index(column_index, output)?;
|
||||
serialize_column_values_u128(
|
||||
|| column_values().map(|val| val.to_u128()),
|
||||
num_vals,
|
||||
output,
|
||||
)?;
|
||||
serialize_column_values_u128(iterable, output)?;
|
||||
output.write_all(&column_index_num_bytes.to_le_bytes())?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn serialize_column_mappable_to_u64<T: MonotonicallyMappableToU64>(
|
||||
column_index: SerializableColumnIndex<'_>,
|
||||
column_values: &impl ColumnValues<T>,
|
||||
column_values: &impl Iterable<T>,
|
||||
output: &mut impl Write,
|
||||
) -> io::Result<()> {
|
||||
let column_index_num_bytes = serialize_column_index(column_index, output)?;
|
||||
serialize_column_values(
|
||||
serialize_u64_based_column_values(
|
||||
column_values,
|
||||
&[
|
||||
FastFieldCodecType::Bitpacked,
|
||||
FastFieldCodecType::BlockwiseLinear,
|
||||
],
|
||||
&[CodecType::Bitpacked, CodecType::BlockwiseLinear],
|
||||
output,
|
||||
)?;
|
||||
output.write_all(&column_index_num_bytes.to_le_bytes())?;
|
||||
@@ -61,7 +49,8 @@ pub fn open_column_u64<T: MonotonicallyMappableToU64>(bytes: OwnedBytes) -> io::
|
||||
);
|
||||
let (column_index_data, column_values_data) = body.split(column_index_num_bytes as usize);
|
||||
let column_index = crate::column_index::open_column_index(column_index_data)?;
|
||||
let column_values = crate::column_values::open_u64_mapped(column_values_data)?;
|
||||
let column_values =
|
||||
crate::column_values::u64_based::load_u64_based_column_values(column_values_data)?;
|
||||
Ok(Column {
|
||||
idx: column_index,
|
||||
values: column_values,
|
||||
@@ -87,15 +76,19 @@ pub fn open_column_u128<T: MonotonicallyMappableToU128>(
|
||||
})
|
||||
}
|
||||
|
||||
pub fn open_column_bytes<T: From<BytesColumn>>(data: OwnedBytes) -> io::Result<T> {
|
||||
pub fn open_column_bytes(data: OwnedBytes) -> io::Result<BytesColumn> {
|
||||
let (body, dictionary_len_bytes) = data.rsplit(4);
|
||||
let dictionary_len = u32::from_le_bytes(dictionary_len_bytes.as_slice().try_into().unwrap());
|
||||
let (dictionary_bytes, column_bytes) = body.split(dictionary_len as usize);
|
||||
let dictionary = Arc::new(Dictionary::from_bytes(dictionary_bytes)?);
|
||||
let term_ord_column = crate::column::open_column_u64::<u64>(column_bytes)?;
|
||||
let bytes_column = BytesColumn {
|
||||
Ok(BytesColumn {
|
||||
dictionary,
|
||||
term_ord_column,
|
||||
};
|
||||
Ok(bytes_column.into())
|
||||
})
|
||||
}
|
||||
|
||||
pub fn open_column_str(data: OwnedBytes) -> io::Result<StrColumn> {
|
||||
let bytes_column = open_column_bytes(data)?;
|
||||
Ok(StrColumn::wrap(bytes_column))
|
||||
}
|
||||
|
||||
136
columnar/src/column_index/merge/mod.rs
Normal file
136
columnar/src/column_index/merge/mod.rs
Normal file
@@ -0,0 +1,136 @@
|
||||
mod shuffled;
|
||||
mod stacked;
|
||||
|
||||
use shuffled::merge_column_index_shuffled;
|
||||
use stacked::merge_column_index_stacked;
|
||||
|
||||
use crate::column_index::SerializableColumnIndex;
|
||||
use crate::{Cardinality, ColumnIndex, MergeRowOrder};
|
||||
|
||||
// For simplification, we never have cardinality go down due to deletes.
|
||||
fn detect_cardinality(columns: &[Option<ColumnIndex>]) -> Cardinality {
|
||||
columns
|
||||
.iter()
|
||||
.flatten()
|
||||
.map(ColumnIndex::get_cardinality)
|
||||
.max()
|
||||
.unwrap_or(Cardinality::Full)
|
||||
}
|
||||
|
||||
pub fn merge_column_index<'a>(
|
||||
columns: &'a [Option<ColumnIndex>],
|
||||
merge_row_order: &'a MergeRowOrder,
|
||||
) -> SerializableColumnIndex<'a> {
|
||||
// For simplification, we do not try to detect whether the cardinality could be
|
||||
// downgraded thanks to deletes.
|
||||
let cardinality_after_merge = detect_cardinality(columns);
|
||||
match merge_row_order {
|
||||
MergeRowOrder::Stack(stack_merge_order) => {
|
||||
merge_column_index_stacked(columns, cardinality_after_merge, stack_merge_order)
|
||||
}
|
||||
MergeRowOrder::Shuffled(complex_merge_order) => {
|
||||
merge_column_index_shuffled(columns, cardinality_after_merge, complex_merge_order)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// TODO actually, the shuffled code path is a bit too general.
|
||||
// In practise, we do not really shuffle everything.
|
||||
// The merge order restricted to a specific column keeps the original row order.
|
||||
//
|
||||
// This may offer some optimization that we have not explored yet.
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use crate::column_index::merge::detect_cardinality;
|
||||
use crate::column_index::multivalued_index::MultiValueIndex;
|
||||
use crate::column_index::{merge_column_index, OptionalIndex, SerializableColumnIndex};
|
||||
use crate::{Cardinality, ColumnIndex, MergeRowOrder, RowAddr, RowId, ShuffleMergeOrder};
|
||||
|
||||
#[test]
|
||||
fn test_detect_cardinality() {
|
||||
assert_eq!(detect_cardinality(&[]), Cardinality::Full);
|
||||
let optional_index: ColumnIndex = OptionalIndex::for_test(1, &[]).into();
|
||||
let multivalued_index: ColumnIndex = MultiValueIndex::for_test(&[0, 1]).into();
|
||||
assert_eq!(
|
||||
detect_cardinality(&[Some(optional_index.clone()), None]),
|
||||
Cardinality::Optional
|
||||
);
|
||||
assert_eq!(
|
||||
detect_cardinality(&[Some(optional_index.clone()), Some(ColumnIndex::Full)]),
|
||||
Cardinality::Optional
|
||||
);
|
||||
assert_eq!(
|
||||
detect_cardinality(&[Some(multivalued_index.clone()), None]),
|
||||
Cardinality::Multivalued
|
||||
);
|
||||
assert_eq!(
|
||||
detect_cardinality(&[
|
||||
Some(multivalued_index.clone()),
|
||||
Some(optional_index.clone())
|
||||
]),
|
||||
Cardinality::Multivalued
|
||||
);
|
||||
assert_eq!(
|
||||
detect_cardinality(&[Some(optional_index), Some(multivalued_index)]),
|
||||
Cardinality::Multivalued
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_merge_index_multivalued_sorted() {
|
||||
let column_indexes: Vec<Option<ColumnIndex>> =
|
||||
vec![Some(MultiValueIndex::for_test(&[0, 2, 5]).into())];
|
||||
let merge_row_order: MergeRowOrder = ShuffleMergeOrder::for_test(
|
||||
&[2],
|
||||
vec![
|
||||
RowAddr {
|
||||
segment_ord: 0u32,
|
||||
row_id: 1u32,
|
||||
},
|
||||
RowAddr {
|
||||
segment_ord: 0u32,
|
||||
row_id: 0u32,
|
||||
},
|
||||
],
|
||||
)
|
||||
.into();
|
||||
let merged_column_index = merge_column_index(&column_indexes[..], &merge_row_order);
|
||||
let SerializableColumnIndex::Multivalued(start_index_iterable) = merged_column_index
|
||||
else { panic!("Excpected a multivalued index") };
|
||||
let start_indexes: Vec<RowId> = start_index_iterable.boxed_iter().collect();
|
||||
assert_eq!(&start_indexes, &[0, 3, 5]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_merge_index_multivalued_sorted_several_segment() {
|
||||
let column_indexes: Vec<Option<ColumnIndex>> = vec![
|
||||
Some(MultiValueIndex::for_test(&[0, 2, 5]).into()),
|
||||
None,
|
||||
Some(MultiValueIndex::for_test(&[0, 1, 4]).into()),
|
||||
];
|
||||
let merge_row_order: MergeRowOrder = ShuffleMergeOrder::for_test(
|
||||
&[2, 0, 2],
|
||||
vec![
|
||||
RowAddr {
|
||||
segment_ord: 2u32,
|
||||
row_id: 1u32,
|
||||
},
|
||||
RowAddr {
|
||||
segment_ord: 0u32,
|
||||
row_id: 0u32,
|
||||
},
|
||||
RowAddr {
|
||||
segment_ord: 2u32,
|
||||
row_id: 0u32,
|
||||
},
|
||||
],
|
||||
)
|
||||
.into();
|
||||
let merged_column_index = merge_column_index(&column_indexes[..], &merge_row_order);
|
||||
let SerializableColumnIndex::Multivalued(start_index_iterable) = merged_column_index
|
||||
else { panic!("Excpected a multivalued index") };
|
||||
let start_indexes: Vec<RowId> = start_index_iterable.boxed_iter().collect();
|
||||
assert_eq!(&start_indexes, &[0, 3, 5, 6]);
|
||||
}
|
||||
}
|
||||
171
columnar/src/column_index/merge/shuffled.rs
Normal file
171
columnar/src/column_index/merge/shuffled.rs
Normal file
@@ -0,0 +1,171 @@
|
||||
use std::iter;
|
||||
|
||||
use crate::column_index::{SerializableColumnIndex, Set};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{Cardinality, ColumnIndex, RowId, ShuffleMergeOrder};
|
||||
|
||||
pub fn merge_column_index_shuffled<'a>(
|
||||
column_indexes: &'a [Option<ColumnIndex>],
|
||||
cardinality_after_merge: Cardinality,
|
||||
shuffle_merge_order: &'a ShuffleMergeOrder,
|
||||
) -> SerializableColumnIndex<'a> {
|
||||
match cardinality_after_merge {
|
||||
Cardinality::Full => SerializableColumnIndex::Full,
|
||||
Cardinality::Optional => {
|
||||
let non_null_row_ids =
|
||||
merge_column_index_shuffled_optional(column_indexes, shuffle_merge_order);
|
||||
SerializableColumnIndex::Optional {
|
||||
non_null_row_ids,
|
||||
num_rows: shuffle_merge_order.num_rows(),
|
||||
}
|
||||
}
|
||||
Cardinality::Multivalued => {
|
||||
let multivalue_start_index =
|
||||
merge_column_index_shuffled_multivalued(column_indexes, shuffle_merge_order);
|
||||
SerializableColumnIndex::Multivalued(multivalue_start_index)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Merge several column indexes into one, ordering rows according to the merge_order passed as
|
||||
/// argument. While it is true that the `merge_order` may imply deletes and hence could in theory a
|
||||
/// multivalued index into an optional one, this is not supported today for simplification.
|
||||
///
|
||||
/// In other words the column_indexes passed as argument may NOT be multivalued.
|
||||
fn merge_column_index_shuffled_optional<'a>(
|
||||
column_indexes: &'a [Option<ColumnIndex>],
|
||||
merge_order: &'a ShuffleMergeOrder,
|
||||
) -> Box<dyn Iterable<RowId> + 'a> {
|
||||
Box::new(ShuffledOptionalIndex {
|
||||
column_indexes,
|
||||
merge_order,
|
||||
})
|
||||
}
|
||||
|
||||
struct ShuffledOptionalIndex<'a> {
|
||||
column_indexes: &'a [Option<ColumnIndex>],
|
||||
merge_order: &'a ShuffleMergeOrder,
|
||||
}
|
||||
|
||||
impl<'a> Iterable<u32> for ShuffledOptionalIndex<'a> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
|
||||
Box::new(self.merge_order
|
||||
.iter_new_to_old_row_addrs()
|
||||
.enumerate()
|
||||
.filter_map(|(new_row_id, old_row_addr)| {
|
||||
let Some(column_index) = &self.column_indexes[old_row_addr.segment_ord as usize] else {
|
||||
return None;
|
||||
};
|
||||
let row_id = new_row_id as u32;
|
||||
if column_index.has_value(old_row_addr.row_id) {
|
||||
Some(row_id)
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}))
|
||||
}
|
||||
}
|
||||
|
||||
fn merge_column_index_shuffled_multivalued<'a>(
|
||||
column_indexes: &'a [Option<ColumnIndex>],
|
||||
merge_order: &'a ShuffleMergeOrder,
|
||||
) -> Box<dyn Iterable<RowId> + 'a> {
|
||||
Box::new(ShuffledMultivaluedIndex {
|
||||
column_indexes,
|
||||
merge_order,
|
||||
})
|
||||
}
|
||||
|
||||
struct ShuffledMultivaluedIndex<'a> {
|
||||
column_indexes: &'a [Option<ColumnIndex>],
|
||||
merge_order: &'a ShuffleMergeOrder,
|
||||
}
|
||||
|
||||
fn iter_num_values<'a>(
|
||||
column_indexes: &'a [Option<ColumnIndex>],
|
||||
merge_order: &'a ShuffleMergeOrder,
|
||||
) -> impl Iterator<Item = u32> + 'a {
|
||||
merge_order.iter_new_to_old_row_addrs().map(|row_addr| {
|
||||
let Some(column_index) = &column_indexes[row_addr.segment_ord as usize] else {
|
||||
// No values in the entire column. It surely means there are 0 values associated to this row.
|
||||
return 0u32;
|
||||
};
|
||||
match column_index {
|
||||
ColumnIndex::Full => 1,
|
||||
ColumnIndex::Optional(optional_index) => {
|
||||
if optional_index.contains(row_addr.row_id) {
|
||||
1u32
|
||||
} else {
|
||||
0u32
|
||||
}
|
||||
}
|
||||
ColumnIndex::Multivalued(multivalued_index) => {
|
||||
multivalued_index.range(row_addr.row_id).len() as u32
|
||||
}
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
/// Transforms an iterator containing the number of vals per row (with `num_rows` elements)
|
||||
/// into a `start_offset` iterator starting at 0 and (with `num_rows + 1` element)
|
||||
fn integrate_num_vals(num_vals: impl Iterator<Item = u32>) -> impl Iterator<Item = RowId> {
|
||||
iter::once(0u32).chain(num_vals.scan(0, |state, num_vals| {
|
||||
*state += num_vals;
|
||||
Some(*state)
|
||||
}))
|
||||
}
|
||||
|
||||
impl<'a> Iterable<u32> for ShuffledMultivaluedIndex<'a> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u32> + '_> {
|
||||
let num_vals_per_row = iter_num_values(self.column_indexes, self.merge_order);
|
||||
Box::new(integrate_num_vals(num_vals_per_row))
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::column_index::OptionalIndex;
|
||||
use crate::RowAddr;
|
||||
|
||||
#[test]
|
||||
fn test_integrate_num_vals_empty() {
|
||||
assert!(integrate_num_vals(iter::empty()).eq(iter::once(0)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_integrate_num_vals_one_el() {
|
||||
assert!(integrate_num_vals(iter::once(10)).eq([0, 10].into_iter()));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_integrate_num_vals_several() {
|
||||
assert!(integrate_num_vals([3, 0, 10, 20].into_iter()).eq([0, 3, 3, 13, 33].into_iter()));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_merge_column_index_optional_shuffle() {
|
||||
let optional_index: ColumnIndex = OptionalIndex::for_test(2, &[0]).into();
|
||||
let column_indexes = vec![Some(optional_index), Some(ColumnIndex::Full)];
|
||||
let row_addrs = vec![
|
||||
RowAddr {
|
||||
segment_ord: 0u32,
|
||||
row_id: 1u32,
|
||||
},
|
||||
RowAddr {
|
||||
segment_ord: 1u32,
|
||||
row_id: 0u32,
|
||||
},
|
||||
];
|
||||
let shuffle_merge_order = ShuffleMergeOrder::for_test(&[2, 1], row_addrs);
|
||||
let serializable_index = merge_column_index_shuffled(
|
||||
&column_indexes[..],
|
||||
Cardinality::Optional,
|
||||
&shuffle_merge_order,
|
||||
);
|
||||
let SerializableColumnIndex::Optional { non_null_row_ids, num_rows } = serializable_index else { panic!() };
|
||||
assert_eq!(num_rows, 2);
|
||||
let non_null_rows: Vec<RowId> = non_null_row_ids.boxed_iter().collect();
|
||||
assert_eq!(&non_null_rows, &[1]);
|
||||
}
|
||||
}
|
||||
154
columnar/src/column_index/merge/stacked.rs
Normal file
154
columnar/src/column_index/merge/stacked.rs
Normal file
@@ -0,0 +1,154 @@
|
||||
use std::iter;
|
||||
|
||||
use crate::column_index::{SerializableColumnIndex, Set};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{Cardinality, ColumnIndex, RowId, StackMergeOrder};
|
||||
|
||||
/// Simple case:
|
||||
/// The new mapping just consists in stacking the different column indexes.
|
||||
///
|
||||
/// There are no sort nor deletes involved.
|
||||
pub fn merge_column_index_stacked<'a>(
|
||||
columns: &'a [Option<ColumnIndex>],
|
||||
cardinality_after_merge: Cardinality,
|
||||
stack_merge_order: &'a StackMergeOrder,
|
||||
) -> SerializableColumnIndex<'a> {
|
||||
match cardinality_after_merge {
|
||||
Cardinality::Full => SerializableColumnIndex::Full,
|
||||
Cardinality::Optional => SerializableColumnIndex::Optional {
|
||||
non_null_row_ids: Box::new(StackedOptionalIndex {
|
||||
columns,
|
||||
stack_merge_order,
|
||||
}),
|
||||
num_rows: stack_merge_order.num_rows(),
|
||||
},
|
||||
Cardinality::Multivalued => {
|
||||
let stacked_multivalued_index = StackedMultivaluedIndex {
|
||||
columns,
|
||||
stack_merge_order,
|
||||
};
|
||||
SerializableColumnIndex::Multivalued(Box::new(stacked_multivalued_index))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
struct StackedOptionalIndex<'a> {
|
||||
columns: &'a [Option<ColumnIndex>],
|
||||
stack_merge_order: &'a StackMergeOrder,
|
||||
}
|
||||
|
||||
impl<'a> Iterable<RowId> for StackedOptionalIndex<'a> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = RowId> + 'a> {
|
||||
Box::new(
|
||||
self.columns
|
||||
.iter()
|
||||
.enumerate()
|
||||
.flat_map(|(columnar_id, column_index_opt)| {
|
||||
let columnar_row_range = self.stack_merge_order.columnar_range(columnar_id);
|
||||
let rows_it: Box<dyn Iterator<Item = RowId>> = match column_index_opt {
|
||||
Some(ColumnIndex::Full) => Box::new(columnar_row_range),
|
||||
Some(ColumnIndex::Optional(optional_index)) => Box::new(
|
||||
optional_index
|
||||
.iter_rows()
|
||||
.map(move |row_id: RowId| columnar_row_range.start + row_id),
|
||||
),
|
||||
Some(ColumnIndex::Multivalued(_)) => {
|
||||
panic!("No multivalued index is allowed when stacking column index");
|
||||
}
|
||||
None => Box::new(std::iter::empty()),
|
||||
};
|
||||
rows_it
|
||||
}),
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Clone, Copy)]
|
||||
struct StackedMultivaluedIndex<'a> {
|
||||
columns: &'a [Option<ColumnIndex>],
|
||||
stack_merge_order: &'a StackMergeOrder,
|
||||
}
|
||||
|
||||
fn convert_column_opt_to_multivalued_index<'a>(
|
||||
column_index_opt: Option<&'a ColumnIndex>,
|
||||
num_rows: RowId,
|
||||
) -> Box<dyn Iterator<Item = RowId> + 'a> {
|
||||
match column_index_opt {
|
||||
None => Box::new(iter::repeat(0u32).take(num_rows as usize + 1)),
|
||||
Some(ColumnIndex::Full) => Box::new(0..num_rows + 1),
|
||||
Some(ColumnIndex::Optional(optional_index)) => {
|
||||
Box::new(
|
||||
(0..num_rows)
|
||||
// TODO optimize
|
||||
.map(|row_id| optional_index.rank(row_id))
|
||||
.chain(std::iter::once(optional_index.num_non_nulls())),
|
||||
)
|
||||
}
|
||||
Some(ColumnIndex::Multivalued(multivalued_index)) => {
|
||||
multivalued_index.start_index_column.iter()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> Iterable<RowId> for StackedMultivaluedIndex<'a> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = RowId> + '_> {
|
||||
let multivalued_indexes =
|
||||
self.columns
|
||||
.iter()
|
||||
.map(Option::as_ref)
|
||||
.enumerate()
|
||||
.map(|(columnar_id, column_opt)| {
|
||||
let num_rows =
|
||||
self.stack_merge_order.columnar_range(columnar_id).len() as RowId;
|
||||
convert_column_opt_to_multivalued_index(column_opt, num_rows)
|
||||
});
|
||||
stack_multivalued_indexes(multivalued_indexes)
|
||||
}
|
||||
}
|
||||
|
||||
// Refactor me
|
||||
fn stack_multivalued_indexes<'a>(
|
||||
mut multivalued_indexes: impl Iterator<Item = Box<dyn Iterator<Item = RowId> + 'a>> + 'a,
|
||||
) -> Box<dyn Iterator<Item = RowId> + 'a> {
|
||||
let mut offset = 0;
|
||||
let mut last_row_id = 0;
|
||||
let mut current_it = multivalued_indexes.next();
|
||||
Box::new(std::iter::from_fn(move || loop {
|
||||
let Some(multivalued_index) = current_it.as_mut() else {
|
||||
return None;
|
||||
};
|
||||
if let Some(row_id) = multivalued_index.next() {
|
||||
last_row_id = offset + row_id;
|
||||
return Some(last_row_id);
|
||||
}
|
||||
offset = last_row_id;
|
||||
loop {
|
||||
current_it = multivalued_indexes.next();
|
||||
if current_it.as_mut()?.next().is_some() {
|
||||
break;
|
||||
}
|
||||
}
|
||||
}))
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use crate::RowId;
|
||||
|
||||
fn it<'a>(row_ids: &'a [RowId]) -> Box<dyn Iterator<Item = RowId> + 'a> {
|
||||
Box::new(row_ids.iter().copied())
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_stack() {
|
||||
let columns = [
|
||||
it(&[0u32, 0u32]),
|
||||
it(&[0u32, 1u32, 1u32, 4u32]),
|
||||
it(&[0u32, 3u32, 5u32]),
|
||||
it(&[0u32, 4u32]),
|
||||
]
|
||||
.into_iter();
|
||||
let start_offsets: Vec<RowId> = super::stack_multivalued_indexes(columns).collect();
|
||||
assert_eq!(start_offsets, &[0, 0, 1, 1, 4, 7, 9, 13]);
|
||||
}
|
||||
}
|
||||
@@ -1,30 +1,39 @@
|
||||
mod merge;
|
||||
mod multivalued_index;
|
||||
mod optional_index;
|
||||
mod serialize;
|
||||
|
||||
use std::ops::Range;
|
||||
use std::sync::Arc;
|
||||
|
||||
pub use optional_index::{OptionalIndex, SerializableOptionalIndex, Set};
|
||||
pub use merge::merge_column_index;
|
||||
pub use optional_index::{OptionalIndex, Set};
|
||||
pub use serialize::{open_column_index, serialize_column_index, SerializableColumnIndex};
|
||||
|
||||
use crate::column_values::ColumnValues;
|
||||
use crate::column_index::multivalued_index::MultiValueIndex;
|
||||
use crate::{Cardinality, RowId};
|
||||
|
||||
#[derive(Clone)]
|
||||
pub enum ColumnIndex<'a> {
|
||||
pub enum ColumnIndex {
|
||||
Full,
|
||||
Optional(OptionalIndex),
|
||||
// TODO Remove the static by fixing the codec if possible.
|
||||
/// The column values enclosed contains for all row_id,
|
||||
/// the value start_index.
|
||||
///
|
||||
/// In addition, at index num_rows, an extra value is added
|
||||
/// containing the overal number of values.
|
||||
Multivalued(Arc<dyn ColumnValues<RowId> + 'a>),
|
||||
Multivalued(MultiValueIndex),
|
||||
}
|
||||
|
||||
impl<'a> ColumnIndex<'a> {
|
||||
impl From<OptionalIndex> for ColumnIndex {
|
||||
fn from(optional_index: OptionalIndex) -> ColumnIndex {
|
||||
ColumnIndex::Optional(optional_index)
|
||||
}
|
||||
}
|
||||
|
||||
impl From<MultiValueIndex> for ColumnIndex {
|
||||
fn from(multi_value_index: MultiValueIndex) -> ColumnIndex {
|
||||
ColumnIndex::Multivalued(multi_value_index)
|
||||
}
|
||||
}
|
||||
|
||||
impl ColumnIndex {
|
||||
pub fn get_cardinality(&self) -> Cardinality {
|
||||
match self {
|
||||
ColumnIndex::Full => Cardinality::Full,
|
||||
@@ -33,6 +42,17 @@ impl<'a> ColumnIndex<'a> {
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns true if and only if there are at least one value associated to the row.
|
||||
pub fn has_value(&self, row_id: RowId) -> bool {
|
||||
match self {
|
||||
ColumnIndex::Full => true,
|
||||
ColumnIndex::Optional(optional_index) => optional_index.contains(row_id),
|
||||
ColumnIndex::Multivalued(multivalued_index) => {
|
||||
multivalued_index.range(row_id).len() > 0
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub fn value_row_ids(&self, row_id: RowId) -> Range<RowId> {
|
||||
match self {
|
||||
ColumnIndex::Full => row_id..row_id + 1,
|
||||
@@ -43,11 +63,22 @@ impl<'a> ColumnIndex<'a> {
|
||||
0..0
|
||||
}
|
||||
}
|
||||
ColumnIndex::Multivalued(multivalued_index) => multivalued_index.range(row_id),
|
||||
}
|
||||
}
|
||||
|
||||
pub fn select_batch_in_place(&self, rank_ids: &mut Vec<RowId>) {
|
||||
match self {
|
||||
ColumnIndex::Full => {
|
||||
// No need to do anything:
|
||||
// value_idx and row_idx are the same.
|
||||
}
|
||||
ColumnIndex::Optional(optional_index) => {
|
||||
optional_index.select_batch(&mut rank_ids[..]);
|
||||
}
|
||||
ColumnIndex::Multivalued(multivalued_index) => {
|
||||
let multivalued_index_ref = &**multivalued_index;
|
||||
let start: u32 = multivalued_index_ref.get_val(row_id);
|
||||
let end: u32 = multivalued_index_ref.get_val(row_id + 1);
|
||||
start..end
|
||||
// TODO important: avoid using 0u32, and restart from the beginning all of the time.
|
||||
multivalued_index.select_batch_in_place(0u32, rank_ids)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,29 +1,141 @@
|
||||
use std::io;
|
||||
use std::io::Write;
|
||||
use std::ops::Range;
|
||||
use std::sync::Arc;
|
||||
|
||||
use common::OwnedBytes;
|
||||
|
||||
use crate::column_values::{ColumnValues, FastFieldCodecType};
|
||||
use crate::column_values::u64_based::CodecType;
|
||||
use crate::column_values::ColumnValues;
|
||||
use crate::iterable::Iterable;
|
||||
use crate::RowId;
|
||||
|
||||
#[derive(Clone)]
|
||||
pub struct MultivaluedIndex(Arc<dyn ColumnValues<RowId>>);
|
||||
|
||||
pub fn serialize_multivalued_index(
|
||||
multivalued_index: &dyn ColumnValues<RowId>,
|
||||
multivalued_index: &dyn Iterable<RowId>,
|
||||
output: &mut impl Write,
|
||||
) -> io::Result<()> {
|
||||
crate::column_values::serialize_column_values(
|
||||
&*multivalued_index,
|
||||
&[FastFieldCodecType::Bitpacked, FastFieldCodecType::Linear],
|
||||
crate::column_values::u64_based::serialize_u64_based_column_values(
|
||||
multivalued_index,
|
||||
&[CodecType::Bitpacked, CodecType::Linear],
|
||||
output,
|
||||
)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn open_multivalued_index(bytes: OwnedBytes) -> io::Result<Arc<dyn ColumnValues<RowId>>> {
|
||||
pub fn open_multivalued_index(bytes: OwnedBytes) -> io::Result<MultiValueIndex> {
|
||||
let start_index_column: Arc<dyn ColumnValues<RowId>> =
|
||||
crate::column_values::open_u64_mapped(bytes)?;
|
||||
Ok(start_index_column)
|
||||
crate::column_values::u64_based::load_u64_based_column_values(bytes)?;
|
||||
Ok(MultiValueIndex { start_index_column })
|
||||
}
|
||||
|
||||
#[derive(Clone)]
|
||||
/// Index to resolve value range for given doc_id.
|
||||
/// Starts at 0.
|
||||
pub struct MultiValueIndex {
|
||||
pub start_index_column: Arc<dyn crate::ColumnValues<RowId>>,
|
||||
}
|
||||
|
||||
impl From<Arc<dyn ColumnValues<RowId>>> for MultiValueIndex {
|
||||
fn from(start_index_column: Arc<dyn ColumnValues<RowId>>) -> Self {
|
||||
MultiValueIndex { start_index_column }
|
||||
}
|
||||
}
|
||||
|
||||
impl MultiValueIndex {
|
||||
pub fn for_test(start_offsets: &[RowId]) -> MultiValueIndex {
|
||||
let mut buffer = Vec::new();
|
||||
serialize_multivalued_index(&start_offsets, &mut buffer).unwrap();
|
||||
let bytes = OwnedBytes::new(buffer);
|
||||
open_multivalued_index(bytes).unwrap()
|
||||
}
|
||||
|
||||
/// Returns `[start, end)`, such that the values associated with
|
||||
/// the given document are `start..end`.
|
||||
#[inline]
|
||||
pub(crate) fn range(&self, row_id: RowId) -> Range<RowId> {
|
||||
let start = self.start_index_column.get_val(row_id);
|
||||
let end = self.start_index_column.get_val(row_id + 1);
|
||||
start..end
|
||||
}
|
||||
|
||||
/// Returns the number of documents in the index.
|
||||
#[inline]
|
||||
pub fn num_rows(&self) -> u32 {
|
||||
self.start_index_column.num_vals() - 1
|
||||
}
|
||||
|
||||
/// Converts a list of ranks (row ids of values) in a 1:n index to the corresponding list of
|
||||
/// row_ids. Positions are converted inplace to docids.
|
||||
///
|
||||
/// Since there is no index for value pos -> docid, but docid -> value pos range, we scan the
|
||||
/// index.
|
||||
///
|
||||
/// Correctness: positions needs to be sorted. idx_reader needs to contain monotonically
|
||||
/// increasing positions.
|
||||
///
|
||||
/// TODO: Instead of a linear scan we can employ a exponential search into binary search to
|
||||
/// match a docid to its value position.
|
||||
#[allow(clippy::bool_to_int_with_if)]
|
||||
pub(crate) fn select_batch_in_place(&self, row_start: RowId, ranks: &mut Vec<u32>) {
|
||||
if ranks.is_empty() {
|
||||
return;
|
||||
}
|
||||
let mut cur_doc = row_start;
|
||||
let mut last_doc = None;
|
||||
|
||||
assert!(self.start_index_column.get_val(row_start) as u32 <= ranks[0]);
|
||||
|
||||
let mut write_doc_pos = 0;
|
||||
for i in 0..ranks.len() {
|
||||
let pos = ranks[i];
|
||||
loop {
|
||||
let end = self.start_index_column.get_val(cur_doc + 1) as u32;
|
||||
if end > pos {
|
||||
ranks[write_doc_pos] = cur_doc;
|
||||
write_doc_pos += if last_doc == Some(cur_doc) { 0 } else { 1 };
|
||||
last_doc = Some(cur_doc);
|
||||
break;
|
||||
}
|
||||
cur_doc += 1;
|
||||
}
|
||||
}
|
||||
ranks.truncate(write_doc_pos);
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use std::ops::Range;
|
||||
use std::sync::Arc;
|
||||
|
||||
use super::MultiValueIndex;
|
||||
use crate::column_values::IterColumn;
|
||||
use crate::{ColumnValues, RowId};
|
||||
|
||||
fn index_to_pos_helper(
|
||||
index: &MultiValueIndex,
|
||||
doc_id_range: Range<u32>,
|
||||
positions: &[u32],
|
||||
) -> Vec<u32> {
|
||||
let mut positions = positions.to_vec();
|
||||
index.select_batch_in_place(doc_id_range.start, &mut positions);
|
||||
positions
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_positions_to_docid() {
|
||||
let offsets: Vec<RowId> = vec![0, 10, 12, 15, 22, 23]; // docid values are [0..10, 10..12, 12..15, etc.]
|
||||
let column: Arc<dyn ColumnValues<RowId>> = Arc::new(IterColumn::from(offsets.into_iter()));
|
||||
let index = MultiValueIndex::from(column);
|
||||
assert_eq!(index.num_rows(), 5);
|
||||
let positions = &[10u32, 11, 15, 20, 21, 22];
|
||||
assert_eq!(index_to_pos_helper(&index, 0..5, positions), vec![1, 3, 4]);
|
||||
assert_eq!(index_to_pos_helper(&index, 1..5, positions), vec![1, 3, 4]);
|
||||
assert_eq!(index_to_pos_helper(&index, 0..5, &[9]), vec![0]);
|
||||
assert_eq!(index_to_pos_helper(&index, 1..5, &[10]), vec![1]);
|
||||
assert_eq!(index_to_pos_helper(&index, 1..5, &[11]), vec![1]);
|
||||
assert_eq!(index_to_pos_helper(&index, 2..5, &[12]), vec![2]);
|
||||
assert_eq!(index_to_pos_helper(&index, 2..5, &[12, 14]), vec![2]);
|
||||
assert_eq!(index_to_pos_helper(&index, 2..5, &[12, 14, 15]), vec![2, 3]);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,16 +1,16 @@
|
||||
use std::io::{self, Write};
|
||||
use std::ops::Range;
|
||||
use std::sync::Arc;
|
||||
|
||||
mod set;
|
||||
mod set_block;
|
||||
|
||||
use common::{BinarySerializable, GroupByIteratorExtended, OwnedBytes, VInt};
|
||||
pub use set::{Set, SetCodec};
|
||||
use common::{BinarySerializable, OwnedBytes, VInt};
|
||||
pub use set::{SelectCursor, Set, SetCodec};
|
||||
use set_block::{
|
||||
DenseBlock, DenseBlockCodec, SparseBlock, SparseBlockCodec, DENSE_BLOCK_NUM_BYTES,
|
||||
};
|
||||
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{InvalidData, RowId};
|
||||
|
||||
/// The threshold for for number of elements after which we switch to dense block encoding.
|
||||
@@ -88,16 +88,6 @@ pub struct OptionalIndex {
|
||||
block_metas: Arc<[BlockMeta]>,
|
||||
}
|
||||
|
||||
impl OptionalIndex {
|
||||
pub fn num_rows(&self) -> RowId {
|
||||
self.num_rows
|
||||
}
|
||||
|
||||
pub fn num_non_nulls(&self) -> RowId {
|
||||
self.num_non_null_rows
|
||||
}
|
||||
}
|
||||
|
||||
/// Splits a value address into lower and upper 16bits.
|
||||
/// The lower 16 bits are the value in the block
|
||||
/// The upper 16 bits are the block index
|
||||
@@ -115,7 +105,63 @@ fn row_addr_from_row_id(row_id: RowId) -> RowAddr {
|
||||
}
|
||||
}
|
||||
|
||||
enum BlockSelectCursor<'a> {
|
||||
Dense(<DenseBlock<'a> as Set<u16>>::SelectCursor<'a>),
|
||||
Sparse(<SparseBlock<'a> as Set<u16>>::SelectCursor<'a>),
|
||||
}
|
||||
|
||||
impl<'a> BlockSelectCursor<'a> {
|
||||
fn select(&mut self, rank: u16) -> u16 {
|
||||
match self {
|
||||
BlockSelectCursor::Dense(dense_select_cursor) => dense_select_cursor.select(rank),
|
||||
BlockSelectCursor::Sparse(sparse_select_cursor) => sparse_select_cursor.select(rank),
|
||||
}
|
||||
}
|
||||
}
|
||||
pub struct OptionalIndexSelectCursor<'a> {
|
||||
current_block_cursor: BlockSelectCursor<'a>,
|
||||
current_block_id: u16,
|
||||
// The current block is guaranteed to contain ranks < end_rank.
|
||||
current_block_end_rank: RowId,
|
||||
optional_index: &'a OptionalIndex,
|
||||
block_doc_idx_start: RowId,
|
||||
num_null_rows_before_block: RowId,
|
||||
}
|
||||
|
||||
impl<'a> OptionalIndexSelectCursor<'a> {
|
||||
fn search_and_load_block(&mut self, rank: RowId) {
|
||||
if rank < self.current_block_end_rank {
|
||||
// we are already in the right block
|
||||
return;
|
||||
}
|
||||
self.current_block_id = self.optional_index.find_block(rank, self.current_block_id);
|
||||
self.current_block_end_rank = self
|
||||
.optional_index
|
||||
.block_metas
|
||||
.get(self.current_block_id as usize + 1)
|
||||
.map(|block_meta| block_meta.non_null_rows_before_block)
|
||||
.unwrap_or(u32::MAX);
|
||||
self.block_doc_idx_start = (self.current_block_id as u32) * ELEMENTS_PER_BLOCK;
|
||||
let block_meta = self.optional_index.block_metas[self.current_block_id as usize];
|
||||
self.num_null_rows_before_block = block_meta.non_null_rows_before_block;
|
||||
let block: Block<'_> = self.optional_index.block(block_meta);
|
||||
self.current_block_cursor = match block {
|
||||
Block::Dense(dense_block) => BlockSelectCursor::Dense(dense_block.select_cursor()),
|
||||
Block::Sparse(sparse_block) => BlockSelectCursor::Sparse(sparse_block.select_cursor()),
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> SelectCursor<RowId> for OptionalIndexSelectCursor<'a> {
|
||||
fn select(&mut self, rank: RowId) -> RowId {
|
||||
self.search_and_load_block(rank);
|
||||
let index_in_block = (rank - self.num_null_rows_before_block) as u16;
|
||||
self.current_block_cursor.select(index_in_block) as RowId + self.block_doc_idx_start
|
||||
}
|
||||
}
|
||||
|
||||
impl Set<RowId> for OptionalIndex {
|
||||
type SelectCursor<'b> = OptionalIndexSelectCursor<'b> where Self: 'b;
|
||||
// Check if value at position is not null.
|
||||
#[inline]
|
||||
fn contains(&self, row_id: RowId) -> bool {
|
||||
@@ -130,6 +176,21 @@ impl Set<RowId> for OptionalIndex {
|
||||
}
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn rank(&self, row_id: RowId) -> RowId {
|
||||
let RowAddr {
|
||||
block_id,
|
||||
in_block_row_id,
|
||||
} = row_addr_from_row_id(row_id);
|
||||
let block_meta = self.block_metas[block_id as usize];
|
||||
let block = self.block(block_meta);
|
||||
let block_offset_row_id = match block {
|
||||
Block::Dense(dense_block) => dense_block.rank(in_block_row_id),
|
||||
Block::Sparse(sparse_block) => sparse_block.rank(in_block_row_id),
|
||||
} as u32;
|
||||
block_meta.non_null_rows_before_block + block_offset_row_id
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn rank_if_exists(&self, row_id: RowId) -> Option<RowId> {
|
||||
let RowAddr {
|
||||
@@ -148,7 +209,7 @@ impl Set<RowId> for OptionalIndex {
|
||||
#[inline]
|
||||
fn select(&self, rank: RowId) -> RowId {
|
||||
let block_pos = self.find_block(rank, 0);
|
||||
let block_doc_idx_start = block_pos * ELEMENTS_PER_BLOCK;
|
||||
let block_doc_idx_start = (block_pos as u32) * ELEMENTS_PER_BLOCK;
|
||||
let block_meta = self.block_metas[block_pos as usize];
|
||||
let block: Block<'_> = self.block(block_meta);
|
||||
let index_in_block = (rank - block_meta.non_null_rows_before_block) as u16;
|
||||
@@ -159,39 +220,53 @@ impl Set<RowId> for OptionalIndex {
|
||||
block_doc_idx_start + in_block_rank as u32
|
||||
}
|
||||
|
||||
fn select_batch(&self, ranks: &[u32], output_idxs: &mut [u32]) {
|
||||
let mut block_pos = 0u32;
|
||||
let mut start = 0;
|
||||
let group_by_it = ranks.iter().copied().group_by(move |codec_idx| {
|
||||
block_pos = self.find_block(*codec_idx, block_pos);
|
||||
block_pos
|
||||
});
|
||||
for (block_pos, block_iter) in group_by_it {
|
||||
let block_doc_idx_start = block_pos * ELEMENTS_PER_BLOCK;
|
||||
let block_meta = self.block_metas[block_pos as usize];
|
||||
let block: Block<'_> = self.block(block_meta);
|
||||
let offset = block_meta.non_null_rows_before_block;
|
||||
let indexes_in_block_iter =
|
||||
block_iter.map(move |codec_idx| (codec_idx - offset) as u16);
|
||||
match block {
|
||||
Block::Dense(dense_block) => {
|
||||
for in_offset in dense_block.select_iter(indexes_in_block_iter) {
|
||||
output_idxs[start] = in_offset as u32 + block_doc_idx_start;
|
||||
start += 1;
|
||||
}
|
||||
}
|
||||
Block::Sparse(sparse_block) => {
|
||||
for in_offset in sparse_block.select_iter(indexes_in_block_iter) {
|
||||
output_idxs[start] = in_offset as u32 + block_doc_idx_start;
|
||||
start += 1;
|
||||
}
|
||||
}
|
||||
};
|
||||
fn select_cursor<'b>(&'b self) -> OptionalIndexSelectCursor<'b> {
|
||||
OptionalIndexSelectCursor {
|
||||
current_block_cursor: BlockSelectCursor::Sparse(
|
||||
SparseBlockCodec::open(b"").select_cursor(),
|
||||
),
|
||||
current_block_id: 0u16,
|
||||
current_block_end_rank: 0u32, //< this is sufficient to force the first load
|
||||
optional_index: self,
|
||||
block_doc_idx_start: 0u32,
|
||||
num_null_rows_before_block: 0u32,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl OptionalIndex {
|
||||
pub fn for_test(num_rows: RowId, row_ids: &[RowId]) -> OptionalIndex {
|
||||
assert!(row_ids
|
||||
.last()
|
||||
.copied()
|
||||
.map(|last_row_id| last_row_id < num_rows)
|
||||
.unwrap_or(true));
|
||||
let mut buffer = Vec::new();
|
||||
serialize_optional_index(&row_ids, num_rows, &mut buffer).unwrap();
|
||||
let bytes = OwnedBytes::new(buffer);
|
||||
open_optional_index(bytes).unwrap()
|
||||
}
|
||||
|
||||
pub fn num_rows(&self) -> RowId {
|
||||
self.num_rows
|
||||
}
|
||||
|
||||
pub fn num_non_nulls(&self) -> RowId {
|
||||
self.num_non_null_rows
|
||||
}
|
||||
|
||||
pub fn iter_rows<'a>(&'a self) -> impl Iterator<Item = RowId> + 'a {
|
||||
// TODO optimize
|
||||
let mut select_batch = self.select_cursor();
|
||||
(0..self.num_non_null_rows).map(move |rank| select_batch.select(rank))
|
||||
}
|
||||
pub fn select_batch(&self, ranks: &mut [RowId]) {
|
||||
let mut select_cursor = self.select_cursor();
|
||||
for rank in ranks.iter_mut() {
|
||||
*rank = select_cursor.select(*rank);
|
||||
}
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn block<'a>(&'a self, block_meta: BlockMeta) -> Block<'a> {
|
||||
let BlockMeta {
|
||||
@@ -214,14 +289,14 @@ impl OptionalIndex {
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn find_block(&self, dense_idx: u32, start_block_pos: u32) -> u32 {
|
||||
for block_pos in start_block_pos..self.block_metas.len() as u32 {
|
||||
fn find_block(&self, dense_idx: u32, start_block_pos: u16) -> u16 {
|
||||
for block_pos in start_block_pos..self.block_metas.len() as u16 {
|
||||
let offset = self.block_metas[block_pos as usize].non_null_rows_before_block;
|
||||
if offset > dense_idx {
|
||||
return block_pos - 1;
|
||||
return block_pos - 1u16;
|
||||
}
|
||||
}
|
||||
self.block_metas.len() as u32 - 1u32
|
||||
self.block_metas.len() as u16 - 1u16
|
||||
}
|
||||
|
||||
// TODO Add a good API for the codec_idx to original_idx translation.
|
||||
@@ -255,7 +330,7 @@ impl OptionalIndexCodec {
|
||||
}
|
||||
|
||||
impl BinarySerializable for OptionalIndexCodec {
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
writer.write_all(&[self.to_code()])
|
||||
}
|
||||
|
||||
@@ -277,12 +352,13 @@ fn serialize_optional_index_block(block_els: &[u16], out: &mut impl io::Write) -
|
||||
}
|
||||
|
||||
pub fn serialize_optional_index<'a, W: io::Write>(
|
||||
serializable_optional_index: &dyn SerializableOptionalIndex<'a>,
|
||||
non_null_rows: &dyn Iterable<RowId>,
|
||||
num_rows: RowId,
|
||||
output: &mut W,
|
||||
) -> io::Result<()> {
|
||||
VInt(serializable_optional_index.num_rows() as u64).serialize(output)?;
|
||||
VInt(num_rows as u64).serialize(output)?;
|
||||
|
||||
let mut rows_it = serializable_optional_index.non_null_rows();
|
||||
let mut rows_it = non_null_rows.boxed_iter();
|
||||
let mut block_metadata: Vec<SerializedBlockMeta> = Vec::new();
|
||||
let mut current_block = Vec::new();
|
||||
|
||||
@@ -435,19 +511,5 @@ pub fn open_optional_index(bytes: OwnedBytes) -> io::Result<OptionalIndex> {
|
||||
Ok(optional_index)
|
||||
}
|
||||
|
||||
pub trait SerializableOptionalIndex<'a> {
|
||||
fn num_rows(&self) -> RowId;
|
||||
fn non_null_rows(&self) -> Box<dyn Iterator<Item = RowId> + 'a>;
|
||||
}
|
||||
|
||||
impl SerializableOptionalIndex<'static> for Range<u32> {
|
||||
fn num_rows(&self) -> RowId {
|
||||
self.end
|
||||
}
|
||||
fn non_null_rows(&self) -> Box<dyn Iterator<Item = RowId> + 'static> {
|
||||
Box::new(self.clone())
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests;
|
||||
|
||||
@@ -13,11 +13,25 @@ pub trait SetCodec {
|
||||
fn open<'a>(data: &'a [u8]) -> Self::Reader<'a>;
|
||||
}
|
||||
|
||||
/// Stateful object that makes it possible to compute several select in a row,
|
||||
/// provided the rank passed as argument are increasing.
|
||||
pub trait SelectCursor<T> {
|
||||
// May panic if rank is greater than the number of elements in the Set,
|
||||
// or if rank is < than value provided in the previous call.
|
||||
fn select(&mut self, rank: T) -> T;
|
||||
}
|
||||
|
||||
pub trait Set<T> {
|
||||
type SelectCursor<'b>: SelectCursor<T>
|
||||
where Self: 'b;
|
||||
|
||||
/// Returns true if the elements is contained in the Set
|
||||
fn contains(&self, el: T) -> bool;
|
||||
|
||||
/// If the set contains `el` returns its position in the sortd set of elements.
|
||||
/// Returns the number of rows in the set that are < `el`
|
||||
fn rank(&self, el: T) -> T;
|
||||
|
||||
/// If the set contains `el` returns the element rank.
|
||||
/// If the set does not contain the element, it returns `None`.
|
||||
fn rank_if_exists(&self, el: T) -> Option<T>;
|
||||
|
||||
@@ -28,11 +42,6 @@ pub trait Set<T> {
|
||||
/// May panic if rank is greater than the number of elements in the Set.
|
||||
fn select(&self, rank: T) -> T;
|
||||
|
||||
/// Batch version of select.
|
||||
/// `ranks` is assumed to be sorted.
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// May panic if rank is greater than the number of elements in the Set.
|
||||
fn select_batch(&self, ranks: &[T], outputs: &mut [T]);
|
||||
/// Creates a brand new select cursor.
|
||||
fn select_cursor<'b>(&'b self) -> Self::SelectCursor<'b>;
|
||||
}
|
||||
|
||||
@@ -3,7 +3,7 @@ use std::io::{self, Write};
|
||||
|
||||
use common::BinarySerializable;
|
||||
|
||||
use crate::column_index::optional_index::{Set, SetCodec, ELEMENTS_PER_BLOCK};
|
||||
use crate::column_index::optional_index::{SelectCursor, Set, SetCodec, ELEMENTS_PER_BLOCK};
|
||||
|
||||
#[inline(always)]
|
||||
fn get_bit_at(input: u64, n: u16) -> bool {
|
||||
@@ -105,7 +105,27 @@ impl DenseMiniBlock {
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct DenseBlock<'a>(&'a [u8]);
|
||||
|
||||
pub struct DenseBlockSelectCursor<'a> {
|
||||
block_id: u16,
|
||||
dense_block: DenseBlock<'a>,
|
||||
}
|
||||
|
||||
impl<'a> SelectCursor<u16> for DenseBlockSelectCursor<'a> {
|
||||
#[inline]
|
||||
fn select(&mut self, rank: u16) -> u16 {
|
||||
self.block_id = self
|
||||
.dense_block
|
||||
.find_miniblock_containing_rank(rank, self.block_id)
|
||||
.unwrap();
|
||||
let index_block = self.dense_block.mini_block(self.block_id);
|
||||
let in_block_rank = rank - index_block.rank;
|
||||
self.block_id * ELEMENTS_PER_MINI_BLOCK + select_u64(index_block.bitvec, in_block_rank)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> Set<u16> for DenseBlock<'a> {
|
||||
type SelectCursor<'b> = DenseBlockSelectCursor<'a> where Self: 'b;
|
||||
|
||||
#[inline(always)]
|
||||
fn contains(&self, el: u16) -> bool {
|
||||
let mini_block_id = el / ELEMENTS_PER_MINI_BLOCK;
|
||||
@@ -128,6 +148,15 @@ impl<'a> Set<u16> for DenseBlock<'a> {
|
||||
}
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn rank(&self, el: u16) -> u16 {
|
||||
let block_pos = el / ELEMENTS_PER_MINI_BLOCK;
|
||||
let index_block = self.mini_block(block_pos);
|
||||
let pos_in_block_bit_vec = el % ELEMENTS_PER_MINI_BLOCK;
|
||||
let ones_in_block = rank_u64(index_block.bitvec, pos_in_block_bit_vec);
|
||||
index_block.rank + ones_in_block
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn select(&self, rank: u16) -> u16 {
|
||||
let block_id = self.find_miniblock_containing_rank(rank, 0).unwrap();
|
||||
@@ -136,37 +165,15 @@ impl<'a> Set<u16> for DenseBlock<'a> {
|
||||
block_id * ELEMENTS_PER_MINI_BLOCK + select_u64(index_block.bitvec, in_block_rank)
|
||||
}
|
||||
|
||||
fn select_batch(&self, ranks: &[u16], outputs: &mut [u16]) {
|
||||
let orig_ids = self.select_iter(ranks.iter().copied());
|
||||
for (output, original_id) in outputs.iter_mut().zip(orig_ids) {
|
||||
*output = original_id;
|
||||
#[inline(always)]
|
||||
fn select_cursor<'b>(&'b self) -> Self::SelectCursor<'b> {
|
||||
DenseBlockSelectCursor {
|
||||
block_id: 0,
|
||||
dense_block: *self,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> DenseBlock<'a> {
|
||||
/// Iterator verison of select.
|
||||
///
|
||||
/// # Panics
|
||||
/// Panics if one of the rank is higher than the number of elements in the set.
|
||||
pub fn select_iter<'b>(
|
||||
&self,
|
||||
rank_it: impl Iterator<Item = u16> + 'b,
|
||||
) -> impl Iterator<Item = u16> + 'b
|
||||
where
|
||||
Self: 'b,
|
||||
{
|
||||
let mut block_id = 0u16;
|
||||
let me = *self;
|
||||
rank_it.map(move |rank| {
|
||||
block_id = me.find_miniblock_containing_rank(rank, block_id).unwrap();
|
||||
let index_block = me.mini_block(block_id);
|
||||
let in_block_rank = rank - index_block.rank;
|
||||
block_id * ELEMENTS_PER_MINI_BLOCK + select_u64(index_block.bitvec, in_block_rank)
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> DenseBlock<'a> {
|
||||
#[inline]
|
||||
fn mini_block(&self, mini_block_id: u16) -> DenseMiniBlock {
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
use crate::column_index::optional_index::{Set, SetCodec};
|
||||
use crate::column_index::optional_index::{SelectCursor, Set, SetCodec};
|
||||
|
||||
pub struct SparseBlockCodec;
|
||||
|
||||
@@ -24,7 +24,16 @@ impl SetCodec for SparseBlockCodec {
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct SparseBlock<'a>(&'a [u8]);
|
||||
|
||||
impl<'a> SelectCursor<u16> for SparseBlock<'a> {
|
||||
#[inline]
|
||||
fn select(&mut self, rank: u16) -> u16 {
|
||||
<SparseBlock<'a> as Set<u16>>::select(self, rank)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> Set<u16> for SparseBlock<'a> {
|
||||
type SelectCursor<'b> = Self where Self: 'b;
|
||||
|
||||
#[inline(always)]
|
||||
fn contains(&self, el: u16) -> bool {
|
||||
self.binary_search(el).is_ok()
|
||||
@@ -35,17 +44,20 @@ impl<'a> Set<u16> for SparseBlock<'a> {
|
||||
self.binary_search(el).ok()
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn rank(&self, el: u16) -> u16 {
|
||||
self.binary_search(el).unwrap_or_else(|el| el)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn select(&self, rank: u16) -> u16 {
|
||||
let offset = rank as usize * 2;
|
||||
u16::from_le_bytes(self.0[offset..offset + 2].try_into().unwrap())
|
||||
}
|
||||
|
||||
fn select_batch(&self, ranks: &[u16], outputs: &mut [u16]) {
|
||||
let orig_ids = self.select_iter(ranks.iter().copied());
|
||||
for (output, original_id) in outputs.iter_mut().zip(orig_ids) {
|
||||
*output = original_id;
|
||||
}
|
||||
#[inline(always)]
|
||||
fn select_cursor<'b>(&'b self) -> Self::SelectCursor<'b> {
|
||||
*self
|
||||
}
|
||||
}
|
||||
|
||||
@@ -96,17 +108,4 @@ impl<'a> SparseBlock<'a> {
|
||||
}
|
||||
Err(left)
|
||||
}
|
||||
|
||||
pub fn select_iter<'b>(
|
||||
&self,
|
||||
iter: impl Iterator<Item = u16> + 'b,
|
||||
) -> impl Iterator<Item = u16> + 'b
|
||||
where
|
||||
Self: 'b,
|
||||
{
|
||||
iter.map(|codec_id| {
|
||||
let offset = codec_id as usize * 2;
|
||||
u16::from_le_bytes(self.0[offset..offset + 2].try_into().unwrap())
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,9 +1,8 @@
|
||||
use std::collections::HashMap;
|
||||
|
||||
use crate::column_index::optional_index::set_block::{
|
||||
DenseBlockCodec, SparseBlockCodec, DENSE_BLOCK_NUM_BYTES,
|
||||
};
|
||||
use crate::column_index::optional_index::{Set, SetCodec};
|
||||
use crate::column_index::optional_index::set_block::dense::DENSE_BLOCK_NUM_BYTES;
|
||||
use crate::column_index::optional_index::set_block::{DenseBlockCodec, SparseBlockCodec};
|
||||
use crate::column_index::optional_index::{SelectCursor, Set, SetCodec};
|
||||
|
||||
fn test_set_helper<C: SetCodec<Item = u16>>(vals: &[u16]) -> usize {
|
||||
let mut buffer = Vec::new();
|
||||
@@ -18,6 +17,10 @@ fn test_set_helper<C: SetCodec<Item = u16>>(vals: &[u16]) -> usize {
|
||||
for val in 0u16..=u16::MAX {
|
||||
assert_eq!(tested_set.contains(val), hash_set.contains_key(&val));
|
||||
assert_eq!(tested_set.rank_if_exists(val), hash_set.get(&val).copied());
|
||||
assert_eq!(
|
||||
tested_set.rank(val),
|
||||
vals.iter().cloned().take_while(|v| *v < val).count() as u16
|
||||
);
|
||||
}
|
||||
for rank in 0..vals.len() {
|
||||
assert_eq!(tested_set.select(rank as u16), vals[rank]);
|
||||
@@ -75,12 +78,10 @@ fn test_simple_translate_codec_codec_idx_to_original_idx_dense() {
|
||||
.unwrap();
|
||||
let tested_set = DenseBlockCodec::open(buffer.as_slice());
|
||||
assert!(tested_set.contains(1));
|
||||
assert_eq!(
|
||||
&tested_set
|
||||
.select_iter([0, 1, 2, 5].iter().copied())
|
||||
.collect::<Vec<u16>>(),
|
||||
&[1, 3, 17, 30_001]
|
||||
);
|
||||
let mut select_cursor = tested_set.select_cursor();
|
||||
assert_eq!(select_cursor.select(0), 1);
|
||||
assert_eq!(select_cursor.select(1), 3);
|
||||
assert_eq!(select_cursor.select(2), 17);
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -89,12 +90,10 @@ fn test_simple_translate_codec_idx_to_original_idx_sparse() {
|
||||
SparseBlockCodec::serialize([1, 3, 17].iter().copied(), &mut buffer).unwrap();
|
||||
let tested_set = SparseBlockCodec::open(buffer.as_slice());
|
||||
assert!(tested_set.contains(1));
|
||||
assert_eq!(
|
||||
&tested_set
|
||||
.select_iter([0, 1, 2].iter().copied())
|
||||
.collect::<Vec<u16>>(),
|
||||
&[1, 3, 17]
|
||||
);
|
||||
let mut select_cursor = tested_set.select_cursor();
|
||||
assert_eq!(SelectCursor::select(&mut select_cursor, 0), 1);
|
||||
assert_eq!(SelectCursor::select(&mut select_cursor, 1), 3);
|
||||
assert_eq!(SelectCursor::select(&mut select_cursor, 2), 17);
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -103,10 +102,8 @@ fn test_simple_translate_codec_idx_to_original_idx_dense() {
|
||||
DenseBlockCodec::serialize(0u16..150u16, &mut buffer).unwrap();
|
||||
let tested_set = DenseBlockCodec::open(buffer.as_slice());
|
||||
assert!(tested_set.contains(1));
|
||||
let rg = 0u16..150u16;
|
||||
let els: Vec<u16> = rg.clone().collect();
|
||||
assert_eq!(
|
||||
&tested_set.select_iter(rg.clone()).collect::<Vec<u16>>(),
|
||||
&els
|
||||
);
|
||||
let mut select_cursor = tested_set.select_cursor();
|
||||
for i in 0..150 {
|
||||
assert_eq!(i, select_cursor.select(i));
|
||||
}
|
||||
}
|
||||
|
||||
@@ -37,13 +37,14 @@ proptest! {
|
||||
fn test_with_random_sets_simple() {
|
||||
let vals = 10..BLOCK_SIZE * 2;
|
||||
let mut out: Vec<u8> = Vec::new();
|
||||
serialize_optional_index(&vals.clone(), &mut out).unwrap();
|
||||
serialize_optional_index(&vals.clone(), 100, &mut out).unwrap();
|
||||
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
|
||||
let ranks: Vec<u32> = (65_472u32..65_473u32).collect();
|
||||
let els: Vec<u32> = ranks.iter().copied().map(|rank| rank + 10).collect();
|
||||
let mut output = vec![0u32; ranks.len()];
|
||||
null_index.select_batch(&ranks[..], &mut output[..]);
|
||||
assert_eq!(&output, &els);
|
||||
let mut select_cursor = null_index.select_cursor();
|
||||
for (rank, el) in ranks.iter().copied().zip(els.iter().copied()) {
|
||||
assert_eq!(select_cursor.select(rank), el);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -65,12 +66,8 @@ fn test_optional_index_one_block_true() {
|
||||
test_null_index(&iter[..]);
|
||||
}
|
||||
|
||||
impl<'a> SerializableOptionalIndex<'a> for &'a [bool] {
|
||||
fn num_rows(&self) -> RowId {
|
||||
self.len() as u32
|
||||
}
|
||||
|
||||
fn non_null_rows(&self) -> Box<dyn Iterator<Item = RowId> + 'a> {
|
||||
impl<'a> Iterable<RowId> for &'a [bool] {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = RowId> + 'a> {
|
||||
Box::new(
|
||||
self.iter()
|
||||
.cloned()
|
||||
@@ -83,7 +80,7 @@ impl<'a> SerializableOptionalIndex<'a> for &'a [bool] {
|
||||
|
||||
fn test_null_index(data: &[bool]) {
|
||||
let mut out: Vec<u8> = Vec::new();
|
||||
serialize_optional_index(&data, &mut out).unwrap();
|
||||
serialize_optional_index(&data, data.len() as RowId, &mut out).unwrap();
|
||||
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
|
||||
let orig_idx_with_value: Vec<u32> = data
|
||||
.iter()
|
||||
@@ -91,11 +88,10 @@ fn test_null_index(data: &[bool]) {
|
||||
.filter(|(_pos, val)| **val)
|
||||
.map(|(pos, _val)| pos as u32)
|
||||
.collect();
|
||||
let ids: Vec<u32> = (0..orig_idx_with_value.len() as u32).collect();
|
||||
let mut output = vec![0u32; ids.len()];
|
||||
null_index.select_batch(&ids[..], &mut output);
|
||||
// assert_eq!(&output[0..100], &orig_idx_with_value[0..100]);
|
||||
assert_eq!(output, orig_idx_with_value);
|
||||
let mut select_iter = null_index.select_cursor();
|
||||
for i in 0..orig_idx_with_value.len() {
|
||||
assert_eq!(select_iter.select(i as u32), orig_idx_with_value[i]);
|
||||
}
|
||||
|
||||
let step_size = (orig_idx_with_value.len() / 100).max(1);
|
||||
for (dense_idx, orig_idx) in orig_idx_with_value.iter().enumerate().step_by(step_size) {
|
||||
@@ -111,51 +107,96 @@ fn test_null_index(data: &[bool]) {
|
||||
|
||||
#[test]
|
||||
fn test_optional_index_test_translation() {
|
||||
let mut out = vec![];
|
||||
let iter = &[true, false, true, false];
|
||||
serialize_optional_index(&&iter[..], &mut out).unwrap();
|
||||
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
|
||||
let mut output = vec![0u32; 2];
|
||||
null_index.select_batch(&[0, 1], &mut output);
|
||||
assert_eq!(output, &[0, 2]);
|
||||
let optional_index = OptionalIndex::for_test(4, &[0, 2]);
|
||||
let mut select_cursor = optional_index.select_cursor();
|
||||
assert_eq!(select_cursor.select(0), 0);
|
||||
assert_eq!(select_cursor.select(1), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_optional_index_translate() {
|
||||
let mut out = vec![];
|
||||
let iter = &[true, false, true, false];
|
||||
serialize_optional_index(&&iter[..], &mut out).unwrap();
|
||||
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
|
||||
assert_eq!(null_index.rank_if_exists(0), Some(0));
|
||||
assert_eq!(null_index.rank_if_exists(2), Some(1));
|
||||
let optional_index = OptionalIndex::for_test(4, &[0, 2]);
|
||||
assert_eq!(optional_index.rank_if_exists(0), Some(0));
|
||||
assert_eq!(optional_index.rank_if_exists(2), Some(1));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_optional_index_small() {
|
||||
let mut out = vec![];
|
||||
let iter = &[true, false, true, false];
|
||||
serialize_optional_index(&&iter[..], &mut out).unwrap();
|
||||
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
|
||||
assert!(null_index.contains(0));
|
||||
assert!(!null_index.contains(1));
|
||||
assert!(null_index.contains(2));
|
||||
assert!(!null_index.contains(3));
|
||||
let optional_index = OptionalIndex::for_test(4, &[0, 2]);
|
||||
assert!(optional_index.contains(0));
|
||||
assert!(!optional_index.contains(1));
|
||||
assert!(optional_index.contains(2));
|
||||
assert!(!optional_index.contains(3));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_optional_index_large() {
|
||||
let mut docs = vec![];
|
||||
docs.extend((0..ELEMENTS_PER_BLOCK).map(|_idx| false));
|
||||
docs.extend((0..=1).map(|_idx| true));
|
||||
let row_ids = &[ELEMENTS_PER_BLOCK, ELEMENTS_PER_BLOCK + 1];
|
||||
let optional_index = OptionalIndex::for_test(ELEMENTS_PER_BLOCK + 2, row_ids);
|
||||
assert!(!optional_index.contains(0));
|
||||
assert!(!optional_index.contains(100));
|
||||
assert!(!optional_index.contains(ELEMENTS_PER_BLOCK - 1));
|
||||
assert!(optional_index.contains(ELEMENTS_PER_BLOCK));
|
||||
assert!(optional_index.contains(ELEMENTS_PER_BLOCK + 1));
|
||||
}
|
||||
|
||||
let mut out = vec![];
|
||||
serialize_optional_index(&&docs[..], &mut out).unwrap();
|
||||
let null_index = open_optional_index(OwnedBytes::new(out)).unwrap();
|
||||
assert!(!null_index.contains(0));
|
||||
assert!(!null_index.contains(100));
|
||||
assert!(!null_index.contains(ELEMENTS_PER_BLOCK - 1));
|
||||
assert!(null_index.contains(ELEMENTS_PER_BLOCK));
|
||||
assert!(null_index.contains(ELEMENTS_PER_BLOCK + 1));
|
||||
fn test_optional_index_iter_aux(row_ids: &[RowId], num_rows: RowId) {
|
||||
let optional_index = OptionalIndex::for_test(num_rows, row_ids);
|
||||
assert_eq!(optional_index.num_rows(), num_rows);
|
||||
assert!(optional_index.iter_rows().eq(row_ids.iter().copied()));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_optional_index_iter_empty() {
|
||||
test_optional_index_iter_aux(&[], 0u32);
|
||||
}
|
||||
|
||||
fn test_optional_index_rank_aux(row_ids: &[RowId]) {
|
||||
let num_rows = row_ids.last().copied().unwrap_or(0u32) + 1;
|
||||
let null_index = OptionalIndex::for_test(num_rows, row_ids);
|
||||
assert_eq!(null_index.num_rows(), num_rows);
|
||||
for (row_id, row_val) in row_ids.iter().copied().enumerate() {
|
||||
assert_eq!(null_index.rank(row_val), row_id as u32);
|
||||
assert_eq!(null_index.rank_if_exists(row_val), Some(row_id as u32));
|
||||
if row_val > 0 && !null_index.contains(&row_val - 1) {
|
||||
assert_eq!(null_index.rank(row_val - 1), row_id as u32);
|
||||
}
|
||||
assert_eq!(null_index.rank(row_val + 1), row_id as u32 + 1);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_optional_index_rank() {
|
||||
test_optional_index_rank_aux(&[1u32]);
|
||||
test_optional_index_rank_aux(&[0u32, 1u32]);
|
||||
let mut block = Vec::new();
|
||||
block.push(3u32);
|
||||
block.extend((0..BLOCK_SIZE).map(|i| i + BLOCK_SIZE + 1));
|
||||
test_optional_index_rank_aux(&block);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_optional_index_iter_empty_one() {
|
||||
test_optional_index_iter_aux(&[1], 2u32);
|
||||
test_optional_index_iter_aux(&[100_000], 200_000u32);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_optional_index_iter_dense_block() {
|
||||
let mut block = Vec::new();
|
||||
block.push(3u32);
|
||||
block.extend((0..BLOCK_SIZE).map(|i| i + BLOCK_SIZE + 1));
|
||||
test_optional_index_iter_aux(&block, 3 * BLOCK_SIZE);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_optional_index_for_tests() {
|
||||
let optional_index = OptionalIndex::for_test(4, &[1, 2]);
|
||||
assert!(!optional_index.contains(0));
|
||||
assert!(optional_index.contains(1));
|
||||
assert!(optional_index.contains(2));
|
||||
assert!(!optional_index.contains(3));
|
||||
assert_eq!(optional_index.num_rows(), 4);
|
||||
}
|
||||
|
||||
#[cfg(all(test, feature = "unstable"))]
|
||||
@@ -175,7 +216,6 @@ mod bench {
|
||||
.map(|_| rng.gen_bool(fill_ratio))
|
||||
.collect();
|
||||
serialize_optional_index(&&vals[..], &mut out).unwrap();
|
||||
|
||||
let codec = open_optional_index(OwnedBytes::new(out)).unwrap();
|
||||
codec
|
||||
}
|
||||
@@ -311,7 +351,8 @@ mod bench {
|
||||
};
|
||||
let mut output = vec![0u32; idxs.len()];
|
||||
bench.iter(|| {
|
||||
codec.select_batch(&idxs[..], &mut output);
|
||||
output.copy_from_slice(&idxs[..]);
|
||||
codec.select_batch(&mut output);
|
||||
});
|
||||
}
|
||||
|
||||
|
||||
@@ -5,23 +5,26 @@ use common::{CountingWriter, OwnedBytes};
|
||||
|
||||
use crate::column_index::multivalued_index::serialize_multivalued_index;
|
||||
use crate::column_index::optional_index::serialize_optional_index;
|
||||
use crate::column_index::{ColumnIndex, SerializableOptionalIndex};
|
||||
use crate::column_values::ColumnValues;
|
||||
use crate::column_index::ColumnIndex;
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{Cardinality, RowId};
|
||||
|
||||
pub enum SerializableColumnIndex<'a> {
|
||||
Full,
|
||||
Optional(Box<dyn SerializableOptionalIndex<'a> + 'a>),
|
||||
Optional {
|
||||
non_null_row_ids: Box<dyn Iterable<RowId> + 'a>,
|
||||
num_rows: RowId,
|
||||
},
|
||||
// TODO remove the Arc<dyn> apart from serialization this is not
|
||||
// dynamic at all.
|
||||
Multivalued(Box<dyn ColumnValues<RowId> + 'a>),
|
||||
Multivalued(Box<dyn Iterable<RowId> + 'a>),
|
||||
}
|
||||
|
||||
impl<'a> SerializableColumnIndex<'a> {
|
||||
pub fn get_cardinality(&self) -> Cardinality {
|
||||
match self {
|
||||
SerializableColumnIndex::Full => Cardinality::Full,
|
||||
SerializableColumnIndex::Optional(_) => Cardinality::Optional,
|
||||
SerializableColumnIndex::Optional { .. } => Cardinality::Optional,
|
||||
SerializableColumnIndex::Multivalued(_) => Cardinality::Multivalued,
|
||||
}
|
||||
}
|
||||
@@ -36,9 +39,10 @@ pub fn serialize_column_index(
|
||||
output.write_all(&[cardinality])?;
|
||||
match column_index {
|
||||
SerializableColumnIndex::Full => {}
|
||||
SerializableColumnIndex::Optional(optional_index) => {
|
||||
serialize_optional_index(&*optional_index, &mut output)?
|
||||
}
|
||||
SerializableColumnIndex::Optional {
|
||||
non_null_row_ids,
|
||||
num_rows,
|
||||
} => serialize_optional_index(non_null_row_ids.as_ref(), num_rows, &mut output)?,
|
||||
SerializableColumnIndex::Multivalued(multivalued_index) => {
|
||||
serialize_multivalued_index(&*multivalued_index, &mut output)?
|
||||
}
|
||||
@@ -47,7 +51,7 @@ pub fn serialize_column_index(
|
||||
Ok(column_index_num_bytes)
|
||||
}
|
||||
|
||||
pub fn open_column_index(mut bytes: OwnedBytes) -> io::Result<ColumnIndex<'static>> {
|
||||
pub fn open_column_index(mut bytes: OwnedBytes) -> io::Result<ColumnIndex> {
|
||||
if bytes.is_empty() {
|
||||
return Err(io::Error::new(
|
||||
io::ErrorKind::UnexpectedEof,
|
||||
@@ -64,8 +68,8 @@ pub fn open_column_index(mut bytes: OwnedBytes) -> io::Result<ColumnIndex<'stati
|
||||
Ok(ColumnIndex::Optional(optional_index))
|
||||
}
|
||||
Cardinality::Multivalued => {
|
||||
let multivalued_index = super::multivalued_index::open_multivalued_index(bytes)?;
|
||||
Ok(ColumnIndex::Multivalued(multivalued_index))
|
||||
let multivalue_index = super::multivalued_index::open_multivalued_index(bytes)?;
|
||||
Ok(ColumnIndex::Multivalued(multivalue_index))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,115 +0,0 @@
|
||||
use std::io::{self, Write};
|
||||
|
||||
use common::OwnedBytes;
|
||||
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
|
||||
|
||||
use super::serialize::NormalizedHeader;
|
||||
use super::{ColumnValues, FastFieldCodec, FastFieldCodecType};
|
||||
|
||||
/// Depending on the field type, a different
|
||||
/// fast field is required.
|
||||
#[derive(Clone)]
|
||||
pub struct BitpackedReader {
|
||||
data: OwnedBytes,
|
||||
bit_unpacker: BitUnpacker,
|
||||
normalized_header: NormalizedHeader,
|
||||
}
|
||||
|
||||
impl ColumnValues for BitpackedReader {
|
||||
#[inline]
|
||||
fn get_val(&self, doc: u32) -> u64 {
|
||||
self.bit_unpacker.get(doc, &self.data)
|
||||
}
|
||||
#[inline]
|
||||
fn min_value(&self) -> u64 {
|
||||
// The BitpackedReader assumes a normalized vector.
|
||||
0
|
||||
}
|
||||
#[inline]
|
||||
fn max_value(&self) -> u64 {
|
||||
self.normalized_header.max_value
|
||||
}
|
||||
#[inline]
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.normalized_header.num_vals
|
||||
}
|
||||
}
|
||||
|
||||
pub struct BitpackedCodec;
|
||||
|
||||
impl FastFieldCodec for BitpackedCodec {
|
||||
/// The CODEC_TYPE is an enum value used for serialization.
|
||||
const CODEC_TYPE: FastFieldCodecType = FastFieldCodecType::Bitpacked;
|
||||
|
||||
type Reader = BitpackedReader;
|
||||
|
||||
/// Opens a fast field given a file.
|
||||
fn open_from_bytes(
|
||||
data: OwnedBytes,
|
||||
normalized_header: NormalizedHeader,
|
||||
) -> io::Result<Self::Reader> {
|
||||
let num_bits = compute_num_bits(normalized_header.max_value);
|
||||
let bit_unpacker = BitUnpacker::new(num_bits);
|
||||
Ok(BitpackedReader {
|
||||
data,
|
||||
bit_unpacker,
|
||||
normalized_header,
|
||||
})
|
||||
}
|
||||
|
||||
/// Serializes data with the BitpackedFastFieldSerializer.
|
||||
///
|
||||
/// The bitpacker assumes that the column has been normalized.
|
||||
/// i.e. It has already been shifted by its minimum value, so that its
|
||||
/// current minimum value is 0.
|
||||
///
|
||||
/// Ideally, we made a shift upstream on the column so that `col.min_value() == 0`.
|
||||
fn serialize(column: &dyn ColumnValues, write: &mut impl Write) -> io::Result<()> {
|
||||
assert_eq!(column.min_value(), 0u64);
|
||||
let num_bits = compute_num_bits(column.max_value());
|
||||
let mut bit_packer = BitPacker::new();
|
||||
for val in column.iter() {
|
||||
bit_packer.write(val, num_bits, write)?;
|
||||
}
|
||||
bit_packer.close(write)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn estimate(column: &dyn ColumnValues) -> Option<f32> {
|
||||
let num_bits = compute_num_bits(column.max_value());
|
||||
let num_bits_uncompressed = 64;
|
||||
Some(num_bits as f32 / num_bits_uncompressed as f32)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::column_values::tests::create_and_validate;
|
||||
|
||||
fn create_and_validate_bitpacked_codec(data: &[u64], name: &str) {
|
||||
create_and_validate::<BitpackedCodec>(data, name);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_with_codec_data_sets() {
|
||||
let data_sets = crate::column_values::tests::get_codec_test_datasets();
|
||||
for (mut data, name) in data_sets {
|
||||
create_and_validate_bitpacked_codec(&data, name);
|
||||
data.reverse();
|
||||
create_and_validate::<BitpackedCodec>(&data, name);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn bitpacked_fast_field_rand() {
|
||||
for _ in 0..500 {
|
||||
let mut data = (0..1 + rand::random::<u8>() as usize)
|
||||
.map(|_| rand::random::<i64>() as u64 / 2)
|
||||
.collect::<Vec<_>>();
|
||||
create_and_validate_bitpacked_codec(&data, "rand");
|
||||
data.reverse();
|
||||
create_and_validate::<BitpackedCodec>(&data, "rand");
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,188 +0,0 @@
|
||||
use std::sync::Arc;
|
||||
use std::{io, iter};
|
||||
|
||||
use common::{BinarySerializable, CountingWriter, DeserializeFrom, OwnedBytes};
|
||||
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
|
||||
|
||||
use crate::column_values::line::Line;
|
||||
use crate::column_values::serialize::NormalizedHeader;
|
||||
use crate::column_values::{ColumnValues, FastFieldCodec, FastFieldCodecType, VecColumn};
|
||||
|
||||
const CHUNK_SIZE: usize = 512;
|
||||
|
||||
#[derive(Debug, Default)]
|
||||
struct Block {
|
||||
line: Line,
|
||||
bit_unpacker: BitUnpacker,
|
||||
data_start_offset: usize,
|
||||
}
|
||||
|
||||
impl BinarySerializable for Block {
|
||||
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
self.line.serialize(writer)?;
|
||||
self.bit_unpacker.bit_width().serialize(writer)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let line = Line::deserialize(reader)?;
|
||||
let bit_width = u8::deserialize(reader)?;
|
||||
Ok(Block {
|
||||
line,
|
||||
bit_unpacker: BitUnpacker::new(bit_width),
|
||||
data_start_offset: 0,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
fn compute_num_blocks(num_vals: u32) -> usize {
|
||||
(num_vals as usize + CHUNK_SIZE - 1) / CHUNK_SIZE
|
||||
}
|
||||
|
||||
pub struct BlockwiseLinearCodec;
|
||||
|
||||
impl FastFieldCodec for BlockwiseLinearCodec {
|
||||
const CODEC_TYPE: FastFieldCodecType = FastFieldCodecType::BlockwiseLinear;
|
||||
type Reader = BlockwiseLinearReader;
|
||||
|
||||
fn open_from_bytes(
|
||||
bytes: common::OwnedBytes,
|
||||
normalized_header: NormalizedHeader,
|
||||
) -> io::Result<Self::Reader> {
|
||||
let footer_len: u32 = (&bytes[bytes.len() - 4..]).deserialize()?;
|
||||
let footer_offset = bytes.len() - 4 - footer_len as usize;
|
||||
let (data, mut footer) = bytes.split(footer_offset);
|
||||
let num_blocks = compute_num_blocks(normalized_header.num_vals);
|
||||
let mut blocks: Vec<Block> = iter::repeat_with(|| Block::deserialize(&mut footer))
|
||||
.take(num_blocks)
|
||||
.collect::<io::Result<_>>()?;
|
||||
|
||||
let mut start_offset = 0;
|
||||
for block in &mut blocks {
|
||||
block.data_start_offset = start_offset;
|
||||
start_offset += (block.bit_unpacker.bit_width() as usize) * CHUNK_SIZE / 8;
|
||||
}
|
||||
Ok(BlockwiseLinearReader {
|
||||
blocks: Arc::new(blocks),
|
||||
data,
|
||||
normalized_header,
|
||||
})
|
||||
}
|
||||
|
||||
// Estimate first_chunk and extrapolate
|
||||
fn estimate(column: &dyn ColumnValues) -> Option<f32> {
|
||||
if column.num_vals() < 10 * CHUNK_SIZE as u32 {
|
||||
return None;
|
||||
}
|
||||
let mut first_chunk: Vec<u64> = column.iter().take(CHUNK_SIZE).collect();
|
||||
let line = Line::train(&VecColumn::from(&first_chunk));
|
||||
for (i, buffer_val) in first_chunk.iter_mut().enumerate() {
|
||||
let interpolated_val = line.eval(i as u32);
|
||||
*buffer_val = buffer_val.wrapping_sub(interpolated_val);
|
||||
}
|
||||
let estimated_bit_width = first_chunk
|
||||
.iter()
|
||||
.map(|el| ((el + 1) as f32 * 3.0) as u64)
|
||||
.map(compute_num_bits)
|
||||
.max()
|
||||
.unwrap();
|
||||
|
||||
let metadata_per_block = {
|
||||
let mut out = vec![];
|
||||
Block::default().serialize(&mut out).unwrap();
|
||||
out.len()
|
||||
};
|
||||
let num_bits = estimated_bit_width as u64 * column.num_vals() as u64
|
||||
// function metadata per block
|
||||
+ metadata_per_block as u64 * (column.num_vals() as u64 / CHUNK_SIZE as u64);
|
||||
let num_bits_uncompressed = 64 * column.num_vals();
|
||||
Some(num_bits as f32 / num_bits_uncompressed as f32)
|
||||
}
|
||||
|
||||
fn serialize(column: &dyn ColumnValues, wrt: &mut impl io::Write) -> io::Result<()> {
|
||||
// The BitpackedReader assumes a normalized vector.
|
||||
assert_eq!(column.min_value(), 0);
|
||||
let mut buffer = Vec::with_capacity(CHUNK_SIZE);
|
||||
let num_vals = column.num_vals();
|
||||
|
||||
let num_blocks = compute_num_blocks(num_vals);
|
||||
let mut blocks = Vec::with_capacity(num_blocks);
|
||||
|
||||
let mut vals = column.iter();
|
||||
|
||||
let mut bit_packer = BitPacker::new();
|
||||
|
||||
for _ in 0..num_blocks {
|
||||
buffer.clear();
|
||||
buffer.extend((&mut vals).take(CHUNK_SIZE));
|
||||
let line = Line::train(&VecColumn::from(&buffer));
|
||||
|
||||
assert!(!buffer.is_empty());
|
||||
|
||||
for (i, buffer_val) in buffer.iter_mut().enumerate() {
|
||||
let interpolated_val = line.eval(i as u32);
|
||||
*buffer_val = buffer_val.wrapping_sub(interpolated_val);
|
||||
}
|
||||
let bit_width = buffer.iter().copied().map(compute_num_bits).max().unwrap();
|
||||
|
||||
for &buffer_val in &buffer {
|
||||
bit_packer.write(buffer_val, bit_width, wrt)?;
|
||||
}
|
||||
|
||||
blocks.push(Block {
|
||||
line,
|
||||
bit_unpacker: BitUnpacker::new(bit_width),
|
||||
data_start_offset: 0,
|
||||
});
|
||||
}
|
||||
|
||||
bit_packer.close(wrt)?;
|
||||
|
||||
assert_eq!(blocks.len(), compute_num_blocks(num_vals));
|
||||
|
||||
let mut counting_wrt = CountingWriter::wrap(wrt);
|
||||
for block in &blocks {
|
||||
block.serialize(&mut counting_wrt)?;
|
||||
}
|
||||
let footer_len = counting_wrt.written_bytes();
|
||||
(footer_len as u32).serialize(&mut counting_wrt)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Clone)]
|
||||
pub struct BlockwiseLinearReader {
|
||||
blocks: Arc<Vec<Block>>,
|
||||
normalized_header: NormalizedHeader,
|
||||
data: OwnedBytes,
|
||||
}
|
||||
|
||||
impl ColumnValues for BlockwiseLinearReader {
|
||||
#[inline(always)]
|
||||
fn get_val(&self, idx: u32) -> u64 {
|
||||
let block_id = (idx / CHUNK_SIZE as u32) as usize;
|
||||
let idx_within_block = idx % (CHUNK_SIZE as u32);
|
||||
let block = &self.blocks[block_id];
|
||||
let interpoled_val: u64 = block.line.eval(idx_within_block);
|
||||
let block_bytes = &self.data[block.data_start_offset..];
|
||||
let bitpacked_diff = block.bit_unpacker.get(idx_within_block, block_bytes);
|
||||
interpoled_val.wrapping_add(bitpacked_diff)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn min_value(&self) -> u64 {
|
||||
// The BlockwiseLinearReader assumes a normalized vector.
|
||||
0u64
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn max_value(&self) -> u64 {
|
||||
self.normalized_header.max_value
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.normalized_header.num_vals
|
||||
}
|
||||
}
|
||||
@@ -1,5 +1,7 @@
|
||||
use std::fmt::Debug;
|
||||
use std::marker::PhantomData;
|
||||
use std::ops::{Range, RangeInclusive};
|
||||
use std::sync::Arc;
|
||||
|
||||
use tantivy_bitpacker::minmax;
|
||||
|
||||
@@ -26,7 +28,7 @@ pub trait ColumnValues<T: PartialOrd = u64>: Send + Sync {
|
||||
///
|
||||
/// Must panic if `start + output.len()` is greater than
|
||||
/// the segment's `maxdoc`.
|
||||
#[inline]
|
||||
#[inline(always)]
|
||||
fn get_range(&self, start: u64, output: &mut [T]) {
|
||||
for (out, idx) in output.iter_mut().zip(start..) {
|
||||
*out = self.get_val(idx as u32);
|
||||
@@ -36,7 +38,7 @@ pub trait ColumnValues<T: PartialOrd = u64>: Send + Sync {
|
||||
/// Get the positions of values which are in the provided value range.
|
||||
///
|
||||
/// Note that position == docid for single value fast fields
|
||||
#[inline]
|
||||
#[inline(always)]
|
||||
fn get_docids_for_value_range(
|
||||
&self,
|
||||
value_range: RangeInclusive<T>,
|
||||
@@ -44,7 +46,6 @@ pub trait ColumnValues<T: PartialOrd = u64>: Send + Sync {
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
let doc_id_range = doc_id_range.start..doc_id_range.end.min(self.num_vals());
|
||||
|
||||
for idx in doc_id_range.start..doc_id_range.end {
|
||||
let val = self.get_val(idx);
|
||||
if value_range.contains(&val) {
|
||||
@@ -78,33 +79,39 @@ pub trait ColumnValues<T: PartialOrd = u64>: Send + Sync {
|
||||
}
|
||||
}
|
||||
|
||||
impl<T: Copy + PartialOrd> ColumnValues<T> for std::sync::Arc<dyn ColumnValues<T>> {
|
||||
impl<T: Copy + PartialOrd + Debug> ColumnValues<T> for Arc<dyn ColumnValues<T>> {
|
||||
#[inline(always)]
|
||||
fn get_val(&self, idx: u32) -> T {
|
||||
self.as_ref().get_val(idx)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn min_value(&self) -> T {
|
||||
self.as_ref().min_value()
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn max_value(&self) -> T {
|
||||
self.as_ref().max_value()
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.as_ref().num_vals()
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn iter<'b>(&'b self) -> Box<dyn Iterator<Item = T> + 'b> {
|
||||
self.as_ref().iter()
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn get_range(&self, start: u64, output: &mut [T]) {
|
||||
self.as_ref().get_range(start, output)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a, C: ColumnValues<T> + ?Sized, T: Copy + PartialOrd> ColumnValues<T> for &'a C {
|
||||
impl<'a, C: ColumnValues<T> + ?Sized, T: Copy + PartialOrd + Debug> ColumnValues<T> for &'a C {
|
||||
fn get_val(&self, idx: u32) -> T {
|
||||
(*self).get_val(idx)
|
||||
}
|
||||
@@ -137,7 +144,7 @@ pub struct VecColumn<'a, T = u64> {
|
||||
pub(crate) max_value: T,
|
||||
}
|
||||
|
||||
impl<'a, T: Copy + PartialOrd + Send + Sync> ColumnValues<T> for VecColumn<'a, T> {
|
||||
impl<'a, T: Copy + PartialOrd + Send + Sync + Debug> ColumnValues<T> for VecColumn<'a, T> {
|
||||
fn get_val(&self, position: u32) -> T {
|
||||
self.values[position as usize]
|
||||
}
|
||||
@@ -205,8 +212,8 @@ pub fn monotonic_map_column<C, T, Input, Output>(
|
||||
where
|
||||
C: ColumnValues<Input>,
|
||||
T: StrictlyMonotonicFn<Input, Output> + Send + Sync,
|
||||
Input: PartialOrd + Send + Sync + Clone,
|
||||
Output: PartialOrd + Send + Sync + Clone,
|
||||
Input: PartialOrd + Debug + Send + Sync + Clone,
|
||||
Output: PartialOrd + Debug + Send + Sync + Clone,
|
||||
{
|
||||
MonotonicMappingColumn {
|
||||
from_column,
|
||||
@@ -219,8 +226,8 @@ impl<C, T, Input, Output> ColumnValues<Output> for MonotonicMappingColumn<C, T,
|
||||
where
|
||||
C: ColumnValues<Input>,
|
||||
T: StrictlyMonotonicFn<Input, Output> + Send + Sync,
|
||||
Input: PartialOrd + Send + Sync + Clone,
|
||||
Output: PartialOrd + Send + Sync + Clone,
|
||||
Input: PartialOrd + Send + Debug + Sync + Clone,
|
||||
Output: PartialOrd + Send + Debug + Sync + Clone,
|
||||
{
|
||||
#[inline]
|
||||
fn get_val(&self, idx: u32) -> Output {
|
||||
@@ -282,7 +289,7 @@ where T: Iterator + Clone + ExactSizeIterator
|
||||
impl<T> ColumnValues<T::Item> for IterColumn<T>
|
||||
where
|
||||
T: Iterator + Clone + ExactSizeIterator + Send + Sync,
|
||||
T::Item: PartialOrd,
|
||||
T::Item: PartialOrd + Debug,
|
||||
{
|
||||
fn get_val(&self, idx: u32) -> T::Item {
|
||||
self.0.clone().nth(idx as usize).unwrap()
|
||||
|
||||
@@ -1,19 +0,0 @@
|
||||
// Copyright (C) 2022 Quickwit, Inc.
|
||||
//
|
||||
// Quickwit is offered under the AGPL v3.0 and as commercial software.
|
||||
// For commercial licensing, contact us at hello@quickwit.io.
|
||||
//
|
||||
// AGPL:
|
||||
// This program is free software: you can redistribute it and/or modify
|
||||
// it under the terms of the GNU Affero General Public License as
|
||||
// published by the Free Software Foundation, either version 3 of the
|
||||
// License, or (at your option) any later version.
|
||||
//
|
||||
// This program is distributed in the hope that it will be useful,
|
||||
// but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
// GNU Affero General Public License for more details.
|
||||
//
|
||||
// You should have received a copy of the GNU Affero General Public License
|
||||
// along with this program. If not, see <http://www.gnu.org/licenses/>.
|
||||
//
|
||||
@@ -22,6 +22,7 @@ use tantivy_bitpacker::{self, BitPacker, BitUnpacker};
|
||||
|
||||
use crate::column_values::compact_space::build_compact_space::get_compact_space;
|
||||
use crate::column_values::ColumnValues;
|
||||
use crate::RowId;
|
||||
|
||||
mod blank_range;
|
||||
mod build_compact_space;
|
||||
@@ -55,7 +56,7 @@ impl RangeMapping {
|
||||
}
|
||||
|
||||
impl BinarySerializable for CompactSpace {
|
||||
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: io::Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
VInt(self.ranges_mapping.len() as u64).serialize(writer)?;
|
||||
|
||||
let mut prev_value = 0;
|
||||
@@ -158,23 +159,30 @@ impl CompactSpace {
|
||||
pub struct CompactSpaceCompressor {
|
||||
params: IPCodecParams,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct IPCodecParams {
|
||||
compact_space: CompactSpace,
|
||||
bit_unpacker: BitUnpacker,
|
||||
min_value: u128,
|
||||
max_value: u128,
|
||||
num_vals: u32,
|
||||
num_vals: RowId,
|
||||
num_bits: u8,
|
||||
}
|
||||
|
||||
impl CompactSpaceCompressor {
|
||||
/// Taking the vals as Vec may cost a lot of memory. It is used to sort the vals.
|
||||
pub fn train_from(iter: impl Iterator<Item = u128>, num_vals: u32) -> Self {
|
||||
let mut values_sorted = BTreeSet::new();
|
||||
values_sorted.extend(iter);
|
||||
let total_num_values = num_vals;
|
||||
pub fn num_vals(&self) -> RowId {
|
||||
self.params.num_vals
|
||||
}
|
||||
|
||||
/// Taking the vals as Vec may cost a lot of memory. It is used to sort the vals.
|
||||
pub fn train_from(iter: impl Iterator<Item = u128>) -> Self {
|
||||
let mut values_sorted = BTreeSet::new();
|
||||
let mut total_num_values = 0u32;
|
||||
for val in iter {
|
||||
total_num_values += 1u32;
|
||||
values_sorted.insert(val);
|
||||
}
|
||||
let compact_space =
|
||||
get_compact_space(&values_sorted, total_num_values, COST_PER_BLANK_IN_BITS);
|
||||
let amplitude_compact_space = compact_space.amplitude_compact_space();
|
||||
@@ -247,7 +255,7 @@ pub struct CompactSpaceDecompressor {
|
||||
}
|
||||
|
||||
impl BinarySerializable for IPCodecParams {
|
||||
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: io::Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
// header flags for future optional dictionary encoding
|
||||
let footer_flags = 0u64;
|
||||
footer_flags.serialize(writer)?;
|
||||
@@ -450,364 +458,352 @@ impl CompactSpaceDecompressor {
|
||||
}
|
||||
}
|
||||
|
||||
// TODO reenable what can be reenabled.
|
||||
// #[cfg(test)]
|
||||
// mod tests {
|
||||
//
|
||||
// use super::*;
|
||||
// use crate::column::format_version::read_format_version;
|
||||
// use crate::column::column_footer::read_null_index_footer;
|
||||
// use crate::column::serialize::U128Header;
|
||||
// use crate::column::{open_u128, serialize_u128};
|
||||
//
|
||||
// #[test]
|
||||
// fn compact_space_test() {
|
||||
// let ips = &[
|
||||
// 2u128, 4u128, 1000, 1001, 1002, 1003, 1004, 1005, 1008, 1010, 1012, 1260,
|
||||
// ]
|
||||
// .into_iter()
|
||||
// .collect();
|
||||
// let compact_space = get_compact_space(ips, ips.len() as u32, 11);
|
||||
// let amplitude = compact_space.amplitude_compact_space();
|
||||
// assert_eq!(amplitude, 17);
|
||||
// assert_eq!(1, compact_space.u128_to_compact(2).unwrap());
|
||||
// assert_eq!(2, compact_space.u128_to_compact(3).unwrap());
|
||||
// assert_eq!(compact_space.u128_to_compact(100).unwrap_err(), 1);
|
||||
//
|
||||
// for (num1, num2) in (0..3).tuple_windows() {
|
||||
// assert_eq!(
|
||||
// compact_space.get_range_mapping(num1).compact_end() + 1,
|
||||
// compact_space.get_range_mapping(num2).compact_start
|
||||
// );
|
||||
// }
|
||||
//
|
||||
// let mut output: Vec<u8> = Vec::new();
|
||||
// compact_space.serialize(&mut output).unwrap();
|
||||
//
|
||||
// assert_eq!(
|
||||
// compact_space,
|
||||
// CompactSpace::deserialize(&mut &output[..]).unwrap()
|
||||
// );
|
||||
//
|
||||
// for ip in ips {
|
||||
// let compact = compact_space.u128_to_compact(*ip).unwrap();
|
||||
// assert_eq!(compact_space.compact_to_u128(compact), *ip);
|
||||
// }
|
||||
// }
|
||||
//
|
||||
// #[test]
|
||||
// fn compact_space_amplitude_test() {
|
||||
// let ips = &[100000u128, 1000000].into_iter().collect();
|
||||
// let compact_space = get_compact_space(ips, ips.len() as u32, 1);
|
||||
// let amplitude = compact_space.amplitude_compact_space();
|
||||
// assert_eq!(amplitude, 2);
|
||||
// }
|
||||
//
|
||||
// fn test_all(mut data: OwnedBytes, expected: &[u128]) {
|
||||
// let _header = U128Header::deserialize(&mut data);
|
||||
// let decompressor = CompactSpaceDecompressor::open(data).unwrap();
|
||||
// for (idx, expected_val) in expected.iter().cloned().enumerate() {
|
||||
// let val = decompressor.get(idx as u32);
|
||||
// assert_eq!(val, expected_val);
|
||||
//
|
||||
// let test_range = |range: RangeInclusive<u128>| {
|
||||
// let expected_positions = expected
|
||||
// .iter()
|
||||
// .positions(|val| range.contains(val))
|
||||
// .map(|pos| pos as u32)
|
||||
// .collect::<Vec<_>>();
|
||||
// let mut positions = Vec::new();
|
||||
// decompressor.get_positions_for_value_range(
|
||||
// range,
|
||||
// 0..decompressor.num_vals(),
|
||||
// &mut positions,
|
||||
// );
|
||||
// assert_eq!(positions, expected_positions);
|
||||
// };
|
||||
//
|
||||
// test_range(expected_val.saturating_sub(1)..=expected_val);
|
||||
// test_range(expected_val..=expected_val);
|
||||
// test_range(expected_val..=expected_val.saturating_add(1));
|
||||
// test_range(expected_val.saturating_sub(1)..=expected_val.saturating_add(1));
|
||||
// }
|
||||
// }
|
||||
//
|
||||
// fn test_aux_vals(u128_vals: &[u128]) -> OwnedBytes {
|
||||
// let mut out = Vec::new();
|
||||
// serialize_u128(
|
||||
// || u128_vals.iter().cloned(),
|
||||
// u128_vals.len() as u32,
|
||||
// &mut out,
|
||||
// )
|
||||
// .unwrap();
|
||||
//
|
||||
// let data = OwnedBytes::new(out);
|
||||
// let (data, _format_version) = read_format_version(data).unwrap();
|
||||
// let (data, _null_index_footer) = read_null_index_footer(data).unwrap();
|
||||
// test_all(data.clone(), u128_vals);
|
||||
//
|
||||
// data
|
||||
// }
|
||||
//
|
||||
// #[test]
|
||||
// fn test_range_1() {
|
||||
// let vals = &[
|
||||
// 1u128,
|
||||
// 100u128,
|
||||
// 3u128,
|
||||
// 99999u128,
|
||||
// 100000u128,
|
||||
// 100001u128,
|
||||
// 4_000_211_221u128,
|
||||
// 4_000_211_222u128,
|
||||
// 333u128,
|
||||
// ];
|
||||
// let mut data = test_aux_vals(vals);
|
||||
//
|
||||
// let _header = U128Header::deserialize(&mut data);
|
||||
// let decomp = CompactSpaceDecompressor::open(data).unwrap();
|
||||
// let complete_range = 0..vals.len() as u32;
|
||||
// for (pos, val) in vals.iter().enumerate() {
|
||||
// let val = *val;
|
||||
// let pos = pos as u32;
|
||||
// let mut positions = Vec::new();
|
||||
// decomp.get_positions_for_value_range(val..=val, pos..pos + 1, &mut positions);
|
||||
// assert_eq!(positions, vec![pos]);
|
||||
// }
|
||||
//
|
||||
// handle docid range out of bounds
|
||||
// let positions: Vec<u32> = get_positions_for_value_range_helper(&decomp, 0..=1, 1..u32::MAX);
|
||||
// assert!(positions.is_empty());
|
||||
//
|
||||
// let positions =
|
||||
// get_positions_for_value_range_helper(&decomp, 0..=1, complete_range.clone());
|
||||
// assert_eq!(positions, vec![0]);
|
||||
// let positions =
|
||||
// get_positions_for_value_range_helper(&decomp, 0..=2, complete_range.clone());
|
||||
// assert_eq!(positions, vec![0]);
|
||||
// let positions =
|
||||
// get_positions_for_value_range_helper(&decomp, 0..=3, complete_range.clone());
|
||||
// assert_eq!(positions, vec![0, 2]);
|
||||
// assert_eq!(
|
||||
// get_positions_for_value_range_helper(
|
||||
// &decomp,
|
||||
// 99999u128..=99999u128,
|
||||
// complete_range.clone()
|
||||
// ),
|
||||
// vec![3]
|
||||
// );
|
||||
// assert_eq!(
|
||||
// get_positions_for_value_range_helper(
|
||||
// &decomp,
|
||||
// 99999u128..=100000u128,
|
||||
// complete_range.clone()
|
||||
// ),
|
||||
// vec![3, 4]
|
||||
// );
|
||||
// assert_eq!(
|
||||
// get_positions_for_value_range_helper(
|
||||
// &decomp,
|
||||
// 99998u128..=100000u128,
|
||||
// complete_range.clone()
|
||||
// ),
|
||||
// vec![3, 4]
|
||||
// );
|
||||
// assert_eq!(
|
||||
// &get_positions_for_value_range_helper(
|
||||
// &decomp,
|
||||
// 99998u128..=99999u128,
|
||||
// complete_range.clone()
|
||||
// ),
|
||||
// &[3]
|
||||
// );
|
||||
// assert!(get_positions_for_value_range_helper(
|
||||
// &decomp,
|
||||
// 99998u128..=99998u128,
|
||||
// complete_range.clone()
|
||||
// )
|
||||
// .is_empty());
|
||||
// assert_eq!(
|
||||
// &get_positions_for_value_range_helper(
|
||||
// &decomp,
|
||||
// 333u128..=333u128,
|
||||
// complete_range.clone()
|
||||
// ),
|
||||
// &[8]
|
||||
// );
|
||||
// assert_eq!(
|
||||
// &get_positions_for_value_range_helper(
|
||||
// &decomp,
|
||||
// 332u128..=333u128,
|
||||
// complete_range.clone()
|
||||
// ),
|
||||
// &[8]
|
||||
// );
|
||||
// assert_eq!(
|
||||
// &get_positions_for_value_range_helper(
|
||||
// &decomp,
|
||||
// 332u128..=334u128,
|
||||
// complete_range.clone()
|
||||
// ),
|
||||
// &[8]
|
||||
// );
|
||||
// assert_eq!(
|
||||
// &get_positions_for_value_range_helper(
|
||||
// &decomp,
|
||||
// 333u128..=334u128,
|
||||
// complete_range.clone()
|
||||
// ),
|
||||
// &[8]
|
||||
// );
|
||||
//
|
||||
// assert_eq!(
|
||||
// &get_positions_for_value_range_helper(
|
||||
// &decomp,
|
||||
// 4_000_211_221u128..=5_000_000_000u128,
|
||||
// complete_range
|
||||
// ),
|
||||
// &[6, 7]
|
||||
// );
|
||||
// }
|
||||
//
|
||||
// #[test]
|
||||
// fn test_empty() {
|
||||
// let vals = &[];
|
||||
// let data = test_aux_vals(vals);
|
||||
// let _decomp = CompactSpaceDecompressor::open(data).unwrap();
|
||||
// }
|
||||
//
|
||||
// #[test]
|
||||
// fn test_range_2() {
|
||||
// let vals = &[
|
||||
// 100u128,
|
||||
// 99999u128,
|
||||
// 100000u128,
|
||||
// 100001u128,
|
||||
// 4_000_211_221u128,
|
||||
// 4_000_211_222u128,
|
||||
// 333u128,
|
||||
// ];
|
||||
// let mut data = test_aux_vals(vals);
|
||||
// let _header = U128Header::deserialize(&mut data);
|
||||
// let decomp = CompactSpaceDecompressor::open(data).unwrap();
|
||||
// let complete_range = 0..vals.len() as u32;
|
||||
// assert!(
|
||||
// &get_positions_for_value_range_helper(&decomp, 0..=5, complete_range.clone())
|
||||
// .is_empty(),
|
||||
// );
|
||||
// assert_eq!(
|
||||
// &get_positions_for_value_range_helper(&decomp, 0..=100, complete_range.clone()),
|
||||
// &[0]
|
||||
// );
|
||||
// assert_eq!(
|
||||
// &get_positions_for_value_range_helper(&decomp, 0..=105, complete_range),
|
||||
// &[0]
|
||||
// );
|
||||
// }
|
||||
//
|
||||
// fn get_positions_for_value_range_helper<C: Column<T> + ?Sized, T: PartialOrd>(
|
||||
// column: &C,
|
||||
// value_range: RangeInclusive<T>,
|
||||
// doc_id_range: Range<u32>,
|
||||
// ) -> Vec<u32> {
|
||||
// let mut positions = Vec::new();
|
||||
// column.get_docids_for_value_range(value_range, doc_id_range, &mut positions);
|
||||
// positions
|
||||
// }
|
||||
//
|
||||
// #[test]
|
||||
// fn test_range_3() {
|
||||
// let vals = &[
|
||||
// 200u128,
|
||||
// 201,
|
||||
// 202,
|
||||
// 203,
|
||||
// 204,
|
||||
// 204,
|
||||
// 206,
|
||||
// 207,
|
||||
// 208,
|
||||
// 209,
|
||||
// 210,
|
||||
// 1_000_000,
|
||||
// 5_000_000_000,
|
||||
// ];
|
||||
// let mut out = Vec::new();
|
||||
// serialize_u128(|| vals.iter().cloned(), vals.len() as u32, &mut out).unwrap();
|
||||
// let decomp = open_u128::<u128>(OwnedBytes::new(out)).unwrap();
|
||||
// let complete_range = 0..vals.len() as u32;
|
||||
//
|
||||
// assert_eq!(
|
||||
// get_positions_for_value_range_helper(&*decomp, 199..=200, complete_range.clone()),
|
||||
// vec![0]
|
||||
// );
|
||||
//
|
||||
// assert_eq!(
|
||||
// get_positions_for_value_range_helper(&*decomp, 199..=201, complete_range.clone()),
|
||||
// vec![0, 1]
|
||||
// );
|
||||
//
|
||||
// assert_eq!(
|
||||
// get_positions_for_value_range_helper(&*decomp, 200..=200, complete_range.clone()),
|
||||
// vec![0]
|
||||
// );
|
||||
//
|
||||
// assert_eq!(
|
||||
// get_positions_for_value_range_helper(&*decomp, 1_000_000..=1_000_000, complete_range),
|
||||
// vec![11]
|
||||
// );
|
||||
// }
|
||||
//
|
||||
// #[test]
|
||||
// fn test_bug1() {
|
||||
// let vals = &[9223372036854775806];
|
||||
// let _data = test_aux_vals(vals);
|
||||
// }
|
||||
//
|
||||
// #[test]
|
||||
// fn test_bug2() {
|
||||
// let vals = &[340282366920938463463374607431768211455u128];
|
||||
// let _data = test_aux_vals(vals);
|
||||
// }
|
||||
//
|
||||
// #[test]
|
||||
// fn test_bug3() {
|
||||
// let vals = &[340282366920938463463374607431768211454];
|
||||
// let _data = test_aux_vals(vals);
|
||||
// }
|
||||
//
|
||||
// #[test]
|
||||
// fn test_bug4() {
|
||||
// let vals = &[340282366920938463463374607431768211455, 0];
|
||||
// let _data = test_aux_vals(vals);
|
||||
// }
|
||||
//
|
||||
// #[test]
|
||||
// fn test_first_large_gaps() {
|
||||
// let vals = &[1_000_000_000u128; 100];
|
||||
// let _data = test_aux_vals(vals);
|
||||
// }
|
||||
// use itertools::Itertools;
|
||||
// use proptest::prelude::*;
|
||||
//
|
||||
// fn num_strategy() -> impl Strategy<Value = u128> {
|
||||
// prop_oneof![
|
||||
// 1 => prop::num::u128::ANY.prop_map(|num| u128::MAX - (num % 10) ),
|
||||
// 1 => prop::num::u128::ANY.prop_map(|num| i64::MAX as u128 + 5 - (num % 10) ),
|
||||
// 1 => prop::num::u128::ANY.prop_map(|num| i128::MAX as u128 + 5 - (num % 10) ),
|
||||
// 1 => prop::num::u128::ANY.prop_map(|num| num % 10 ),
|
||||
// 20 => prop::num::u128::ANY,
|
||||
// ]
|
||||
// }
|
||||
//
|
||||
// proptest! {
|
||||
// #![proptest_config(ProptestConfig::with_cases(10))]
|
||||
//
|
||||
// #[test]
|
||||
// fn compress_decompress_random(vals in proptest::collection::vec(num_strategy()
|
||||
// , 1..1000)) {
|
||||
// let _data = test_aux_vals(&vals);
|
||||
// }
|
||||
// }
|
||||
// }
|
||||
//
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
|
||||
use itertools::Itertools;
|
||||
|
||||
use super::*;
|
||||
use crate::column_values::serialize::U128Header;
|
||||
use crate::column_values::{open_u128_mapped, serialize_column_values_u128};
|
||||
|
||||
#[test]
|
||||
fn compact_space_test() {
|
||||
let ips = &[
|
||||
2u128, 4u128, 1000, 1001, 1002, 1003, 1004, 1005, 1008, 1010, 1012, 1260,
|
||||
]
|
||||
.into_iter()
|
||||
.collect();
|
||||
let compact_space = get_compact_space(ips, ips.len() as u32, 11);
|
||||
let amplitude = compact_space.amplitude_compact_space();
|
||||
assert_eq!(amplitude, 17);
|
||||
assert_eq!(1, compact_space.u128_to_compact(2).unwrap());
|
||||
assert_eq!(2, compact_space.u128_to_compact(3).unwrap());
|
||||
assert_eq!(compact_space.u128_to_compact(100).unwrap_err(), 1);
|
||||
|
||||
for (num1, num2) in (0..3).tuple_windows() {
|
||||
assert_eq!(
|
||||
compact_space.get_range_mapping(num1).compact_end() + 1,
|
||||
compact_space.get_range_mapping(num2).compact_start
|
||||
);
|
||||
}
|
||||
|
||||
let mut output: Vec<u8> = Vec::new();
|
||||
compact_space.serialize(&mut output).unwrap();
|
||||
|
||||
assert_eq!(
|
||||
compact_space,
|
||||
CompactSpace::deserialize(&mut &output[..]).unwrap()
|
||||
);
|
||||
|
||||
for ip in ips {
|
||||
let compact = compact_space.u128_to_compact(*ip).unwrap();
|
||||
assert_eq!(compact_space.compact_to_u128(compact), *ip);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn compact_space_amplitude_test() {
|
||||
let ips = &[100000u128, 1000000].into_iter().collect();
|
||||
let compact_space = get_compact_space(ips, ips.len() as u32, 1);
|
||||
let amplitude = compact_space.amplitude_compact_space();
|
||||
assert_eq!(amplitude, 2);
|
||||
}
|
||||
|
||||
fn test_all(mut data: OwnedBytes, expected: &[u128]) {
|
||||
let _header = U128Header::deserialize(&mut data);
|
||||
let decompressor = CompactSpaceDecompressor::open(data).unwrap();
|
||||
for (idx, expected_val) in expected.iter().cloned().enumerate() {
|
||||
let val = decompressor.get(idx as u32);
|
||||
assert_eq!(val, expected_val);
|
||||
|
||||
let test_range = |range: RangeInclusive<u128>| {
|
||||
let expected_positions = expected
|
||||
.iter()
|
||||
.positions(|val| range.contains(val))
|
||||
.map(|pos| pos as u32)
|
||||
.collect::<Vec<_>>();
|
||||
let mut positions = Vec::new();
|
||||
decompressor.get_positions_for_value_range(
|
||||
range,
|
||||
0..decompressor.num_vals(),
|
||||
&mut positions,
|
||||
);
|
||||
assert_eq!(positions, expected_positions);
|
||||
};
|
||||
|
||||
test_range(expected_val.saturating_sub(1)..=expected_val);
|
||||
test_range(expected_val..=expected_val);
|
||||
test_range(expected_val..=expected_val.saturating_add(1));
|
||||
test_range(expected_val.saturating_sub(1)..=expected_val.saturating_add(1));
|
||||
}
|
||||
}
|
||||
|
||||
fn test_aux_vals(u128_vals: &[u128]) -> OwnedBytes {
|
||||
let mut out = Vec::new();
|
||||
serialize_column_values_u128(&u128_vals, &mut out).unwrap();
|
||||
let data = OwnedBytes::new(out);
|
||||
test_all(data.clone(), u128_vals);
|
||||
data
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_range_1() {
|
||||
let vals = &[
|
||||
1u128,
|
||||
100u128,
|
||||
3u128,
|
||||
99999u128,
|
||||
100000u128,
|
||||
100001u128,
|
||||
4_000_211_221u128,
|
||||
4_000_211_222u128,
|
||||
333u128,
|
||||
];
|
||||
let mut data = test_aux_vals(vals);
|
||||
|
||||
let _header = U128Header::deserialize(&mut data);
|
||||
let decomp = CompactSpaceDecompressor::open(data).unwrap();
|
||||
let complete_range = 0..vals.len() as u32;
|
||||
for (pos, val) in vals.iter().enumerate() {
|
||||
let val = *val;
|
||||
let pos = pos as u32;
|
||||
let mut positions = Vec::new();
|
||||
decomp.get_positions_for_value_range(val..=val, pos..pos + 1, &mut positions);
|
||||
assert_eq!(positions, vec![pos]);
|
||||
}
|
||||
|
||||
// handle docid range out of bounds
|
||||
let positions: Vec<u32> = get_positions_for_value_range_helper(&decomp, 0..=1, 1..u32::MAX);
|
||||
assert!(positions.is_empty());
|
||||
|
||||
let positions =
|
||||
get_positions_for_value_range_helper(&decomp, 0..=1, complete_range.clone());
|
||||
assert_eq!(positions, vec![0]);
|
||||
let positions =
|
||||
get_positions_for_value_range_helper(&decomp, 0..=2, complete_range.clone());
|
||||
assert_eq!(positions, vec![0]);
|
||||
let positions =
|
||||
get_positions_for_value_range_helper(&decomp, 0..=3, complete_range.clone());
|
||||
assert_eq!(positions, vec![0, 2]);
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
99999u128..=99999u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
vec![3]
|
||||
);
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
99999u128..=100000u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
vec![3, 4]
|
||||
);
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
99998u128..=100000u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
vec![3, 4]
|
||||
);
|
||||
assert_eq!(
|
||||
&get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
99998u128..=99999u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
&[3]
|
||||
);
|
||||
assert!(get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
99998u128..=99998u128,
|
||||
complete_range.clone()
|
||||
)
|
||||
.is_empty());
|
||||
assert_eq!(
|
||||
&get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
333u128..=333u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
&[8]
|
||||
);
|
||||
assert_eq!(
|
||||
&get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
332u128..=333u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
&[8]
|
||||
);
|
||||
assert_eq!(
|
||||
&get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
332u128..=334u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
&[8]
|
||||
);
|
||||
assert_eq!(
|
||||
&get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
333u128..=334u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
&[8]
|
||||
);
|
||||
|
||||
assert_eq!(
|
||||
&get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
4_000_211_221u128..=5_000_000_000u128,
|
||||
complete_range
|
||||
),
|
||||
&[6, 7]
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_empty() {
|
||||
let vals = &[];
|
||||
let data = test_aux_vals(vals);
|
||||
let _decomp = CompactSpaceDecompressor::open(data).unwrap();
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_range_2() {
|
||||
let vals = &[
|
||||
100u128,
|
||||
99999u128,
|
||||
100000u128,
|
||||
100001u128,
|
||||
4_000_211_221u128,
|
||||
4_000_211_222u128,
|
||||
333u128,
|
||||
];
|
||||
let mut data = test_aux_vals(vals);
|
||||
let _header = U128Header::deserialize(&mut data);
|
||||
let decomp = CompactSpaceDecompressor::open(data).unwrap();
|
||||
let complete_range = 0..vals.len() as u32;
|
||||
assert!(
|
||||
&get_positions_for_value_range_helper(&decomp, 0..=5, complete_range.clone())
|
||||
.is_empty(),
|
||||
);
|
||||
assert_eq!(
|
||||
&get_positions_for_value_range_helper(&decomp, 0..=100, complete_range.clone()),
|
||||
&[0]
|
||||
);
|
||||
assert_eq!(
|
||||
&get_positions_for_value_range_helper(&decomp, 0..=105, complete_range),
|
||||
&[0]
|
||||
);
|
||||
}
|
||||
|
||||
fn get_positions_for_value_range_helper<C: ColumnValues<T> + ?Sized, T: PartialOrd>(
|
||||
column: &C,
|
||||
value_range: RangeInclusive<T>,
|
||||
doc_id_range: Range<u32>,
|
||||
) -> Vec<u32> {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(value_range, doc_id_range, &mut positions);
|
||||
positions
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_range_3() {
|
||||
let vals = &[
|
||||
200u128,
|
||||
201,
|
||||
202,
|
||||
203,
|
||||
204,
|
||||
204,
|
||||
206,
|
||||
207,
|
||||
208,
|
||||
209,
|
||||
210,
|
||||
1_000_000,
|
||||
5_000_000_000,
|
||||
];
|
||||
let mut out = Vec::new();
|
||||
serialize_column_values_u128(&&vals[..], &mut out).unwrap();
|
||||
let decomp = open_u128_mapped(OwnedBytes::new(out)).unwrap();
|
||||
let complete_range = 0..vals.len() as u32;
|
||||
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(&*decomp, 199..=200, complete_range.clone()),
|
||||
vec![0]
|
||||
);
|
||||
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(&*decomp, 199..=201, complete_range.clone()),
|
||||
vec![0, 1]
|
||||
);
|
||||
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(&*decomp, 200..=200, complete_range.clone()),
|
||||
vec![0]
|
||||
);
|
||||
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(&*decomp, 1_000_000..=1_000_000, complete_range),
|
||||
vec![11]
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_bug1() {
|
||||
let vals = &[9223372036854775806];
|
||||
let _data = test_aux_vals(vals);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_bug2() {
|
||||
let vals = &[340282366920938463463374607431768211455u128];
|
||||
let _data = test_aux_vals(vals);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_bug3() {
|
||||
let vals = &[340282366920938463463374607431768211454];
|
||||
let _data = test_aux_vals(vals);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_bug4() {
|
||||
let vals = &[340282366920938463463374607431768211455, 0];
|
||||
let _data = test_aux_vals(vals);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_first_large_gaps() {
|
||||
let vals = &[1_000_000_000u128; 100];
|
||||
let _data = test_aux_vals(vals);
|
||||
}
|
||||
|
||||
use proptest::prelude::*;
|
||||
|
||||
fn num_strategy() -> impl Strategy<Value = u128> {
|
||||
prop_oneof![
|
||||
1 => prop::num::u128::ANY.prop_map(|num| u128::MAX - (num % 10) ),
|
||||
1 => prop::num::u128::ANY.prop_map(|num| i64::MAX as u128 + 5 - (num % 10) ),
|
||||
1 => prop::num::u128::ANY.prop_map(|num| i128::MAX as u128 + 5 - (num % 10) ),
|
||||
1 => prop::num::u128::ANY.prop_map(|num| num % 10 ),
|
||||
20 => prop::num::u128::ANY,
|
||||
]
|
||||
}
|
||||
|
||||
proptest! {
|
||||
#![proptest_config(ProptestConfig::with_cases(10))]
|
||||
|
||||
#[test]
|
||||
fn compress_decompress_random(vals in proptest::collection::vec(num_strategy() , 1..1000)) {
|
||||
let _data = test_aux_vals(&vals);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,75 +0,0 @@
|
||||
use std::num::NonZeroU64;
|
||||
|
||||
use fastdivide::DividerU64;
|
||||
|
||||
/// Compute the gcd of two non null numbers.
|
||||
///
|
||||
/// It is recommended, but not required, to feed values such that `large >= small`.
|
||||
fn compute_gcd(mut large: NonZeroU64, mut small: NonZeroU64) -> NonZeroU64 {
|
||||
loop {
|
||||
let rem: u64 = large.get() % small;
|
||||
if let Some(new_small) = NonZeroU64::new(rem) {
|
||||
(large, small) = (small, new_small);
|
||||
} else {
|
||||
return small;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Find GCD for iterator of numbers
|
||||
pub fn find_gcd(numbers: impl Iterator<Item = u64>) -> Option<NonZeroU64> {
|
||||
let mut numbers = numbers.flat_map(NonZeroU64::new);
|
||||
let mut gcd: NonZeroU64 = numbers.next()?;
|
||||
if gcd.get() == 1 {
|
||||
return Some(gcd);
|
||||
}
|
||||
|
||||
let mut gcd_divider = DividerU64::divide_by(gcd.get());
|
||||
for val in numbers {
|
||||
let remainder = val.get() - (gcd_divider.divide(val.get())) * gcd.get();
|
||||
if remainder == 0 {
|
||||
continue;
|
||||
}
|
||||
gcd = compute_gcd(val, gcd);
|
||||
if gcd.get() == 1 {
|
||||
return Some(gcd);
|
||||
}
|
||||
|
||||
gcd_divider = DividerU64::divide_by(gcd.get());
|
||||
}
|
||||
Some(gcd)
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use std::num::NonZeroU64;
|
||||
|
||||
use crate::column_values::gcd::{compute_gcd, find_gcd};
|
||||
|
||||
#[test]
|
||||
fn test_compute_gcd() {
|
||||
let test_compute_gcd_aux = |large, small, expected| {
|
||||
let large = NonZeroU64::new(large).unwrap();
|
||||
let small = NonZeroU64::new(small).unwrap();
|
||||
let expected = NonZeroU64::new(expected).unwrap();
|
||||
assert_eq!(compute_gcd(small, large), expected);
|
||||
assert_eq!(compute_gcd(large, small), expected);
|
||||
};
|
||||
test_compute_gcd_aux(1, 4, 1);
|
||||
test_compute_gcd_aux(2, 4, 2);
|
||||
test_compute_gcd_aux(10, 25, 5);
|
||||
test_compute_gcd_aux(25, 25, 25);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn find_gcd_test() {
|
||||
assert_eq!(find_gcd([0].into_iter()), None);
|
||||
assert_eq!(find_gcd([0, 10].into_iter()), NonZeroU64::new(10));
|
||||
assert_eq!(find_gcd([10, 0].into_iter()), NonZeroU64::new(10));
|
||||
assert_eq!(find_gcd([].into_iter()), None);
|
||||
assert_eq!(find_gcd([15, 30, 5, 10].into_iter()), NonZeroU64::new(5));
|
||||
assert_eq!(find_gcd([15, 16, 10].into_iter()), NonZeroU64::new(1));
|
||||
assert_eq!(find_gcd([0, 5, 5, 5].into_iter()), NonZeroU64::new(5));
|
||||
assert_eq!(find_gcd([0, 0].into_iter()), None);
|
||||
}
|
||||
}
|
||||
@@ -1,230 +0,0 @@
|
||||
use std::io::{self, Write};
|
||||
|
||||
use common::{BinarySerializable, OwnedBytes};
|
||||
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
|
||||
|
||||
use super::line::Line;
|
||||
use super::serialize::NormalizedHeader;
|
||||
use super::{ColumnValues, FastFieldCodec, FastFieldCodecType};
|
||||
|
||||
/// Depending on the field type, a different
|
||||
/// fast field is required.
|
||||
#[derive(Clone)]
|
||||
pub struct LinearReader {
|
||||
data: OwnedBytes,
|
||||
linear_params: LinearParams,
|
||||
header: NormalizedHeader,
|
||||
}
|
||||
|
||||
impl ColumnValues for LinearReader {
|
||||
#[inline]
|
||||
fn get_val(&self, doc: u32) -> u64 {
|
||||
let interpoled_val: u64 = self.linear_params.line.eval(doc);
|
||||
let bitpacked_diff = self.linear_params.bit_unpacker.get(doc, &self.data);
|
||||
interpoled_val.wrapping_add(bitpacked_diff)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn min_value(&self) -> u64 {
|
||||
// The LinearReader assumes a normalized vector.
|
||||
0u64
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn max_value(&self) -> u64 {
|
||||
self.header.max_value
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.header.num_vals
|
||||
}
|
||||
}
|
||||
|
||||
/// Fastfield serializer, which tries to guess values by linear interpolation
|
||||
/// and stores the difference bitpacked.
|
||||
pub struct LinearCodec;
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
struct LinearParams {
|
||||
line: Line,
|
||||
bit_unpacker: BitUnpacker,
|
||||
}
|
||||
|
||||
impl BinarySerializable for LinearParams {
|
||||
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
self.line.serialize(writer)?;
|
||||
self.bit_unpacker.bit_width().serialize(writer)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let line = Line::deserialize(reader)?;
|
||||
let bit_width = u8::deserialize(reader)?;
|
||||
Ok(Self {
|
||||
line,
|
||||
bit_unpacker: BitUnpacker::new(bit_width),
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
impl FastFieldCodec for LinearCodec {
|
||||
const CODEC_TYPE: FastFieldCodecType = FastFieldCodecType::Linear;
|
||||
|
||||
type Reader = LinearReader;
|
||||
|
||||
/// Opens a fast field given a file.
|
||||
fn open_from_bytes(mut data: OwnedBytes, header: NormalizedHeader) -> io::Result<Self::Reader> {
|
||||
let linear_params = LinearParams::deserialize(&mut data)?;
|
||||
Ok(LinearReader {
|
||||
data,
|
||||
linear_params,
|
||||
header,
|
||||
})
|
||||
}
|
||||
|
||||
/// Creates a new fast field serializer.
|
||||
fn serialize(column: &dyn ColumnValues, write: &mut impl Write) -> io::Result<()> {
|
||||
assert_eq!(column.min_value(), 0);
|
||||
let line = Line::train(column);
|
||||
|
||||
let max_offset_from_line = column
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(pos, actual_value)| {
|
||||
let calculated_value = line.eval(pos as u32);
|
||||
actual_value.wrapping_sub(calculated_value)
|
||||
})
|
||||
.max()
|
||||
.unwrap();
|
||||
|
||||
let num_bits = compute_num_bits(max_offset_from_line);
|
||||
let linear_params = LinearParams {
|
||||
line,
|
||||
bit_unpacker: BitUnpacker::new(num_bits),
|
||||
};
|
||||
linear_params.serialize(write)?;
|
||||
|
||||
let mut bit_packer = BitPacker::new();
|
||||
for (pos, actual_value) in column.iter().enumerate() {
|
||||
let calculated_value = line.eval(pos as u32);
|
||||
let offset = actual_value.wrapping_sub(calculated_value);
|
||||
bit_packer.write(offset, num_bits, write)?;
|
||||
}
|
||||
bit_packer.close(write)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// estimation for linear interpolation is hard because, you don't know
|
||||
/// where the local maxima for the deviation of the calculated value are and
|
||||
/// the offset to shift all values to >=0 is also unknown.
|
||||
#[allow(clippy::question_mark)]
|
||||
fn estimate(column: &dyn ColumnValues) -> Option<f32> {
|
||||
if column.num_vals() < 3 {
|
||||
return None; // disable compressor for this case
|
||||
}
|
||||
|
||||
let limit_num_vals = column.num_vals().min(100_000);
|
||||
|
||||
let num_samples = 100;
|
||||
let step_size = (limit_num_vals / num_samples).max(1); // 20 samples
|
||||
let mut sample_positions_and_values: Vec<_> = Vec::new();
|
||||
for (pos, val) in column.iter().enumerate().step_by(step_size as usize) {
|
||||
sample_positions_and_values.push((pos as u64, val));
|
||||
}
|
||||
|
||||
let line = Line::estimate(&sample_positions_and_values);
|
||||
|
||||
let estimated_bit_width = sample_positions_and_values
|
||||
.into_iter()
|
||||
.map(|(pos, actual_value)| {
|
||||
let interpolated_val = line.eval(pos as u32);
|
||||
actual_value.wrapping_sub(interpolated_val)
|
||||
})
|
||||
.map(|diff| ((diff as f32 * 1.5) * 2.0) as u64)
|
||||
.map(compute_num_bits)
|
||||
.max()
|
||||
.unwrap_or(0);
|
||||
|
||||
// Extrapolate to whole column
|
||||
let num_bits = (estimated_bit_width as u64 * column.num_vals() as u64) + 64;
|
||||
let num_bits_uncompressed = 64 * column.num_vals();
|
||||
Some(num_bits as f32 / num_bits_uncompressed as f32)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use rand::RngCore;
|
||||
|
||||
use super::*;
|
||||
use crate::column_values::tests;
|
||||
|
||||
fn create_and_validate(data: &[u64], name: &str) -> Option<(f32, f32)> {
|
||||
tests::create_and_validate::<LinearCodec>(data, name)
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_compression() {
|
||||
let data = (10..=6_000_u64).collect::<Vec<_>>();
|
||||
let (estimate, actual_compression) =
|
||||
create_and_validate(&data, "simple monotonically large").unwrap();
|
||||
|
||||
assert_le!(actual_compression, 0.001);
|
||||
assert_le!(estimate, 0.02);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_with_codec_datasets() {
|
||||
let data_sets = tests::get_codec_test_datasets();
|
||||
for (mut data, name) in data_sets {
|
||||
create_and_validate(&data, name);
|
||||
data.reverse();
|
||||
create_and_validate(&data, name);
|
||||
}
|
||||
}
|
||||
#[test]
|
||||
fn linear_interpol_fast_field_test_large_amplitude() {
|
||||
let data = vec![
|
||||
i64::MAX as u64 / 2,
|
||||
i64::MAX as u64 / 3,
|
||||
i64::MAX as u64 / 2,
|
||||
];
|
||||
|
||||
create_and_validate(&data, "large amplitude");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn overflow_error_test() {
|
||||
let data = vec![1572656989877777, 1170935903116329, 720575940379279, 0];
|
||||
create_and_validate(&data, "overflow test");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn linear_interpol_fast_concave_data() {
|
||||
let data = vec![0, 1, 2, 5, 8, 10, 20, 50];
|
||||
create_and_validate(&data, "concave data");
|
||||
}
|
||||
#[test]
|
||||
fn linear_interpol_fast_convex_data() {
|
||||
let data = vec![0, 40, 60, 70, 75, 77];
|
||||
create_and_validate(&data, "convex data");
|
||||
}
|
||||
#[test]
|
||||
fn linear_interpol_fast_field_test_simple() {
|
||||
let data = (10..=20_u64).collect::<Vec<_>>();
|
||||
create_and_validate(&data, "simple monotonically");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn linear_interpol_fast_field_rand() {
|
||||
let mut rng = rand::thread_rng();
|
||||
for _ in 0..50 {
|
||||
let mut data = (0..10_000).map(|_| rng.next_u64()).collect::<Vec<_>>();
|
||||
create_and_validate(&data, "random");
|
||||
data.reverse();
|
||||
create_and_validate(&data, "random");
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,222 +0,0 @@
|
||||
#[macro_use]
|
||||
extern crate prettytable;
|
||||
use std::collections::HashSet;
|
||||
use std::env;
|
||||
use std::io::BufRead;
|
||||
use std::net::{IpAddr, Ipv6Addr};
|
||||
use std::str::FromStr;
|
||||
|
||||
use common::OwnedBytes;
|
||||
use fastfield_codecs::{open_u128, serialize_u128, Column, FastFieldCodecType, VecColumn};
|
||||
use itertools::Itertools;
|
||||
use measure_time::print_time;
|
||||
use prettytable::{Cell, Row, Table};
|
||||
|
||||
fn print_set_stats(ip_addrs: &[u128]) {
|
||||
println!("NumIps\t{}", ip_addrs.len());
|
||||
let ip_addr_set: HashSet<u128> = ip_addrs.iter().cloned().collect();
|
||||
println!("NumUniqueIps\t{}", ip_addr_set.len());
|
||||
let ratio_unique = ip_addr_set.len() as f64 / ip_addrs.len() as f64;
|
||||
println!("RatioUniqueOverTotal\t{ratio_unique:.4}");
|
||||
|
||||
// histogram
|
||||
let mut ip_addrs = ip_addrs.to_vec();
|
||||
ip_addrs.sort();
|
||||
let mut cnts: Vec<usize> = ip_addrs
|
||||
.into_iter()
|
||||
.dedup_with_count()
|
||||
.map(|(cnt, _)| cnt)
|
||||
.collect();
|
||||
cnts.sort();
|
||||
|
||||
let top_256_cnt: usize = cnts.iter().rev().take(256).sum();
|
||||
let top_128_cnt: usize = cnts.iter().rev().take(128).sum();
|
||||
let top_64_cnt: usize = cnts.iter().rev().take(64).sum();
|
||||
let top_8_cnt: usize = cnts.iter().rev().take(8).sum();
|
||||
let total: usize = cnts.iter().sum();
|
||||
|
||||
println!("{}", total);
|
||||
println!("{}", top_256_cnt);
|
||||
println!("{}", top_128_cnt);
|
||||
println!("Percentage Top8 {:02}", top_8_cnt as f32 / total as f32);
|
||||
println!("Percentage Top64 {:02}", top_64_cnt as f32 / total as f32);
|
||||
println!("Percentage Top128 {:02}", top_128_cnt as f32 / total as f32);
|
||||
println!("Percentage Top256 {:02}", top_256_cnt as f32 / total as f32);
|
||||
|
||||
let mut cnts: Vec<(usize, usize)> = cnts.into_iter().dedup_with_count().collect();
|
||||
cnts.sort_by(|a, b| {
|
||||
if a.1 == b.1 {
|
||||
a.0.cmp(&b.0)
|
||||
} else {
|
||||
b.1.cmp(&a.1)
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
fn ip_dataset() -> Vec<u128> {
|
||||
let mut ip_addr_v4 = 0;
|
||||
|
||||
let stdin = std::io::stdin();
|
||||
let ip_addrs: Vec<u128> = stdin
|
||||
.lock()
|
||||
.lines()
|
||||
.flat_map(|line| {
|
||||
let line = line.unwrap();
|
||||
let line = line.trim();
|
||||
let ip_addr = IpAddr::from_str(line.trim()).ok()?;
|
||||
if ip_addr.is_ipv4() {
|
||||
ip_addr_v4 += 1;
|
||||
}
|
||||
let ip_addr_v6: Ipv6Addr = match ip_addr {
|
||||
IpAddr::V4(v4) => v4.to_ipv6_mapped(),
|
||||
IpAddr::V6(v6) => v6,
|
||||
};
|
||||
Some(ip_addr_v6)
|
||||
})
|
||||
.map(|ip_v6| u128::from_be_bytes(ip_v6.octets()))
|
||||
.collect();
|
||||
|
||||
println!("IpAddrsAny\t{}", ip_addrs.len());
|
||||
println!("IpAddrsV4\t{}", ip_addr_v4);
|
||||
|
||||
ip_addrs
|
||||
}
|
||||
|
||||
fn bench_ip() {
|
||||
let dataset = ip_dataset();
|
||||
print_set_stats(&dataset);
|
||||
|
||||
// Chunks
|
||||
{
|
||||
let mut data = vec![];
|
||||
for dataset in dataset.chunks(500_000) {
|
||||
serialize_u128(|| dataset.iter().cloned(), dataset.len() as u32, &mut data).unwrap();
|
||||
}
|
||||
let compression = data.len() as f64 / (dataset.len() * 16) as f64;
|
||||
println!("Compression 50_000 chunks {:.4}", compression);
|
||||
println!(
|
||||
"Num Bits per elem {:.2}",
|
||||
(data.len() * 8) as f32 / dataset.len() as f32
|
||||
);
|
||||
}
|
||||
|
||||
let mut data = vec![];
|
||||
{
|
||||
print_time!("creation");
|
||||
serialize_u128(|| dataset.iter().cloned(), dataset.len() as u32, &mut data).unwrap();
|
||||
}
|
||||
|
||||
let compression = data.len() as f64 / (dataset.len() * 16) as f64;
|
||||
println!("Compression {:.2}", compression);
|
||||
println!(
|
||||
"Num Bits per elem {:.2}",
|
||||
(data.len() * 8) as f32 / dataset.len() as f32
|
||||
);
|
||||
|
||||
let decompressor = open_u128::<u128>(OwnedBytes::new(data)).unwrap();
|
||||
// Sample some ranges
|
||||
let mut doc_values = Vec::new();
|
||||
for value in dataset.iter().take(1110).skip(1100).cloned() {
|
||||
doc_values.clear();
|
||||
print_time!("get range");
|
||||
decompressor.get_docids_for_value_range(
|
||||
value..=value,
|
||||
0..decompressor.num_vals(),
|
||||
&mut doc_values,
|
||||
);
|
||||
println!("{:?}", doc_values.len());
|
||||
}
|
||||
}
|
||||
|
||||
fn main() {
|
||||
if env::args().nth(1).unwrap() == "bench_ip" {
|
||||
bench_ip();
|
||||
return;
|
||||
}
|
||||
|
||||
let mut table = Table::new();
|
||||
|
||||
// Add a row per time
|
||||
table.add_row(row!["", "Compression Ratio", "Compression Estimation"]);
|
||||
|
||||
for (data, data_set_name) in get_codec_test_data_sets() {
|
||||
let results: Vec<(f32, f32, FastFieldCodecType)> = [
|
||||
serialize_with_codec(&data, FastFieldCodecType::Bitpacked),
|
||||
serialize_with_codec(&data, FastFieldCodecType::Linear),
|
||||
serialize_with_codec(&data, FastFieldCodecType::BlockwiseLinear),
|
||||
]
|
||||
.into_iter()
|
||||
.flatten()
|
||||
.collect();
|
||||
let best_compression_ratio_codec = results
|
||||
.iter()
|
||||
.min_by(|&res1, &res2| res1.partial_cmp(res2).unwrap())
|
||||
.cloned()
|
||||
.unwrap();
|
||||
|
||||
table.add_row(Row::new(vec![Cell::new(data_set_name).style_spec("Bbb")]));
|
||||
for (est, comp, codec_type) in results {
|
||||
let est_cell = est.to_string();
|
||||
let ratio_cell = comp.to_string();
|
||||
let style = if comp == best_compression_ratio_codec.1 {
|
||||
"Fb"
|
||||
} else {
|
||||
""
|
||||
};
|
||||
table.add_row(Row::new(vec![
|
||||
Cell::new(&format!("{codec_type:?}")).style_spec("bFg"),
|
||||
Cell::new(&ratio_cell).style_spec(style),
|
||||
Cell::new(&est_cell).style_spec(""),
|
||||
]));
|
||||
}
|
||||
}
|
||||
|
||||
table.printstd();
|
||||
}
|
||||
|
||||
pub fn get_codec_test_data_sets() -> Vec<(Vec<u64>, &'static str)> {
|
||||
let mut data_and_names = vec![];
|
||||
|
||||
let data = (1000..=200_000_u64).collect::<Vec<_>>();
|
||||
data_and_names.push((data, "Autoincrement"));
|
||||
|
||||
let mut current_cumulative = 0;
|
||||
let data = (1..=200_000_u64)
|
||||
.map(|num| {
|
||||
let num = (num as f32 + num as f32).log10() as u64;
|
||||
current_cumulative += num;
|
||||
current_cumulative
|
||||
})
|
||||
.collect::<Vec<_>>();
|
||||
// let data = (1..=200000_u64).map(|num| num + num).collect::<Vec<_>>();
|
||||
data_and_names.push((data, "Monotonically increasing concave"));
|
||||
|
||||
let mut current_cumulative = 0;
|
||||
let data = (1..=200_000_u64)
|
||||
.map(|num| {
|
||||
let num = (200_000.0 - num as f32).log10() as u64;
|
||||
current_cumulative += num;
|
||||
current_cumulative
|
||||
})
|
||||
.collect::<Vec<_>>();
|
||||
data_and_names.push((data, "Monotonically increasing convex"));
|
||||
|
||||
let data = (1000..=200_000_u64)
|
||||
.map(|num| num + rand::random::<u8>() as u64)
|
||||
.collect::<Vec<_>>();
|
||||
data_and_names.push((data, "Almost monotonically increasing"));
|
||||
|
||||
data_and_names
|
||||
}
|
||||
|
||||
pub fn serialize_with_codec(
|
||||
data: &[u64],
|
||||
codec_type: FastFieldCodecType,
|
||||
) -> Option<(f32, f32, FastFieldCodecType)> {
|
||||
let col = VecColumn::from(data);
|
||||
let estimation = fastfield_codecs::estimate(&col, codec_type)?;
|
||||
let mut out = Vec::new();
|
||||
fastfield_codecs::serialize(&col, &mut out, &[codec_type]).ok()?;
|
||||
let actual_compression = out.len() as f32 / (col.num_vals() * 8) as f32;
|
||||
Some((estimation, actual_compression, codec_type))
|
||||
}
|
||||
@@ -7,83 +7,74 @@
|
||||
//! - Encode data in different codecs.
|
||||
//! - Monotonically map values to u64/u128
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests;
|
||||
|
||||
use std::fmt::Debug;
|
||||
use std::io;
|
||||
use std::io::Write;
|
||||
use std::sync::Arc;
|
||||
|
||||
use common::{BinarySerializable, OwnedBytes};
|
||||
use compact_space::CompactSpaceDecompressor;
|
||||
use monotonic_mapping::{
|
||||
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
|
||||
StrictlyMonotonicMappingToInternalBaseval, StrictlyMonotonicMappingToInternalGCDBaseval,
|
||||
};
|
||||
use serialize::{Header, U128Header};
|
||||
pub use monotonic_mapping::{MonotonicallyMappableToU64, StrictlyMonotonicFn};
|
||||
use monotonic_mapping::{StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal};
|
||||
pub use monotonic_mapping_u128::MonotonicallyMappableToU128;
|
||||
use serialize::U128Header;
|
||||
|
||||
mod bitpacked;
|
||||
mod blockwise_linear;
|
||||
mod compact_space;
|
||||
mod line;
|
||||
mod linear;
|
||||
pub(crate) mod monotonic_mapping;
|
||||
pub(crate) mod monotonic_mapping_u128;
|
||||
mod stats;
|
||||
pub(crate) mod u64_based;
|
||||
|
||||
mod column;
|
||||
mod column_with_cardinality;
|
||||
mod gcd;
|
||||
pub mod serialize;
|
||||
|
||||
pub use serialize::serialize_column_values_u128;
|
||||
pub use stats::Stats;
|
||||
pub use u64_based::{
|
||||
load_u64_based_column_values, serialize_and_load_u64_based_column_values,
|
||||
serialize_u64_based_column_values, CodecType, ALL_U64_CODEC_TYPES,
|
||||
};
|
||||
|
||||
pub use self::column::{monotonic_map_column, ColumnValues, IterColumn, VecColumn};
|
||||
pub use self::monotonic_mapping::{MonotonicallyMappableToU64, StrictlyMonotonicFn};
|
||||
pub use self::monotonic_mapping_u128::MonotonicallyMappableToU128;
|
||||
#[cfg(test)]
|
||||
pub use self::serialize::tests::serialize_and_load;
|
||||
pub use self::serialize::{serialize_column_values, NormalizedHeader};
|
||||
use crate::column_values::bitpacked::BitpackedCodec;
|
||||
use crate::column_values::blockwise_linear::BlockwiseLinearCodec;
|
||||
use crate::column_values::linear::LinearCodec;
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{ColumnIndex, MergeRowOrder};
|
||||
|
||||
#[derive(PartialEq, Eq, PartialOrd, Ord, Debug, Clone, Copy)]
|
||||
#[repr(u8)]
|
||||
/// Available codecs to use to encode the u64 (via [`MonotonicallyMappableToU64`]) converted data.
|
||||
pub enum FastFieldCodecType {
|
||||
/// Bitpack all values in the value range. The number of bits is defined by the amplitude
|
||||
/// `column.max_value() - column.min_value()`
|
||||
Bitpacked = 1,
|
||||
/// Linear interpolation puts a line between the first and last value and then bitpacks the
|
||||
/// values by the offset from the line. The number of bits is defined by the max deviation from
|
||||
/// the line.
|
||||
Linear = 2,
|
||||
/// Same as [`FastFieldCodecType::Linear`], but encodes in blocks of 512 elements.
|
||||
BlockwiseLinear = 3,
|
||||
pub(crate) struct MergedColumnValues<'a, T> {
|
||||
pub(crate) column_indexes: &'a [Option<ColumnIndex>],
|
||||
pub(crate) column_values: &'a [Option<Arc<dyn ColumnValues<T>>>],
|
||||
pub(crate) merge_row_order: &'a MergeRowOrder,
|
||||
}
|
||||
|
||||
impl BinarySerializable for FastFieldCodecType {
|
||||
fn serialize<W: Write>(&self, wrt: &mut W) -> io::Result<()> {
|
||||
self.to_code().serialize(wrt)
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let code = u8::deserialize(reader)?;
|
||||
let codec_type: Self = Self::from_code(code)
|
||||
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "Unknown code `{code}.`"))?;
|
||||
Ok(codec_type)
|
||||
}
|
||||
}
|
||||
|
||||
impl FastFieldCodecType {
|
||||
pub(crate) fn to_code(self) -> u8 {
|
||||
self as u8
|
||||
}
|
||||
|
||||
pub(crate) fn from_code(code: u8) -> Option<Self> {
|
||||
match code {
|
||||
1 => Some(Self::Bitpacked),
|
||||
2 => Some(Self::Linear),
|
||||
3 => Some(Self::BlockwiseLinear),
|
||||
_ => None,
|
||||
impl<'a, T: Copy + PartialOrd + Debug> Iterable<T> for MergedColumnValues<'a, T> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
|
||||
match self.merge_row_order {
|
||||
MergeRowOrder::Stack(_) => {
|
||||
Box::new(self
|
||||
.column_values
|
||||
.iter()
|
||||
.flatten()
|
||||
.flat_map(|column_value| column_value.iter()))
|
||||
},
|
||||
MergeRowOrder::Shuffled(shuffle_merge_order) => {
|
||||
Box::new(shuffle_merge_order
|
||||
.iter_new_to_old_row_addrs()
|
||||
.flat_map(|row_addr| {
|
||||
let Some(column_index) = self.column_indexes[row_addr.segment_ord as usize].as_ref() else {
|
||||
return None;
|
||||
};
|
||||
let Some(column_values) = self.column_values[row_addr.segment_ord as usize].as_ref() else {
|
||||
return None;
|
||||
};
|
||||
let value_range = column_index.value_row_ids(row_addr.row_id);
|
||||
Some((value_range, column_values))
|
||||
})
|
||||
.flat_map(|(value_range, column_values)| {
|
||||
value_range
|
||||
.into_iter()
|
||||
.map(|val| column_values.get_val(val))
|
||||
})
|
||||
)
|
||||
},
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -98,7 +89,7 @@ pub enum U128FastFieldCodecType {
|
||||
}
|
||||
|
||||
impl BinarySerializable for U128FastFieldCodecType {
|
||||
fn serialize<W: Write>(&self, wrt: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, wrt: &mut W) -> io::Result<()> {
|
||||
self.to_code().serialize(wrt)
|
||||
}
|
||||
|
||||
@@ -124,7 +115,7 @@ impl U128FastFieldCodecType {
|
||||
}
|
||||
|
||||
/// Returns the correct codec reader wrapped in the `Arc` for the data.
|
||||
pub fn open_u128_mapped<T: MonotonicallyMappableToU128>(
|
||||
pub fn open_u128_mapped<T: MonotonicallyMappableToU128 + Debug>(
|
||||
mut bytes: OwnedBytes,
|
||||
) -> io::Result<Arc<dyn ColumnValues<T>>> {
|
||||
let header = U128Header::deserialize(&mut bytes)?;
|
||||
@@ -136,68 +127,6 @@ pub fn open_u128_mapped<T: MonotonicallyMappableToU128>(
|
||||
Ok(Arc::new(monotonic_map_column(reader, inverted)))
|
||||
}
|
||||
|
||||
/// Returns the correct codec reader wrapped in the `Arc` for the data.
|
||||
pub fn open_u64_mapped<T: MonotonicallyMappableToU64>(
|
||||
mut bytes: OwnedBytes,
|
||||
) -> io::Result<Arc<dyn ColumnValues<T>>> {
|
||||
let header = Header::deserialize(&mut bytes)?;
|
||||
match header.codec_type {
|
||||
FastFieldCodecType::Bitpacked => open_specific_codec::<BitpackedCodec, _>(bytes, &header),
|
||||
FastFieldCodecType::Linear => open_specific_codec::<LinearCodec, _>(bytes, &header),
|
||||
FastFieldCodecType::BlockwiseLinear => {
|
||||
open_specific_codec::<BlockwiseLinearCodec, _>(bytes, &header)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn open_specific_codec<C: FastFieldCodec, Item: MonotonicallyMappableToU64>(
|
||||
bytes: OwnedBytes,
|
||||
header: &Header,
|
||||
) -> io::Result<Arc<dyn ColumnValues<Item>>> {
|
||||
let normalized_header = header.normalized();
|
||||
let reader = C::open_from_bytes(bytes, normalized_header)?;
|
||||
let min_value = header.min_value;
|
||||
if let Some(gcd) = header.gcd {
|
||||
let mapping = StrictlyMonotonicMappingInverter::from(
|
||||
StrictlyMonotonicMappingToInternalGCDBaseval::new(gcd.get(), min_value),
|
||||
);
|
||||
Ok(Arc::new(monotonic_map_column(reader, mapping)))
|
||||
} else {
|
||||
let mapping = StrictlyMonotonicMappingInverter::from(
|
||||
StrictlyMonotonicMappingToInternalBaseval::new(min_value),
|
||||
);
|
||||
Ok(Arc::new(monotonic_map_column(reader, mapping)))
|
||||
}
|
||||
}
|
||||
|
||||
/// The FastFieldSerializerEstimate trait is required on all variants
|
||||
/// of fast field compressions, to decide which one to choose.
|
||||
pub(crate) trait FastFieldCodec: 'static {
|
||||
/// A codex needs to provide a unique name and id, which is
|
||||
/// used for debugging and de/serialization.
|
||||
const CODEC_TYPE: FastFieldCodecType;
|
||||
|
||||
type Reader: ColumnValues<u64> + 'static;
|
||||
|
||||
/// Reads the metadata and returns the CodecReader
|
||||
fn open_from_bytes(bytes: OwnedBytes, header: NormalizedHeader) -> io::Result<Self::Reader>;
|
||||
|
||||
/// Serializes the data using the serializer into write.
|
||||
///
|
||||
/// The column iterator should be preferred over using column `get_val` method for
|
||||
/// performance reasons.
|
||||
fn serialize(column: &dyn ColumnValues, write: &mut impl Write) -> io::Result<()>;
|
||||
|
||||
/// Returns an estimate of the compression ratio.
|
||||
/// If the codec is not applicable, returns `None`.
|
||||
///
|
||||
/// The baseline is uncompressed 64bit data.
|
||||
///
|
||||
/// It could make sense to also return a value representing
|
||||
/// computational complexity.
|
||||
fn estimate(column: &dyn ColumnValues) -> Option<f32>;
|
||||
}
|
||||
|
||||
#[cfg(all(test, feature = "unstable"))]
|
||||
mod bench {
|
||||
use std::sync::Arc;
|
||||
|
||||
@@ -1,3 +1,4 @@
|
||||
use std::fmt::Debug;
|
||||
use std::marker::PhantomData;
|
||||
|
||||
use fastdivide::DividerU64;
|
||||
@@ -7,7 +8,7 @@ use crate::RowId;
|
||||
|
||||
/// Monotonic maps a value to u64 value space.
|
||||
/// Monotonic mapping enables `PartialOrd` on u64 space without conversion to original space.
|
||||
pub trait MonotonicallyMappableToU64: 'static + PartialOrd + Copy + Send + Sync {
|
||||
pub trait MonotonicallyMappableToU64: 'static + PartialOrd + Debug + Copy + Send + Sync {
|
||||
/// Converts a value to u64.
|
||||
///
|
||||
/// Internally all fast field values are encoded as u64.
|
||||
|
||||
@@ -1,8 +1,9 @@
|
||||
use std::fmt::Debug;
|
||||
use std::net::Ipv6Addr;
|
||||
|
||||
/// Montonic maps a value to u128 value space
|
||||
/// Monotonic mapping enables `PartialOrd` on u128 space without conversion to original space.
|
||||
pub trait MonotonicallyMappableToU128: 'static + PartialOrd + Copy + Send + Sync {
|
||||
pub trait MonotonicallyMappableToU128: 'static + PartialOrd + Copy + Debug + Send + Sync {
|
||||
/// Converts a value to u128.
|
||||
///
|
||||
/// Internally all fast field values are encoded as u64.
|
||||
|
||||
@@ -1,40 +1,12 @@
|
||||
// Copyright (C) 2022 Quickwit, Inc.
|
||||
//
|
||||
// Quickwit is offered under the AGPL v3.0 and as commercial software.
|
||||
// For commercial licensing, contact us at hello@quickwit.io.
|
||||
//
|
||||
// AGPL:
|
||||
// This program is free software: you can redistribute it and/or modify
|
||||
// it under the terms of the GNU Affero General Public License as
|
||||
// published by the Free Software Foundation, either version 3 of the
|
||||
// License, or (at your option) any later version.
|
||||
//
|
||||
// This program is distributed in the hope that it will be useful,
|
||||
// but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
// GNU Affero General Public License for more details.
|
||||
//
|
||||
// You should have received a copy of the GNU Affero General Public License
|
||||
// along with this program. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
use std::fmt::Debug;
|
||||
use std::io;
|
||||
use std::num::NonZeroU64;
|
||||
|
||||
use common::{BinarySerializable, VInt};
|
||||
use log::warn;
|
||||
|
||||
use super::bitpacked::BitpackedCodec;
|
||||
use super::blockwise_linear::BlockwiseLinearCodec;
|
||||
use super::linear::LinearCodec;
|
||||
use super::monotonic_mapping::{
|
||||
StrictlyMonotonicFn, StrictlyMonotonicMappingToInternal,
|
||||
StrictlyMonotonicMappingToInternalGCDBaseval,
|
||||
};
|
||||
use super::{
|
||||
monotonic_map_column, ColumnValues, FastFieldCodec, FastFieldCodecType,
|
||||
MonotonicallyMappableToU64, U128FastFieldCodecType,
|
||||
};
|
||||
use crate::column_values::compact_space::CompactSpaceCompressor;
|
||||
use crate::column_values::U128FastFieldCodecType;
|
||||
use crate::iterable::Iterable;
|
||||
use crate::MonotonicallyMappableToU128;
|
||||
|
||||
/// The normalized header gives some parameters after applying the following
|
||||
/// normalization of the vector:
|
||||
@@ -49,53 +21,6 @@ pub struct NormalizedHeader {
|
||||
pub max_value: u64,
|
||||
}
|
||||
|
||||
#[derive(Debug, Copy, Clone)]
|
||||
pub(crate) struct Header {
|
||||
pub num_vals: u32,
|
||||
pub min_value: u64,
|
||||
pub max_value: u64,
|
||||
pub gcd: Option<NonZeroU64>,
|
||||
pub codec_type: FastFieldCodecType,
|
||||
}
|
||||
|
||||
impl Header {
|
||||
pub fn normalized(self) -> NormalizedHeader {
|
||||
let gcd = self.gcd.map(|gcd| gcd.get()).unwrap_or(1);
|
||||
let gcd_min_val_mapping =
|
||||
StrictlyMonotonicMappingToInternalGCDBaseval::new(gcd, self.min_value);
|
||||
|
||||
let max_value = gcd_min_val_mapping.mapping(self.max_value);
|
||||
NormalizedHeader {
|
||||
num_vals: self.num_vals,
|
||||
max_value,
|
||||
}
|
||||
}
|
||||
|
||||
pub(crate) fn normalize_column<C: ColumnValues>(&self, from_column: C) -> impl ColumnValues {
|
||||
normalize_column(from_column, self.min_value, self.gcd)
|
||||
}
|
||||
|
||||
pub fn compute_header(
|
||||
column: impl ColumnValues<u64>,
|
||||
codecs: &[FastFieldCodecType],
|
||||
) -> Option<Header> {
|
||||
let num_vals = column.num_vals();
|
||||
let min_value = column.min_value();
|
||||
let max_value = column.max_value();
|
||||
let gcd = super::gcd::find_gcd(column.iter().map(|val| val - min_value))
|
||||
.filter(|gcd| gcd.get() > 1u64);
|
||||
let normalized_column = normalize_column(column, min_value, gcd);
|
||||
let codec_type = detect_codec(normalized_column, codecs)?;
|
||||
Some(Header {
|
||||
num_vals,
|
||||
min_value,
|
||||
max_value,
|
||||
gcd,
|
||||
codec_type,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
|
||||
pub(crate) struct U128Header {
|
||||
pub num_vals: u32,
|
||||
@@ -103,7 +28,7 @@ pub(crate) struct U128Header {
|
||||
}
|
||||
|
||||
impl BinarySerializable for U128Header {
|
||||
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: io::Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
VInt(self.num_vals as u64).serialize(writer)?;
|
||||
self.codec_type.serialize(writer)?;
|
||||
Ok(())
|
||||
@@ -119,157 +44,39 @@ impl BinarySerializable for U128Header {
|
||||
}
|
||||
}
|
||||
|
||||
fn normalize_column<C: ColumnValues>(
|
||||
from_column: C,
|
||||
min_value: u64,
|
||||
gcd: Option<NonZeroU64>,
|
||||
) -> impl ColumnValues {
|
||||
let gcd = gcd.map(|gcd| gcd.get()).unwrap_or(1);
|
||||
let mapping = StrictlyMonotonicMappingToInternalGCDBaseval::new(gcd, min_value);
|
||||
monotonic_map_column(from_column, mapping)
|
||||
}
|
||||
|
||||
impl BinarySerializable for Header {
|
||||
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
VInt(self.num_vals as u64).serialize(writer)?;
|
||||
VInt(self.min_value).serialize(writer)?;
|
||||
VInt(self.max_value - self.min_value).serialize(writer)?;
|
||||
if let Some(gcd) = self.gcd {
|
||||
VInt(gcd.get()).serialize(writer)?;
|
||||
} else {
|
||||
VInt(0u64).serialize(writer)?;
|
||||
}
|
||||
self.codec_type.serialize(writer)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let num_vals = VInt::deserialize(reader)?.0 as u32;
|
||||
let min_value = VInt::deserialize(reader)?.0;
|
||||
let amplitude = VInt::deserialize(reader)?.0;
|
||||
let max_value = min_value + amplitude;
|
||||
let gcd_u64 = VInt::deserialize(reader)?.0;
|
||||
let codec_type = FastFieldCodecType::deserialize(reader)?;
|
||||
Ok(Header {
|
||||
num_vals,
|
||||
min_value,
|
||||
max_value,
|
||||
gcd: NonZeroU64::new(gcd_u64),
|
||||
codec_type,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
/// Serializes u128 values with the compact space codec.
|
||||
pub fn serialize_column_values_u128<F: Fn() -> I, I: Iterator<Item = u128>>(
|
||||
iter_gen: F,
|
||||
num_vals: u32,
|
||||
pub fn serialize_column_values_u128<T: MonotonicallyMappableToU128>(
|
||||
iterable: &dyn Iterable<T>,
|
||||
output: &mut impl io::Write,
|
||||
) -> io::Result<()> {
|
||||
let compressor = CompactSpaceCompressor::train_from(
|
||||
iterable
|
||||
.boxed_iter()
|
||||
.map(MonotonicallyMappableToU128::to_u128),
|
||||
);
|
||||
let header = U128Header {
|
||||
num_vals,
|
||||
num_vals: compressor.num_vals(),
|
||||
codec_type: U128FastFieldCodecType::CompactSpace,
|
||||
};
|
||||
header.serialize(output)?;
|
||||
let compressor = CompactSpaceCompressor::train_from(iter_gen(), num_vals);
|
||||
compressor.compress_into(iter_gen(), output)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Serializes the column with the codec with the best estimate on the data.
|
||||
pub fn serialize_column_values<T: MonotonicallyMappableToU64>(
|
||||
typed_column: impl ColumnValues<T>,
|
||||
codecs: &[FastFieldCodecType],
|
||||
output: &mut impl io::Write,
|
||||
) -> io::Result<()> {
|
||||
let column = monotonic_map_column(typed_column, StrictlyMonotonicMappingToInternal::<T>::new());
|
||||
let header = Header::compute_header(&column, codecs).ok_or_else(|| {
|
||||
io::Error::new(
|
||||
io::ErrorKind::InvalidInput,
|
||||
format!(
|
||||
"Data cannot be serialized with this list of codec. {:?}",
|
||||
codecs
|
||||
),
|
||||
)
|
||||
})?;
|
||||
header.serialize(output)?;
|
||||
let normalized_column = header.normalize_column(column);
|
||||
assert_eq!(normalized_column.min_value(), 0u64);
|
||||
serialize_given_codec(normalized_column, header.codec_type, output)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn detect_codec(
|
||||
column: impl ColumnValues<u64>,
|
||||
codecs: &[FastFieldCodecType],
|
||||
) -> Option<FastFieldCodecType> {
|
||||
let mut estimations = Vec::new();
|
||||
for &codec in codecs {
|
||||
let estimation_opt = match codec {
|
||||
FastFieldCodecType::Bitpacked => BitpackedCodec::estimate(&column),
|
||||
FastFieldCodecType::Linear => LinearCodec::estimate(&column),
|
||||
FastFieldCodecType::BlockwiseLinear => BlockwiseLinearCodec::estimate(&column),
|
||||
};
|
||||
if let Some(estimation) = estimation_opt {
|
||||
estimations.push((estimation, codec));
|
||||
}
|
||||
}
|
||||
if let Some(broken_estimation) = estimations.iter().find(|estimation| estimation.0.is_nan()) {
|
||||
warn!(
|
||||
"broken estimation for fast field codec {:?}",
|
||||
broken_estimation.1
|
||||
);
|
||||
}
|
||||
// removing nan values for codecs with broken calculations, and max values which disables
|
||||
// codecs
|
||||
estimations.retain(|estimation| !estimation.0.is_nan() && estimation.0 != f32::MAX);
|
||||
estimations.sort_by(|(score_left, _), (score_right, _)| score_left.total_cmp(score_right));
|
||||
Some(estimations.first()?.1)
|
||||
}
|
||||
|
||||
pub(crate) fn serialize_given_codec(
|
||||
column: impl ColumnValues<u64>,
|
||||
codec_type: FastFieldCodecType,
|
||||
output: &mut impl io::Write,
|
||||
) -> io::Result<()> {
|
||||
match codec_type {
|
||||
FastFieldCodecType::Bitpacked => {
|
||||
BitpackedCodec::serialize(&column, output)?;
|
||||
}
|
||||
FastFieldCodecType::Linear => {
|
||||
LinearCodec::serialize(&column, output)?;
|
||||
}
|
||||
FastFieldCodecType::BlockwiseLinear => {
|
||||
BlockwiseLinearCodec::serialize(&column, output)?;
|
||||
}
|
||||
}
|
||||
compressor.compress_into(
|
||||
iterable
|
||||
.boxed_iter()
|
||||
.map(MonotonicallyMappableToU128::to_u128),
|
||||
output,
|
||||
)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
pub mod tests {
|
||||
use std::sync::Arc;
|
||||
|
||||
use common::OwnedBytes;
|
||||
|
||||
use super::*;
|
||||
use crate::column_values::{open_u64_mapped, VecColumn};
|
||||
use crate::column_values::u64_based::{
|
||||
serialize_and_load_u64_based_column_values, serialize_u64_based_column_values,
|
||||
ALL_U64_CODEC_TYPES,
|
||||
};
|
||||
use crate::column_values::CodecType;
|
||||
|
||||
const ALL_CODEC_TYPES: [FastFieldCodecType; 3] = [
|
||||
FastFieldCodecType::Bitpacked,
|
||||
FastFieldCodecType::Linear,
|
||||
FastFieldCodecType::BlockwiseLinear,
|
||||
];
|
||||
|
||||
/// Helper function to serialize a column (autodetect from all codecs) and then open it
|
||||
pub fn serialize_and_load<T: MonotonicallyMappableToU64 + Ord + Default>(
|
||||
column: &[T],
|
||||
) -> Arc<dyn ColumnValues<T>> {
|
||||
let mut buffer = Vec::new();
|
||||
serialize_column_values(&VecColumn::from(&column), &ALL_CODEC_TYPES, &mut buffer).unwrap();
|
||||
open_u64_mapped(OwnedBytes::new(buffer)).unwrap()
|
||||
}
|
||||
#[test]
|
||||
fn test_serialize_deserialize_u128_header() {
|
||||
let original = U128Header {
|
||||
@@ -285,15 +92,22 @@ pub mod tests {
|
||||
#[test]
|
||||
fn test_serialize_deserialize() {
|
||||
let original = [1u64, 5u64, 10u64];
|
||||
let restored: Vec<u64> = serialize_and_load(&original[..]).iter().collect();
|
||||
let restored: Vec<u64> =
|
||||
serialize_and_load_u64_based_column_values(&&original[..], &ALL_U64_CODEC_TYPES)
|
||||
.iter()
|
||||
.collect();
|
||||
assert_eq!(&restored, &original[..]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_fastfield_bool_size_bitwidth_1() {
|
||||
let mut buffer = Vec::new();
|
||||
let col = VecColumn::from(&[false, true][..]);
|
||||
serialize_column_values(&col, &ALL_CODEC_TYPES, &mut buffer).unwrap();
|
||||
serialize_u64_based_column_values::<bool>(
|
||||
&&[false, true][..],
|
||||
&ALL_U64_CODEC_TYPES,
|
||||
&mut buffer,
|
||||
)
|
||||
.unwrap();
|
||||
// TODO put the header as a footer so that it serves as a padding.
|
||||
// 5 bytes of header, 1 byte of value, 7 bytes of padding.
|
||||
assert_eq!(buffer.len(), 5 + 1);
|
||||
@@ -302,19 +116,23 @@ pub mod tests {
|
||||
#[test]
|
||||
fn test_fastfield_bool_bit_size_bitwidth_0() {
|
||||
let mut buffer = Vec::new();
|
||||
let col = VecColumn::from(&[true][..]);
|
||||
serialize_column_values(&col, &ALL_CODEC_TYPES, &mut buffer).unwrap();
|
||||
// 5 bytes of header, 0 bytes of value, 7 bytes of padding.
|
||||
assert_eq!(buffer.len(), 5);
|
||||
serialize_u64_based_column_values::<bool>(
|
||||
&&[false, true][..],
|
||||
&ALL_U64_CODEC_TYPES,
|
||||
&mut buffer,
|
||||
)
|
||||
.unwrap();
|
||||
// 6 bytes of header, 0 bytes of value, 7 bytes of padding.
|
||||
assert_eq!(buffer.len(), 6);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_fastfield_gcd() {
|
||||
let mut buffer = Vec::new();
|
||||
let vals: Vec<u64> = (0..80).map(|val| (val % 7) * 1_000u64).collect();
|
||||
let col = VecColumn::from(&vals[..]);
|
||||
serialize_column_values(&col, &[FastFieldCodecType::Bitpacked], &mut buffer).unwrap();
|
||||
serialize_u64_based_column_values(&&vals[..], &[CodecType::Bitpacked], &mut buffer)
|
||||
.unwrap();
|
||||
// Values are stored over 3 bits.
|
||||
assert_eq!(buffer.len(), 7 + (3 * 80 / 8));
|
||||
assert_eq!(buffer.len(), 6 + (3 * 80 / 8));
|
||||
}
|
||||
}
|
||||
|
||||
96
columnar/src/column_values/stats.rs
Normal file
96
columnar/src/column_values/stats.rs
Normal file
@@ -0,0 +1,96 @@
|
||||
use std::io;
|
||||
use std::io::Write;
|
||||
use std::num::NonZeroU64;
|
||||
|
||||
use common::{BinarySerializable, VInt};
|
||||
|
||||
use crate::RowId;
|
||||
|
||||
#[derive(Debug, Clone, Eq, PartialEq)]
|
||||
pub struct Stats {
|
||||
pub gcd: NonZeroU64,
|
||||
pub min_value: u64,
|
||||
pub max_value: u64,
|
||||
pub num_rows: RowId,
|
||||
}
|
||||
|
||||
impl Stats {
|
||||
pub fn amplitude(&self) -> u64 {
|
||||
self.max_value - self.min_value
|
||||
}
|
||||
}
|
||||
|
||||
impl BinarySerializable for Stats {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
VInt(self.min_value).serialize(writer)?;
|
||||
VInt(self.gcd.get()).serialize(writer)?;
|
||||
VInt(self.amplitude() / self.gcd).serialize(writer)?;
|
||||
VInt(self.num_rows as u64).serialize(writer)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let min_value = VInt::deserialize(reader)?.0;
|
||||
let gcd = VInt::deserialize(reader)?.0;
|
||||
let gcd = NonZeroU64::new(gcd)
|
||||
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "GCD of 0 is forbidden"))?;
|
||||
let amplitude = VInt::deserialize(reader)?.0 * gcd.get();
|
||||
let max_value = min_value + amplitude;
|
||||
let num_rows = VInt::deserialize(reader)?.0 as RowId;
|
||||
Ok(Stats {
|
||||
min_value,
|
||||
max_value,
|
||||
num_rows,
|
||||
gcd,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use std::num::NonZeroU64;
|
||||
|
||||
use common::BinarySerializable;
|
||||
|
||||
use crate::column_values::Stats;
|
||||
|
||||
#[track_caller]
|
||||
fn test_stats_ser_deser_aux(stats: &Stats, num_bytes: usize) {
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
stats.serialize(&mut buffer).unwrap();
|
||||
assert_eq!(buffer.len(), num_bytes);
|
||||
let deser_stats = Stats::deserialize(&mut &buffer[..]).unwrap();
|
||||
assert_eq!(stats, &deser_stats);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_stats_serialization() {
|
||||
test_stats_ser_deser_aux(
|
||||
&(Stats {
|
||||
gcd: NonZeroU64::new(3).unwrap(),
|
||||
min_value: 1,
|
||||
max_value: 3001,
|
||||
num_rows: 10,
|
||||
}),
|
||||
5,
|
||||
);
|
||||
test_stats_ser_deser_aux(
|
||||
&(Stats {
|
||||
gcd: NonZeroU64::new(1_000).unwrap(),
|
||||
min_value: 1,
|
||||
max_value: 3001,
|
||||
num_rows: 10,
|
||||
}),
|
||||
5,
|
||||
);
|
||||
test_stats_ser_deser_aux(
|
||||
&(Stats {
|
||||
gcd: NonZeroU64::new(1).unwrap(),
|
||||
min_value: 0,
|
||||
max_value: 0,
|
||||
num_rows: 0,
|
||||
}),
|
||||
4,
|
||||
);
|
||||
}
|
||||
}
|
||||
127
columnar/src/column_values/u64_based/bitpacked.rs
Normal file
127
columnar/src/column_values/u64_based/bitpacked.rs
Normal file
@@ -0,0 +1,127 @@
|
||||
use std::io::{self, Write};
|
||||
|
||||
use common::{BinarySerializable, OwnedBytes};
|
||||
use fastdivide::DividerU64;
|
||||
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
|
||||
|
||||
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, Stats};
|
||||
use crate::{ColumnValues, RowId};
|
||||
|
||||
/// Depending on the field type, a different
|
||||
/// fast field is required.
|
||||
#[derive(Clone)]
|
||||
pub struct BitpackedReader {
|
||||
data: OwnedBytes,
|
||||
bit_unpacker: BitUnpacker,
|
||||
stats: Stats,
|
||||
}
|
||||
|
||||
impl ColumnValues for BitpackedReader {
|
||||
#[inline(always)]
|
||||
fn get_val(&self, doc: u32) -> u64 {
|
||||
self.stats.min_value + self.stats.gcd.get() * self.bit_unpacker.get(doc, &self.data)
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn min_value(&self) -> u64 {
|
||||
self.stats.min_value
|
||||
}
|
||||
#[inline]
|
||||
fn max_value(&self) -> u64 {
|
||||
self.stats.max_value
|
||||
}
|
||||
#[inline]
|
||||
fn num_vals(&self) -> RowId {
|
||||
self.stats.num_rows
|
||||
}
|
||||
}
|
||||
|
||||
fn num_bits(stats: &Stats) -> u8 {
|
||||
compute_num_bits(stats.amplitude() / stats.gcd)
|
||||
}
|
||||
|
||||
#[derive(Default)]
|
||||
pub struct BitpackedCodecEstimator;
|
||||
|
||||
impl ColumnCodecEstimator for BitpackedCodecEstimator {
|
||||
fn collect(&mut self, _value: u64) {}
|
||||
|
||||
fn estimate(&self, stats: &Stats) -> Option<u64> {
|
||||
let num_bits_per_value = num_bits(stats);
|
||||
Some(stats.num_bytes() + (stats.num_rows as u64 * (num_bits_per_value as u64) + 7) / 8)
|
||||
}
|
||||
|
||||
fn serialize(
|
||||
&self,
|
||||
stats: &Stats,
|
||||
vals: &mut dyn Iterator<Item = u64>,
|
||||
wrt: &mut dyn Write,
|
||||
) -> io::Result<()> {
|
||||
stats.serialize(wrt)?;
|
||||
let num_bits = num_bits(stats);
|
||||
let mut bit_packer = BitPacker::new();
|
||||
let divider = DividerU64::divide_by(stats.gcd.get());
|
||||
for val in vals {
|
||||
bit_packer.write(divider.divide(val - stats.min_value), num_bits, wrt)?;
|
||||
}
|
||||
bit_packer.close(wrt)?;
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
pub struct BitpackedCodec;
|
||||
|
||||
impl ColumnCodec for BitpackedCodec {
|
||||
type Reader = BitpackedReader;
|
||||
type Estimator = BitpackedCodecEstimator;
|
||||
|
||||
/// Opens a fast field given a file.
|
||||
fn load(mut data: OwnedBytes) -> io::Result<Self::Reader> {
|
||||
let stats = Stats::deserialize(&mut data)?;
|
||||
let num_bits = num_bits(&stats);
|
||||
let bit_unpacker = BitUnpacker::new(num_bits);
|
||||
Ok(BitpackedReader {
|
||||
data,
|
||||
bit_unpacker,
|
||||
stats,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::column_values::u64_based::tests::create_and_validate;
|
||||
|
||||
#[test]
|
||||
fn test_with_codec_data_sets_simple() {
|
||||
create_and_validate::<BitpackedCodec>(&[4, 3, 12], "name");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_with_codec_data_sets_simple_gcd() {
|
||||
create_and_validate::<BitpackedCodec>(&[1000, 2000, 3000], "name");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_with_codec_data_sets() {
|
||||
let data_sets = crate::column_values::u64_based::tests::get_codec_test_datasets();
|
||||
for (mut data, name) in data_sets {
|
||||
create_and_validate::<BitpackedCodec>(&data, name);
|
||||
data.reverse();
|
||||
create_and_validate::<BitpackedCodec>(&data, name);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn bitpacked_fast_field_rand() {
|
||||
for _ in 0..500 {
|
||||
let mut data = (0..1 + rand::random::<u8>() as usize)
|
||||
.map(|_| rand::random::<i64>() as u64 / 2)
|
||||
.collect::<Vec<_>>();
|
||||
create_and_validate::<BitpackedCodec>(&data, "rand");
|
||||
data.reverse();
|
||||
create_and_validate::<BitpackedCodec>(&data, "rand");
|
||||
}
|
||||
}
|
||||
}
|
||||
281
columnar/src/column_values/u64_based/blockwise_linear.rs
Normal file
281
columnar/src/column_values/u64_based/blockwise_linear.rs
Normal file
@@ -0,0 +1,281 @@
|
||||
use std::io::Write;
|
||||
use std::sync::Arc;
|
||||
use std::{io, iter};
|
||||
|
||||
use common::{BinarySerializable, CountingWriter, DeserializeFrom, OwnedBytes};
|
||||
use fastdivide::DividerU64;
|
||||
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
|
||||
|
||||
use crate::column_values::u64_based::line::Line;
|
||||
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, Stats};
|
||||
use crate::column_values::{ColumnValues, VecColumn};
|
||||
use crate::MonotonicallyMappableToU64;
|
||||
|
||||
const BLOCK_SIZE: u32 = 512u32;
|
||||
|
||||
#[derive(Debug, Default)]
|
||||
struct Block {
|
||||
line: Line,
|
||||
bit_unpacker: BitUnpacker,
|
||||
data_start_offset: usize,
|
||||
}
|
||||
|
||||
impl BinarySerializable for Block {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
self.line.serialize(writer)?;
|
||||
self.bit_unpacker.bit_width().serialize(writer)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let line = Line::deserialize(reader)?;
|
||||
let bit_width = u8::deserialize(reader)?;
|
||||
Ok(Block {
|
||||
line,
|
||||
bit_unpacker: BitUnpacker::new(bit_width),
|
||||
data_start_offset: 0,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
fn compute_num_blocks(num_vals: u32) -> u32 {
|
||||
(num_vals + BLOCK_SIZE - 1) / BLOCK_SIZE
|
||||
}
|
||||
|
||||
pub struct BlockwiseLinearEstimator {
|
||||
block: Vec<u64>,
|
||||
values_num_bytes: u64,
|
||||
meta_num_bytes: u64,
|
||||
}
|
||||
|
||||
impl Default for BlockwiseLinearEstimator {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
block: Vec::with_capacity(BLOCK_SIZE as usize),
|
||||
values_num_bytes: 0u64,
|
||||
meta_num_bytes: 0u64,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl BlockwiseLinearEstimator {
|
||||
fn flush_block_estimate(&mut self) {
|
||||
if self.block.is_empty() {
|
||||
return;
|
||||
}
|
||||
let line = Line::train(&VecColumn::from(&self.block));
|
||||
let mut max_value = 0u64;
|
||||
for (i, buffer_val) in self.block.iter().enumerate() {
|
||||
let interpolated_val = line.eval(i as u32);
|
||||
let val = buffer_val.wrapping_sub(interpolated_val);
|
||||
max_value = val.max(max_value);
|
||||
}
|
||||
let bit_width = compute_num_bits(max_value) as usize;
|
||||
self.values_num_bytes += (bit_width * self.block.len() + 7) as u64 / 8;
|
||||
self.meta_num_bytes += 1 + line.num_bytes();
|
||||
}
|
||||
}
|
||||
|
||||
impl ColumnCodecEstimator for BlockwiseLinearEstimator {
|
||||
fn collect(&mut self, value: u64) {
|
||||
self.block.push(value);
|
||||
if self.block.len() == BLOCK_SIZE as usize {
|
||||
self.flush_block_estimate();
|
||||
self.block.clear();
|
||||
}
|
||||
}
|
||||
fn estimate(&self, stats: &Stats) -> Option<u64> {
|
||||
let mut estimate = 4 + stats.num_bytes() + self.meta_num_bytes + self.values_num_bytes;
|
||||
if stats.gcd.get() > 1 {
|
||||
let estimate_gain_from_gcd =
|
||||
(stats.gcd.get() as f32).log2().floor() * stats.num_rows as f32 / 8.0f32;
|
||||
estimate = estimate.saturating_sub(estimate_gain_from_gcd as u64);
|
||||
}
|
||||
Some(estimate)
|
||||
}
|
||||
|
||||
fn finalize(&mut self) {
|
||||
self.flush_block_estimate();
|
||||
}
|
||||
|
||||
fn serialize(
|
||||
&self,
|
||||
stats: &Stats,
|
||||
mut vals: &mut dyn Iterator<Item = u64>,
|
||||
wrt: &mut dyn Write,
|
||||
) -> io::Result<()> {
|
||||
stats.serialize(wrt)?;
|
||||
let mut buffer = Vec::with_capacity(BLOCK_SIZE as usize);
|
||||
let num_blocks = compute_num_blocks(stats.num_rows) as usize;
|
||||
let mut blocks = Vec::with_capacity(num_blocks);
|
||||
|
||||
let mut bit_packer = BitPacker::new();
|
||||
|
||||
let gcd_divider = DividerU64::divide_by(stats.gcd.get());
|
||||
|
||||
for _ in 0..num_blocks {
|
||||
buffer.clear();
|
||||
buffer.extend(
|
||||
(&mut vals)
|
||||
.map(MonotonicallyMappableToU64::to_u64)
|
||||
.take(BLOCK_SIZE as usize),
|
||||
);
|
||||
|
||||
for buffer_val in buffer.iter_mut() {
|
||||
*buffer_val = gcd_divider.divide(*buffer_val - stats.min_value);
|
||||
}
|
||||
|
||||
let line = Line::train(&VecColumn::from(&buffer));
|
||||
|
||||
assert!(!buffer.is_empty());
|
||||
|
||||
for (i, buffer_val) in buffer.iter_mut().enumerate() {
|
||||
let interpolated_val = line.eval(i as u32);
|
||||
*buffer_val = buffer_val.wrapping_sub(interpolated_val);
|
||||
}
|
||||
|
||||
let bit_width = buffer.iter().copied().map(compute_num_bits).max().unwrap();
|
||||
|
||||
for &buffer_val in &buffer {
|
||||
bit_packer.write(buffer_val, bit_width, wrt)?;
|
||||
}
|
||||
|
||||
blocks.push(Block {
|
||||
line,
|
||||
bit_unpacker: BitUnpacker::new(bit_width),
|
||||
data_start_offset: 0,
|
||||
});
|
||||
}
|
||||
|
||||
bit_packer.close(wrt)?;
|
||||
|
||||
assert_eq!(blocks.len(), num_blocks);
|
||||
|
||||
let mut counting_wrt = CountingWriter::wrap(wrt);
|
||||
for block in &blocks {
|
||||
block.serialize(&mut counting_wrt)?;
|
||||
}
|
||||
let footer_len = counting_wrt.written_bytes();
|
||||
(footer_len as u32).serialize(&mut counting_wrt)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
pub struct BlockwiseLinearCodec;
|
||||
|
||||
impl ColumnCodec<u64> for BlockwiseLinearCodec {
|
||||
type Reader = BlockwiseLinearReader;
|
||||
|
||||
type Estimator = BlockwiseLinearEstimator;
|
||||
|
||||
fn load(mut bytes: OwnedBytes) -> io::Result<Self::Reader> {
|
||||
let stats = Stats::deserialize(&mut bytes)?;
|
||||
let footer_len: u32 = (&bytes[bytes.len() - 4..]).deserialize()?;
|
||||
let footer_offset = bytes.len() - 4 - footer_len as usize;
|
||||
let (data, mut footer) = bytes.split(footer_offset);
|
||||
let num_blocks = compute_num_blocks(stats.num_rows);
|
||||
let mut blocks: Vec<Block> = iter::repeat_with(|| Block::deserialize(&mut footer))
|
||||
.take(num_blocks as usize)
|
||||
.collect::<io::Result<_>>()?;
|
||||
let mut start_offset = 0;
|
||||
for block in &mut blocks {
|
||||
block.data_start_offset = start_offset;
|
||||
start_offset += (block.bit_unpacker.bit_width() as usize) * BLOCK_SIZE as usize / 8;
|
||||
}
|
||||
Ok(BlockwiseLinearReader {
|
||||
blocks: blocks.into_boxed_slice().into(),
|
||||
data,
|
||||
stats,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Clone)]
|
||||
pub struct BlockwiseLinearReader {
|
||||
blocks: Arc<[Block]>,
|
||||
data: OwnedBytes,
|
||||
stats: Stats,
|
||||
}
|
||||
|
||||
impl ColumnValues for BlockwiseLinearReader {
|
||||
#[inline(always)]
|
||||
fn get_val(&self, idx: u32) -> u64 {
|
||||
let block_id = (idx / BLOCK_SIZE as u32) as usize;
|
||||
let idx_within_block = idx % (BLOCK_SIZE as u32);
|
||||
let block = &self.blocks[block_id];
|
||||
let interpoled_val: u64 = block.line.eval(idx_within_block);
|
||||
let block_bytes = &self.data[block.data_start_offset..];
|
||||
let bitpacked_diff = block.bit_unpacker.get(idx_within_block, block_bytes);
|
||||
// TODO optimize me! the line parameters could be tweaked to include the multiplication and
|
||||
// remove the dependency.
|
||||
self.stats.min_value
|
||||
+ self
|
||||
.stats
|
||||
.gcd
|
||||
.get()
|
||||
.wrapping_mul(interpoled_val.wrapping_add(bitpacked_diff))
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn min_value(&self) -> u64 {
|
||||
self.stats.min_value
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn max_value(&self) -> u64 {
|
||||
self.stats.max_value
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.stats.num_rows
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::column_values::u64_based::tests::create_and_validate;
|
||||
|
||||
#[test]
|
||||
fn test_with_codec_data_sets_simple() {
|
||||
create_and_validate::<BlockwiseLinearCodec>(
|
||||
&[11, 20, 40, 20, 10, 10, 10, 10, 10, 10],
|
||||
"simple test",
|
||||
)
|
||||
.unwrap();
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_with_codec_data_sets_simple_gcd() {
|
||||
let (_, actual_compression_rate) = create_and_validate::<BlockwiseLinearCodec>(
|
||||
&[10, 20, 40, 20, 10, 10, 10, 10, 10, 10],
|
||||
"name",
|
||||
)
|
||||
.unwrap();
|
||||
assert_eq!(actual_compression_rate, 0.175);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_with_codec_data_sets() {
|
||||
let data_sets = crate::column_values::u64_based::tests::get_codec_test_datasets();
|
||||
for (mut data, name) in data_sets {
|
||||
create_and_validate::<BlockwiseLinearCodec>(&data, name);
|
||||
data.reverse();
|
||||
create_and_validate::<BlockwiseLinearCodec>(&data, name);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_blockwise_linear_fast_field_rand() {
|
||||
for _ in 0..500 {
|
||||
let mut data = (0..1 + rand::random::<u8>() as usize)
|
||||
.map(|_| rand::random::<i64>() as u64 / 2)
|
||||
.collect::<Vec<_>>();
|
||||
create_and_validate::<BlockwiseLinearCodec>(&data, "rand");
|
||||
data.reverse();
|
||||
create_and_validate::<BlockwiseLinearCodec>(&data, "rand");
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -17,8 +17,8 @@ const MID_POINT: u64 = (1u64 << 32) - 1u64;
|
||||
/// `y = m * x >> 32 + b`
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Line {
|
||||
slope: u64,
|
||||
intercept: u64,
|
||||
pub(crate) slope: u64,
|
||||
pub(crate) intercept: u64,
|
||||
}
|
||||
|
||||
/// Compute the line slope.
|
||||
@@ -67,21 +67,8 @@ impl Line {
|
||||
self.intercept.wrapping_add(linear_part)
|
||||
}
|
||||
|
||||
// Same as train, but the intercept is only estimated from provided sample positions
|
||||
pub fn estimate(sample_positions_and_values: &[(u64, u64)]) -> Self {
|
||||
let first_val = sample_positions_and_values[0].1;
|
||||
let last_val = sample_positions_and_values[sample_positions_and_values.len() - 1].1;
|
||||
let num_vals = sample_positions_and_values[sample_positions_and_values.len() - 1].0 + 1;
|
||||
Self::train_from(
|
||||
first_val,
|
||||
last_val,
|
||||
num_vals as u32,
|
||||
sample_positions_and_values.iter().cloned(),
|
||||
)
|
||||
}
|
||||
|
||||
// Intercept is only computed from provided positions
|
||||
fn train_from(
|
||||
pub fn train_from(
|
||||
first_val: u64,
|
||||
last_val: u64,
|
||||
num_vals: u32,
|
||||
@@ -145,6 +132,7 @@ impl Line {
|
||||
///
|
||||
/// This function is only invariable by translation if all of the
|
||||
/// `ys` are packaged into half of the space. (See heuristic below)
|
||||
/// TODO USE array
|
||||
pub fn train(ys: &dyn ColumnValues) -> Self {
|
||||
let first_val = ys.iter().next().unwrap();
|
||||
let last_val = ys.iter().nth(ys.num_vals() as usize - 1).unwrap();
|
||||
@@ -158,7 +146,7 @@ impl Line {
|
||||
}
|
||||
|
||||
impl BinarySerializable for Line {
|
||||
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: io::Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
VInt(self.slope).serialize(writer)?;
|
||||
VInt(self.intercept).serialize(writer)?;
|
||||
Ok(())
|
||||
277
columnar/src/column_values/u64_based/linear.rs
Normal file
277
columnar/src/column_values/u64_based/linear.rs
Normal file
@@ -0,0 +1,277 @@
|
||||
use std::io;
|
||||
|
||||
use common::{BinarySerializable, OwnedBytes};
|
||||
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
|
||||
|
||||
use super::line::Line;
|
||||
use super::ColumnValues;
|
||||
use crate::column_values::u64_based::{ColumnCodec, ColumnCodecEstimator, Stats};
|
||||
use crate::column_values::VecColumn;
|
||||
use crate::RowId;
|
||||
|
||||
const HALF_SPACE: u64 = u64::MAX / 2;
|
||||
const LINE_ESTIMATION_BLOCK_LEN: usize = 512;
|
||||
|
||||
/// Depending on the field type, a different
|
||||
/// fast field is required.
|
||||
#[derive(Clone)]
|
||||
pub struct LinearReader {
|
||||
data: OwnedBytes,
|
||||
linear_params: LinearParams,
|
||||
stats: Stats,
|
||||
}
|
||||
|
||||
impl ColumnValues for LinearReader {
|
||||
#[inline]
|
||||
fn get_val(&self, doc: u32) -> u64 {
|
||||
let interpoled_val: u64 = self.linear_params.line.eval(doc);
|
||||
let bitpacked_diff = self.linear_params.bit_unpacker.get(doc, &self.data);
|
||||
interpoled_val.wrapping_add(bitpacked_diff)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn min_value(&self) -> u64 {
|
||||
self.stats.min_value
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn max_value(&self) -> u64 {
|
||||
self.stats.max_value
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.stats.num_rows
|
||||
}
|
||||
}
|
||||
|
||||
/// Fastfield serializer, which tries to guess values by linear interpolation
|
||||
/// and stores the difference bitpacked.
|
||||
pub struct LinearCodec;
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
struct LinearParams {
|
||||
line: Line,
|
||||
bit_unpacker: BitUnpacker,
|
||||
}
|
||||
|
||||
impl BinarySerializable for LinearParams {
|
||||
fn serialize<W: io::Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
self.line.serialize(writer)?;
|
||||
self.bit_unpacker.bit_width().serialize(writer)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let line = Line::deserialize(reader)?;
|
||||
let bit_width = u8::deserialize(reader)?;
|
||||
Ok(Self {
|
||||
line,
|
||||
bit_unpacker: BitUnpacker::new(bit_width),
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
pub struct LinearCodecEstimator {
|
||||
block: Vec<u64>,
|
||||
line: Option<Line>,
|
||||
row_id: RowId,
|
||||
min_deviation: u64,
|
||||
max_deviation: u64,
|
||||
first_val: u64,
|
||||
last_val: u64,
|
||||
}
|
||||
|
||||
impl Default for LinearCodecEstimator {
|
||||
fn default() -> LinearCodecEstimator {
|
||||
LinearCodecEstimator {
|
||||
block: Vec::with_capacity(LINE_ESTIMATION_BLOCK_LEN),
|
||||
line: None,
|
||||
row_id: 0,
|
||||
min_deviation: u64::MAX,
|
||||
max_deviation: u64::MIN,
|
||||
first_val: 0u64,
|
||||
last_val: 0u64,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl ColumnCodecEstimator for LinearCodecEstimator {
|
||||
fn finalize(&mut self) {
|
||||
if let Some(line) = self.line.as_mut() {
|
||||
line.intercept = line
|
||||
.intercept
|
||||
.wrapping_add(self.min_deviation)
|
||||
.wrapping_sub(HALF_SPACE);
|
||||
}
|
||||
}
|
||||
|
||||
fn estimate(&self, stats: &Stats) -> Option<u64> {
|
||||
let line = self.line?;
|
||||
let amplitude = self.max_deviation - self.min_deviation;
|
||||
let num_bits = compute_num_bits(amplitude);
|
||||
let linear_params = LinearParams {
|
||||
line,
|
||||
bit_unpacker: BitUnpacker::new(num_bits),
|
||||
};
|
||||
Some(
|
||||
stats.num_bytes()
|
||||
+ linear_params.num_bytes()
|
||||
+ (num_bits as u64 * stats.num_rows as u64 + 7) / 8,
|
||||
)
|
||||
}
|
||||
|
||||
fn serialize(
|
||||
&self,
|
||||
stats: &Stats,
|
||||
vals: &mut dyn Iterator<Item = u64>,
|
||||
wrt: &mut dyn io::Write,
|
||||
) -> io::Result<()> {
|
||||
stats.serialize(wrt)?;
|
||||
let line = self.line.unwrap();
|
||||
let amplitude = self.max_deviation - self.min_deviation;
|
||||
let num_bits = compute_num_bits(amplitude);
|
||||
let linear_params = LinearParams {
|
||||
line,
|
||||
bit_unpacker: BitUnpacker::new(num_bits),
|
||||
};
|
||||
linear_params.serialize(wrt)?;
|
||||
let mut bit_packer = BitPacker::new();
|
||||
for (pos, value) in vals.enumerate() {
|
||||
let calculated_value = line.eval(pos as u32);
|
||||
let offset = value.wrapping_sub(calculated_value);
|
||||
bit_packer.write(offset, num_bits, wrt)?;
|
||||
}
|
||||
bit_packer.close(wrt)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn collect(&mut self, value: u64) {
|
||||
if let Some(line) = self.line {
|
||||
self.collect_after_line_estimation(&line, value);
|
||||
} else {
|
||||
self.collect_before_line_estimation(value);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl LinearCodecEstimator {
|
||||
#[inline]
|
||||
fn collect_after_line_estimation(&mut self, line: &Line, value: u64) {
|
||||
let interpoled_val: u64 = line.eval(self.row_id);
|
||||
let deviation = value.wrapping_add(HALF_SPACE).wrapping_sub(interpoled_val);
|
||||
self.min_deviation = self.min_deviation.min(deviation);
|
||||
self.max_deviation = self.max_deviation.max(deviation);
|
||||
if self.row_id == 0 {
|
||||
self.first_val = value;
|
||||
}
|
||||
self.last_val = value;
|
||||
self.row_id += 1u32;
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn collect_before_line_estimation(&mut self, value: u64) {
|
||||
self.block.push(value);
|
||||
if self.block.len() == LINE_ESTIMATION_BLOCK_LEN {
|
||||
let line = Line::train(&VecColumn::from(&self.block));
|
||||
let block = std::mem::take(&mut self.block);
|
||||
for val in block {
|
||||
self.collect_after_line_estimation(&line, val);
|
||||
}
|
||||
self.line = Some(line);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl ColumnCodec for LinearCodec {
|
||||
type Reader = LinearReader;
|
||||
|
||||
type Estimator = LinearCodecEstimator;
|
||||
|
||||
fn load(mut data: OwnedBytes) -> io::Result<Self::Reader> {
|
||||
let stats = Stats::deserialize(&mut data)?;
|
||||
let linear_params = LinearParams::deserialize(&mut data)?;
|
||||
Ok(LinearReader {
|
||||
stats,
|
||||
linear_params,
|
||||
data,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use rand::RngCore;
|
||||
|
||||
use super::*;
|
||||
use crate::column_values::u64_based::tests::{create_and_validate, get_codec_test_datasets};
|
||||
|
||||
#[test]
|
||||
fn test_compression_simple() {
|
||||
let vals = (100u64..)
|
||||
.take(super::LINE_ESTIMATION_BLOCK_LEN)
|
||||
.collect::<Vec<_>>();
|
||||
create_and_validate::<LinearCodec>(&vals, "simple monotonically large").unwrap();
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_compression() {
|
||||
let data = (10..=6_000_u64).collect::<Vec<_>>();
|
||||
let (estimate, actual_compression) =
|
||||
create_and_validate::<LinearCodec>(&data, "simple monotonically large").unwrap();
|
||||
assert_le!(actual_compression, 0.001);
|
||||
assert_le!(estimate, 0.02);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_with_codec_datasets() {
|
||||
let data_sets = get_codec_test_datasets();
|
||||
for (mut data, name) in data_sets {
|
||||
create_and_validate::<LinearCodec>(&data, name);
|
||||
data.reverse();
|
||||
create_and_validate::<LinearCodec>(&data, name);
|
||||
}
|
||||
}
|
||||
#[test]
|
||||
fn linear_interpol_fast_field_test_large_amplitude() {
|
||||
let data = vec![
|
||||
i64::MAX as u64 / 2,
|
||||
i64::MAX as u64 / 3,
|
||||
i64::MAX as u64 / 2,
|
||||
];
|
||||
create_and_validate::<LinearCodec>(&data, "large amplitude");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn overflow_error_test() {
|
||||
let data = vec![1572656989877777, 1170935903116329, 720575940379279, 0];
|
||||
create_and_validate::<LinearCodec>(&data, "overflow test");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn linear_interpol_fast_concave_data() {
|
||||
let data = vec![0, 1, 2, 5, 8, 10, 20, 50];
|
||||
create_and_validate::<LinearCodec>(&data, "concave data");
|
||||
}
|
||||
#[test]
|
||||
fn linear_interpol_fast_convex_data() {
|
||||
let data = vec![0, 40, 60, 70, 75, 77];
|
||||
create_and_validate::<LinearCodec>(&data, "convex data");
|
||||
}
|
||||
#[test]
|
||||
fn linear_interpol_fast_field_test_simple() {
|
||||
let data = (10..=20_u64).collect::<Vec<_>>();
|
||||
create_and_validate::<LinearCodec>(&data, "simple monotonically");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn linear_interpol_fast_field_rand() {
|
||||
let mut rng = rand::thread_rng();
|
||||
for _ in 0..50 {
|
||||
let mut data = (0..10_000).map(|_| rng.next_u64()).collect::<Vec<_>>();
|
||||
create_and_validate::<LinearCodec>(&data, "random");
|
||||
data.reverse();
|
||||
create_and_validate::<LinearCodec>(&data, "random");
|
||||
}
|
||||
}
|
||||
}
|
||||
182
columnar/src/column_values/u64_based/mod.rs
Normal file
182
columnar/src/column_values/u64_based/mod.rs
Normal file
@@ -0,0 +1,182 @@
|
||||
mod bitpacked;
|
||||
mod blockwise_linear;
|
||||
mod line;
|
||||
mod linear;
|
||||
mod stats_collector;
|
||||
|
||||
use std::io;
|
||||
use std::io::Write;
|
||||
use std::sync::Arc;
|
||||
|
||||
use common::{BinarySerializable, OwnedBytes};
|
||||
|
||||
use crate::column_values::monotonic_mapping::{
|
||||
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
|
||||
};
|
||||
use crate::column_values::u64_based::bitpacked::BitpackedCodec;
|
||||
use crate::column_values::u64_based::blockwise_linear::BlockwiseLinearCodec;
|
||||
use crate::column_values::u64_based::linear::LinearCodec;
|
||||
use crate::column_values::u64_based::stats_collector::StatsCollector;
|
||||
use crate::column_values::{monotonic_map_column, Stats};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{ColumnValues, MonotonicallyMappableToU64};
|
||||
|
||||
pub trait ColumnCodecEstimator<T = u64>: 'static {
|
||||
fn collect(&mut self, value: u64);
|
||||
fn estimate(&self, stats: &Stats) -> Option<u64>;
|
||||
fn finalize(&mut self) {}
|
||||
fn serialize(
|
||||
&self,
|
||||
stats: &Stats,
|
||||
vals: &mut dyn Iterator<Item = T>,
|
||||
wrt: &mut dyn io::Write,
|
||||
) -> io::Result<()>;
|
||||
}
|
||||
|
||||
pub trait ColumnCodec<T: PartialOrd = u64> {
|
||||
type Reader: ColumnValues<T> + 'static;
|
||||
type Estimator: ColumnCodecEstimator + Default;
|
||||
|
||||
fn load(bytes: OwnedBytes) -> io::Result<Self::Reader>;
|
||||
|
||||
fn estimator() -> Self::Estimator {
|
||||
Self::Estimator::default()
|
||||
}
|
||||
fn boxed_estimator() -> Box<dyn ColumnCodecEstimator> {
|
||||
Box::new(Self::estimator())
|
||||
}
|
||||
}
|
||||
|
||||
/// Available codecs to use to encode the u64 (via [`MonotonicallyMappableToU64`]) converted data.
|
||||
#[derive(PartialEq, Eq, PartialOrd, Ord, Debug, Clone, Copy)]
|
||||
#[repr(u8)]
|
||||
pub enum CodecType {
|
||||
/// Bitpack all values in the value range. The number of bits is defined by the amplitude
|
||||
/// `column.max_value() - column.min_value()`
|
||||
Bitpacked = 0u8,
|
||||
/// Linear interpolation puts a line between the first and last value and then bitpacks the
|
||||
/// values by the offset from the line. The number of bits is defined by the max deviation from
|
||||
/// the line.
|
||||
Linear = 1u8,
|
||||
/// Same as [`CodecType::Linear`], but encodes in blocks of 512 elements.
|
||||
BlockwiseLinear = 2u8,
|
||||
}
|
||||
|
||||
pub const ALL_U64_CODEC_TYPES: [CodecType; 3] = [
|
||||
CodecType::Bitpacked,
|
||||
CodecType::Linear,
|
||||
CodecType::BlockwiseLinear,
|
||||
];
|
||||
|
||||
impl CodecType {
|
||||
fn to_code(self) -> u8 {
|
||||
self as u8
|
||||
}
|
||||
|
||||
fn try_from_code(code: u8) -> Option<CodecType> {
|
||||
match code {
|
||||
0u8 => Some(CodecType::Bitpacked),
|
||||
1u8 => Some(CodecType::Linear),
|
||||
2u8 => Some(CodecType::BlockwiseLinear),
|
||||
_ => None,
|
||||
}
|
||||
}
|
||||
|
||||
fn load<T: MonotonicallyMappableToU64>(
|
||||
&self,
|
||||
bytes: OwnedBytes,
|
||||
) -> io::Result<Arc<dyn ColumnValues<T>>> {
|
||||
match self {
|
||||
CodecType::Bitpacked => load_specific_codec::<BitpackedCodec, T>(bytes),
|
||||
CodecType::Linear => load_specific_codec::<LinearCodec, T>(bytes),
|
||||
CodecType::BlockwiseLinear => load_specific_codec::<BlockwiseLinearCodec, T>(bytes),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn load_specific_codec<C: ColumnCodec, T: MonotonicallyMappableToU64>(
|
||||
bytes: OwnedBytes,
|
||||
) -> io::Result<Arc<dyn ColumnValues<T>>> {
|
||||
let reader = C::load(bytes)?;
|
||||
let reader_typed = monotonic_map_column(
|
||||
reader,
|
||||
StrictlyMonotonicMappingInverter::from(StrictlyMonotonicMappingToInternal::<T>::new()),
|
||||
);
|
||||
Ok(Arc::new(reader_typed))
|
||||
}
|
||||
|
||||
impl CodecType {
|
||||
pub fn estimator(&self) -> Box<dyn ColumnCodecEstimator> {
|
||||
match self {
|
||||
CodecType::Bitpacked => BitpackedCodec::boxed_estimator(),
|
||||
CodecType::Linear => LinearCodec::boxed_estimator(),
|
||||
CodecType::BlockwiseLinear => BlockwiseLinearCodec::boxed_estimator(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub fn serialize_u64_based_column_values<'a, T: MonotonicallyMappableToU64>(
|
||||
vals: &dyn Iterable<T>,
|
||||
codec_types: &[CodecType],
|
||||
wrt: &mut dyn Write,
|
||||
) -> io::Result<()> {
|
||||
let mut stats_collector = StatsCollector::default();
|
||||
let mut estimators: Vec<(CodecType, Box<dyn ColumnCodecEstimator>)> =
|
||||
Vec::with_capacity(codec_types.len());
|
||||
for &codec_type in codec_types {
|
||||
estimators.push((codec_type, codec_type.estimator()));
|
||||
}
|
||||
for val in vals.boxed_iter() {
|
||||
let val_u64 = val.to_u64();
|
||||
stats_collector.collect(val_u64);
|
||||
for (_, estimator) in &mut estimators {
|
||||
estimator.collect(val_u64);
|
||||
}
|
||||
}
|
||||
for (_, estimator) in &mut estimators {
|
||||
estimator.finalize();
|
||||
}
|
||||
let stats = stats_collector.stats();
|
||||
let (_, best_codec, best_codec_estimator) = estimators
|
||||
.into_iter()
|
||||
.flat_map(|(codec_type, estimator)| {
|
||||
let num_bytes = estimator.estimate(&stats)?;
|
||||
Some((num_bytes, codec_type, estimator))
|
||||
})
|
||||
.min_by_key(|(num_bytes, _, _)| *num_bytes)
|
||||
.ok_or_else(|| {
|
||||
io::Error::new(io::ErrorKind::InvalidData, "No available applicable codec.")
|
||||
})?;
|
||||
best_codec.to_code().serialize(wrt)?;
|
||||
best_codec_estimator.serialize(
|
||||
&stats,
|
||||
&mut vals.boxed_iter().map(MonotonicallyMappableToU64::to_u64),
|
||||
wrt,
|
||||
)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn load_u64_based_column_values<T: MonotonicallyMappableToU64>(
|
||||
mut bytes: OwnedBytes,
|
||||
) -> io::Result<Arc<dyn ColumnValues<T>>> {
|
||||
let codec_type: CodecType = bytes
|
||||
.get(0)
|
||||
.copied()
|
||||
.and_then(CodecType::try_from_code)
|
||||
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "Failed to read codec type"))?;
|
||||
bytes.advance(1);
|
||||
codec_type.load(bytes)
|
||||
}
|
||||
|
||||
/// Helper function to serialize a column (autodetect from all codecs) and then open it
|
||||
pub fn serialize_and_load_u64_based_column_values<T: MonotonicallyMappableToU64>(
|
||||
vals: &dyn Iterable,
|
||||
codec_types: &[CodecType],
|
||||
) -> Arc<dyn ColumnValues<T>> {
|
||||
let mut buffer = Vec::new();
|
||||
serialize_u64_based_column_values(vals, codec_types, &mut buffer).unwrap();
|
||||
load_u64_based_column_values::<T>(OwnedBytes::new(buffer)).unwrap()
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests;
|
||||
200
columnar/src/column_values/u64_based/stats_collector.rs
Normal file
200
columnar/src/column_values/u64_based/stats_collector.rs
Normal file
@@ -0,0 +1,200 @@
|
||||
use std::num::NonZeroU64;
|
||||
|
||||
use fastdivide::DividerU64;
|
||||
|
||||
use crate::column_values::Stats;
|
||||
use crate::RowId;
|
||||
|
||||
/// Compute the gcd of two non null numbers.
|
||||
///
|
||||
/// It is recommended, but not required, to feed values such that `large >= small`.
|
||||
fn compute_gcd(mut large: NonZeroU64, mut small: NonZeroU64) -> NonZeroU64 {
|
||||
loop {
|
||||
let rem: u64 = large.get() % small;
|
||||
if let Some(new_small) = NonZeroU64::new(rem) {
|
||||
(large, small) = (small, new_small);
|
||||
} else {
|
||||
return small;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Default)]
|
||||
pub struct StatsCollector {
|
||||
min_max_opt: Option<(u64, u64)>,
|
||||
num_rows: RowId,
|
||||
// We measure the GCD of the difference between the values and the minimal value.
|
||||
// This is the same as computing the difference between the values and the first value.
|
||||
//
|
||||
// This way, we can compress i64-converted-to-u64 (e.g. timestamp that were supplied in
|
||||
// seconds, only to be converted in microseconds).
|
||||
increment_gcd_opt: Option<(NonZeroU64, DividerU64)>,
|
||||
first_value_opt: Option<u64>,
|
||||
}
|
||||
|
||||
impl StatsCollector {
|
||||
pub fn stats(&self) -> Stats {
|
||||
let (min_value, max_value) = self.min_max_opt.unwrap_or((0u64, 0u64));
|
||||
let increment_gcd = if let Some((increment_gcd, _)) = self.increment_gcd_opt {
|
||||
increment_gcd
|
||||
} else {
|
||||
NonZeroU64::new(1u64).unwrap()
|
||||
};
|
||||
Stats {
|
||||
min_value,
|
||||
max_value,
|
||||
num_rows: self.num_rows,
|
||||
gcd: increment_gcd,
|
||||
}
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn update_increment_gcd(&mut self, value: u64) {
|
||||
let Some(first_value) = self.first_value_opt else {
|
||||
// We set the first value and just quit.
|
||||
self.first_value_opt = Some(value);
|
||||
return;
|
||||
};
|
||||
let Some(non_zero_value) = NonZeroU64::new(value.abs_diff(first_value)) else {
|
||||
// We can simply skip 0 values.
|
||||
return;
|
||||
};
|
||||
let Some((gcd, gcd_divider)) = self.increment_gcd_opt else {
|
||||
self.set_increment_gcd(non_zero_value);
|
||||
return;
|
||||
};
|
||||
if gcd.get() == 1 {
|
||||
// It won't see any update now.
|
||||
return;
|
||||
}
|
||||
let remainder =
|
||||
non_zero_value.get() - (gcd_divider.divide(non_zero_value.get())) * gcd.get();
|
||||
if remainder == 0 {
|
||||
return;
|
||||
}
|
||||
let new_gcd = compute_gcd(non_zero_value, gcd);
|
||||
self.set_increment_gcd(new_gcd);
|
||||
}
|
||||
|
||||
fn set_increment_gcd(&mut self, gcd: NonZeroU64) {
|
||||
let new_divider = DividerU64::divide_by(gcd.get());
|
||||
self.increment_gcd_opt = Some((gcd, new_divider));
|
||||
}
|
||||
|
||||
pub fn collect(&mut self, value: u64) {
|
||||
self.min_max_opt = Some(if let Some((min, max)) = self.min_max_opt {
|
||||
(min.min(value), max.max(value))
|
||||
} else {
|
||||
(value, value)
|
||||
});
|
||||
self.num_rows += 1;
|
||||
self.update_increment_gcd(value);
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use std::num::NonZeroU64;
|
||||
|
||||
use crate::column_values::u64_based::stats_collector::{compute_gcd, StatsCollector};
|
||||
use crate::column_values::u64_based::Stats;
|
||||
|
||||
fn compute_stats(vals: impl Iterator<Item = u64>) -> Stats {
|
||||
let mut stats_collector = StatsCollector::default();
|
||||
for val in vals {
|
||||
stats_collector.collect(val);
|
||||
}
|
||||
stats_collector.stats()
|
||||
}
|
||||
|
||||
fn find_gcd(vals: impl Iterator<Item = u64>) -> u64 {
|
||||
compute_stats(vals).gcd.get()
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_compute_gcd() {
|
||||
let test_compute_gcd_aux = |large, small, expected| {
|
||||
let large = NonZeroU64::new(large).unwrap();
|
||||
let small = NonZeroU64::new(small).unwrap();
|
||||
let expected = NonZeroU64::new(expected).unwrap();
|
||||
assert_eq!(compute_gcd(small, large), expected);
|
||||
assert_eq!(compute_gcd(large, small), expected);
|
||||
};
|
||||
test_compute_gcd_aux(1, 4, 1);
|
||||
test_compute_gcd_aux(2, 4, 2);
|
||||
test_compute_gcd_aux(10, 25, 5);
|
||||
test_compute_gcd_aux(25, 25, 25);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_gcd() {
|
||||
assert_eq!(find_gcd([0].into_iter()), 1);
|
||||
assert_eq!(find_gcd([0, 10].into_iter()), 10);
|
||||
assert_eq!(find_gcd([10, 0].into_iter()), 10);
|
||||
assert_eq!(find_gcd([].into_iter()), 1);
|
||||
assert_eq!(find_gcd([15, 30, 5, 10].into_iter()), 5);
|
||||
assert_eq!(find_gcd([15, 16, 10].into_iter()), 1);
|
||||
assert_eq!(find_gcd([0, 5, 5, 5].into_iter()), 5);
|
||||
assert_eq!(find_gcd([0, 0].into_iter()), 1);
|
||||
assert_eq!(find_gcd([1, 10, 4, 1, 7, 10].into_iter()), 3);
|
||||
assert_eq!(find_gcd([1, 10, 0, 4, 1, 7, 10].into_iter()), 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_stats() {
|
||||
assert_eq!(
|
||||
compute_stats([].into_iter()),
|
||||
Stats {
|
||||
gcd: NonZeroU64::new(1).unwrap(),
|
||||
min_value: 0,
|
||||
max_value: 0,
|
||||
num_rows: 0
|
||||
}
|
||||
);
|
||||
assert_eq!(
|
||||
compute_stats([0, 1].into_iter()),
|
||||
Stats {
|
||||
gcd: NonZeroU64::new(1).unwrap(),
|
||||
min_value: 0,
|
||||
max_value: 1,
|
||||
num_rows: 2
|
||||
}
|
||||
);
|
||||
assert_eq!(
|
||||
compute_stats([0, 1].into_iter()),
|
||||
Stats {
|
||||
gcd: NonZeroU64::new(1).unwrap(),
|
||||
min_value: 0,
|
||||
max_value: 1,
|
||||
num_rows: 2
|
||||
}
|
||||
);
|
||||
assert_eq!(
|
||||
compute_stats([10, 20, 30].into_iter()),
|
||||
Stats {
|
||||
gcd: NonZeroU64::new(10).unwrap(),
|
||||
min_value: 10,
|
||||
max_value: 30,
|
||||
num_rows: 3
|
||||
}
|
||||
);
|
||||
assert_eq!(
|
||||
compute_stats([10, 50, 10, 30].into_iter()),
|
||||
Stats {
|
||||
gcd: NonZeroU64::new(20).unwrap(),
|
||||
min_value: 10,
|
||||
max_value: 50,
|
||||
num_rows: 4
|
||||
}
|
||||
);
|
||||
assert_eq!(
|
||||
compute_stats([10, 0, 30].into_iter()),
|
||||
Stats {
|
||||
gcd: NonZeroU64::new(10).unwrap(),
|
||||
min_value: 0,
|
||||
max_value: 30,
|
||||
num_rows: 3
|
||||
}
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -2,53 +2,88 @@ use proptest::prelude::*;
|
||||
use proptest::strategy::Strategy;
|
||||
use proptest::{prop_oneof, proptest};
|
||||
|
||||
use super::bitpacked::BitpackedCodec;
|
||||
use super::blockwise_linear::BlockwiseLinearCodec;
|
||||
use super::linear::LinearCodec;
|
||||
use super::serialize::Header;
|
||||
|
||||
pub(crate) fn create_and_validate<Codec: FastFieldCodec>(
|
||||
data: &[u64],
|
||||
#[test]
|
||||
fn test_serialize_and_load_simple() {
|
||||
let mut buffer = Vec::new();
|
||||
let vals = &[1u64, 2u64, 5u64];
|
||||
serialize_u64_based_column_values(
|
||||
&&vals[..],
|
||||
&[CodecType::Bitpacked, CodecType::BlockwiseLinear],
|
||||
&mut buffer,
|
||||
)
|
||||
.unwrap();
|
||||
assert_eq!(buffer.len(), 7);
|
||||
let col = load_u64_based_column_values::<u64>(OwnedBytes::new(buffer)).unwrap();
|
||||
assert_eq!(col.num_vals(), 3);
|
||||
assert_eq!(col.get_val(0), 1);
|
||||
assert_eq!(col.get_val(1), 2);
|
||||
assert_eq!(col.get_val(2), 5);
|
||||
}
|
||||
pub(crate) fn create_and_validate<TColumnCodec: ColumnCodec>(
|
||||
vals: &[u64],
|
||||
name: &str,
|
||||
) -> Option<(f32, f32)> {
|
||||
let col = &VecColumn::from(data);
|
||||
let header = Header::compute_header(col, &[Codec::CODEC_TYPE])?;
|
||||
let normalized_col = header.normalize_column(col);
|
||||
let estimation = Codec::estimate(&normalized_col)?;
|
||||
let mut stats_collector = StatsCollector::default();
|
||||
let mut codec_estimator: TColumnCodec::Estimator = Default::default();
|
||||
|
||||
let mut out = Vec::new();
|
||||
let col = VecColumn::from(data);
|
||||
serialize_column_values(&col, &[Codec::CODEC_TYPE], &mut out).unwrap();
|
||||
for val in vals.boxed_iter() {
|
||||
stats_collector.collect(val);
|
||||
codec_estimator.collect(val);
|
||||
}
|
||||
codec_estimator.finalize();
|
||||
let stats = stats_collector.stats();
|
||||
let estimation = codec_estimator.estimate(&stats)?;
|
||||
|
||||
let actual_compression = out.len() as f32 / (data.len() as f32 * 8.0);
|
||||
let mut buffer = Vec::new();
|
||||
codec_estimator
|
||||
.serialize(&stats, vals.boxed_iter().as_mut(), &mut buffer)
|
||||
.unwrap();
|
||||
|
||||
let reader = super::open_u64_mapped::<u64>(OwnedBytes::new(out)).unwrap();
|
||||
assert_eq!(reader.num_vals(), data.len() as u32);
|
||||
for (doc, orig_val) in data.iter().copied().enumerate() {
|
||||
let actual_compression = buffer.len() as u64;
|
||||
|
||||
let reader = TColumnCodec::load(OwnedBytes::new(buffer)).unwrap();
|
||||
assert_eq!(reader.num_vals(), vals.len() as u32);
|
||||
for (doc, orig_val) in vals.iter().copied().enumerate() {
|
||||
let val = reader.get_val(doc as u32);
|
||||
assert_eq!(
|
||||
val, orig_val,
|
||||
"val `{val}` does not match orig_val {orig_val:?}, in data set {name}, data `{data:?}`",
|
||||
"val `{val}` does not match orig_val {orig_val:?}, in data set {name}, data `{vals:?}`",
|
||||
);
|
||||
}
|
||||
|
||||
if !data.is_empty() {
|
||||
let test_rand_idx = rand::thread_rng().gen_range(0..=data.len() - 1);
|
||||
let expected_positions: Vec<u32> = data
|
||||
if !vals.is_empty() {
|
||||
let test_rand_idx = rand::thread_rng().gen_range(0..=vals.len() - 1);
|
||||
let expected_positions: Vec<u32> = vals
|
||||
.iter()
|
||||
.enumerate()
|
||||
.filter(|(_, el)| **el == data[test_rand_idx])
|
||||
.filter(|(_, el)| **el == vals[test_rand_idx])
|
||||
.map(|(pos, _)| pos as u32)
|
||||
.collect();
|
||||
let mut positions = Vec::new();
|
||||
reader.get_docids_for_value_range(
|
||||
data[test_rand_idx]..=data[test_rand_idx],
|
||||
0..data.len() as u32,
|
||||
vals[test_rand_idx]..=vals[test_rand_idx],
|
||||
0..vals.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
assert_eq!(expected_positions, positions);
|
||||
}
|
||||
Some((estimation, actual_compression))
|
||||
if actual_compression > 1000 {
|
||||
assert!(relative_difference(estimation, actual_compression) < 0.10f32);
|
||||
}
|
||||
Some((
|
||||
compression_rate(estimation, stats.num_rows),
|
||||
compression_rate(actual_compression, stats.num_rows),
|
||||
))
|
||||
}
|
||||
|
||||
fn compression_rate(num_bytes: u64, num_values: u32) -> f32 {
|
||||
num_bytes as f32 / (num_values as f32 * 8.0)
|
||||
}
|
||||
|
||||
fn relative_difference(left: u64, right: u64) -> f32 {
|
||||
let left = left as f32;
|
||||
let right = right as f32;
|
||||
2.0f32 * (left - right).abs() / (left + right)
|
||||
}
|
||||
|
||||
proptest! {
|
||||
@@ -64,12 +99,21 @@ proptest! {
|
||||
create_and_validate::<LinearCodec>(&data, "proptest linearinterpol");
|
||||
}
|
||||
|
||||
|
||||
#[test]
|
||||
fn test_proptest_small_blockwise_linear(data in proptest::collection::vec(num_strategy(), 1..10)) {
|
||||
create_and_validate::<BlockwiseLinearCodec>(&data, "proptest multilinearinterpol");
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_small_blockwise_linear_example() {
|
||||
create_and_validate::<BlockwiseLinearCodec>(
|
||||
&[9223372036854775808, 9223370937344622593],
|
||||
"proptest multilinearinterpol",
|
||||
);
|
||||
}
|
||||
|
||||
proptest! {
|
||||
#![proptest_config(ProptestConfig::with_cases(10))]
|
||||
|
||||
@@ -118,8 +162,8 @@ pub fn get_codec_test_datasets() -> Vec<(Vec<u64>, &'static str)> {
|
||||
data_and_names
|
||||
}
|
||||
|
||||
fn test_codec<C: FastFieldCodec>() {
|
||||
let codec_name = format!("{:?}", C::CODEC_TYPE);
|
||||
fn test_codec<C: ColumnCodec>() {
|
||||
let codec_name = std::any::type_name::<C>();
|
||||
for (data, dataset_name) in get_codec_test_datasets() {
|
||||
let estimate_actual_opt: Option<(f32, f32)> =
|
||||
tests::create_and_validate::<C>(&data, dataset_name);
|
||||
@@ -146,53 +190,48 @@ fn test_codec_multi_interpolation() {
|
||||
|
||||
use super::*;
|
||||
|
||||
fn estimate<C: ColumnCodec>(vals: &[u64]) -> Option<f32> {
|
||||
let mut stats_collector = StatsCollector::default();
|
||||
let mut estimator = C::Estimator::default();
|
||||
for &val in vals {
|
||||
stats_collector.collect(val);
|
||||
estimator.collect(val);
|
||||
}
|
||||
estimator.finalize();
|
||||
let stats = stats_collector.stats();
|
||||
let num_bytes = estimator.estimate(&stats)?;
|
||||
if stats.num_rows == 0 {
|
||||
return None;
|
||||
}
|
||||
Some(num_bytes as f32 / (8.0 * stats.num_rows as f32))
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn estimation_good_interpolation_case() {
|
||||
let data = (10..=20000_u64).collect::<Vec<_>>();
|
||||
let data: VecColumn = data.as_slice().into();
|
||||
|
||||
let linear_interpol_estimation = LinearCodec::estimate(&data).unwrap();
|
||||
let linear_interpol_estimation = estimate::<LinearCodec>(&data).unwrap();
|
||||
assert_le!(linear_interpol_estimation, 0.01);
|
||||
|
||||
let multi_linear_interpol_estimation = BlockwiseLinearCodec::estimate(&data).unwrap();
|
||||
let multi_linear_interpol_estimation = estimate::<BlockwiseLinearCodec>(&data).unwrap();
|
||||
assert_le!(multi_linear_interpol_estimation, 0.2);
|
||||
assert_lt!(linear_interpol_estimation, multi_linear_interpol_estimation);
|
||||
|
||||
let bitpacked_estimation = BitpackedCodec::estimate(&data).unwrap();
|
||||
let bitpacked_estimation = estimate::<BitpackedCodec>(&data).unwrap();
|
||||
assert_lt!(linear_interpol_estimation, bitpacked_estimation);
|
||||
}
|
||||
#[test]
|
||||
fn estimation_test_bad_interpolation_case() {
|
||||
let data: &[u64] = &[200, 10, 10, 10, 10, 1000, 20];
|
||||
|
||||
let data: VecColumn = data.into();
|
||||
let linear_interpol_estimation = LinearCodec::estimate(&data).unwrap();
|
||||
assert_le!(linear_interpol_estimation, 0.34);
|
||||
|
||||
let bitpacked_estimation = BitpackedCodec::estimate(&data).unwrap();
|
||||
assert_lt!(bitpacked_estimation, linear_interpol_estimation);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn estimation_prefer_bitpacked() {
|
||||
let data = VecColumn::from(&[10, 10, 10, 10]);
|
||||
let linear_interpol_estimation = LinearCodec::estimate(&data).unwrap();
|
||||
let bitpacked_estimation = BitpackedCodec::estimate(&data).unwrap();
|
||||
assert_lt!(bitpacked_estimation, linear_interpol_estimation);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn estimation_test_bad_interpolation_case_monotonically_increasing() {
|
||||
let mut data: Vec<u64> = (201..=20000_u64).collect();
|
||||
data.push(1_000_000);
|
||||
let data: VecColumn = data.as_slice().into();
|
||||
|
||||
// in this case the linear interpolation can't in fact not be worse than bitpacking,
|
||||
// but the estimator adds some threshold, which leads to estimated worse behavior
|
||||
let linear_interpol_estimation = LinearCodec::estimate(&data).unwrap();
|
||||
let linear_interpol_estimation = estimate::<LinearCodec>(&data[..]).unwrap();
|
||||
assert_le!(linear_interpol_estimation, 0.35);
|
||||
|
||||
let bitpacked_estimation = BitpackedCodec::estimate(&data).unwrap();
|
||||
let bitpacked_estimation = estimate::<BitpackedCodec>(&data).unwrap();
|
||||
assert_le!(bitpacked_estimation, 0.32);
|
||||
assert_le!(bitpacked_estimation, linear_interpol_estimation);
|
||||
}
|
||||
@@ -201,7 +240,7 @@ fn estimation_test_bad_interpolation_case_monotonically_increasing() {
|
||||
fn test_fast_field_codec_type_to_code() {
|
||||
let mut count_codec = 0;
|
||||
for code in 0..=255 {
|
||||
if let Some(codec_type) = FastFieldCodecType::from_code(code) {
|
||||
if let Some(codec_type) = CodecType::try_from_code(code) {
|
||||
assert_eq!(codec_type.to_code(), code);
|
||||
count_codec += 1;
|
||||
}
|
||||
@@ -209,19 +248,16 @@ fn test_fast_field_codec_type_to_code() {
|
||||
assert_eq!(count_codec, 3);
|
||||
}
|
||||
|
||||
fn test_fastfield_gcd_i64_with_codec(
|
||||
codec_type: FastFieldCodecType,
|
||||
num_vals: usize,
|
||||
) -> io::Result<()> {
|
||||
fn test_fastfield_gcd_i64_with_codec(codec_type: CodecType, num_vals: usize) -> io::Result<()> {
|
||||
let mut vals: Vec<i64> = (-4..=(num_vals as i64) - 5).map(|val| val * 1000).collect();
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
crate::column_values::serialize_column_values(
|
||||
&VecColumn::from(&vals),
|
||||
crate::column_values::serialize_u64_based_column_values(
|
||||
&&vals[..],
|
||||
&[codec_type],
|
||||
&mut buffer,
|
||||
)?;
|
||||
let buffer = OwnedBytes::new(buffer);
|
||||
let column = crate::column_values::open_u64_mapped::<i64>(buffer.clone())?;
|
||||
let column = crate::column_values::load_u64_based_column_values::<i64>(buffer.clone())?;
|
||||
assert_eq!(column.get_val(0), -4000i64);
|
||||
assert_eq!(column.get_val(1), -3000i64);
|
||||
assert_eq!(column.get_val(2), -2000i64);
|
||||
@@ -232,8 +268,8 @@ fn test_fastfield_gcd_i64_with_codec(
|
||||
let mut buffer_without_gcd = Vec::new();
|
||||
vals.pop();
|
||||
vals.push(1001i64);
|
||||
crate::column_values::serialize_column_values(
|
||||
&VecColumn::from(&vals),
|
||||
crate::column_values::serialize_u64_based_column_values(
|
||||
&&vals[..],
|
||||
&[codec_type],
|
||||
&mut buffer_without_gcd,
|
||||
)?;
|
||||
@@ -246,28 +282,25 @@ fn test_fastfield_gcd_i64_with_codec(
|
||||
#[test]
|
||||
fn test_fastfield_gcd_i64() -> io::Result<()> {
|
||||
for &codec_type in &[
|
||||
FastFieldCodecType::Bitpacked,
|
||||
FastFieldCodecType::BlockwiseLinear,
|
||||
FastFieldCodecType::Linear,
|
||||
CodecType::Bitpacked,
|
||||
CodecType::BlockwiseLinear,
|
||||
CodecType::Linear,
|
||||
] {
|
||||
test_fastfield_gcd_i64_with_codec(codec_type, 5500)?;
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn test_fastfield_gcd_u64_with_codec(
|
||||
codec_type: FastFieldCodecType,
|
||||
num_vals: usize,
|
||||
) -> io::Result<()> {
|
||||
fn test_fastfield_gcd_u64_with_codec(codec_type: CodecType, num_vals: usize) -> io::Result<()> {
|
||||
let mut vals: Vec<u64> = (1..=num_vals).map(|i| i as u64 * 1000u64).collect();
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
crate::column_values::serialize_column_values(
|
||||
&VecColumn::from(&vals),
|
||||
crate::column_values::serialize_u64_based_column_values(
|
||||
&&vals[..],
|
||||
&[codec_type],
|
||||
&mut buffer,
|
||||
)?;
|
||||
let buffer = OwnedBytes::new(buffer);
|
||||
let column = crate::column_values::open_u64_mapped::<u64>(buffer.clone())?;
|
||||
let column = crate::column_values::load_u64_based_column_values::<u64>(buffer.clone())?;
|
||||
assert_eq!(column.get_val(0), 1000u64);
|
||||
assert_eq!(column.get_val(1), 2000u64);
|
||||
assert_eq!(column.get_val(2), 3000u64);
|
||||
@@ -278,8 +311,8 @@ fn test_fastfield_gcd_u64_with_codec(
|
||||
let mut buffer_without_gcd = Vec::new();
|
||||
vals.pop();
|
||||
vals.push(1001u64);
|
||||
crate::column_values::serialize_column_values(
|
||||
&VecColumn::from(&vals),
|
||||
crate::column_values::serialize_u64_based_column_values(
|
||||
&&vals[..],
|
||||
&[codec_type],
|
||||
&mut buffer_without_gcd,
|
||||
)?;
|
||||
@@ -291,9 +324,9 @@ fn test_fastfield_gcd_u64_with_codec(
|
||||
#[test]
|
||||
fn test_fastfield_gcd_u64() -> io::Result<()> {
|
||||
for &codec_type in &[
|
||||
FastFieldCodecType::Bitpacked,
|
||||
FastFieldCodecType::BlockwiseLinear,
|
||||
FastFieldCodecType::Linear,
|
||||
CodecType::Bitpacked,
|
||||
CodecType::BlockwiseLinear,
|
||||
CodecType::Linear,
|
||||
] {
|
||||
test_fastfield_gcd_u64_with_codec(codec_type, 5500)?;
|
||||
}
|
||||
@@ -302,7 +335,10 @@ fn test_fastfield_gcd_u64() -> io::Result<()> {
|
||||
|
||||
#[test]
|
||||
pub fn test_fastfield2() {
|
||||
let test_fastfield = crate::column_values::serialize_and_load(&[100u64, 200u64, 300u64]);
|
||||
let test_fastfield = crate::column_values::serialize_and_load_u64_based_column_values::<u64>(
|
||||
&&[100u64, 200u64, 300u64][..],
|
||||
&ALL_U64_CODEC_TYPES,
|
||||
);
|
||||
assert_eq!(test_fastfield.get_val(0), 100);
|
||||
assert_eq!(test_fastfield.get_val(1), 200);
|
||||
assert_eq!(test_fastfield.get_val(2), 300);
|
||||
@@ -1,3 +1,4 @@
|
||||
use std::fmt::Debug;
|
||||
use std::net::Ipv6Addr;
|
||||
|
||||
use crate::value::NumericalType;
|
||||
@@ -66,7 +67,7 @@ impl ColumnType {
|
||||
}
|
||||
|
||||
// TODO remove if possible
|
||||
pub trait HasAssociatedColumnType: 'static + Send + Sync + Copy + PartialOrd {
|
||||
pub trait HasAssociatedColumnType: 'static + Debug + Send + Sync + Copy + PartialOrd {
|
||||
fn column_type() -> ColumnType;
|
||||
fn default_value() -> Self;
|
||||
}
|
||||
|
||||
@@ -1,208 +0,0 @@
|
||||
use std::collections::HashMap;
|
||||
use std::io;
|
||||
|
||||
use crate::columnar::ColumnarReader;
|
||||
use crate::dynamic_column::DynamicColumn;
|
||||
use crate::ColumnType;
|
||||
|
||||
pub enum MergeDocOrder {
|
||||
/// Columnar tables are simply stacked one above the other.
|
||||
/// If the i-th columnar_readers has n_rows_i rows, then
|
||||
/// in the resulting columnar,
|
||||
/// rows [r0..n_row_0) contains the row of columnar_readers[0], in ordder
|
||||
/// rows [n_row_0..n_row_0 + n_row_1 contains the row of columnar_readers[1], in order.
|
||||
/// ..
|
||||
Stack,
|
||||
/// Some more complex mapping, that can interleaves rows from the different readers and
|
||||
/// possibly drop rows.
|
||||
Complex(()),
|
||||
}
|
||||
|
||||
pub fn merge_columnar(
|
||||
_columnar_readers: &[ColumnarReader],
|
||||
mapping: MergeDocOrder,
|
||||
_output: &mut impl io::Write,
|
||||
) -> io::Result<()> {
|
||||
match mapping {
|
||||
MergeDocOrder::Stack => {
|
||||
// implement me :)
|
||||
todo!();
|
||||
}
|
||||
MergeDocOrder::Complex(_) => {
|
||||
// for later
|
||||
todo!();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Column types are grouped into different categories.
|
||||
/// After merge, all columns belonging to the same category are coerced to
|
||||
/// the same column type.
|
||||
///
|
||||
/// In practise, today, only Numerical colummns are coerced into one type today.
|
||||
///
|
||||
/// See also [README.md].
|
||||
#[derive(Copy, Clone, Eq, PartialEq, Hash, Debug)]
|
||||
#[repr(u8)]
|
||||
enum ColumnTypeCategory {
|
||||
Bool,
|
||||
Str,
|
||||
Numerical,
|
||||
DateTime,
|
||||
Bytes,
|
||||
IpAddr,
|
||||
}
|
||||
|
||||
impl From<ColumnType> for ColumnTypeCategory {
|
||||
fn from(column_type: ColumnType) -> Self {
|
||||
match column_type {
|
||||
ColumnType::I64 => ColumnTypeCategory::Numerical,
|
||||
ColumnType::U64 => ColumnTypeCategory::Numerical,
|
||||
ColumnType::F64 => ColumnTypeCategory::Numerical,
|
||||
ColumnType::Bytes => ColumnTypeCategory::Bytes,
|
||||
ColumnType::Str => ColumnTypeCategory::Str,
|
||||
ColumnType::Bool => ColumnTypeCategory::Bool,
|
||||
ColumnType::IpAddr => ColumnTypeCategory::IpAddr,
|
||||
ColumnType::DateTime => ColumnTypeCategory::DateTime,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn collect_columns(
|
||||
columnar_readers: &[&ColumnarReader],
|
||||
) -> io::Result<HashMap<String, HashMap<ColumnTypeCategory, Vec<DynamicColumn>>>> {
|
||||
// Each column name may have multiple types of column associated.
|
||||
// For merging we are interested in the same column type category since they can be merged.
|
||||
let mut field_name_to_group: HashMap<String, HashMap<ColumnTypeCategory, Vec<DynamicColumn>>> =
|
||||
HashMap::new();
|
||||
|
||||
for columnar_reader in columnar_readers {
|
||||
let column_name_and_handle = columnar_reader.list_columns()?;
|
||||
for (column_name, handle) in column_name_and_handle {
|
||||
let column_type_to_handles = field_name_to_group
|
||||
.entry(column_name.to_string())
|
||||
.or_default();
|
||||
|
||||
let columns = column_type_to_handles
|
||||
.entry(handle.column_type().into())
|
||||
.or_default();
|
||||
columns.push(handle.open()?);
|
||||
}
|
||||
}
|
||||
|
||||
normalize_columns(&mut field_name_to_group);
|
||||
|
||||
Ok(field_name_to_group)
|
||||
}
|
||||
|
||||
/// Coerce numerical type columns to the same type
|
||||
/// TODO rename to `coerce_columns`
|
||||
fn normalize_columns(map: &mut HashMap<String, HashMap<ColumnTypeCategory, Vec<DynamicColumn>>>) {
|
||||
for (_field_name, type_category_to_columns) in map.iter_mut() {
|
||||
for (type_category, columns) in type_category_to_columns {
|
||||
if type_category == &ColumnTypeCategory::Numerical {
|
||||
let casted_columns = cast_to_common_numerical_column(&columns);
|
||||
*columns = casted_columns;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Receives a list of columns of numerical types (u64, i64, f64)
|
||||
///
|
||||
/// Returns a list of `DynamicColumn` which are all of the same numerical type
|
||||
fn cast_to_common_numerical_column(columns: &[DynamicColumn]) -> Vec<DynamicColumn> {
|
||||
assert!(columns
|
||||
.iter()
|
||||
.all(|column| column.column_type().numerical_type().is_some()));
|
||||
let coerce_to_i64: Vec<_> = columns
|
||||
.iter()
|
||||
.map(|column| column.clone().coerce_to_i64())
|
||||
.collect();
|
||||
|
||||
if coerce_to_i64.iter().all(|column| column.is_some()) {
|
||||
return coerce_to_i64
|
||||
.into_iter()
|
||||
.map(|column| column.unwrap())
|
||||
.collect();
|
||||
}
|
||||
|
||||
let coerce_to_u64: Vec<_> = columns
|
||||
.iter()
|
||||
.map(|column| column.clone().coerce_to_u64())
|
||||
.collect();
|
||||
|
||||
if coerce_to_u64.iter().all(|column| column.is_some()) {
|
||||
return coerce_to_u64
|
||||
.into_iter()
|
||||
.map(|column| column.unwrap())
|
||||
.collect();
|
||||
}
|
||||
|
||||
columns
|
||||
.iter()
|
||||
.map(|column| {
|
||||
column
|
||||
.clone()
|
||||
.coerce_to_f64()
|
||||
.expect("couldn't cast column to f64")
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::ColumnarWriter;
|
||||
|
||||
#[test]
|
||||
fn test_column_coercion() {
|
||||
// i64 type
|
||||
let columnar1 = {
|
||||
let mut dataframe_writer = ColumnarWriter::default();
|
||||
dataframe_writer.record_numerical(1u32, "numbers", 1i64);
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer.serialize(2, &mut buffer).unwrap();
|
||||
ColumnarReader::open(buffer).unwrap()
|
||||
};
|
||||
// u64 type
|
||||
let columnar2 = {
|
||||
let mut dataframe_writer = ColumnarWriter::default();
|
||||
dataframe_writer.record_numerical(1u32, "numbers", u64::MAX - 100);
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer.serialize(2, &mut buffer).unwrap();
|
||||
ColumnarReader::open(buffer).unwrap()
|
||||
};
|
||||
|
||||
// f64 type
|
||||
let columnar3 = {
|
||||
let mut dataframe_writer = ColumnarWriter::default();
|
||||
dataframe_writer.record_numerical(1u32, "numbers", 30.5);
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer.serialize(2, &mut buffer).unwrap();
|
||||
ColumnarReader::open(buffer).unwrap()
|
||||
};
|
||||
|
||||
let column_map = collect_columns(&[&columnar1, &columnar2, &columnar3]).unwrap();
|
||||
assert_eq!(column_map.len(), 1);
|
||||
let cat_to_columns = column_map.get("numbers").unwrap();
|
||||
assert_eq!(cat_to_columns.len(), 1);
|
||||
|
||||
let numerical = cat_to_columns.get(&ColumnTypeCategory::Numerical).unwrap();
|
||||
assert!(numerical.iter().all(|column| column.is_f64()));
|
||||
|
||||
let column_map = collect_columns(&[&columnar1, &columnar1]).unwrap();
|
||||
assert_eq!(column_map.len(), 1);
|
||||
let cat_to_columns = column_map.get("numbers").unwrap();
|
||||
assert_eq!(cat_to_columns.len(), 1);
|
||||
let numerical = cat_to_columns.get(&ColumnTypeCategory::Numerical).unwrap();
|
||||
assert!(numerical.iter().all(|column| column.is_i64()));
|
||||
|
||||
let column_map = collect_columns(&[&columnar2, &columnar2]).unwrap();
|
||||
assert_eq!(column_map.len(), 1);
|
||||
let cat_to_columns = column_map.get("numbers").unwrap();
|
||||
assert_eq!(cat_to_columns.len(), 1);
|
||||
let numerical = cat_to_columns.get(&ColumnTypeCategory::Numerical).unwrap();
|
||||
assert!(numerical.iter().all(|column| column.is_u64()));
|
||||
}
|
||||
}
|
||||
204
columnar/src/columnar/merge/merge_dict_column.rs
Normal file
204
columnar/src/columnar/merge/merge_dict_column.rs
Normal file
@@ -0,0 +1,204 @@
|
||||
use std::io::{self, Write};
|
||||
|
||||
use common::{BitSet, CountingWriter, ReadOnlyBitSet};
|
||||
use sstable::{SSTable, TermOrdinal};
|
||||
|
||||
use super::term_merger::TermMerger;
|
||||
use crate::column::serialize_column_mappable_to_u64;
|
||||
use crate::column_index::SerializableColumnIndex;
|
||||
use crate::iterable::Iterable;
|
||||
use crate::{BytesColumn, MergeRowOrder, ShuffleMergeOrder};
|
||||
|
||||
// Serialize [Dictionary, Column, dictionary num bytes U32::LE]
|
||||
// Column: [Column Index, Column Values, column index num bytes U32::LE]
|
||||
pub fn merge_bytes_or_str_column(
|
||||
column_index: SerializableColumnIndex<'_>,
|
||||
bytes_columns: &[Option<BytesColumn>],
|
||||
merge_row_order: &MergeRowOrder,
|
||||
output: &mut impl Write,
|
||||
) -> io::Result<()> {
|
||||
// Serialize dict and generate mapping for values
|
||||
let mut output = CountingWriter::wrap(output);
|
||||
// TODO !!! Remove useless terms.
|
||||
let term_ord_mapping = serialize_merged_dict(bytes_columns, merge_row_order, &mut output)?;
|
||||
let dictionary_num_bytes: u32 = output.written_bytes() as u32;
|
||||
let output = output.finish();
|
||||
let remapped_term_ordinals_values = RemappedTermOrdinalsValues {
|
||||
bytes_columns,
|
||||
term_ord_mapping: &term_ord_mapping,
|
||||
merge_row_order,
|
||||
};
|
||||
serialize_column_mappable_to_u64(column_index, &remapped_term_ordinals_values, output)?;
|
||||
output.write_all(&dictionary_num_bytes.to_le_bytes())?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
struct RemappedTermOrdinalsValues<'a> {
|
||||
bytes_columns: &'a [Option<BytesColumn>],
|
||||
term_ord_mapping: &'a TermOrdinalMapping,
|
||||
merge_row_order: &'a MergeRowOrder,
|
||||
}
|
||||
|
||||
impl<'a> Iterable for RemappedTermOrdinalsValues<'a> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = u64> + '_> {
|
||||
match self.merge_row_order {
|
||||
MergeRowOrder::Stack(_) => self.boxed_iter_stacked(),
|
||||
MergeRowOrder::Shuffled(shuffle_merge_order) => {
|
||||
self.boxed_iter_shuffled(shuffle_merge_order)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> RemappedTermOrdinalsValues<'a> {
|
||||
fn boxed_iter_stacked(&self) -> Box<dyn Iterator<Item = u64> + '_> {
|
||||
let iter = self
|
||||
.bytes_columns
|
||||
.iter()
|
||||
.enumerate()
|
||||
.flat_map(|(segment_ord, byte_column)| {
|
||||
let segment_ord = self.term_ord_mapping.get_segment(segment_ord as u32);
|
||||
byte_column.into_iter().flat_map(move |bytes_column| {
|
||||
bytes_column
|
||||
.ords()
|
||||
.values
|
||||
.iter()
|
||||
.map(move |term_ord| segment_ord[term_ord as usize])
|
||||
})
|
||||
});
|
||||
// TODO see if we can better decompose the mapping / and the stacking
|
||||
Box::new(iter)
|
||||
}
|
||||
|
||||
fn boxed_iter_shuffled<'b>(
|
||||
&'b self,
|
||||
shuffle_merge_order: &'b ShuffleMergeOrder,
|
||||
) -> Box<dyn Iterator<Item = u64> + 'b> {
|
||||
Box::new(
|
||||
shuffle_merge_order
|
||||
.iter_new_to_old_row_addrs()
|
||||
.flat_map(move |old_addr| {
|
||||
let segment_ord = self.term_ord_mapping.get_segment(old_addr.segment_ord);
|
||||
self.bytes_columns[old_addr.segment_ord as usize]
|
||||
.as_ref()
|
||||
.into_iter()
|
||||
.flat_map(move |bytes_column| {
|
||||
bytes_column
|
||||
.term_ords(old_addr.row_id)
|
||||
.map(|old_term_ord: u64| segment_ord[old_term_ord as usize])
|
||||
})
|
||||
}),
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
fn compute_term_bitset(column: &BytesColumn, row_bitset: &ReadOnlyBitSet) -> BitSet {
|
||||
let num_terms = column.dictionary().num_terms();
|
||||
let mut term_bitset = BitSet::with_max_value(num_terms as u32);
|
||||
for row_id in row_bitset.iter() {
|
||||
for term_ord in column.term_ord_column.values(row_id) {
|
||||
term_bitset.insert(term_ord as u32);
|
||||
}
|
||||
}
|
||||
term_bitset
|
||||
}
|
||||
|
||||
fn is_term_present(bitsets: &[Option<BitSet>], term_merger: &TermMerger) -> bool {
|
||||
for (segment_ord, from_term_ord) in term_merger.matching_segments() {
|
||||
if let Some(bitset) = bitsets[segment_ord].as_ref() {
|
||||
if bitset.contains(from_term_ord as u32) {
|
||||
return true;
|
||||
}
|
||||
} else {
|
||||
return true;
|
||||
}
|
||||
}
|
||||
false
|
||||
}
|
||||
|
||||
fn serialize_merged_dict(
|
||||
bytes_columns: &[Option<BytesColumn>],
|
||||
merge_row_order: &MergeRowOrder,
|
||||
output: &mut impl Write,
|
||||
) -> io::Result<TermOrdinalMapping> {
|
||||
let mut term_ord_mapping = TermOrdinalMapping::default();
|
||||
|
||||
let mut field_term_streams = Vec::new();
|
||||
for column in bytes_columns.iter().flatten() {
|
||||
term_ord_mapping.add_segment(column.dictionary.num_terms());
|
||||
let terms = column.dictionary.stream()?;
|
||||
field_term_streams.push(terms);
|
||||
}
|
||||
|
||||
let mut merged_terms = TermMerger::new(field_term_streams);
|
||||
let mut sstable_builder = sstable::VoidSSTable::writer(output);
|
||||
|
||||
// TODO support complex `merge_row_order`.
|
||||
match merge_row_order {
|
||||
MergeRowOrder::Stack(_) => {
|
||||
let mut current_term_ord = 0;
|
||||
while merged_terms.advance() {
|
||||
let term_bytes: &[u8] = merged_terms.key();
|
||||
sstable_builder.insert(term_bytes, &())?;
|
||||
for (segment_ord, from_term_ord) in merged_terms.matching_segments() {
|
||||
term_ord_mapping.register_from_to(segment_ord, from_term_ord, current_term_ord);
|
||||
}
|
||||
current_term_ord += 1;
|
||||
}
|
||||
sstable_builder.finish()?;
|
||||
}
|
||||
MergeRowOrder::Shuffled(shuffle_merge_order) => {
|
||||
assert_eq!(shuffle_merge_order.alive_bitsets.len(), bytes_columns.len());
|
||||
let mut term_bitsets: Vec<Option<BitSet>> = Vec::with_capacity(bytes_columns.len());
|
||||
for (alive_bitset_opt, bytes_column_opt) in shuffle_merge_order
|
||||
.alive_bitsets
|
||||
.iter()
|
||||
.zip(bytes_columns.iter())
|
||||
{
|
||||
match (alive_bitset_opt, bytes_column_opt) {
|
||||
(Some(alive_bitset), Some(bytes_column)) => {
|
||||
let term_bitset = compute_term_bitset(bytes_column, alive_bitset);
|
||||
term_bitsets.push(Some(term_bitset));
|
||||
}
|
||||
_ => {
|
||||
term_bitsets.push(None);
|
||||
}
|
||||
}
|
||||
}
|
||||
let mut current_term_ord = 0;
|
||||
while merged_terms.advance() {
|
||||
let term_bytes: &[u8] = merged_terms.key();
|
||||
if !is_term_present(&term_bitsets[..], &merged_terms) {
|
||||
continue;
|
||||
}
|
||||
sstable_builder.insert(term_bytes, &())?;
|
||||
for (segment_ord, from_term_ord) in merged_terms.matching_segments() {
|
||||
term_ord_mapping.register_from_to(segment_ord, from_term_ord, current_term_ord);
|
||||
}
|
||||
current_term_ord += 1;
|
||||
}
|
||||
sstable_builder.finish()?;
|
||||
}
|
||||
}
|
||||
Ok(term_ord_mapping)
|
||||
}
|
||||
|
||||
#[derive(Default, Debug)]
|
||||
struct TermOrdinalMapping {
|
||||
per_segment_new_term_ordinals: Vec<Vec<TermOrdinal>>,
|
||||
}
|
||||
|
||||
impl TermOrdinalMapping {
|
||||
fn add_segment(&mut self, max_term_ord: usize) {
|
||||
self.per_segment_new_term_ordinals
|
||||
.push(vec![TermOrdinal::default(); max_term_ord as usize]);
|
||||
}
|
||||
|
||||
fn register_from_to(&mut self, segment_ord: usize, from_ord: TermOrdinal, to_ord: TermOrdinal) {
|
||||
self.per_segment_new_term_ordinals[segment_ord][from_ord as usize] = to_ord;
|
||||
}
|
||||
|
||||
fn get_segment(&self, segment_ord: u32) -> &[TermOrdinal] {
|
||||
&(self.per_segment_new_term_ordinals[segment_ord as usize])[..]
|
||||
}
|
||||
}
|
||||
118
columnar/src/columnar/merge/merge_mapping.rs
Normal file
118
columnar/src/columnar/merge/merge_mapping.rs
Normal file
@@ -0,0 +1,118 @@
|
||||
use std::ops::Range;
|
||||
|
||||
use common::{BitSet, OwnedBytes, ReadOnlyBitSet};
|
||||
|
||||
use crate::{ColumnarReader, RowAddr, RowId};
|
||||
|
||||
pub struct StackMergeOrder {
|
||||
// This does not start at 0. The first row is the number of
|
||||
// rows in the first columnar.
|
||||
cumulated_row_ids: Vec<RowId>,
|
||||
}
|
||||
|
||||
impl StackMergeOrder {
|
||||
pub fn stack(columnars: &[&ColumnarReader]) -> StackMergeOrder {
|
||||
let mut cumulated_row_ids: Vec<RowId> = Vec::with_capacity(columnars.len());
|
||||
let mut cumulated_row_id = 0;
|
||||
for columnar in columnars {
|
||||
cumulated_row_id += columnar.num_rows();
|
||||
cumulated_row_ids.push(cumulated_row_id);
|
||||
}
|
||||
StackMergeOrder { cumulated_row_ids }
|
||||
}
|
||||
|
||||
pub fn num_rows(&self) -> RowId {
|
||||
self.cumulated_row_ids.last().copied().unwrap_or(0)
|
||||
}
|
||||
|
||||
pub fn offset(&self, columnar_id: usize) -> RowId {
|
||||
if columnar_id == 0 {
|
||||
return 0;
|
||||
}
|
||||
self.cumulated_row_ids[columnar_id - 1]
|
||||
}
|
||||
|
||||
pub fn columnar_range(&self, columnar_id: usize) -> Range<RowId> {
|
||||
self.offset(columnar_id)..self.offset(columnar_id + 1)
|
||||
}
|
||||
}
|
||||
|
||||
pub enum MergeRowOrder {
|
||||
/// Columnar tables are simply stacked one above the other.
|
||||
/// If the i-th columnar_readers has n_rows_i rows, then
|
||||
/// in the resulting columnar,
|
||||
/// rows [r0..n_row_0) contains the row of columnar_readers[0], in ordder
|
||||
/// rows [n_row_0..n_row_0 + n_row_1 contains the row of columnar_readers[1], in order.
|
||||
/// ..
|
||||
/// No documents is deleted.
|
||||
Stack(StackMergeOrder),
|
||||
/// Some more complex mapping, that may interleaves rows from the different readers and
|
||||
/// drop rows, or do both.
|
||||
Shuffled(ShuffleMergeOrder),
|
||||
}
|
||||
|
||||
impl From<StackMergeOrder> for MergeRowOrder {
|
||||
fn from(stack_merge_order: StackMergeOrder) -> MergeRowOrder {
|
||||
MergeRowOrder::Stack(stack_merge_order)
|
||||
}
|
||||
}
|
||||
|
||||
impl From<ShuffleMergeOrder> for MergeRowOrder {
|
||||
fn from(shuffle_merge_order: ShuffleMergeOrder) -> MergeRowOrder {
|
||||
MergeRowOrder::Shuffled(shuffle_merge_order)
|
||||
}
|
||||
}
|
||||
|
||||
impl MergeRowOrder {
|
||||
pub fn num_rows(&self) -> RowId {
|
||||
match self {
|
||||
MergeRowOrder::Stack(stack_row_order) => stack_row_order.num_rows(),
|
||||
MergeRowOrder::Shuffled(complex_mapping) => complex_mapping.num_rows(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub struct ShuffleMergeOrder {
|
||||
pub new_row_id_to_old_row_id: Vec<RowAddr>,
|
||||
pub alive_bitsets: Vec<Option<ReadOnlyBitSet>>,
|
||||
}
|
||||
|
||||
impl ShuffleMergeOrder {
|
||||
pub fn for_test(
|
||||
segment_num_rows: &[RowId],
|
||||
new_row_id_to_old_row_id: Vec<RowAddr>,
|
||||
) -> ShuffleMergeOrder {
|
||||
let mut alive_bitsets: Vec<BitSet> = segment_num_rows
|
||||
.iter()
|
||||
.map(|&num_rows| BitSet::with_max_value(num_rows))
|
||||
.collect();
|
||||
for &RowAddr {
|
||||
segment_ord,
|
||||
row_id,
|
||||
} in &new_row_id_to_old_row_id
|
||||
{
|
||||
alive_bitsets[segment_ord as usize].insert(row_id);
|
||||
}
|
||||
let alive_bitsets: Vec<Option<ReadOnlyBitSet>> = alive_bitsets
|
||||
.into_iter()
|
||||
.map(|alive_bitset| {
|
||||
let mut buffer = Vec::new();
|
||||
alive_bitset.serialize(&mut buffer).unwrap();
|
||||
let data = OwnedBytes::new(buffer);
|
||||
Some(ReadOnlyBitSet::open(data))
|
||||
})
|
||||
.collect();
|
||||
ShuffleMergeOrder {
|
||||
new_row_id_to_old_row_id,
|
||||
alive_bitsets,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn num_rows(&self) -> RowId {
|
||||
self.new_row_id_to_old_row_id.len() as RowId
|
||||
}
|
||||
|
||||
pub fn iter_new_to_old_row_addrs(&self) -> impl Iterator<Item = RowAddr> + '_ {
|
||||
self.new_row_id_to_old_row_id.iter().copied()
|
||||
}
|
||||
}
|
||||
271
columnar/src/columnar/merge/mod.rs
Normal file
271
columnar/src/columnar/merge/mod.rs
Normal file
@@ -0,0 +1,271 @@
|
||||
mod merge_dict_column;
|
||||
mod merge_mapping;
|
||||
mod term_merger;
|
||||
|
||||
// mod sorted_doc_id_column;
|
||||
|
||||
use std::collections::{BTreeMap, HashMap, HashSet};
|
||||
use std::io;
|
||||
use std::net::Ipv6Addr;
|
||||
use std::sync::Arc;
|
||||
|
||||
pub use merge_mapping::{MergeRowOrder, ShuffleMergeOrder, StackMergeOrder};
|
||||
|
||||
use super::writer::ColumnarSerializer;
|
||||
use crate::column::{serialize_column_mappable_to_u128, serialize_column_mappable_to_u64};
|
||||
use crate::column_values::MergedColumnValues;
|
||||
use crate::columnar::merge::merge_dict_column::merge_bytes_or_str_column;
|
||||
use crate::columnar::writer::CompatibleNumericalTypes;
|
||||
use crate::columnar::ColumnarReader;
|
||||
use crate::dynamic_column::DynamicColumn;
|
||||
use crate::{
|
||||
BytesColumn, Column, ColumnIndex, ColumnType, ColumnValues, NumericalType, NumericalValue,
|
||||
};
|
||||
|
||||
/// Column types are grouped into different categories.
|
||||
/// After merge, all columns belonging to the same category are coerced to
|
||||
/// the same column type.
|
||||
///
|
||||
/// In practise, today, only Numerical colummns are coerced into one type today.
|
||||
///
|
||||
/// See also [README.md].
|
||||
#[derive(Copy, Clone, Eq, PartialEq, Hash, Debug)]
|
||||
enum ColumnTypeCategory {
|
||||
Bool,
|
||||
Str,
|
||||
Numerical,
|
||||
DateTime,
|
||||
Bytes,
|
||||
IpAddr,
|
||||
}
|
||||
|
||||
impl From<ColumnType> for ColumnTypeCategory {
|
||||
fn from(column_type: ColumnType) -> Self {
|
||||
match column_type {
|
||||
ColumnType::I64 => ColumnTypeCategory::Numerical,
|
||||
ColumnType::U64 => ColumnTypeCategory::Numerical,
|
||||
ColumnType::F64 => ColumnTypeCategory::Numerical,
|
||||
ColumnType::Bytes => ColumnTypeCategory::Bytes,
|
||||
ColumnType::Str => ColumnTypeCategory::Str,
|
||||
ColumnType::Bool => ColumnTypeCategory::Bool,
|
||||
ColumnType::IpAddr => ColumnTypeCategory::IpAddr,
|
||||
ColumnType::DateTime => ColumnTypeCategory::DateTime,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub fn merge_columnar(
|
||||
columnar_readers: &[&ColumnarReader],
|
||||
merge_row_order: MergeRowOrder,
|
||||
output: &mut impl io::Write,
|
||||
) -> io::Result<()> {
|
||||
let mut serializer = ColumnarSerializer::new(output);
|
||||
|
||||
let columns_to_merge = group_columns_for_merge(columnar_readers)?;
|
||||
for ((column_name, column_type), columns) in columns_to_merge {
|
||||
let mut column_serializer =
|
||||
serializer.serialize_column(column_name.as_bytes(), column_type);
|
||||
merge_column(
|
||||
column_type,
|
||||
columns,
|
||||
&merge_row_order,
|
||||
&mut column_serializer,
|
||||
)?;
|
||||
}
|
||||
serializer.finalize(merge_row_order.num_rows())?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn dynamic_column_to_u64_monotonic(dynamic_column: DynamicColumn) -> Option<Column<u64>> {
|
||||
match dynamic_column {
|
||||
DynamicColumn::Bool(column) => Some(column.to_u64_monotonic()),
|
||||
DynamicColumn::I64(column) => Some(column.to_u64_monotonic()),
|
||||
DynamicColumn::U64(column) => Some(column.to_u64_monotonic()),
|
||||
DynamicColumn::F64(column) => Some(column.to_u64_monotonic()),
|
||||
DynamicColumn::DateTime(column) => Some(column.to_u64_monotonic()),
|
||||
DynamicColumn::IpAddr(_) | DynamicColumn::Bytes(_) | DynamicColumn::Str(_) => None,
|
||||
}
|
||||
}
|
||||
|
||||
fn merge_column(
|
||||
column_type: ColumnType,
|
||||
columns: Vec<Option<DynamicColumn>>,
|
||||
merge_row_order: &MergeRowOrder,
|
||||
wrt: &mut impl io::Write,
|
||||
) -> io::Result<()> {
|
||||
match column_type {
|
||||
ColumnType::I64
|
||||
| ColumnType::U64
|
||||
| ColumnType::F64
|
||||
| ColumnType::DateTime
|
||||
| ColumnType::Bool => {
|
||||
let mut column_indexes: Vec<Option<ColumnIndex>> = Vec::with_capacity(columns.len());
|
||||
let mut column_values: Vec<Option<Arc<dyn ColumnValues>>> =
|
||||
Vec::with_capacity(columns.len());
|
||||
for dynamic_column_opt in columns {
|
||||
if let Some(Column { idx, values }) =
|
||||
dynamic_column_opt.and_then(dynamic_column_to_u64_monotonic)
|
||||
{
|
||||
column_indexes.push(Some(idx));
|
||||
column_values.push(Some(values));
|
||||
} else {
|
||||
column_indexes.push(None);
|
||||
column_values.push(None);
|
||||
}
|
||||
}
|
||||
let merged_column_index =
|
||||
crate::column_index::merge_column_index(&column_indexes[..], merge_row_order);
|
||||
let merge_column_values = MergedColumnValues {
|
||||
column_indexes: &column_indexes[..],
|
||||
column_values: &column_values[..],
|
||||
merge_row_order,
|
||||
};
|
||||
serialize_column_mappable_to_u64(merged_column_index, &merge_column_values, wrt)?;
|
||||
}
|
||||
ColumnType::IpAddr => {
|
||||
let mut column_indexes: Vec<Option<ColumnIndex>> = Vec::with_capacity(columns.len());
|
||||
let mut column_values: Vec<Option<Arc<dyn ColumnValues<Ipv6Addr>>>> =
|
||||
Vec::with_capacity(columns.len());
|
||||
for dynamic_column_opt in columns {
|
||||
if let Some(DynamicColumn::IpAddr(Column { idx, values })) = dynamic_column_opt {
|
||||
column_indexes.push(Some(idx));
|
||||
column_values.push(Some(values));
|
||||
} else {
|
||||
column_indexes.push(None);
|
||||
column_values.push(None);
|
||||
}
|
||||
}
|
||||
|
||||
let merged_column_index =
|
||||
crate::column_index::merge_column_index(&column_indexes[..], merge_row_order);
|
||||
let merge_column_values = MergedColumnValues {
|
||||
column_indexes: &column_indexes[..],
|
||||
column_values: &column_values,
|
||||
merge_row_order,
|
||||
};
|
||||
|
||||
serialize_column_mappable_to_u128(merged_column_index, &merge_column_values, wrt)?;
|
||||
}
|
||||
ColumnType::Bytes | ColumnType::Str => {
|
||||
let mut column_indexes: Vec<Option<ColumnIndex>> = Vec::with_capacity(columns.len());
|
||||
let mut bytes_columns: Vec<Option<BytesColumn>> = Vec::with_capacity(columns.len());
|
||||
for dynamic_column_opt in columns {
|
||||
match dynamic_column_opt {
|
||||
Some(DynamicColumn::Str(str_column)) => {
|
||||
column_indexes.push(Some(str_column.term_ord_column.idx.clone()));
|
||||
bytes_columns.push(Some(str_column.into()));
|
||||
}
|
||||
Some(DynamicColumn::Bytes(bytes_column)) => {
|
||||
column_indexes.push(Some(bytes_column.term_ord_column.idx.clone()));
|
||||
bytes_columns.push(Some(bytes_column));
|
||||
}
|
||||
_ => {
|
||||
column_indexes.push(None);
|
||||
bytes_columns.push(None);
|
||||
}
|
||||
}
|
||||
}
|
||||
let merged_column_index =
|
||||
crate::column_index::merge_column_index(&column_indexes[..], merge_row_order);
|
||||
merge_bytes_or_str_column(merged_column_index, &bytes_columns, merge_row_order, wrt)?;
|
||||
}
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn group_columns_for_merge(
|
||||
columnar_readers: &[&ColumnarReader],
|
||||
) -> io::Result<BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>>> {
|
||||
// Each column name may have multiple types of column associated.
|
||||
// For merging we are interested in the same column type category since they can be merged.
|
||||
let mut columns_grouped: HashMap<(String, ColumnTypeCategory), Vec<Option<DynamicColumn>>> =
|
||||
HashMap::new();
|
||||
|
||||
let num_columnars = columnar_readers.len();
|
||||
|
||||
for (columnar_id, columnar_reader) in columnar_readers.iter().enumerate() {
|
||||
let column_name_and_handle = columnar_reader.list_columns()?;
|
||||
for (column_name, handle) in column_name_and_handle {
|
||||
let column_type_category: ColumnTypeCategory = handle.column_type().into();
|
||||
let columns = columns_grouped
|
||||
.entry((column_name, column_type_category))
|
||||
.or_insert_with(|| vec![None; num_columnars]);
|
||||
let column = handle.open()?;
|
||||
columns[columnar_id] = Some(column);
|
||||
}
|
||||
}
|
||||
|
||||
let mut merge_columns: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
|
||||
BTreeMap::default();
|
||||
|
||||
for ((column_name, col_category), mut columns) in columns_grouped {
|
||||
if col_category == ColumnTypeCategory::Numerical {
|
||||
coerce_numerical_columns_to_same_type(&mut columns);
|
||||
}
|
||||
let column_type = columns
|
||||
.iter()
|
||||
.flatten()
|
||||
.map(|col| col.column_type())
|
||||
.next()
|
||||
.unwrap();
|
||||
merge_columns.insert((column_name, column_type), columns);
|
||||
}
|
||||
|
||||
Ok(merge_columns)
|
||||
}
|
||||
|
||||
/// Coerce a set of numerical columns to the same type.
|
||||
///
|
||||
/// If all columns are already from the same type, keep this type
|
||||
/// (even if they could all be coerced to i64).
|
||||
fn coerce_numerical_columns_to_same_type(columns: &mut [Option<DynamicColumn>]) {
|
||||
let mut column_types: HashSet<NumericalType> = HashSet::default();
|
||||
let mut compatible_numerical_types = CompatibleNumericalTypes::default();
|
||||
for column in columns.iter().flatten() {
|
||||
let min_value: NumericalValue;
|
||||
let max_value: NumericalValue;
|
||||
match column {
|
||||
DynamicColumn::I64(column) => {
|
||||
min_value = column.min_value().into();
|
||||
max_value = column.max_value().into();
|
||||
}
|
||||
DynamicColumn::U64(column) => {
|
||||
min_value = column.min_value().into();
|
||||
max_value = column.min_value().into();
|
||||
}
|
||||
DynamicColumn::F64(column) => {
|
||||
min_value = column.min_value().into();
|
||||
max_value = column.min_value().into();
|
||||
}
|
||||
DynamicColumn::Bool(_)
|
||||
| DynamicColumn::IpAddr(_)
|
||||
| DynamicColumn::DateTime(_)
|
||||
| DynamicColumn::Bytes(_)
|
||||
| DynamicColumn::Str(_) => {
|
||||
panic!("We expected only numerical columns.");
|
||||
}
|
||||
}
|
||||
column_types.insert(column.column_type().numerical_type().unwrap());
|
||||
compatible_numerical_types.accept_value(min_value);
|
||||
compatible_numerical_types.accept_value(max_value);
|
||||
}
|
||||
if column_types.len() <= 1 {
|
||||
// No need to do anything. The columns are already all from the same type.
|
||||
// This is necessary to let use force a given type.
|
||||
|
||||
// TODO This works in a world where we do not allow a change of schema,
|
||||
// but in the future, we will have to pass some kind of schema to enforce
|
||||
// the logic.
|
||||
return;
|
||||
}
|
||||
let coerce_type = compatible_numerical_types.to_numerical_type();
|
||||
for column_opt in columns.iter_mut() {
|
||||
if let Some(column) = column_opt.take() {
|
||||
*column_opt = column.coerce_numerical(coerce_type);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests;
|
||||
107
columnar/src/columnar/merge/term_merger.rs
Normal file
107
columnar/src/columnar/merge/term_merger.rs
Normal file
@@ -0,0 +1,107 @@
|
||||
use std::cmp::Ordering;
|
||||
use std::collections::BinaryHeap;
|
||||
|
||||
use sstable::TermOrdinal;
|
||||
|
||||
use crate::Streamer;
|
||||
|
||||
pub struct HeapItem<'a> {
|
||||
pub streamer: Streamer<'a>,
|
||||
pub segment_ord: usize,
|
||||
}
|
||||
|
||||
impl<'a> PartialEq for HeapItem<'a> {
|
||||
fn eq(&self, other: &Self) -> bool {
|
||||
self.segment_ord == other.segment_ord
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> Eq for HeapItem<'a> {}
|
||||
|
||||
impl<'a> PartialOrd for HeapItem<'a> {
|
||||
fn partial_cmp(&self, other: &HeapItem<'a>) -> Option<Ordering> {
|
||||
Some(self.cmp(other))
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> Ord for HeapItem<'a> {
|
||||
fn cmp(&self, other: &HeapItem<'a>) -> Ordering {
|
||||
(&other.streamer.key(), &other.segment_ord).cmp(&(&self.streamer.key(), &self.segment_ord))
|
||||
}
|
||||
}
|
||||
|
||||
/// Given a list of sorted term streams,
|
||||
/// returns an iterator over sorted unique terms.
|
||||
///
|
||||
/// The item yield is actually a pair with
|
||||
/// - the term
|
||||
/// - a slice with the ordinal of the segments containing
|
||||
/// the terms.
|
||||
pub struct TermMerger<'a> {
|
||||
heap: BinaryHeap<HeapItem<'a>>,
|
||||
current_streamers: Vec<HeapItem<'a>>,
|
||||
}
|
||||
|
||||
impl<'a> TermMerger<'a> {
|
||||
/// Stream of merged term dictionary
|
||||
pub fn new(streams: Vec<Streamer<'a>>) -> TermMerger<'a> {
|
||||
TermMerger {
|
||||
heap: BinaryHeap::new(),
|
||||
current_streamers: streams
|
||||
.into_iter()
|
||||
.enumerate()
|
||||
.map(|(ord, streamer)| HeapItem {
|
||||
streamer,
|
||||
segment_ord: ord,
|
||||
})
|
||||
.collect(),
|
||||
}
|
||||
}
|
||||
|
||||
pub(crate) fn matching_segments<'b: 'a>(
|
||||
&'b self,
|
||||
) -> impl 'b + Iterator<Item = (usize, TermOrdinal)> {
|
||||
self.current_streamers
|
||||
.iter()
|
||||
.map(|heap_item| (heap_item.segment_ord, heap_item.streamer.term_ord()))
|
||||
}
|
||||
|
||||
fn advance_segments(&mut self) {
|
||||
let streamers = &mut self.current_streamers;
|
||||
let heap = &mut self.heap;
|
||||
for mut heap_item in streamers.drain(..) {
|
||||
if heap_item.streamer.advance() {
|
||||
heap.push(heap_item);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Advance the term iterator to the next term.
|
||||
/// Returns true if there is indeed another term
|
||||
/// False if there is none.
|
||||
pub fn advance(&mut self) -> bool {
|
||||
self.advance_segments();
|
||||
if let Some(head) = self.heap.pop() {
|
||||
self.current_streamers.push(head);
|
||||
while let Some(next_streamer) = self.heap.peek() {
|
||||
if self.current_streamers[0].streamer.key() != next_streamer.streamer.key() {
|
||||
break;
|
||||
}
|
||||
let next_heap_it = self.heap.pop().unwrap(); // safe : we peeked beforehand
|
||||
self.current_streamers.push(next_heap_it);
|
||||
}
|
||||
true
|
||||
} else {
|
||||
false
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the current term.
|
||||
///
|
||||
/// This method may be called
|
||||
/// if and only if advance() has been called before
|
||||
/// and "true" was returned.
|
||||
pub fn key(&self) -> &[u8] {
|
||||
self.current_streamers[0].streamer.key()
|
||||
}
|
||||
}
|
||||
258
columnar/src/columnar/merge/tests.rs
Normal file
258
columnar/src/columnar/merge/tests.rs
Normal file
@@ -0,0 +1,258 @@
|
||||
use super::*;
|
||||
use crate::{Cardinality, ColumnarWriter, HasAssociatedColumnType, RowId};
|
||||
|
||||
fn make_columnar<T: Into<NumericalValue> + HasAssociatedColumnType + Copy>(
|
||||
column_name: &str,
|
||||
vals: &[T],
|
||||
) -> ColumnarReader {
|
||||
let mut dataframe_writer = ColumnarWriter::default();
|
||||
dataframe_writer.record_column_type(column_name, T::column_type(), false);
|
||||
for (row_id, val) in vals.iter().copied().enumerate() {
|
||||
dataframe_writer.record_numerical(row_id as RowId, column_name, val.into());
|
||||
}
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer
|
||||
.serialize(vals.len() as RowId, None, &mut buffer)
|
||||
.unwrap();
|
||||
ColumnarReader::open(buffer).unwrap()
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_column_coercion_to_u64() {
|
||||
// i64 type
|
||||
let columnar1 = make_columnar("numbers", &[1i64]);
|
||||
// u64 type
|
||||
let columnar2 = make_columnar("numbers", &[u64::MAX]);
|
||||
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
|
||||
group_columns_for_merge(&[&columnar1, &columnar2]).unwrap();
|
||||
assert_eq!(column_map.len(), 1);
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnType::U64)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_column_no_coercion_if_all_the_same() {
|
||||
let columnar1 = make_columnar("numbers", &[1u64]);
|
||||
let columnar2 = make_columnar("numbers", &[2u64]);
|
||||
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
|
||||
group_columns_for_merge(&[&columnar1, &columnar2]).unwrap();
|
||||
assert_eq!(column_map.len(), 1);
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnType::U64)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_column_coercion_to_i64() {
|
||||
let columnar1 = make_columnar("numbers", &[-1i64]);
|
||||
let columnar2 = make_columnar("numbers", &[2u64]);
|
||||
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
|
||||
group_columns_for_merge(&[&columnar1, &columnar2]).unwrap();
|
||||
assert_eq!(column_map.len(), 1);
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnType::I64)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_missing_column() {
|
||||
let columnar1 = make_columnar("numbers", &[-1i64]);
|
||||
let columnar2 = make_columnar("numbers2", &[2u64]);
|
||||
let column_map: BTreeMap<(String, ColumnType), Vec<Option<DynamicColumn>>> =
|
||||
group_columns_for_merge(&[&columnar1, &columnar2]).unwrap();
|
||||
assert_eq!(column_map.len(), 2);
|
||||
assert!(column_map.contains_key(&("numbers".to_string(), ColumnType::I64)));
|
||||
{
|
||||
let columns = column_map
|
||||
.get(&("numbers".to_string(), ColumnType::I64))
|
||||
.unwrap();
|
||||
assert!(columns[0].is_some());
|
||||
assert!(columns[1].is_none());
|
||||
}
|
||||
{
|
||||
let columns = column_map
|
||||
.get(&("numbers2".to_string(), ColumnType::U64))
|
||||
.unwrap();
|
||||
assert!(columns[0].is_none());
|
||||
assert!(columns[1].is_some());
|
||||
}
|
||||
}
|
||||
|
||||
fn make_numerical_columnar_multiple_columns(
|
||||
columns: &[(&str, &[&[NumericalValue]])],
|
||||
) -> ColumnarReader {
|
||||
let mut dataframe_writer = ColumnarWriter::default();
|
||||
for (column_name, column_values) in columns {
|
||||
for (row_id, vals) in column_values.iter().enumerate() {
|
||||
for val in vals.iter() {
|
||||
dataframe_writer.record_numerical(row_id as u32, column_name, *val);
|
||||
}
|
||||
}
|
||||
}
|
||||
let num_rows = columns
|
||||
.iter()
|
||||
.map(|(_, val_rows)| val_rows.len() as RowId)
|
||||
.max()
|
||||
.unwrap_or(0u32);
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer
|
||||
.serialize(num_rows, None, &mut buffer)
|
||||
.unwrap();
|
||||
ColumnarReader::open(buffer).unwrap()
|
||||
}
|
||||
|
||||
fn make_byte_columnar_multiple_columns(columns: &[(&str, &[&[&[u8]]])]) -> ColumnarReader {
|
||||
let mut dataframe_writer = ColumnarWriter::default();
|
||||
for (column_name, column_values) in columns {
|
||||
for (row_id, vals) in column_values.iter().enumerate() {
|
||||
for val in vals.iter() {
|
||||
dataframe_writer.record_bytes(row_id as u32, column_name, *val);
|
||||
}
|
||||
}
|
||||
}
|
||||
let num_rows = columns
|
||||
.iter()
|
||||
.map(|(_, val_rows)| val_rows.len() as RowId)
|
||||
.max()
|
||||
.unwrap_or(0u32);
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer
|
||||
.serialize(num_rows, None, &mut buffer)
|
||||
.unwrap();
|
||||
ColumnarReader::open(buffer).unwrap()
|
||||
}
|
||||
|
||||
fn make_text_columnar_multiple_columns(columns: &[(&str, &[&[&str]])]) -> ColumnarReader {
|
||||
let mut dataframe_writer = ColumnarWriter::default();
|
||||
for (column_name, column_values) in columns {
|
||||
for (row_id, vals) in column_values.iter().enumerate() {
|
||||
for val in vals.iter() {
|
||||
dataframe_writer.record_str(row_id as u32, column_name, *val);
|
||||
}
|
||||
}
|
||||
}
|
||||
let num_rows = columns
|
||||
.iter()
|
||||
.map(|(_, val_rows)| val_rows.len() as RowId)
|
||||
.max()
|
||||
.unwrap_or(0u32);
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer
|
||||
.serialize(num_rows, None, &mut buffer)
|
||||
.unwrap();
|
||||
ColumnarReader::open(buffer).unwrap()
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_merge_columnar_numbers() {
|
||||
let columnar1 =
|
||||
make_numerical_columnar_multiple_columns(&[("numbers", &[&[NumericalValue::from(-1f64)]])]);
|
||||
let columnar2 = make_numerical_columnar_multiple_columns(&[(
|
||||
"numbers",
|
||||
&[&[], &[NumericalValue::from(-3f64)]],
|
||||
)]);
|
||||
let mut buffer = Vec::new();
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let stack_merge_order = StackMergeOrder::stack(columnars);
|
||||
crate::columnar::merge_columnar(
|
||||
columnars,
|
||||
MergeRowOrder::Stack(stack_merge_order),
|
||||
&mut buffer,
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_rows(), 3);
|
||||
assert_eq!(columnar_reader.num_columns(), 1);
|
||||
let cols = columnar_reader.read_columns("numbers").unwrap();
|
||||
let dynamic_column = cols[0].open().unwrap();
|
||||
let DynamicColumn::F64(vals) = dynamic_column else { panic!() };
|
||||
assert_eq!(vals.get_cardinality(), Cardinality::Optional);
|
||||
assert_eq!(vals.first(0u32), Some(-1f64));
|
||||
assert_eq!(vals.first(1u32), None);
|
||||
assert_eq!(vals.first(2u32), Some(-3f64));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_merge_columnar_texts() {
|
||||
let columnar1 = make_text_columnar_multiple_columns(&[("texts", &[&["a"]])]);
|
||||
let columnar2 = make_text_columnar_multiple_columns(&[("texts", &[&[], &["b"]])]);
|
||||
let mut buffer = Vec::new();
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let stack_merge_order = StackMergeOrder::stack(columnars);
|
||||
crate::columnar::merge_columnar(
|
||||
columnars,
|
||||
MergeRowOrder::Stack(stack_merge_order),
|
||||
&mut buffer,
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_rows(), 3);
|
||||
assert_eq!(columnar_reader.num_columns(), 1);
|
||||
let cols = columnar_reader.read_columns("texts").unwrap();
|
||||
let dynamic_column = cols[0].open().unwrap();
|
||||
let DynamicColumn::Str(vals) = dynamic_column else { panic!() };
|
||||
let get_str_for_ord = |ord| {
|
||||
let mut out = String::new();
|
||||
vals.ord_to_str(ord, &mut out).unwrap();
|
||||
out
|
||||
};
|
||||
|
||||
assert_eq!(vals.dictionary.num_terms(), 2);
|
||||
assert_eq!(get_str_for_ord(0), "a");
|
||||
assert_eq!(get_str_for_ord(1), "b");
|
||||
|
||||
let get_str_for_row = |row_id| {
|
||||
let term_ords: Vec<u64> = vals.term_ords(row_id).collect();
|
||||
assert!(term_ords.len() <= 1);
|
||||
let mut out = String::new();
|
||||
if term_ords.len() == 1 {
|
||||
vals.ord_to_str(term_ords[0], &mut out).unwrap();
|
||||
}
|
||||
out
|
||||
};
|
||||
|
||||
assert_eq!(get_str_for_row(0), "a");
|
||||
assert_eq!(get_str_for_row(1), "");
|
||||
assert_eq!(get_str_for_row(2), "b");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_merge_columnar_byte() {
|
||||
let columnar1 = make_byte_columnar_multiple_columns(&[("bytes", &[&[b"bbbb"], &[b"baaa"]])]);
|
||||
let columnar2 = make_byte_columnar_multiple_columns(&[("bytes", &[&[], &[b"a"]])]);
|
||||
let mut buffer = Vec::new();
|
||||
let columnars = &[&columnar1, &columnar2];
|
||||
let stack_merge_order = StackMergeOrder::stack(columnars);
|
||||
crate::columnar::merge_columnar(
|
||||
columnars,
|
||||
MergeRowOrder::Stack(stack_merge_order),
|
||||
&mut buffer,
|
||||
)
|
||||
.unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_rows(), 4);
|
||||
assert_eq!(columnar_reader.num_columns(), 1);
|
||||
let cols = columnar_reader.read_columns("bytes").unwrap();
|
||||
let dynamic_column = cols[0].open().unwrap();
|
||||
let DynamicColumn::Bytes(vals) = dynamic_column else { panic!() };
|
||||
let get_bytes_for_ord = |ord| {
|
||||
let mut out = Vec::new();
|
||||
vals.ord_to_bytes(ord, &mut out).unwrap();
|
||||
out
|
||||
};
|
||||
|
||||
assert_eq!(vals.dictionary.num_terms(), 3);
|
||||
assert_eq!(get_bytes_for_ord(0), b"a");
|
||||
assert_eq!(get_bytes_for_ord(1), b"baaa");
|
||||
assert_eq!(get_bytes_for_ord(2), b"bbbb");
|
||||
|
||||
let get_bytes_for_row = |row_id| {
|
||||
let term_ords: Vec<u64> = vals.term_ords(row_id).collect();
|
||||
assert!(term_ords.len() <= 1);
|
||||
let mut out = Vec::new();
|
||||
if term_ords.len() == 1 {
|
||||
vals.ord_to_bytes(term_ords[0], &mut out).unwrap();
|
||||
}
|
||||
out
|
||||
};
|
||||
|
||||
assert_eq!(get_bytes_for_row(0), b"bbbb");
|
||||
assert_eq!(get_bytes_for_row(1), b"baaa");
|
||||
assert_eq!(get_bytes_for_row(2), b"");
|
||||
assert_eq!(get_bytes_for_row(3), b"a");
|
||||
}
|
||||
1
columnar/src/columnar/merge_index.rs
Normal file
1
columnar/src/columnar/merge_index.rs
Normal file
@@ -0,0 +1 @@
|
||||
|
||||
@@ -1,10 +1,11 @@
|
||||
mod column_type;
|
||||
mod format_version;
|
||||
mod merge;
|
||||
mod merge_index;
|
||||
mod reader;
|
||||
mod writer;
|
||||
|
||||
pub use column_type::{ColumnType, HasAssociatedColumnType};
|
||||
pub use merge::{merge_columnar, MergeDocOrder};
|
||||
pub use merge::{merge_columnar, MergeRowOrder, ShuffleMergeOrder, StackMergeOrder};
|
||||
pub use reader::ColumnarReader;
|
||||
pub use writer::ColumnarWriter;
|
||||
|
||||
@@ -6,6 +6,7 @@ use sstable::{Dictionary, RangeSSTable};
|
||||
|
||||
use crate::columnar::{format_version, ColumnType};
|
||||
use crate::dynamic_column::DynamicColumnHandle;
|
||||
use crate::RowId;
|
||||
|
||||
fn io_invalid_data(msg: String) -> io::Error {
|
||||
io::Error::new(io::ErrorKind::InvalidData, msg)
|
||||
@@ -13,9 +14,11 @@ fn io_invalid_data(msg: String) -> io::Error {
|
||||
|
||||
/// The ColumnarReader makes it possible to access a set of columns
|
||||
/// associated to field names.
|
||||
#[derive(Clone)]
|
||||
pub struct ColumnarReader {
|
||||
column_dictionary: Dictionary<RangeSSTable>,
|
||||
column_data: FileSlice,
|
||||
num_rows: RowId,
|
||||
}
|
||||
|
||||
impl ColumnarReader {
|
||||
@@ -27,23 +30,27 @@ impl ColumnarReader {
|
||||
|
||||
fn open_inner(file_slice: FileSlice) -> io::Result<ColumnarReader> {
|
||||
let (file_slice_without_sstable_len, footer_slice) = file_slice
|
||||
.split_from_end(mem::size_of::<u64>() + format_version::VERSION_FOOTER_NUM_BYTES);
|
||||
.split_from_end(mem::size_of::<u64>() + 4 + format_version::VERSION_FOOTER_NUM_BYTES);
|
||||
let footer_bytes = footer_slice.read_bytes()?;
|
||||
let (mut sstable_len_bytes, version_footer_bytes) =
|
||||
footer_bytes.rsplit(format_version::VERSION_FOOTER_NUM_BYTES);
|
||||
let sstable_len = u64::deserialize(&mut &footer_bytes[0..8])?;
|
||||
let num_rows = u32::deserialize(&mut &footer_bytes[8..12])?;
|
||||
let version_footer_bytes: [u8; format_version::VERSION_FOOTER_NUM_BYTES] =
|
||||
version_footer_bytes.as_slice().try_into().unwrap();
|
||||
footer_bytes[12..].try_into().unwrap();
|
||||
let _version = format_version::parse_footer(version_footer_bytes)?;
|
||||
let sstable_len = u64::deserialize(&mut sstable_len_bytes)?;
|
||||
let (column_data, sstable) =
|
||||
file_slice_without_sstable_len.split_from_end(sstable_len as usize);
|
||||
let column_dictionary = Dictionary::open(sstable)?;
|
||||
Ok(ColumnarReader {
|
||||
column_dictionary,
|
||||
column_data,
|
||||
num_rows,
|
||||
})
|
||||
}
|
||||
|
||||
pub fn num_rows(&self) -> RowId {
|
||||
self.num_rows
|
||||
}
|
||||
|
||||
// TODO Add unit tests
|
||||
pub fn list_columns(&self) -> io::Result<Vec<(String, DynamicColumnHandle)>> {
|
||||
let mut stream = self.column_dictionary.stream()?;
|
||||
@@ -73,7 +80,6 @@ impl ColumnarReader {
|
||||
///
|
||||
/// There can be more than one column associated to a given column name, provided they have
|
||||
/// different types.
|
||||
// TODO fix ugly API
|
||||
pub fn read_columns(&self, column_name: &str) -> io::Result<Vec<DynamicColumnHandle>> {
|
||||
// Each column is a associated to a given `column_key`,
|
||||
// that starts by `column_name\0column_header`.
|
||||
@@ -120,3 +126,46 @@ impl ColumnarReader {
|
||||
self.column_dictionary.num_terms()
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use crate::{ColumnType, ColumnarReader, ColumnarWriter};
|
||||
|
||||
#[test]
|
||||
fn test_list_columns() {
|
||||
let mut columnar_writer = ColumnarWriter::default();
|
||||
columnar_writer.record_column_type("col1", ColumnType::Str, false);
|
||||
columnar_writer.record_column_type("col2", ColumnType::U64, false);
|
||||
let mut buffer = Vec::new();
|
||||
columnar_writer.serialize(1, None, &mut buffer).unwrap();
|
||||
let columnar = ColumnarReader::open(buffer).unwrap();
|
||||
let columns = columnar.list_columns().unwrap();
|
||||
assert_eq!(columns.len(), 2);
|
||||
assert_eq!(&columns[0].0, "col1");
|
||||
assert_eq!(columns[0].1.column_type(), ColumnType::Str);
|
||||
assert_eq!(&columns[1].0, "col2");
|
||||
assert_eq!(columns[1].1.column_type(), ColumnType::U64);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_list_columns_strict_typing_prevents_coercion() {
|
||||
let mut columnar_writer = ColumnarWriter::default();
|
||||
columnar_writer.record_column_type("count", ColumnType::U64, false);
|
||||
columnar_writer.record_numerical(1, "count", 1u64);
|
||||
let mut buffer = Vec::new();
|
||||
columnar_writer.serialize(2, None, &mut buffer).unwrap();
|
||||
let columnar = ColumnarReader::open(buffer).unwrap();
|
||||
let columns = columnar.list_columns().unwrap();
|
||||
assert_eq!(columns.len(), 1);
|
||||
assert_eq!(&columns[0].0, "count");
|
||||
assert_eq!(columns[0].1.column_type(), ColumnType::U64);
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expect = "Input type forbidden")]
|
||||
fn test_list_columns_strict_typing_panics_on_wrong_types() {
|
||||
let mut columnar_writer = ColumnarWriter::default();
|
||||
columnar_writer.record_column_type("count", ColumnType::U64, false);
|
||||
columnar_writer.record_numerical(1, "count", 1i64);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -41,10 +41,31 @@ impl ColumnWriter {
|
||||
pub(super) fn operation_iterator<'a, V: SymbolValue>(
|
||||
&self,
|
||||
arena: &MemoryArena,
|
||||
old_to_new_ids_opt: Option<&[RowId]>,
|
||||
buffer: &'a mut Vec<u8>,
|
||||
) -> impl Iterator<Item = ColumnOperation<V>> + 'a {
|
||||
buffer.clear();
|
||||
self.values.read_to_end(arena, buffer);
|
||||
if let Some(old_to_new_ids) = old_to_new_ids_opt {
|
||||
// TODO avoid the extra deserialization / serialization.
|
||||
let mut sorted_ops: Vec<(RowId, ColumnOperation<V>)> = Vec::new();
|
||||
let mut new_doc = 0u32;
|
||||
let mut cursor = &buffer[..];
|
||||
for op in std::iter::from_fn(|| ColumnOperation::<V>::deserialize(&mut cursor)) {
|
||||
if let ColumnOperation::NewDoc(doc) = &op {
|
||||
new_doc = old_to_new_ids[*doc as usize];
|
||||
sorted_ops.push((new_doc, ColumnOperation::NewDoc(new_doc)));
|
||||
} else {
|
||||
sorted_ops.push((new_doc, op));
|
||||
}
|
||||
}
|
||||
// stable sort is crucial here.
|
||||
sorted_ops.sort_by_key(|(new_doc_id, _)| *new_doc_id);
|
||||
buffer.clear();
|
||||
for (_, op) in sorted_ops {
|
||||
buffer.extend_from_slice(op.serialize().as_ref());
|
||||
}
|
||||
}
|
||||
let mut cursor: &[u8] = &buffer[..];
|
||||
std::iter::from_fn(move || ColumnOperation::deserialize(&mut cursor))
|
||||
}
|
||||
@@ -114,7 +135,7 @@ impl NumericalColumnWriter {
|
||||
/// State used to store what types are still acceptable
|
||||
/// after having seen a set of numerical values.
|
||||
#[derive(Clone, Copy)]
|
||||
enum CompatibleNumericalTypes {
|
||||
pub(crate) enum CompatibleNumericalTypes {
|
||||
Dynamic {
|
||||
all_values_within_i64_range: bool,
|
||||
all_values_within_u64_range: bool,
|
||||
@@ -132,7 +153,7 @@ impl Default for CompatibleNumericalTypes {
|
||||
}
|
||||
|
||||
impl CompatibleNumericalTypes {
|
||||
fn is_type_accepted(&self, numerical_type: NumericalType) -> bool {
|
||||
pub fn is_type_accepted(&self, numerical_type: NumericalType) -> bool {
|
||||
match self {
|
||||
CompatibleNumericalTypes::Dynamic {
|
||||
all_values_within_i64_range,
|
||||
@@ -148,7 +169,7 @@ impl CompatibleNumericalTypes {
|
||||
}
|
||||
}
|
||||
|
||||
fn accept_value(&mut self, numerical_value: NumericalValue) {
|
||||
pub fn accept_value(&mut self, numerical_value: NumericalValue) {
|
||||
match self {
|
||||
CompatibleNumericalTypes::Dynamic {
|
||||
all_values_within_i64_range,
|
||||
@@ -168,7 +189,12 @@ impl CompatibleNumericalTypes {
|
||||
}
|
||||
},
|
||||
CompatibleNumericalTypes::StaticType(typ) => {
|
||||
assert_eq!(numerical_value.numerical_type(), *typ);
|
||||
assert_eq!(
|
||||
numerical_value.numerical_type(),
|
||||
*typ,
|
||||
"Input type forbidden. This column has been forced to type {typ:?}, received \
|
||||
{numerical_value:?}"
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -205,9 +231,11 @@ impl NumericalColumnWriter {
|
||||
pub(super) fn operation_iterator<'a>(
|
||||
self,
|
||||
arena: &MemoryArena,
|
||||
old_to_new_ids: Option<&[RowId]>,
|
||||
buffer: &'a mut Vec<u8>,
|
||||
) -> impl Iterator<Item = ColumnOperation<NumericalValue>> + 'a {
|
||||
self.column_writer.operation_iterator(arena, buffer)
|
||||
self.column_writer
|
||||
.operation_iterator(arena, old_to_new_ids, buffer)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -215,6 +243,14 @@ impl NumericalColumnWriter {
|
||||
pub(crate) struct StrOrBytesColumnWriter {
|
||||
pub(crate) dictionary_id: u32,
|
||||
pub(crate) column_writer: ColumnWriter,
|
||||
// If true, when facing a multivalued cardinality,
|
||||
// values associated to a given document will be sorted.
|
||||
//
|
||||
// This is useful for facets.
|
||||
//
|
||||
// If false, the order of appearance in the document will be
|
||||
// observed.
|
||||
pub(crate) sort_values_within_row: bool,
|
||||
}
|
||||
|
||||
impl StrOrBytesColumnWriter {
|
||||
@@ -222,6 +258,7 @@ impl StrOrBytesColumnWriter {
|
||||
StrOrBytesColumnWriter {
|
||||
dictionary_id,
|
||||
column_writer: Default::default(),
|
||||
sort_values_within_row: false,
|
||||
}
|
||||
}
|
||||
|
||||
@@ -239,9 +276,11 @@ impl StrOrBytesColumnWriter {
|
||||
pub(super) fn operation_iterator<'a>(
|
||||
&self,
|
||||
arena: &MemoryArena,
|
||||
old_to_new_ids: Option<&[RowId]>,
|
||||
byte_buffer: &'a mut Vec<u8>,
|
||||
) -> impl Iterator<Item = ColumnOperation<UnorderedId>> + 'a {
|
||||
self.column_writer.operation_iterator(arena, byte_buffer)
|
||||
self.column_writer
|
||||
.operation_iterator(arena, old_to_new_ids, byte_buffer)
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -7,8 +7,9 @@ use std::io;
|
||||
use std::net::Ipv6Addr;
|
||||
|
||||
use column_operation::ColumnOperation;
|
||||
pub(crate) use column_writers::CompatibleNumericalTypes;
|
||||
use common::CountingWriter;
|
||||
use serializer::ColumnarSerializer;
|
||||
pub(crate) use serializer::ColumnarSerializer;
|
||||
use stacker::{Addr, ArenaHashMap, MemoryArena};
|
||||
|
||||
use crate::column_index::SerializableColumnIndex;
|
||||
@@ -29,10 +30,7 @@ use crate::{Cardinality, RowId};
|
||||
#[derive(Default)]
|
||||
struct SpareBuffers {
|
||||
value_index_builders: PreallocatedIndexBuilders,
|
||||
i64_values: Vec<i64>,
|
||||
u64_values: Vec<u64>,
|
||||
f64_values: Vec<f64>,
|
||||
bool_values: Vec<bool>,
|
||||
ip_addr_values: Vec<Ipv6Addr>,
|
||||
}
|
||||
|
||||
@@ -47,7 +45,7 @@ struct SpareBuffers {
|
||||
/// columnar_writer.record_str(1u32 /* doc id */, "product_name", "Apple");
|
||||
/// columnar_writer.record_numerical(0u32 /* doc id */, "price", 10.5f64); //< uh oh we ended up mixing integer and floats.
|
||||
/// let mut wrt: Vec<u8> = Vec::new();
|
||||
/// columnar_writer.serialize(2u32, &mut wrt).unwrap();
|
||||
/// columnar_writer.serialize(2u32, None, &mut wrt).unwrap();
|
||||
/// ```
|
||||
pub struct ColumnarWriter {
|
||||
numerical_field_hash_map: ArenaHashMap,
|
||||
@@ -106,7 +104,65 @@ impl ColumnarWriter {
|
||||
+ self.datetime_field_hash_map.mem_usage()
|
||||
}
|
||||
|
||||
pub fn record_column_type(&mut self, column_name: &str, column_type: ColumnType) {
|
||||
/// Returns the list of doc ids from 0..num_docs sorted by the `sort_field`
|
||||
/// column.
|
||||
///
|
||||
/// If the column is multivalued, use the first value for scoring.
|
||||
/// If no value is associated to a specific row, the document is assigned
|
||||
/// the lowest possible score.
|
||||
///
|
||||
/// The sort applied is stable.
|
||||
pub fn sort_order(&self, sort_field: &str, num_docs: RowId, reversed: bool) -> Vec<u32> {
|
||||
let Some(numerical_col_writer) =
|
||||
self.numerical_field_hash_map.get::<NumericalColumnWriter>(sort_field.as_bytes()) else {
|
||||
return Vec::new();
|
||||
};
|
||||
let mut symbols_buffer = Vec::new();
|
||||
let mut values = Vec::new();
|
||||
let mut last_doc_opt: Option<RowId> = None;
|
||||
for op in numerical_col_writer.operation_iterator(&self.arena, None, &mut symbols_buffer) {
|
||||
match op {
|
||||
ColumnOperation::NewDoc(doc) => {
|
||||
last_doc_opt = Some(doc);
|
||||
}
|
||||
ColumnOperation::Value(numerical_value) => {
|
||||
if let Some(last_doc) = last_doc_opt {
|
||||
let score: f32 = f64::coerce(numerical_value) as f32;
|
||||
values.push((score, last_doc));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
for doc in values.len() as u32..num_docs {
|
||||
values.push((0.0f32, doc));
|
||||
}
|
||||
values.sort_by(|(left_score, _), (right_score, _)| {
|
||||
if reversed {
|
||||
right_score.partial_cmp(left_score).unwrap()
|
||||
} else {
|
||||
left_score.partial_cmp(right_score).unwrap()
|
||||
}
|
||||
});
|
||||
values.into_iter().map(|(_score, doc)| doc).collect()
|
||||
}
|
||||
|
||||
/// Records a column type. This is useful to bypass the coercion process,
|
||||
/// makes sure the empty is present in the resulting columnar, or set
|
||||
/// the `sort_values_within_row`.
|
||||
///
|
||||
/// `sort_values_within_row` is only allowed for `Bytes` or `Str` columns.
|
||||
pub fn record_column_type(
|
||||
&mut self,
|
||||
column_name: &str,
|
||||
column_type: ColumnType,
|
||||
sort_values_within_row: bool,
|
||||
) {
|
||||
if sort_values_within_row {
|
||||
assert!(
|
||||
column_type == ColumnType::Bytes || column_type == ColumnType::Str,
|
||||
"sort_values_within_row is only allowed for Bytes and Str columns",
|
||||
);
|
||||
}
|
||||
match column_type {
|
||||
ColumnType::Str | ColumnType::Bytes => {
|
||||
let (hash_map, dictionaries) = (
|
||||
@@ -121,13 +177,15 @@ impl ColumnarWriter {
|
||||
hash_map,
|
||||
column_name,
|
||||
|column_opt: Option<StrOrBytesColumnWriter>| {
|
||||
if let Some(column_writer) = column_opt {
|
||||
let mut column_writer = if let Some(column_writer) = column_opt {
|
||||
column_writer
|
||||
} else {
|
||||
let dictionary_id = dictionaries.len() as u32;
|
||||
dictionaries.push(DictionaryBuilder::default());
|
||||
StrOrBytesColumnWriter::with_dictionary_id(dictionary_id)
|
||||
}
|
||||
};
|
||||
column_writer.sort_values_within_row = sort_values_within_row;
|
||||
column_writer
|
||||
},
|
||||
);
|
||||
}
|
||||
@@ -165,18 +223,6 @@ impl ColumnarWriter {
|
||||
}
|
||||
}
|
||||
|
||||
pub fn force_numerical_type(&mut self, column_name: &str, numerical_type: NumericalType) {
|
||||
mutate_or_create_column(
|
||||
&mut self.numerical_field_hash_map,
|
||||
column_name,
|
||||
|column_opt: Option<NumericalColumnWriter>| {
|
||||
let mut column: NumericalColumnWriter = column_opt.unwrap_or_default();
|
||||
column.force_numerical_type(numerical_type);
|
||||
column
|
||||
},
|
||||
);
|
||||
}
|
||||
|
||||
pub fn record_numerical<T: Into<NumericalValue> + Copy>(
|
||||
&mut self,
|
||||
doc: RowId,
|
||||
@@ -274,7 +320,12 @@ impl ColumnarWriter {
|
||||
},
|
||||
);
|
||||
}
|
||||
pub fn serialize(&mut self, num_docs: RowId, wrt: &mut dyn io::Write) -> io::Result<()> {
|
||||
pub fn serialize(
|
||||
&mut self,
|
||||
num_docs: RowId,
|
||||
old_to_new_row_ids: Option<&[RowId]>,
|
||||
wrt: &mut dyn io::Write,
|
||||
) -> io::Result<()> {
|
||||
let mut serializer = ColumnarSerializer::new(wrt);
|
||||
let mut columns: Vec<(&[u8], ColumnType, Addr)> = self
|
||||
.numerical_field_hash_map
|
||||
@@ -317,19 +368,19 @@ impl ColumnarWriter {
|
||||
let mut symbol_byte_buffer: Vec<u8> = Vec::new();
|
||||
for (column_name, column_type, addr) in columns {
|
||||
match column_type {
|
||||
ColumnType::Bool | ColumnType::DateTime => {
|
||||
let column_writer: ColumnWriter = if column_type == ColumnType::Bool {
|
||||
self.bool_field_hash_map.read(addr)
|
||||
} else {
|
||||
self.datetime_field_hash_map.read(addr)
|
||||
};
|
||||
ColumnType::Bool => {
|
||||
let column_writer: ColumnWriter = self.bool_field_hash_map.read(addr);
|
||||
let cardinality = column_writer.get_cardinality(num_docs);
|
||||
let mut column_serializer =
|
||||
serializer.serialize_column(column_name, ColumnType::Bool);
|
||||
serializer.serialize_column(column_name, column_type);
|
||||
serialize_bool_column(
|
||||
cardinality,
|
||||
num_docs,
|
||||
column_writer.operation_iterator(arena, &mut symbol_byte_buffer),
|
||||
column_writer.operation_iterator(
|
||||
arena,
|
||||
old_to_new_row_ids,
|
||||
&mut symbol_byte_buffer,
|
||||
),
|
||||
buffers,
|
||||
&mut column_serializer,
|
||||
)?;
|
||||
@@ -342,7 +393,11 @@ impl ColumnarWriter {
|
||||
serialize_ip_addr_column(
|
||||
cardinality,
|
||||
num_docs,
|
||||
column_writer.operation_iterator(arena, &mut symbol_byte_buffer),
|
||||
column_writer.operation_iterator(
|
||||
arena,
|
||||
old_to_new_row_ids,
|
||||
&mut symbol_byte_buffer,
|
||||
),
|
||||
buffers,
|
||||
&mut column_serializer,
|
||||
)?;
|
||||
@@ -364,39 +419,68 @@ impl ColumnarWriter {
|
||||
serialize_bytes_or_str_column(
|
||||
cardinality,
|
||||
num_docs,
|
||||
str_or_bytes_column_writer.sort_values_within_row,
|
||||
dictionary_builder,
|
||||
str_or_bytes_column_writer
|
||||
.operation_iterator(arena, &mut symbol_byte_buffer),
|
||||
str_or_bytes_column_writer.operation_iterator(
|
||||
arena,
|
||||
old_to_new_row_ids,
|
||||
&mut symbol_byte_buffer,
|
||||
),
|
||||
buffers,
|
||||
&mut column_serializer,
|
||||
)?;
|
||||
}
|
||||
ColumnType::I64 | ColumnType::F64 | ColumnType::U64 => {
|
||||
ColumnType::F64 | ColumnType::I64 | ColumnType::U64 => {
|
||||
let numerical_column_writer: NumericalColumnWriter =
|
||||
self.numerical_field_hash_map.read(addr);
|
||||
let numerical_type = column_type.numerical_type().unwrap();
|
||||
let cardinality = numerical_column_writer.cardinality(num_docs);
|
||||
let mut column_serializer =
|
||||
serializer.serialize_column(column_name, ColumnType::from(numerical_type));
|
||||
serializer.serialize_column(column_name, column_type);
|
||||
let numerical_type = column_type.numerical_type().unwrap();
|
||||
serialize_numerical_column(
|
||||
cardinality,
|
||||
num_docs,
|
||||
numerical_type,
|
||||
numerical_column_writer.operation_iterator(arena, &mut symbol_byte_buffer),
|
||||
numerical_column_writer.operation_iterator(
|
||||
arena,
|
||||
old_to_new_row_ids,
|
||||
&mut symbol_byte_buffer,
|
||||
),
|
||||
buffers,
|
||||
&mut column_serializer,
|
||||
)?;
|
||||
}
|
||||
ColumnType::DateTime => {
|
||||
let column_writer: ColumnWriter = self.datetime_field_hash_map.read(addr);
|
||||
let cardinality = column_writer.get_cardinality(num_docs);
|
||||
let mut column_serializer =
|
||||
serializer.serialize_column(column_name, ColumnType::DateTime);
|
||||
serialize_numerical_column(
|
||||
cardinality,
|
||||
num_docs,
|
||||
NumericalType::I64,
|
||||
column_writer.operation_iterator(
|
||||
arena,
|
||||
old_to_new_row_ids,
|
||||
&mut symbol_byte_buffer,
|
||||
),
|
||||
buffers,
|
||||
&mut column_serializer,
|
||||
)?;
|
||||
}
|
||||
};
|
||||
}
|
||||
serializer.finalize()?;
|
||||
serializer.finalize(num_docs)?;
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
// Serialize [Dictionary, Column, dictionary num bytes U32::LE]
|
||||
// Column: [Column Index, Column Values, column index num bytes U32::LE]
|
||||
fn serialize_bytes_or_str_column(
|
||||
cardinality: Cardinality,
|
||||
num_docs: RowId,
|
||||
sort_values_within_row: bool,
|
||||
dictionary_builder: &DictionaryBuilder,
|
||||
operation_it: impl Iterator<Item = ColumnOperation<UnorderedId>>,
|
||||
buffers: &mut SpareBuffers,
|
||||
@@ -425,6 +509,7 @@ fn serialize_bytes_or_str_column(
|
||||
operation_iterator,
|
||||
cardinality,
|
||||
num_docs,
|
||||
sort_values_within_row,
|
||||
value_index_builders,
|
||||
u64_values,
|
||||
&mut wrt,
|
||||
@@ -444,8 +529,6 @@ fn serialize_numerical_column(
|
||||
let SpareBuffers {
|
||||
value_index_builders,
|
||||
u64_values,
|
||||
i64_values,
|
||||
f64_values,
|
||||
..
|
||||
} = buffers;
|
||||
match numerical_type {
|
||||
@@ -454,8 +537,9 @@ fn serialize_numerical_column(
|
||||
coerce_numerical_symbol::<i64>(op_iterator),
|
||||
cardinality,
|
||||
num_docs,
|
||||
false,
|
||||
value_index_builders,
|
||||
i64_values,
|
||||
u64_values,
|
||||
wrt,
|
||||
)?;
|
||||
}
|
||||
@@ -464,6 +548,7 @@ fn serialize_numerical_column(
|
||||
coerce_numerical_symbol::<u64>(op_iterator),
|
||||
cardinality,
|
||||
num_docs,
|
||||
false,
|
||||
value_index_builders,
|
||||
u64_values,
|
||||
wrt,
|
||||
@@ -474,8 +559,9 @@ fn serialize_numerical_column(
|
||||
coerce_numerical_symbol::<f64>(op_iterator),
|
||||
cardinality,
|
||||
num_docs,
|
||||
false,
|
||||
value_index_builders,
|
||||
f64_values,
|
||||
u64_values,
|
||||
wrt,
|
||||
)?;
|
||||
}
|
||||
@@ -492,15 +578,19 @@ fn serialize_bool_column(
|
||||
) -> io::Result<()> {
|
||||
let SpareBuffers {
|
||||
value_index_builders,
|
||||
bool_values,
|
||||
u64_values,
|
||||
..
|
||||
} = buffers;
|
||||
send_to_serialize_column_mappable_to_u64(
|
||||
column_operations_it,
|
||||
column_operations_it.map(|bool_column_operation| match bool_column_operation {
|
||||
ColumnOperation::NewDoc(doc) => ColumnOperation::NewDoc(doc),
|
||||
ColumnOperation::Value(bool_val) => ColumnOperation::Value(bool_val.to_u64()),
|
||||
}),
|
||||
cardinality,
|
||||
num_docs,
|
||||
false,
|
||||
value_index_builders,
|
||||
bool_values,
|
||||
u64_values,
|
||||
wrt,
|
||||
)?;
|
||||
Ok(())
|
||||
@@ -530,11 +620,11 @@ fn serialize_ip_addr_column(
|
||||
}
|
||||
|
||||
fn send_to_serialize_column_mappable_to_u128<
|
||||
T: Copy + std::fmt::Debug + Send + Sync + MonotonicallyMappableToU128 + PartialOrd,
|
||||
T: Copy + Ord + std::fmt::Debug + Send + Sync + MonotonicallyMappableToU128 + PartialOrd,
|
||||
>(
|
||||
op_iterator: impl Iterator<Item = ColumnOperation<T>>,
|
||||
cardinality: Cardinality,
|
||||
num_docs: RowId,
|
||||
num_rows: RowId,
|
||||
value_index_builders: &mut PreallocatedIndexBuilders,
|
||||
values: &mut Vec<T>,
|
||||
mut wrt: impl io::Write,
|
||||
@@ -556,37 +646,47 @@ where
|
||||
Cardinality::Optional => {
|
||||
let optional_index_builder = value_index_builders.borrow_optional_index_builder();
|
||||
consume_operation_iterator(op_iterator, optional_index_builder, values);
|
||||
let optional_index = optional_index_builder.finish(num_docs);
|
||||
SerializableColumnIndex::Optional(Box::new(optional_index))
|
||||
let optional_index = optional_index_builder.finish(num_rows);
|
||||
SerializableColumnIndex::Optional {
|
||||
num_rows,
|
||||
non_null_row_ids: Box::new(optional_index),
|
||||
}
|
||||
}
|
||||
Cardinality::Multivalued => {
|
||||
let multivalued_index_builder = value_index_builders.borrow_multivalued_index_builder();
|
||||
consume_operation_iterator(op_iterator, multivalued_index_builder, values);
|
||||
let multivalued_index = multivalued_index_builder.finish(num_docs);
|
||||
let multivalued_index = multivalued_index_builder.finish(num_rows);
|
||||
SerializableColumnIndex::Multivalued(Box::new(multivalued_index))
|
||||
}
|
||||
};
|
||||
crate::column::serialize_column_mappable_to_u128(
|
||||
serializable_column_index,
|
||||
|| values.iter().cloned(),
|
||||
values.len() as u32,
|
||||
&&values[..],
|
||||
&mut wrt,
|
||||
)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn send_to_serialize_column_mappable_to_u64<
|
||||
T: Copy + Default + std::fmt::Debug + Send + Sync + MonotonicallyMappableToU64 + PartialOrd,
|
||||
>(
|
||||
op_iterator: impl Iterator<Item = ColumnOperation<T>>,
|
||||
fn sort_values_within_row_in_place(multivalued_index: &[RowId], values: &mut Vec<u64>) {
|
||||
let mut start_index: usize = 0;
|
||||
for end_index in multivalued_index.iter().copied() {
|
||||
let end_index = end_index as usize;
|
||||
values[start_index..end_index].sort_unstable();
|
||||
start_index = end_index;
|
||||
}
|
||||
}
|
||||
|
||||
fn send_to_serialize_column_mappable_to_u64(
|
||||
op_iterator: impl Iterator<Item = ColumnOperation<u64>>,
|
||||
cardinality: Cardinality,
|
||||
num_docs: RowId,
|
||||
num_rows: RowId,
|
||||
sort_values_within_row: bool,
|
||||
value_index_builders: &mut PreallocatedIndexBuilders,
|
||||
values: &mut Vec<T>,
|
||||
values: &mut Vec<u64>,
|
||||
mut wrt: impl io::Write,
|
||||
) -> io::Result<()>
|
||||
where
|
||||
for<'a> VecColumn<'a, T>: ColumnValues<T>,
|
||||
for<'a> VecColumn<'a, u64>: ColumnValues<u64>,
|
||||
{
|
||||
values.clear();
|
||||
let serializable_column_index = match cardinality {
|
||||
@@ -601,19 +701,25 @@ where
|
||||
Cardinality::Optional => {
|
||||
let optional_index_builder = value_index_builders.borrow_optional_index_builder();
|
||||
consume_operation_iterator(op_iterator, optional_index_builder, values);
|
||||
let optional_index = optional_index_builder.finish(num_docs);
|
||||
SerializableColumnIndex::Optional(Box::new(optional_index))
|
||||
let optional_index = optional_index_builder.finish(num_rows);
|
||||
SerializableColumnIndex::Optional {
|
||||
non_null_row_ids: Box::new(optional_index),
|
||||
num_rows,
|
||||
}
|
||||
}
|
||||
Cardinality::Multivalued => {
|
||||
let multivalued_index_builder = value_index_builders.borrow_multivalued_index_builder();
|
||||
consume_operation_iterator(op_iterator, multivalued_index_builder, values);
|
||||
let multivalued_index = multivalued_index_builder.finish(num_docs);
|
||||
let multivalued_index = multivalued_index_builder.finish(num_rows);
|
||||
if sort_values_within_row {
|
||||
sort_values_within_row_in_place(multivalued_index, values);
|
||||
}
|
||||
SerializableColumnIndex::Multivalued(Box::new(multivalued_index))
|
||||
}
|
||||
};
|
||||
crate::column::serialize_column_mappable_to_u64(
|
||||
serializable_column_index,
|
||||
&VecColumn::from(&values[..]),
|
||||
&&values[..],
|
||||
&mut wrt,
|
||||
)?;
|
||||
Ok(())
|
||||
@@ -621,17 +727,17 @@ where
|
||||
|
||||
fn coerce_numerical_symbol<T>(
|
||||
operation_iterator: impl Iterator<Item = ColumnOperation<NumericalValue>>,
|
||||
) -> impl Iterator<Item = ColumnOperation<T>>
|
||||
where T: Coerce {
|
||||
) -> impl Iterator<Item = ColumnOperation<u64>>
|
||||
where T: Coerce + MonotonicallyMappableToU64 {
|
||||
operation_iterator.map(|symbol| match symbol {
|
||||
ColumnOperation::NewDoc(doc) => ColumnOperation::NewDoc(doc),
|
||||
ColumnOperation::Value(numerical_value) => {
|
||||
ColumnOperation::Value(Coerce::coerce(numerical_value))
|
||||
ColumnOperation::Value(T::coerce(numerical_value).to_u64())
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
fn consume_operation_iterator<T: std::fmt::Debug, TIndexBuilder: IndexBuilder>(
|
||||
fn consume_operation_iterator<T: Ord, TIndexBuilder: IndexBuilder>(
|
||||
operation_iterator: impl Iterator<Item = ColumnOperation<T>>,
|
||||
index_builder: &mut TIndexBuilder,
|
||||
values: &mut Vec<T>,
|
||||
@@ -666,7 +772,7 @@ mod tests {
|
||||
assert_eq!(column_writer.get_cardinality(3), Cardinality::Full);
|
||||
let mut buffer = Vec::new();
|
||||
let symbols: Vec<ColumnOperation<NumericalValue>> = column_writer
|
||||
.operation_iterator(&mut arena, &mut buffer)
|
||||
.operation_iterator(&mut arena, None, &mut buffer)
|
||||
.collect();
|
||||
assert_eq!(symbols.len(), 6);
|
||||
assert!(matches!(symbols[0], ColumnOperation::NewDoc(0u32)));
|
||||
@@ -695,7 +801,7 @@ mod tests {
|
||||
assert_eq!(column_writer.get_cardinality(3), Cardinality::Optional);
|
||||
let mut buffer = Vec::new();
|
||||
let symbols: Vec<ColumnOperation<NumericalValue>> = column_writer
|
||||
.operation_iterator(&mut arena, &mut buffer)
|
||||
.operation_iterator(&mut arena, None, &mut buffer)
|
||||
.collect();
|
||||
assert_eq!(symbols.len(), 4);
|
||||
assert!(matches!(symbols[0], ColumnOperation::NewDoc(1u32)));
|
||||
@@ -718,7 +824,7 @@ mod tests {
|
||||
assert_eq!(column_writer.get_cardinality(2), Cardinality::Optional);
|
||||
let mut buffer = Vec::new();
|
||||
let symbols: Vec<ColumnOperation<NumericalValue>> = column_writer
|
||||
.operation_iterator(&mut arena, &mut buffer)
|
||||
.operation_iterator(&mut arena, None, &mut buffer)
|
||||
.collect();
|
||||
assert_eq!(symbols.len(), 2);
|
||||
assert!(matches!(symbols[0], ColumnOperation::NewDoc(0u32)));
|
||||
@@ -737,7 +843,7 @@ mod tests {
|
||||
assert_eq!(column_writer.get_cardinality(1), Cardinality::Multivalued);
|
||||
let mut buffer = Vec::new();
|
||||
let symbols: Vec<ColumnOperation<NumericalValue>> = column_writer
|
||||
.operation_iterator(&mut arena, &mut buffer)
|
||||
.operation_iterator(&mut arena, None, &mut buffer)
|
||||
.collect();
|
||||
assert_eq!(symbols.len(), 3);
|
||||
assert!(matches!(symbols[0], ColumnOperation::NewDoc(0u32)));
|
||||
|
||||
@@ -1,11 +1,12 @@
|
||||
use std::io;
|
||||
use std::io::Write;
|
||||
|
||||
use common::CountingWriter;
|
||||
use common::{BinarySerializable, CountingWriter};
|
||||
use sstable::value::RangeValueWriter;
|
||||
use sstable::RangeSSTable;
|
||||
|
||||
use crate::columnar::ColumnType;
|
||||
use crate::RowId;
|
||||
|
||||
pub struct ColumnarSerializer<W: io::Write> {
|
||||
wrt: CountingWriter<W>,
|
||||
@@ -46,11 +47,12 @@ impl<W: io::Write> ColumnarSerializer<W> {
|
||||
}
|
||||
}
|
||||
|
||||
pub(crate) fn finalize(mut self) -> io::Result<()> {
|
||||
pub(crate) fn finalize(mut self, num_rows: RowId) -> io::Result<()> {
|
||||
let sstable_bytes: Vec<u8> = self.sstable_range.finish()?;
|
||||
let sstable_num_bytes: u64 = sstable_bytes.len() as u64;
|
||||
self.wrt.write_all(&sstable_bytes)?;
|
||||
self.wrt.write_all(&sstable_num_bytes.to_le_bytes()[..])?;
|
||||
num_rows.serialize(&mut self.wrt)?;
|
||||
self.wrt
|
||||
.write_all(&super::super::format_version::footer())?;
|
||||
self.wrt.flush()?;
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
use crate::column_index::SerializableOptionalIndex;
|
||||
use crate::column_values::{ColumnValues, VecColumn};
|
||||
use crate::iterable::Iterable;
|
||||
use crate::RowId;
|
||||
|
||||
/// The `IndexBuilder` interprets a sequence of
|
||||
@@ -29,34 +28,15 @@ pub struct OptionalIndexBuilder {
|
||||
docs: Vec<RowId>,
|
||||
}
|
||||
|
||||
struct SingleValueArrayIndex<'a> {
|
||||
// RowIds with a value, in a strictly increasing order
|
||||
row_ids: &'a [RowId],
|
||||
num_rows: RowId,
|
||||
}
|
||||
|
||||
impl<'a> SerializableOptionalIndex<'a> for SingleValueArrayIndex<'a> {
|
||||
fn num_rows(&self) -> RowId {
|
||||
self.num_rows
|
||||
}
|
||||
|
||||
fn non_null_rows(&self) -> Box<dyn Iterator<Item = RowId> + 'a> {
|
||||
Box::new(self.row_ids.iter().copied())
|
||||
}
|
||||
}
|
||||
|
||||
impl OptionalIndexBuilder {
|
||||
pub fn finish<'a>(&'a mut self, num_rows: RowId) -> impl SerializableOptionalIndex + 'a {
|
||||
pub fn finish<'a>(&'a mut self, num_rows: RowId) -> impl Iterable<RowId> + 'a {
|
||||
debug_assert!(self
|
||||
.docs
|
||||
.last()
|
||||
.copied()
|
||||
.map(|last_doc| last_doc < num_rows)
|
||||
.unwrap_or(true));
|
||||
SingleValueArrayIndex {
|
||||
row_ids: &self.docs[..],
|
||||
num_rows,
|
||||
}
|
||||
&self.docs[..]
|
||||
}
|
||||
|
||||
fn reset(&mut self) {
|
||||
@@ -84,14 +64,10 @@ pub struct MultivaluedIndexBuilder {
|
||||
}
|
||||
|
||||
impl MultivaluedIndexBuilder {
|
||||
pub fn finish(&mut self, num_docs: RowId) -> impl ColumnValues<u32> + '_ {
|
||||
pub fn finish(&mut self, num_docs: RowId) -> &[u32] {
|
||||
self.start_offsets
|
||||
.resize(num_docs as usize + 1, self.total_num_vals_seen);
|
||||
VecColumn {
|
||||
values: &&self.start_offsets[..],
|
||||
min_value: 0,
|
||||
max_value: self.start_offsets.last().copied().unwrap_or(0),
|
||||
}
|
||||
&self.start_offsets[..]
|
||||
}
|
||||
|
||||
fn reset(&mut self) {
|
||||
@@ -149,7 +125,7 @@ mod tests {
|
||||
assert_eq!(
|
||||
&opt_value_index_builder
|
||||
.finish(1u32)
|
||||
.non_null_rows()
|
||||
.boxed_iter()
|
||||
.collect::<Vec<u32>>(),
|
||||
&[0]
|
||||
);
|
||||
@@ -159,7 +135,7 @@ mod tests {
|
||||
assert_eq!(
|
||||
&opt_value_index_builder
|
||||
.finish(2u32)
|
||||
.non_null_rows()
|
||||
.boxed_iter()
|
||||
.collect::<Vec<u32>>(),
|
||||
&[1]
|
||||
);
|
||||
@@ -177,6 +153,7 @@ mod tests {
|
||||
multivalued_value_index_builder
|
||||
.finish(4u32)
|
||||
.iter()
|
||||
.copied()
|
||||
.collect::<Vec<u32>>(),
|
||||
vec![0, 0, 2, 3, 3]
|
||||
);
|
||||
@@ -188,6 +165,7 @@ mod tests {
|
||||
multivalued_value_index_builder
|
||||
.finish(4u32)
|
||||
.iter()
|
||||
.copied()
|
||||
.collect::<Vec<u32>>(),
|
||||
vec![0, 0, 0, 2, 2]
|
||||
);
|
||||
|
||||
@@ -8,7 +8,7 @@ use common::{HasLen, OwnedBytes};
|
||||
use crate::column::{BytesColumn, Column, StrColumn};
|
||||
use crate::column_values::{monotonic_map_column, StrictlyMonotonicFn};
|
||||
use crate::columnar::ColumnType;
|
||||
use crate::{DateTime, NumericalType};
|
||||
use crate::{Cardinality, DateTime, NumericalType};
|
||||
|
||||
#[derive(Clone)]
|
||||
pub enum DynamicColumn {
|
||||
@@ -23,6 +23,18 @@ pub enum DynamicColumn {
|
||||
}
|
||||
|
||||
impl DynamicColumn {
|
||||
pub fn get_cardinality(&self) -> Cardinality {
|
||||
match self {
|
||||
DynamicColumn::Bool(c) => c.get_cardinality(),
|
||||
DynamicColumn::I64(c) => c.get_cardinality(),
|
||||
DynamicColumn::U64(c) => c.get_cardinality(),
|
||||
DynamicColumn::F64(c) => c.get_cardinality(),
|
||||
DynamicColumn::IpAddr(c) => c.get_cardinality(),
|
||||
DynamicColumn::DateTime(c) => c.get_cardinality(),
|
||||
DynamicColumn::Bytes(c) => c.ords().get_cardinality(),
|
||||
DynamicColumn::Str(c) => c.ords().get_cardinality(),
|
||||
}
|
||||
}
|
||||
pub fn column_type(&self) -> ColumnType {
|
||||
match self {
|
||||
DynamicColumn::Bool(_) => ColumnType::Bool,
|
||||
@@ -36,6 +48,14 @@ impl DynamicColumn {
|
||||
}
|
||||
}
|
||||
|
||||
pub fn coerce_numerical(self, target_numerical_type: NumericalType) -> Option<Self> {
|
||||
match target_numerical_type {
|
||||
NumericalType::I64 => self.coerce_to_i64(),
|
||||
NumericalType::U64 => self.coerce_to_u64(),
|
||||
NumericalType::F64 => self.coerce_to_f64(),
|
||||
}
|
||||
}
|
||||
|
||||
pub fn is_numerical(&self) -> bool {
|
||||
self.column_type().numerical_type().is_some()
|
||||
}
|
||||
@@ -50,7 +70,7 @@ impl DynamicColumn {
|
||||
self.column_type().numerical_type() == Some(NumericalType::U64)
|
||||
}
|
||||
|
||||
pub fn coerce_to_f64(self) -> Option<DynamicColumn> {
|
||||
fn coerce_to_f64(self) -> Option<DynamicColumn> {
|
||||
match self {
|
||||
DynamicColumn::I64(column) => Some(DynamicColumn::F64(Column {
|
||||
idx: column.idx,
|
||||
@@ -64,7 +84,7 @@ impl DynamicColumn {
|
||||
_ => None,
|
||||
}
|
||||
}
|
||||
pub fn coerce_to_i64(self) -> Option<DynamicColumn> {
|
||||
fn coerce_to_i64(self) -> Option<DynamicColumn> {
|
||||
match self {
|
||||
DynamicColumn::U64(column) => {
|
||||
if column.max_value() > i64::MAX as u64 {
|
||||
@@ -79,7 +99,7 @@ impl DynamicColumn {
|
||||
_ => None,
|
||||
}
|
||||
}
|
||||
pub fn coerce_to_u64(self) -> Option<DynamicColumn> {
|
||||
fn coerce_to_u64(self) -> Option<DynamicColumn> {
|
||||
match self {
|
||||
DynamicColumn::I64(column) => {
|
||||
if column.min_value() < 0 {
|
||||
@@ -215,10 +235,8 @@ impl DynamicColumnHandle {
|
||||
|
||||
fn open_internal(&self, column_bytes: OwnedBytes) -> io::Result<DynamicColumn> {
|
||||
let dynamic_column: DynamicColumn = match self.column_type {
|
||||
ColumnType::Bytes => {
|
||||
crate::column::open_column_bytes::<BytesColumn>(column_bytes)?.into()
|
||||
}
|
||||
ColumnType::Str => crate::column::open_column_bytes::<StrColumn>(column_bytes)?.into(),
|
||||
ColumnType::Bytes => crate::column::open_column_bytes(column_bytes)?.into(),
|
||||
ColumnType::Str => crate::column::open_column_str(column_bytes)?.into(),
|
||||
ColumnType::I64 => crate::column::open_column_u64::<i64>(column_bytes)?.into(),
|
||||
ColumnType::U64 => crate::column::open_column_u64::<u64>(column_bytes)?.into(),
|
||||
ColumnType::F64 => crate::column::open_column_u64::<f64>(column_bytes)?.into(),
|
||||
|
||||
19
columnar/src/iterable.rs
Normal file
19
columnar/src/iterable.rs
Normal file
@@ -0,0 +1,19 @@
|
||||
use std::ops::Range;
|
||||
|
||||
pub trait Iterable<T = u64> {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = T> + '_>;
|
||||
}
|
||||
|
||||
impl<'a, T: Copy> Iterable<T> for &'a [T] {
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
|
||||
Box::new(self.iter().copied())
|
||||
}
|
||||
}
|
||||
|
||||
impl<T: Copy> Iterable<T> for Range<T>
|
||||
where Range<T>: Iterator<Item = T>
|
||||
{
|
||||
fn boxed_iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
|
||||
Box::new(self.clone())
|
||||
}
|
||||
}
|
||||
@@ -11,30 +11,48 @@ use std::io;
|
||||
|
||||
mod column;
|
||||
mod column_index;
|
||||
mod column_values;
|
||||
pub mod column_values;
|
||||
mod columnar;
|
||||
mod dictionary;
|
||||
mod dynamic_column;
|
||||
mod iterable;
|
||||
pub(crate) mod utils;
|
||||
mod value;
|
||||
|
||||
pub use column::{BytesColumn, Column, StrColumn};
|
||||
pub use column_values::ColumnValues;
|
||||
pub use column_index::ColumnIndex;
|
||||
pub use column_values::{ColumnValues, MonotonicallyMappableToU128, MonotonicallyMappableToU64};
|
||||
pub use columnar::{
|
||||
merge_columnar, ColumnType, ColumnarReader, ColumnarWriter, HasAssociatedColumnType,
|
||||
MergeDocOrder,
|
||||
MergeRowOrder, ShuffleMergeOrder, StackMergeOrder,
|
||||
};
|
||||
use sstable::VoidSSTable;
|
||||
pub use value::{NumericalType, NumericalValue};
|
||||
|
||||
pub use self::dynamic_column::{DynamicColumn, DynamicColumnHandle};
|
||||
|
||||
pub type RowId = u32;
|
||||
|
||||
#[derive(Clone, Copy)]
|
||||
pub struct RowAddr {
|
||||
pub segment_ord: u32,
|
||||
pub row_id: RowId,
|
||||
}
|
||||
|
||||
pub use sstable::Dictionary;
|
||||
pub type Streamer<'a> = sstable::Streamer<'a, VoidSSTable>;
|
||||
|
||||
#[derive(Clone, Copy, PartialOrd, PartialEq, Default, Debug)]
|
||||
pub struct DateTime {
|
||||
pub timestamp_micros: i64,
|
||||
}
|
||||
|
||||
impl DateTime {
|
||||
pub fn into_timestamp_micros(self) -> i64 {
|
||||
self.timestamp_micros
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Copy, Clone, Debug)]
|
||||
pub struct InvalidData;
|
||||
|
||||
@@ -62,6 +80,12 @@ pub enum Cardinality {
|
||||
}
|
||||
|
||||
impl Cardinality {
|
||||
pub fn is_optional(&self) -> bool {
|
||||
matches!(self, Cardinality::Optional)
|
||||
}
|
||||
pub fn is_multivalue(&self) -> bool {
|
||||
matches!(self, Cardinality::Multivalued)
|
||||
}
|
||||
pub(crate) fn to_code(self) -> u8 {
|
||||
self as u8
|
||||
}
|
||||
|
||||
@@ -12,7 +12,7 @@ fn test_dataframe_writer_str() {
|
||||
dataframe_writer.record_str(1u32, "my_string", "hello");
|
||||
dataframe_writer.record_str(3u32, "my_string", "helloeee");
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer.serialize(5, &mut buffer).unwrap();
|
||||
dataframe_writer.serialize(5, None, &mut buffer).unwrap();
|
||||
let columnar = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar.num_columns(), 1);
|
||||
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("my_string").unwrap();
|
||||
@@ -26,7 +26,7 @@ fn test_dataframe_writer_bytes() {
|
||||
dataframe_writer.record_bytes(1u32, "my_string", b"hello");
|
||||
dataframe_writer.record_bytes(3u32, "my_string", b"helloeee");
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer.serialize(5, &mut buffer).unwrap();
|
||||
dataframe_writer.serialize(5, None, &mut buffer).unwrap();
|
||||
let columnar = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar.num_columns(), 1);
|
||||
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("my_string").unwrap();
|
||||
@@ -40,7 +40,7 @@ fn test_dataframe_writer_bool() {
|
||||
dataframe_writer.record_bool(1u32, "bool.value", false);
|
||||
dataframe_writer.record_bool(3u32, "bool.value", true);
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer.serialize(5, &mut buffer).unwrap();
|
||||
dataframe_writer.serialize(5, None, &mut buffer).unwrap();
|
||||
let columnar = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar.num_columns(), 1);
|
||||
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("bool.value").unwrap();
|
||||
@@ -63,7 +63,7 @@ fn test_dataframe_writer_u64_multivalued() {
|
||||
dataframe_writer.record_numerical(6u32, "divisor", 2u64);
|
||||
dataframe_writer.record_numerical(6u32, "divisor", 3u64);
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer.serialize(7, &mut buffer).unwrap();
|
||||
dataframe_writer.serialize(7, None, &mut buffer).unwrap();
|
||||
let columnar = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar.num_columns(), 1);
|
||||
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("divisor").unwrap();
|
||||
@@ -84,7 +84,7 @@ fn test_dataframe_writer_ip_addr() {
|
||||
dataframe_writer.record_ip_addr(1, "ip_addr", Ipv6Addr::from_u128(1001));
|
||||
dataframe_writer.record_ip_addr(3, "ip_addr", Ipv6Addr::from_u128(1050));
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer.serialize(5, &mut buffer).unwrap();
|
||||
dataframe_writer.serialize(5, None, &mut buffer).unwrap();
|
||||
let columnar = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar.num_columns(), 1);
|
||||
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("ip_addr").unwrap();
|
||||
@@ -113,7 +113,7 @@ fn test_dataframe_writer_numerical() {
|
||||
dataframe_writer.record_numerical(2u32, "srical.value", NumericalValue::U64(13u64));
|
||||
dataframe_writer.record_numerical(4u32, "srical.value", NumericalValue::U64(15u64));
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
dataframe_writer.serialize(6, &mut buffer).unwrap();
|
||||
dataframe_writer.serialize(6, None, &mut buffer).unwrap();
|
||||
let columnar = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar.num_columns(), 1);
|
||||
let cols: Vec<DynamicColumnHandle> = columnar.read_columns("srical.value").unwrap();
|
||||
@@ -144,7 +144,7 @@ fn test_dictionary_encoded_str() {
|
||||
columnar_writer.record_str(3, "my.column", "c");
|
||||
columnar_writer.record_str(3, "my.column2", "different_column!");
|
||||
columnar_writer.record_str(4, "my.column", "b");
|
||||
columnar_writer.serialize(5, &mut buffer).unwrap();
|
||||
columnar_writer.serialize(5, None, &mut buffer).unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_columns(), 2);
|
||||
let col_handles = columnar_reader.read_columns("my.column").unwrap();
|
||||
@@ -176,7 +176,7 @@ fn test_dictionary_encoded_bytes() {
|
||||
columnar_writer.record_bytes(3, "my.column", b"c");
|
||||
columnar_writer.record_bytes(3, "my.column2", b"different_column!");
|
||||
columnar_writer.record_bytes(4, "my.column", b"b");
|
||||
columnar_writer.serialize(5, &mut buffer).unwrap();
|
||||
columnar_writer.serialize(5, None, &mut buffer).unwrap();
|
||||
let columnar_reader = ColumnarReader::open(buffer).unwrap();
|
||||
assert_eq!(columnar_reader.num_columns(), 2);
|
||||
let col_handles = columnar_reader.read_columns("my.column").unwrap();
|
||||
|
||||
@@ -5,12 +5,37 @@ use byteorder::{ReadBytesExt, WriteBytesExt};
|
||||
|
||||
use crate::{Endianness, VInt};
|
||||
|
||||
#[derive(Default)]
|
||||
struct Counter(u64);
|
||||
|
||||
impl io::Write for Counter {
|
||||
fn write(&mut self, buf: &[u8]) -> io::Result<usize> {
|
||||
self.0 += buf.len() as u64;
|
||||
Ok(buf.len())
|
||||
}
|
||||
|
||||
fn write_all(&mut self, buf: &[u8]) -> io::Result<()> {
|
||||
self.0 += buf.len() as u64;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn flush(&mut self) -> io::Result<()> {
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
/// Trait for a simple binary serialization.
|
||||
pub trait BinarySerializable: fmt::Debug + Sized {
|
||||
/// Serialize
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()>;
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()>;
|
||||
/// Deserialize
|
||||
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self>;
|
||||
|
||||
fn num_bytes(&self) -> u64 {
|
||||
let mut counter = Counter::default();
|
||||
self.serialize(&mut counter).unwrap();
|
||||
counter.0
|
||||
}
|
||||
}
|
||||
|
||||
pub trait DeserializeFrom<T: BinarySerializable> {
|
||||
@@ -34,7 +59,7 @@ pub trait FixedSize: BinarySerializable {
|
||||
}
|
||||
|
||||
impl BinarySerializable for () {
|
||||
fn serialize<W: Write>(&self, _: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, _: &mut W) -> io::Result<()> {
|
||||
Ok(())
|
||||
}
|
||||
fn deserialize<R: Read>(_: &mut R) -> io::Result<Self> {
|
||||
@@ -47,7 +72,7 @@ impl FixedSize for () {
|
||||
}
|
||||
|
||||
impl<T: BinarySerializable> BinarySerializable for Vec<T> {
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
VInt(self.len() as u64).serialize(writer)?;
|
||||
for it in self {
|
||||
it.serialize(writer)?;
|
||||
@@ -66,7 +91,7 @@ impl<T: BinarySerializable> BinarySerializable for Vec<T> {
|
||||
}
|
||||
|
||||
impl<Left: BinarySerializable, Right: BinarySerializable> BinarySerializable for (Left, Right) {
|
||||
fn serialize<W: Write>(&self, write: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, write: &mut W) -> io::Result<()> {
|
||||
self.0.serialize(write)?;
|
||||
self.1.serialize(write)
|
||||
}
|
||||
@@ -81,7 +106,7 @@ impl<Left: BinarySerializable + FixedSize, Right: BinarySerializable + FixedSize
|
||||
}
|
||||
|
||||
impl BinarySerializable for u32 {
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
writer.write_u32::<Endianness>(*self)
|
||||
}
|
||||
|
||||
@@ -95,7 +120,7 @@ impl FixedSize for u32 {
|
||||
}
|
||||
|
||||
impl BinarySerializable for u16 {
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
writer.write_u16::<Endianness>(*self)
|
||||
}
|
||||
|
||||
@@ -109,7 +134,7 @@ impl FixedSize for u16 {
|
||||
}
|
||||
|
||||
impl BinarySerializable for u64 {
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
writer.write_u64::<Endianness>(*self)
|
||||
}
|
||||
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self> {
|
||||
@@ -122,7 +147,7 @@ impl FixedSize for u64 {
|
||||
}
|
||||
|
||||
impl BinarySerializable for u128 {
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
writer.write_u128::<Endianness>(*self)
|
||||
}
|
||||
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self> {
|
||||
@@ -135,7 +160,7 @@ impl FixedSize for u128 {
|
||||
}
|
||||
|
||||
impl BinarySerializable for f32 {
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
writer.write_f32::<Endianness>(*self)
|
||||
}
|
||||
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self> {
|
||||
@@ -148,7 +173,7 @@ impl FixedSize for f32 {
|
||||
}
|
||||
|
||||
impl BinarySerializable for i64 {
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
writer.write_i64::<Endianness>(*self)
|
||||
}
|
||||
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self> {
|
||||
@@ -161,7 +186,7 @@ impl FixedSize for i64 {
|
||||
}
|
||||
|
||||
impl BinarySerializable for f64 {
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
writer.write_f64::<Endianness>(*self)
|
||||
}
|
||||
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self> {
|
||||
@@ -174,7 +199,7 @@ impl FixedSize for f64 {
|
||||
}
|
||||
|
||||
impl BinarySerializable for u8 {
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
writer.write_u8(*self)
|
||||
}
|
||||
fn deserialize<R: Read>(reader: &mut R) -> io::Result<u8> {
|
||||
@@ -187,7 +212,7 @@ impl FixedSize for u8 {
|
||||
}
|
||||
|
||||
impl BinarySerializable for bool {
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
writer.write_u8(u8::from(*self))
|
||||
}
|
||||
fn deserialize<R: Read>(reader: &mut R) -> io::Result<bool> {
|
||||
@@ -208,7 +233,7 @@ impl FixedSize for bool {
|
||||
}
|
||||
|
||||
impl BinarySerializable for String {
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
let data: &[u8] = self.as_bytes();
|
||||
VInt(data.len() as u64).serialize(writer)?;
|
||||
writer.write_all(data)
|
||||
|
||||
@@ -44,7 +44,7 @@ pub fn deserialize_vint_u128(data: &[u8]) -> io::Result<(u128, &[u8])> {
|
||||
pub struct VIntU128(pub u128);
|
||||
|
||||
impl BinarySerializable for VIntU128 {
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
let mut buffer = vec![];
|
||||
serialize_vint_u128(self.0, &mut buffer);
|
||||
writer.write_all(&buffer)
|
||||
@@ -211,7 +211,7 @@ impl VInt {
|
||||
}
|
||||
|
||||
impl BinarySerializable for VInt {
|
||||
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
|
||||
let mut buffer = [0u8; 10];
|
||||
let num_bytes = self.serialize_into(&mut buffer);
|
||||
writer.write_all(&buffer[0..num_bytes])
|
||||
|
||||
@@ -13,7 +13,7 @@ use tantivy::aggregation::agg_result::AggregationResults;
|
||||
use tantivy::aggregation::metric::AverageAggregation;
|
||||
use tantivy::aggregation::AggregationCollector;
|
||||
use tantivy::query::TermQuery;
|
||||
use tantivy::schema::{self, Cardinality, IndexRecordOption, Schema, TextFieldIndexing};
|
||||
use tantivy::schema::{self, IndexRecordOption, Schema, TextFieldIndexing};
|
||||
use tantivy::{doc, Index, Term};
|
||||
|
||||
fn main() -> tantivy::Result<()> {
|
||||
@@ -25,7 +25,7 @@ fn main() -> tantivy::Result<()> {
|
||||
.set_stored();
|
||||
let text_field = schema_builder.add_text_field("text", text_fieldtype);
|
||||
let score_fieldtype =
|
||||
crate::schema::NumericOptions::default().set_fast(Cardinality::SingleValue);
|
||||
crate::schema::NumericOptions::default().set_fast();
|
||||
let highscore_field = schema_builder.add_f64_field("highscore", score_fieldtype.clone());
|
||||
let price_field = schema_builder.add_f64_field("price", score_fieldtype);
|
||||
|
||||
@@ -4,7 +4,7 @@
|
||||
|
||||
use tantivy::collector::TopDocs;
|
||||
use tantivy::query::QueryParser;
|
||||
use tantivy::schema::{Cardinality, DateOptions, Schema, Value, INDEXED, STORED, STRING};
|
||||
use tantivy::schema::{DateOptions, Schema, Value, INDEXED, STORED, STRING};
|
||||
use tantivy::Index;
|
||||
|
||||
fn main() -> tantivy::Result<()> {
|
||||
@@ -12,7 +12,7 @@ fn main() -> tantivy::Result<()> {
|
||||
let mut schema_builder = Schema::builder();
|
||||
let opts = DateOptions::from(INDEXED)
|
||||
.set_stored()
|
||||
.set_fast(Cardinality::SingleValue)
|
||||
.set_fast()
|
||||
.set_precision(tantivy::DatePrecision::Seconds);
|
||||
let occurred_at = schema_builder.add_date_field("occurred_at", opts);
|
||||
let event_type = schema_builder.add_text_field("event", STRING | STORED);
|
||||
@@ -1,33 +0,0 @@
|
||||
[package]
|
||||
name = "fastfield_codecs"
|
||||
version = "0.3.0"
|
||||
authors = ["Pascal Seitz <pascal@quickwit.io>"]
|
||||
license = "MIT"
|
||||
edition = "2021"
|
||||
description = "Fast field codecs used by tantivy"
|
||||
documentation = "https://docs.rs/fastfield_codecs/"
|
||||
homepage = "https://github.com/quickwit-oss/tantivy"
|
||||
repository = "https://github.com/quickwit-oss/tantivy"
|
||||
|
||||
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
|
||||
|
||||
[dependencies]
|
||||
common = { version = "0.5", path = "../common/", package = "tantivy-common" }
|
||||
tantivy-bitpacker = { version= "0.3", path = "../bitpacker/" }
|
||||
prettytable-rs = {version="0.10.0", optional= true}
|
||||
rand = {version="0.8.3", optional= true}
|
||||
fastdivide = "0.4"
|
||||
log = "0.4"
|
||||
itertools = { version = "0.10.3" }
|
||||
measure_time = { version="0.8.2", optional=true}
|
||||
|
||||
[dev-dependencies]
|
||||
more-asserts = "0.3.0"
|
||||
proptest = "1.0.0"
|
||||
rand = "0.8.3"
|
||||
|
||||
[features]
|
||||
bin = ["prettytable-rs", "rand", "measure_time"]
|
||||
default = ["bin"]
|
||||
unstable = []
|
||||
|
||||
@@ -1,68 +0,0 @@
|
||||
|
||||
|
||||
# Fast Field Codecs
|
||||
|
||||
This crate contains various fast field codecs, used to compress/decompress fast field data in tantivy.
|
||||
|
||||
## Contributing
|
||||
|
||||
Contributing is pretty straightforward. Since the bitpacking is the simplest compressor, you can check it for reference.
|
||||
|
||||
A codec needs to implement 2 traits:
|
||||
|
||||
- A reader implementing `FastFieldCodecReader` to read the codec.
|
||||
- A serializer implementing `FastFieldCodecSerializer` for compression estimation and codec name + id.
|
||||
|
||||
### Tests
|
||||
|
||||
Once the traits are implemented test and benchmark integration is pretty easy (see `test_with_codec_data_sets` and `bench.rs`).
|
||||
|
||||
Make sure to add the codec to the main.rs, which tests the compression ratio and estimation against different data sets. You can run it with:
|
||||
```
|
||||
cargo run --features bin
|
||||
```
|
||||
|
||||
### TODO
|
||||
- Add real world data sets in comparison
|
||||
- Add codec to cover sparse data sets
|
||||
|
||||
|
||||
### Codec Comparison
|
||||
```
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| | Compression Ratio | Compression Estimation |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| Autoincrement | | |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| LinearInterpol | 0.000039572664 | 0.000004396963 |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| MultiLinearInterpol | 0.1477348 | 0.17275847 |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| Bitpacked | 0.28126493 | 0.28125 |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| Monotonically increasing concave | | |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| LinearInterpol | 0.25003937 | 0.26562938 |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| MultiLinearInterpol | 0.190665 | 0.1883836 |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| Bitpacked | 0.31251436 | 0.3125 |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| Monotonically increasing convex | | |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| LinearInterpol | 0.25003937 | 0.28125438 |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| MultiLinearInterpol | 0.18676 | 0.2040086 |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| Bitpacked | 0.31251436 | 0.3125 |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| Almost monotonically increasing | | |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| LinearInterpol | 0.14066513 | 0.1562544 |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| MultiLinearInterpol | 0.16335973 | 0.17275847 |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
| Bitpacked | 0.28126493 | 0.28125 |
|
||||
+----------------------------------+-------------------+------------------------+
|
||||
|
||||
```
|
||||
@@ -1,311 +0,0 @@
|
||||
#![feature(test)]
|
||||
|
||||
extern crate test;
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use std::ops::RangeInclusive;
|
||||
use std::sync::Arc;
|
||||
|
||||
use common::OwnedBytes;
|
||||
use fastfield_codecs::*;
|
||||
use rand::prelude::*;
|
||||
use test::Bencher;
|
||||
|
||||
use super::*;
|
||||
|
||||
// Warning: this generates the same permutation at each call
|
||||
fn generate_permutation() -> Vec<u64> {
|
||||
let mut permutation: Vec<u64> = (0u64..100_000u64).collect();
|
||||
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
|
||||
permutation
|
||||
}
|
||||
|
||||
fn generate_random() -> Vec<u64> {
|
||||
let mut permutation: Vec<u64> = (0u64..100_000u64)
|
||||
.map(|el| el + random::<u16>() as u64)
|
||||
.collect();
|
||||
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
|
||||
permutation
|
||||
}
|
||||
|
||||
// Warning: this generates the same permutation at each call
|
||||
fn generate_permutation_gcd() -> Vec<u64> {
|
||||
let mut permutation: Vec<u64> = (1u64..100_000u64).map(|el| el * 1000).collect();
|
||||
permutation.shuffle(&mut StdRng::from_seed([1u8; 32]));
|
||||
permutation
|
||||
}
|
||||
|
||||
pub fn serialize_and_load<T: MonotonicallyMappableToU64 + Ord + Default>(
|
||||
column: &[T],
|
||||
) -> Arc<dyn Column<T>> {
|
||||
let mut buffer = Vec::new();
|
||||
serialize(VecColumn::from(&column), &mut buffer, &ALL_CODEC_TYPES).unwrap();
|
||||
open(OwnedBytes::new(buffer)).unwrap()
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_jumpy_veclookup(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
let n = permutation.len();
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for _ in 0..n {
|
||||
a = permutation[a as usize];
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_jumpy_fflookup(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
let n = permutation.len();
|
||||
let column: Arc<dyn Column<u64>> = serialize_and_load(&permutation);
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for _ in 0..n {
|
||||
a = column.get_val(a as u32);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
const FIFTY_PERCENT_RANGE: RangeInclusive<u64> = 1..=50;
|
||||
const SINGLE_ITEM: u64 = 90;
|
||||
const SINGLE_ITEM_RANGE: RangeInclusive<u64> = 90..=90;
|
||||
const ONE_PERCENT_ITEM_RANGE: RangeInclusive<u64> = 49..=49;
|
||||
fn get_data_50percent_item() -> Vec<u128> {
|
||||
let mut rng = StdRng::from_seed([1u8; 32]);
|
||||
|
||||
let mut data = vec![];
|
||||
for _ in 0..300_000 {
|
||||
let val = rng.gen_range(1..=100);
|
||||
data.push(val);
|
||||
}
|
||||
data.push(SINGLE_ITEM);
|
||||
|
||||
data.shuffle(&mut rng);
|
||||
let data = data.iter().map(|el| *el as u128).collect::<Vec<_>>();
|
||||
data
|
||||
}
|
||||
fn get_u128_column_random() -> Arc<dyn Column<u128>> {
|
||||
let permutation = generate_random();
|
||||
let permutation = permutation.iter().map(|el| *el as u128).collect::<Vec<_>>();
|
||||
get_u128_column_from_data(&permutation)
|
||||
}
|
||||
|
||||
fn get_u128_column_from_data(data: &[u128]) -> Arc<dyn Column<u128>> {
|
||||
let mut out = vec![];
|
||||
let iter_gen = || data.iter().cloned();
|
||||
serialize_u128(iter_gen, data.len() as u32, &mut out).unwrap();
|
||||
let out = OwnedBytes::new(out);
|
||||
open_u128::<u128>(out).unwrap()
|
||||
}
|
||||
|
||||
// U64 RANGE START
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u64_50percent_hit(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
|
||||
let column: Arc<dyn Column<u64>> = serialize_and_load(&data);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(
|
||||
FIFTY_PERCENT_RANGE,
|
||||
0..data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
positions
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u64_1percent_hit(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
|
||||
let column: Arc<dyn Column<u64>> = serialize_and_load(&data);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(
|
||||
ONE_PERCENT_ITEM_RANGE,
|
||||
0..data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
positions
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u64_single_hit(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
|
||||
let column: Arc<dyn Column<u64>> = serialize_and_load(&data);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(
|
||||
SINGLE_ITEM_RANGE,
|
||||
0..data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
positions
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u64_hit_all(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let data = data.iter().map(|el| *el as u64).collect::<Vec<_>>();
|
||||
let column: Arc<dyn Column<u64>> = serialize_and_load(&data);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(0..=u64::MAX, 0..data.len() as u32, &mut positions);
|
||||
positions
|
||||
});
|
||||
}
|
||||
// U64 RANGE END
|
||||
|
||||
// U128 RANGE START
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u128_50percent_hit(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let column = get_u128_column_from_data(&data);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(
|
||||
*FIFTY_PERCENT_RANGE.start() as u128..=*FIFTY_PERCENT_RANGE.end() as u128,
|
||||
0..data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
positions
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u128_single_hit(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let column = get_u128_column_from_data(&data);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(
|
||||
*SINGLE_ITEM_RANGE.start() as u128..=*SINGLE_ITEM_RANGE.end() as u128,
|
||||
0..data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
positions
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_getrange_u128_hit_all(b: &mut Bencher) {
|
||||
let data = get_data_50percent_item();
|
||||
let column = get_u128_column_from_data(&data);
|
||||
|
||||
b.iter(|| {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(0..=u128::MAX, 0..data.len() as u32, &mut positions);
|
||||
positions
|
||||
});
|
||||
}
|
||||
// U128 RANGE END
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_scan_all_fflookup_u128(b: &mut Bencher) {
|
||||
let column = get_u128_column_random();
|
||||
|
||||
b.iter(|| {
|
||||
let mut a = 0u128;
|
||||
for i in 0u64..column.num_vals() as u64 {
|
||||
a += column.get_val(i as u32);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_jumpy_stride5_u128(b: &mut Bencher) {
|
||||
let column = get_u128_column_random();
|
||||
|
||||
b.iter(|| {
|
||||
let n = column.num_vals();
|
||||
let mut a = 0u128;
|
||||
for i in (0..n / 5).map(|val| val * 5) {
|
||||
a += column.get_val(i);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_stride7_vec(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
let n = permutation.len();
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for i in (0..n / 7).map(|val| val * 7) {
|
||||
a += permutation[i as usize];
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_stride7_fflookup(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
let n = permutation.len();
|
||||
let column: Arc<dyn Column<u64>> = serialize_and_load(&permutation);
|
||||
b.iter(|| {
|
||||
let mut a = 0;
|
||||
for i in (0..n / 7).map(|val| val * 7) {
|
||||
a += column.get_val(i as u32);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_scan_all_fflookup(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
let n = permutation.len();
|
||||
let column: Arc<dyn Column<u64>> = serialize_and_load(&permutation);
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for i in 0u32..n as u32 {
|
||||
a += column.get_val(i);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_scan_all_fflookup_gcd(b: &mut Bencher) {
|
||||
let permutation = generate_permutation_gcd();
|
||||
let n = permutation.len();
|
||||
let column: Arc<dyn Column<u64>> = serialize_and_load(&permutation);
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for i in 0..n {
|
||||
a += column.get_val(i as u32);
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_intfastfield_scan_all_vec(b: &mut Bencher) {
|
||||
let permutation = generate_permutation();
|
||||
b.iter(|| {
|
||||
let mut a = 0u64;
|
||||
for i in 0..permutation.len() {
|
||||
a += permutation[i as usize] as u64;
|
||||
}
|
||||
a
|
||||
});
|
||||
}
|
||||
}
|
||||
@@ -1,116 +0,0 @@
|
||||
use std::io::{self, Write};
|
||||
|
||||
use common::OwnedBytes;
|
||||
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
|
||||
|
||||
use crate::serialize::NormalizedHeader;
|
||||
use crate::{Column, FastFieldCodec, FastFieldCodecType};
|
||||
|
||||
/// Depending on the field type, a different
|
||||
/// fast field is required.
|
||||
#[derive(Clone)]
|
||||
pub struct BitpackedReader {
|
||||
data: OwnedBytes,
|
||||
bit_unpacker: BitUnpacker,
|
||||
normalized_header: NormalizedHeader,
|
||||
}
|
||||
|
||||
impl Column for BitpackedReader {
|
||||
#[inline]
|
||||
fn get_val(&self, doc: u32) -> u64 {
|
||||
self.bit_unpacker.get(doc, &self.data)
|
||||
}
|
||||
#[inline]
|
||||
fn min_value(&self) -> u64 {
|
||||
// The BitpackedReader assumes a normalized vector.
|
||||
0
|
||||
}
|
||||
#[inline]
|
||||
fn max_value(&self) -> u64 {
|
||||
self.normalized_header.max_value
|
||||
}
|
||||
#[inline]
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.normalized_header.num_vals
|
||||
}
|
||||
}
|
||||
|
||||
pub struct BitpackedCodec;
|
||||
|
||||
impl FastFieldCodec for BitpackedCodec {
|
||||
/// The CODEC_TYPE is an enum value used for serialization.
|
||||
const CODEC_TYPE: FastFieldCodecType = FastFieldCodecType::Bitpacked;
|
||||
|
||||
type Reader = BitpackedReader;
|
||||
|
||||
/// Opens a fast field given a file.
|
||||
fn open_from_bytes(
|
||||
data: OwnedBytes,
|
||||
normalized_header: NormalizedHeader,
|
||||
) -> io::Result<Self::Reader> {
|
||||
let num_bits = compute_num_bits(normalized_header.max_value);
|
||||
let bit_unpacker = BitUnpacker::new(num_bits);
|
||||
Ok(BitpackedReader {
|
||||
data,
|
||||
bit_unpacker,
|
||||
normalized_header,
|
||||
})
|
||||
}
|
||||
|
||||
/// Serializes data with the BitpackedFastFieldSerializer.
|
||||
///
|
||||
/// The bitpacker assumes that the column has been normalized.
|
||||
/// i.e. It has already been shifted by its minimum value, so that its
|
||||
/// current minimum value is 0.
|
||||
///
|
||||
/// Ideally, we made a shift upstream on the column so that `col.min_value() == 0`.
|
||||
fn serialize(column: &dyn Column, write: &mut impl Write) -> io::Result<()> {
|
||||
assert_eq!(column.min_value(), 0u64);
|
||||
let num_bits = compute_num_bits(column.max_value());
|
||||
let mut bit_packer = BitPacker::new();
|
||||
for val in column.iter() {
|
||||
bit_packer.write(val, num_bits, write)?;
|
||||
}
|
||||
bit_packer.close(write)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn estimate(column: &dyn Column) -> Option<f32> {
|
||||
let num_bits = compute_num_bits(column.max_value());
|
||||
let num_bits_uncompressed = 64;
|
||||
Some(num_bits as f32 / num_bits_uncompressed as f32)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::tests::get_codec_test_datasets;
|
||||
|
||||
fn create_and_validate(data: &[u64], name: &str) {
|
||||
crate::tests::create_and_validate::<BitpackedCodec>(data, name);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_with_codec_data_sets() {
|
||||
let data_sets = get_codec_test_datasets();
|
||||
for (mut data, name) in data_sets {
|
||||
create_and_validate(&data, name);
|
||||
data.reverse();
|
||||
create_and_validate(&data, name);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn bitpacked_fast_field_rand() {
|
||||
for _ in 0..500 {
|
||||
let mut data = (0..1 + rand::random::<u8>() as usize)
|
||||
.map(|_| rand::random::<i64>() as u64 / 2)
|
||||
.collect::<Vec<_>>();
|
||||
create_and_validate(&data, "rand");
|
||||
|
||||
data.reverse();
|
||||
create_and_validate(&data, "rand");
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,188 +0,0 @@
|
||||
use std::sync::Arc;
|
||||
use std::{io, iter};
|
||||
|
||||
use common::{BinarySerializable, CountingWriter, DeserializeFrom, OwnedBytes};
|
||||
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
|
||||
|
||||
use crate::line::Line;
|
||||
use crate::serialize::NormalizedHeader;
|
||||
use crate::{Column, FastFieldCodec, FastFieldCodecType, VecColumn};
|
||||
|
||||
const CHUNK_SIZE: usize = 512;
|
||||
|
||||
#[derive(Debug, Default)]
|
||||
struct Block {
|
||||
line: Line,
|
||||
bit_unpacker: BitUnpacker,
|
||||
data_start_offset: usize,
|
||||
}
|
||||
|
||||
impl BinarySerializable for Block {
|
||||
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
self.line.serialize(writer)?;
|
||||
self.bit_unpacker.bit_width().serialize(writer)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let line = Line::deserialize(reader)?;
|
||||
let bit_width = u8::deserialize(reader)?;
|
||||
Ok(Block {
|
||||
line,
|
||||
bit_unpacker: BitUnpacker::new(bit_width),
|
||||
data_start_offset: 0,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
fn compute_num_blocks(num_vals: u32) -> usize {
|
||||
(num_vals as usize + CHUNK_SIZE - 1) / CHUNK_SIZE
|
||||
}
|
||||
|
||||
pub struct BlockwiseLinearCodec;
|
||||
|
||||
impl FastFieldCodec for BlockwiseLinearCodec {
|
||||
const CODEC_TYPE: crate::FastFieldCodecType = FastFieldCodecType::BlockwiseLinear;
|
||||
type Reader = BlockwiseLinearReader;
|
||||
|
||||
fn open_from_bytes(
|
||||
bytes: common::OwnedBytes,
|
||||
normalized_header: NormalizedHeader,
|
||||
) -> io::Result<Self::Reader> {
|
||||
let footer_len: u32 = (&bytes[bytes.len() - 4..]).deserialize()?;
|
||||
let footer_offset = bytes.len() - 4 - footer_len as usize;
|
||||
let (data, mut footer) = bytes.split(footer_offset);
|
||||
let num_blocks = compute_num_blocks(normalized_header.num_vals);
|
||||
let mut blocks: Vec<Block> = iter::repeat_with(|| Block::deserialize(&mut footer))
|
||||
.take(num_blocks)
|
||||
.collect::<io::Result<_>>()?;
|
||||
|
||||
let mut start_offset = 0;
|
||||
for block in &mut blocks {
|
||||
block.data_start_offset = start_offset;
|
||||
start_offset += (block.bit_unpacker.bit_width() as usize) * CHUNK_SIZE / 8;
|
||||
}
|
||||
Ok(BlockwiseLinearReader {
|
||||
blocks: Arc::new(blocks),
|
||||
data,
|
||||
normalized_header,
|
||||
})
|
||||
}
|
||||
|
||||
// Estimate first_chunk and extrapolate
|
||||
fn estimate(column: &dyn crate::Column) -> Option<f32> {
|
||||
if column.num_vals() < 10 * CHUNK_SIZE as u32 {
|
||||
return None;
|
||||
}
|
||||
let mut first_chunk: Vec<u64> = column.iter().take(CHUNK_SIZE).collect();
|
||||
let line = Line::train(&VecColumn::from(&first_chunk));
|
||||
for (i, buffer_val) in first_chunk.iter_mut().enumerate() {
|
||||
let interpolated_val = line.eval(i as u32);
|
||||
*buffer_val = buffer_val.wrapping_sub(interpolated_val);
|
||||
}
|
||||
let estimated_bit_width = first_chunk
|
||||
.iter()
|
||||
.map(|el| ((el + 1) as f32 * 3.0) as u64)
|
||||
.map(compute_num_bits)
|
||||
.max()
|
||||
.unwrap();
|
||||
|
||||
let metadata_per_block = {
|
||||
let mut out = vec![];
|
||||
Block::default().serialize(&mut out).unwrap();
|
||||
out.len()
|
||||
};
|
||||
let num_bits = estimated_bit_width as u64 * column.num_vals() as u64
|
||||
// function metadata per block
|
||||
+ metadata_per_block as u64 * (column.num_vals() as u64 / CHUNK_SIZE as u64);
|
||||
let num_bits_uncompressed = 64 * column.num_vals();
|
||||
Some(num_bits as f32 / num_bits_uncompressed as f32)
|
||||
}
|
||||
|
||||
fn serialize(column: &dyn Column, wrt: &mut impl io::Write) -> io::Result<()> {
|
||||
// The BitpackedReader assumes a normalized vector.
|
||||
assert_eq!(column.min_value(), 0);
|
||||
let mut buffer = Vec::with_capacity(CHUNK_SIZE);
|
||||
let num_vals = column.num_vals();
|
||||
|
||||
let num_blocks = compute_num_blocks(num_vals);
|
||||
let mut blocks = Vec::with_capacity(num_blocks);
|
||||
|
||||
let mut vals = column.iter();
|
||||
|
||||
let mut bit_packer = BitPacker::new();
|
||||
|
||||
for _ in 0..num_blocks {
|
||||
buffer.clear();
|
||||
buffer.extend((&mut vals).take(CHUNK_SIZE));
|
||||
let line = Line::train(&VecColumn::from(&buffer));
|
||||
|
||||
assert!(!buffer.is_empty());
|
||||
|
||||
for (i, buffer_val) in buffer.iter_mut().enumerate() {
|
||||
let interpolated_val = line.eval(i as u32);
|
||||
*buffer_val = buffer_val.wrapping_sub(interpolated_val);
|
||||
}
|
||||
let bit_width = buffer.iter().copied().map(compute_num_bits).max().unwrap();
|
||||
|
||||
for &buffer_val in &buffer {
|
||||
bit_packer.write(buffer_val, bit_width, wrt)?;
|
||||
}
|
||||
|
||||
blocks.push(Block {
|
||||
line,
|
||||
bit_unpacker: BitUnpacker::new(bit_width),
|
||||
data_start_offset: 0,
|
||||
});
|
||||
}
|
||||
|
||||
bit_packer.close(wrt)?;
|
||||
|
||||
assert_eq!(blocks.len(), compute_num_blocks(num_vals));
|
||||
|
||||
let mut counting_wrt = CountingWriter::wrap(wrt);
|
||||
for block in &blocks {
|
||||
block.serialize(&mut counting_wrt)?;
|
||||
}
|
||||
let footer_len = counting_wrt.written_bytes();
|
||||
(footer_len as u32).serialize(&mut counting_wrt)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Clone)]
|
||||
pub struct BlockwiseLinearReader {
|
||||
blocks: Arc<Vec<Block>>,
|
||||
normalized_header: NormalizedHeader,
|
||||
data: OwnedBytes,
|
||||
}
|
||||
|
||||
impl Column for BlockwiseLinearReader {
|
||||
#[inline(always)]
|
||||
fn get_val(&self, idx: u32) -> u64 {
|
||||
let block_id = (idx / CHUNK_SIZE as u32) as usize;
|
||||
let idx_within_block = idx % (CHUNK_SIZE as u32);
|
||||
let block = &self.blocks[block_id];
|
||||
let interpoled_val: u64 = block.line.eval(idx_within_block);
|
||||
let block_bytes = &self.data[block.data_start_offset..];
|
||||
let bitpacked_diff = block.bit_unpacker.get(idx_within_block, block_bytes);
|
||||
interpoled_val.wrapping_add(bitpacked_diff)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn min_value(&self) -> u64 {
|
||||
// The BlockwiseLinearReader assumes a normalized vector.
|
||||
0u64
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn max_value(&self) -> u64 {
|
||||
self.normalized_header.max_value
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.normalized_header.num_vals
|
||||
}
|
||||
}
|
||||
@@ -1,352 +0,0 @@
|
||||
use std::fmt::{self, Debug};
|
||||
use std::marker::PhantomData;
|
||||
use std::ops::{Range, RangeInclusive};
|
||||
|
||||
use tantivy_bitpacker::minmax;
|
||||
|
||||
use crate::monotonic_mapping::StrictlyMonotonicFn;
|
||||
|
||||
/// `Column` provides columnar access on a field.
|
||||
pub trait Column<T: PartialOrd + Debug = u64>: Send + Sync {
|
||||
/// Return the value associated with the given idx.
|
||||
///
|
||||
/// This accessor should return as fast as possible.
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// May panic if `idx` is greater than the column length.
|
||||
fn get_val(&self, idx: u32) -> T;
|
||||
|
||||
/// Fills an output buffer with the fast field values
|
||||
/// associated with the `DocId` going from
|
||||
/// `start` to `start + output.len()`.
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// Must panic if `start + output.len()` is greater than
|
||||
/// the segment's `maxdoc`.
|
||||
#[inline]
|
||||
fn get_range(&self, start: u64, output: &mut [T]) {
|
||||
for (out, idx) in output.iter_mut().zip(start..) {
|
||||
*out = self.get_val(idx as u32);
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the positions of values which are in the provided value range.
|
||||
///
|
||||
/// Note that position == docid for single value fast fields
|
||||
#[inline]
|
||||
fn get_docids_for_value_range(
|
||||
&self,
|
||||
value_range: RangeInclusive<T>,
|
||||
doc_id_range: Range<u32>,
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
let doc_id_range = doc_id_range.start..doc_id_range.end.min(self.num_vals());
|
||||
|
||||
for idx in doc_id_range.start..doc_id_range.end {
|
||||
let val = self.get_val(idx);
|
||||
if value_range.contains(&val) {
|
||||
positions.push(idx);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the minimum value for this fast field.
|
||||
///
|
||||
/// This min_value may not be exact.
|
||||
/// For instance, the min value does not take in account of possible
|
||||
/// deleted document. All values are however guaranteed to be higher than
|
||||
/// `.min_value()`.
|
||||
fn min_value(&self) -> T;
|
||||
|
||||
/// Returns the maximum value for this fast field.
|
||||
///
|
||||
/// This max_value may not be exact.
|
||||
/// For instance, the max value does not take in account of possible
|
||||
/// deleted document. All values are however guaranteed to be higher than
|
||||
/// `.max_value()`.
|
||||
fn max_value(&self) -> T;
|
||||
|
||||
/// The number of values in the column.
|
||||
fn num_vals(&self) -> u32;
|
||||
|
||||
/// Returns a iterator over the data
|
||||
fn iter<'a>(&'a self) -> Box<dyn Iterator<Item = T> + 'a> {
|
||||
Box::new((0..self.num_vals()).map(|idx| self.get_val(idx)))
|
||||
}
|
||||
}
|
||||
|
||||
/// VecColumn provides `Column` over a slice.
|
||||
pub struct VecColumn<'a, T = u64> {
|
||||
values: &'a [T],
|
||||
min_value: T,
|
||||
max_value: T,
|
||||
}
|
||||
|
||||
impl<'a, C: Column<T>, T: Copy + PartialOrd + fmt::Debug> Column<T> for &'a C {
|
||||
fn get_val(&self, idx: u32) -> T {
|
||||
(*self).get_val(idx)
|
||||
}
|
||||
|
||||
fn min_value(&self) -> T {
|
||||
(*self).min_value()
|
||||
}
|
||||
|
||||
fn max_value(&self) -> T {
|
||||
(*self).max_value()
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
(*self).num_vals()
|
||||
}
|
||||
|
||||
fn iter<'b>(&'b self) -> Box<dyn Iterator<Item = T> + 'b> {
|
||||
(*self).iter()
|
||||
}
|
||||
|
||||
fn get_range(&self, start: u64, output: &mut [T]) {
|
||||
(*self).get_range(start, output)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a, T: Copy + PartialOrd + Send + Sync + Debug> Column<T> for VecColumn<'a, T> {
|
||||
fn get_val(&self, position: u32) -> T {
|
||||
self.values[position as usize]
|
||||
}
|
||||
|
||||
fn iter(&self) -> Box<dyn Iterator<Item = T> + '_> {
|
||||
Box::new(self.values.iter().copied())
|
||||
}
|
||||
|
||||
fn min_value(&self) -> T {
|
||||
self.min_value
|
||||
}
|
||||
|
||||
fn max_value(&self) -> T {
|
||||
self.max_value
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.values.len() as u32
|
||||
}
|
||||
|
||||
fn get_range(&self, start: u64, output: &mut [T]) {
|
||||
output.copy_from_slice(&self.values[start as usize..][..output.len()])
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a, T: Copy + PartialOrd + Default, V> From<&'a V> for VecColumn<'a, T>
|
||||
where V: AsRef<[T]> + ?Sized
|
||||
{
|
||||
fn from(values: &'a V) -> Self {
|
||||
let values = values.as_ref();
|
||||
let (min_value, max_value) = minmax(values.iter().copied()).unwrap_or_default();
|
||||
Self {
|
||||
values,
|
||||
min_value,
|
||||
max_value,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
struct MonotonicMappingColumn<C, T, Input> {
|
||||
from_column: C,
|
||||
monotonic_mapping: T,
|
||||
_phantom: PhantomData<Input>,
|
||||
}
|
||||
|
||||
/// Creates a view of a column transformed by a strictly monotonic mapping. See
|
||||
/// [`StrictlyMonotonicFn`].
|
||||
///
|
||||
/// E.g. apply a gcd monotonic_mapping([100, 200, 300]) == [1, 2, 3]
|
||||
/// monotonic_mapping.mapping() is expected to be injective, and we should always have
|
||||
/// monotonic_mapping.inverse(monotonic_mapping.mapping(el)) == el
|
||||
///
|
||||
/// The inverse of the mapping is required for:
|
||||
/// `fn get_positions_for_value_range(&self, range: RangeInclusive<T>) -> Vec<u64> `
|
||||
/// The user provides the original value range and we need to monotonic map them in the same way the
|
||||
/// serialization does before calling the underlying column.
|
||||
///
|
||||
/// Note that when opening a codec, the monotonic_mapping should be the inverse of the mapping
|
||||
/// during serialization. And therefore the monotonic_mapping_inv when opening is the same as
|
||||
/// monotonic_mapping during serialization.
|
||||
pub fn monotonic_map_column<C, T, Input, Output>(
|
||||
from_column: C,
|
||||
monotonic_mapping: T,
|
||||
) -> impl Column<Output>
|
||||
where
|
||||
C: Column<Input>,
|
||||
T: StrictlyMonotonicFn<Input, Output> + Send + Sync,
|
||||
Input: PartialOrd + Send + Sync + Copy + Debug,
|
||||
Output: PartialOrd + Send + Sync + Copy + Debug,
|
||||
{
|
||||
MonotonicMappingColumn {
|
||||
from_column,
|
||||
monotonic_mapping,
|
||||
_phantom: PhantomData,
|
||||
}
|
||||
}
|
||||
|
||||
impl<C, T, Input, Output> Column<Output> for MonotonicMappingColumn<C, T, Input>
|
||||
where
|
||||
C: Column<Input>,
|
||||
T: StrictlyMonotonicFn<Input, Output> + Send + Sync,
|
||||
Input: PartialOrd + Send + Sync + Copy + Debug,
|
||||
Output: PartialOrd + Send + Sync + Copy + Debug,
|
||||
{
|
||||
#[inline]
|
||||
fn get_val(&self, idx: u32) -> Output {
|
||||
let from_val = self.from_column.get_val(idx);
|
||||
self.monotonic_mapping.mapping(from_val)
|
||||
}
|
||||
|
||||
fn min_value(&self) -> Output {
|
||||
let from_min_value = self.from_column.min_value();
|
||||
self.monotonic_mapping.mapping(from_min_value)
|
||||
}
|
||||
|
||||
fn max_value(&self) -> Output {
|
||||
let from_max_value = self.from_column.max_value();
|
||||
self.monotonic_mapping.mapping(from_max_value)
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.from_column.num_vals()
|
||||
}
|
||||
|
||||
fn iter(&self) -> Box<dyn Iterator<Item = Output> + '_> {
|
||||
Box::new(
|
||||
self.from_column
|
||||
.iter()
|
||||
.map(|el| self.monotonic_mapping.mapping(el)),
|
||||
)
|
||||
}
|
||||
|
||||
fn get_docids_for_value_range(
|
||||
&self,
|
||||
range: RangeInclusive<Output>,
|
||||
doc_id_range: Range<u32>,
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
if range.start() > &self.max_value() || range.end() < &self.min_value() {
|
||||
return;
|
||||
}
|
||||
let range = self.monotonic_mapping.inverse_coerce(range);
|
||||
if range.start() > range.end() {
|
||||
return;
|
||||
}
|
||||
self.from_column
|
||||
.get_docids_for_value_range(range, doc_id_range, positions)
|
||||
}
|
||||
|
||||
// We voluntarily do not implement get_range as it yields a regression,
|
||||
// and we do not have any specialized implementation anyway.
|
||||
}
|
||||
|
||||
/// Wraps an iterator into a `Column`.
|
||||
pub struct IterColumn<T>(T);
|
||||
|
||||
impl<T> From<T> for IterColumn<T>
|
||||
where T: Iterator + Clone + ExactSizeIterator
|
||||
{
|
||||
fn from(iter: T) -> Self {
|
||||
IterColumn(iter)
|
||||
}
|
||||
}
|
||||
|
||||
impl<T> Column<T::Item> for IterColumn<T>
|
||||
where
|
||||
T: Iterator + Clone + ExactSizeIterator + Send + Sync,
|
||||
T::Item: PartialOrd + fmt::Debug,
|
||||
{
|
||||
fn get_val(&self, idx: u32) -> T::Item {
|
||||
self.0.clone().nth(idx as usize).unwrap()
|
||||
}
|
||||
|
||||
fn min_value(&self) -> T::Item {
|
||||
self.0.clone().next().unwrap()
|
||||
}
|
||||
|
||||
fn max_value(&self) -> T::Item {
|
||||
self.0.clone().last().unwrap()
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.0.len() as u32
|
||||
}
|
||||
|
||||
fn iter(&self) -> Box<dyn Iterator<Item = T::Item> + '_> {
|
||||
Box::new(self.0.clone())
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::monotonic_mapping::{
|
||||
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternalBaseval,
|
||||
StrictlyMonotonicMappingToInternalGCDBaseval,
|
||||
};
|
||||
|
||||
#[test]
|
||||
fn test_monotonic_mapping() {
|
||||
let vals = &[3u64, 5u64][..];
|
||||
let col = VecColumn::from(vals);
|
||||
let mapped = monotonic_map_column(col, StrictlyMonotonicMappingToInternalBaseval::new(2));
|
||||
assert_eq!(mapped.min_value(), 1u64);
|
||||
assert_eq!(mapped.max_value(), 3u64);
|
||||
assert_eq!(mapped.num_vals(), 2);
|
||||
assert_eq!(mapped.num_vals(), 2);
|
||||
assert_eq!(mapped.get_val(0), 1);
|
||||
assert_eq!(mapped.get_val(1), 3);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_range_as_col() {
|
||||
let col = IterColumn::from(10..100);
|
||||
assert_eq!(col.num_vals(), 90);
|
||||
assert_eq!(col.max_value(), 99);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_monotonic_mapping_iter() {
|
||||
let vals: Vec<u64> = (10..110u64).map(|el| el * 10).collect();
|
||||
let col = VecColumn::from(&vals);
|
||||
let mapped = monotonic_map_column(
|
||||
col,
|
||||
StrictlyMonotonicMappingInverter::from(
|
||||
StrictlyMonotonicMappingToInternalGCDBaseval::new(10, 100),
|
||||
),
|
||||
);
|
||||
let val_i64s: Vec<u64> = mapped.iter().collect();
|
||||
for i in 0..100 {
|
||||
assert_eq!(val_i64s[i as usize], mapped.get_val(i));
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_monotonic_mapping_get_range() {
|
||||
let vals: Vec<u64> = (0..100u64).map(|el| el * 10).collect();
|
||||
let col = VecColumn::from(&vals);
|
||||
let mapped = monotonic_map_column(
|
||||
col,
|
||||
StrictlyMonotonicMappingInverter::from(
|
||||
StrictlyMonotonicMappingToInternalGCDBaseval::new(10, 0),
|
||||
),
|
||||
);
|
||||
|
||||
assert_eq!(mapped.min_value(), 0u64);
|
||||
assert_eq!(mapped.max_value(), 9900u64);
|
||||
assert_eq!(mapped.num_vals(), 100);
|
||||
let val_u64s: Vec<u64> = mapped.iter().collect();
|
||||
assert_eq!(val_u64s.len(), 100);
|
||||
for i in 0..100 {
|
||||
assert_eq!(val_u64s[i as usize], mapped.get_val(i));
|
||||
assert_eq!(val_u64s[i as usize], vals[i as usize] * 10);
|
||||
}
|
||||
let mut buf = [0u64; 20];
|
||||
mapped.get_range(7, &mut buf[..]);
|
||||
assert_eq!(&val_u64s[7..][..20], &buf);
|
||||
}
|
||||
}
|
||||
@@ -1,43 +0,0 @@
|
||||
use std::ops::RangeInclusive;
|
||||
|
||||
/// The range of a blank in value space.
|
||||
///
|
||||
/// A blank is an unoccupied space in the data.
|
||||
/// Use try_into() to construct.
|
||||
/// A range has to have at least length of 3. Invalid ranges will be rejected.
|
||||
///
|
||||
/// Ordered by range length.
|
||||
#[derive(Debug, Eq, PartialEq, Clone)]
|
||||
pub(crate) struct BlankRange {
|
||||
blank_range: RangeInclusive<u128>,
|
||||
}
|
||||
impl TryFrom<RangeInclusive<u128>> for BlankRange {
|
||||
type Error = &'static str;
|
||||
fn try_from(range: RangeInclusive<u128>) -> Result<Self, Self::Error> {
|
||||
let blank_size = range.end().saturating_sub(*range.start());
|
||||
if blank_size < 2 {
|
||||
Err("invalid range")
|
||||
} else {
|
||||
Ok(BlankRange { blank_range: range })
|
||||
}
|
||||
}
|
||||
}
|
||||
impl BlankRange {
|
||||
pub(crate) fn blank_size(&self) -> u128 {
|
||||
self.blank_range.end() - self.blank_range.start() + 1
|
||||
}
|
||||
pub(crate) fn blank_range(&self) -> RangeInclusive<u128> {
|
||||
self.blank_range.clone()
|
||||
}
|
||||
}
|
||||
|
||||
impl Ord for BlankRange {
|
||||
fn cmp(&self, other: &Self) -> std::cmp::Ordering {
|
||||
self.blank_size().cmp(&other.blank_size())
|
||||
}
|
||||
}
|
||||
impl PartialOrd for BlankRange {
|
||||
fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
|
||||
Some(self.blank_size().cmp(&other.blank_size()))
|
||||
}
|
||||
}
|
||||
@@ -1,231 +0,0 @@
|
||||
use std::collections::{BTreeSet, BinaryHeap};
|
||||
use std::iter;
|
||||
use std::ops::RangeInclusive;
|
||||
|
||||
use itertools::Itertools;
|
||||
|
||||
use super::blank_range::BlankRange;
|
||||
use super::{CompactSpace, RangeMapping};
|
||||
|
||||
/// Put the blanks for the sorted values into a binary heap
|
||||
fn get_blanks(values_sorted: &BTreeSet<u128>) -> BinaryHeap<BlankRange> {
|
||||
let mut blanks: BinaryHeap<BlankRange> = BinaryHeap::new();
|
||||
for (first, second) in values_sorted.iter().tuple_windows() {
|
||||
// Correctness Overflow: the values are deduped and sorted (BTreeSet property), that means
|
||||
// there's always space between two values.
|
||||
let blank_range = first + 1..=second - 1;
|
||||
let blank_range: Result<BlankRange, _> = blank_range.try_into();
|
||||
if let Ok(blank_range) = blank_range {
|
||||
blanks.push(blank_range);
|
||||
}
|
||||
}
|
||||
|
||||
blanks
|
||||
}
|
||||
|
||||
struct BlankCollector {
|
||||
blanks: Vec<BlankRange>,
|
||||
staged_blanks_sum: u128,
|
||||
}
|
||||
impl BlankCollector {
|
||||
fn new() -> Self {
|
||||
Self {
|
||||
blanks: vec![],
|
||||
staged_blanks_sum: 0,
|
||||
}
|
||||
}
|
||||
fn stage_blank(&mut self, blank: BlankRange) {
|
||||
self.staged_blanks_sum += blank.blank_size();
|
||||
self.blanks.push(blank);
|
||||
}
|
||||
fn drain(&mut self) -> impl Iterator<Item = BlankRange> + '_ {
|
||||
self.staged_blanks_sum = 0;
|
||||
self.blanks.drain(..)
|
||||
}
|
||||
fn staged_blanks_sum(&self) -> u128 {
|
||||
self.staged_blanks_sum
|
||||
}
|
||||
fn num_staged_blanks(&self) -> usize {
|
||||
self.blanks.len()
|
||||
}
|
||||
}
|
||||
fn num_bits(val: u128) -> u8 {
|
||||
(128u32 - val.leading_zeros()) as u8
|
||||
}
|
||||
|
||||
/// Will collect blanks and add them to compact space if more bits are saved than cost from
|
||||
/// metadata.
|
||||
pub fn get_compact_space(
|
||||
values_deduped_sorted: &BTreeSet<u128>,
|
||||
total_num_values: u32,
|
||||
cost_per_blank: usize,
|
||||
) -> CompactSpace {
|
||||
let mut compact_space_builder = CompactSpaceBuilder::new();
|
||||
if values_deduped_sorted.is_empty() {
|
||||
return compact_space_builder.finish();
|
||||
}
|
||||
|
||||
let mut blanks: BinaryHeap<BlankRange> = get_blanks(values_deduped_sorted);
|
||||
// Replace after stabilization of https://github.com/rust-lang/rust/issues/62924
|
||||
|
||||
// We start by space that's limited to min_value..=max_value
|
||||
let min_value = *values_deduped_sorted.iter().next().unwrap_or(&0);
|
||||
let max_value = *values_deduped_sorted.iter().last().unwrap_or(&0);
|
||||
|
||||
// +1 for null, in case min and max covers the whole space, we are off by one.
|
||||
let mut amplitude_compact_space = (max_value - min_value).saturating_add(1);
|
||||
if min_value != 0 {
|
||||
compact_space_builder.add_blanks(iter::once(0..=min_value - 1));
|
||||
}
|
||||
if max_value != u128::MAX {
|
||||
compact_space_builder.add_blanks(iter::once(max_value + 1..=u128::MAX));
|
||||
}
|
||||
|
||||
let mut amplitude_bits: u8 = num_bits(amplitude_compact_space);
|
||||
|
||||
let mut blank_collector = BlankCollector::new();
|
||||
// We will stage blanks until they reduce the compact space by at least 1 bit and then flush
|
||||
// them if the metadata cost is lower than the total number of saved bits.
|
||||
// Binary heap to process the gaps by their size
|
||||
while let Some(blank_range) = blanks.pop() {
|
||||
blank_collector.stage_blank(blank_range);
|
||||
|
||||
let staged_spaces_sum: u128 = blank_collector.staged_blanks_sum();
|
||||
let amplitude_new_compact_space = amplitude_compact_space - staged_spaces_sum;
|
||||
let amplitude_new_bits = num_bits(amplitude_new_compact_space);
|
||||
if amplitude_bits == amplitude_new_bits {
|
||||
continue;
|
||||
}
|
||||
let saved_bits = (amplitude_bits - amplitude_new_bits) as usize * total_num_values as usize;
|
||||
// TODO: Maybe calculate exact cost of blanks and run this more expensive computation only,
|
||||
// when amplitude_new_bits changes
|
||||
let cost = blank_collector.num_staged_blanks() * cost_per_blank;
|
||||
if cost >= saved_bits {
|
||||
// Continue here, since although we walk over the blanks by size,
|
||||
// we can potentially save a lot at the last bits, which are smaller blanks
|
||||
//
|
||||
// E.g. if the first range reduces the compact space by 1000 from 2000 to 1000, which
|
||||
// saves 11-10=1 bit and the next range reduces the compact space by 950 to
|
||||
// 50, which saves 10-6=4 bit
|
||||
continue;
|
||||
}
|
||||
|
||||
amplitude_compact_space = amplitude_new_compact_space;
|
||||
amplitude_bits = amplitude_new_bits;
|
||||
compact_space_builder.add_blanks(blank_collector.drain().map(|blank| blank.blank_range()));
|
||||
}
|
||||
|
||||
// special case, when we don't collected any blanks because:
|
||||
// * the data is empty (early exit)
|
||||
// * the algorithm did decide it's not worth the cost, which can be the case for single values
|
||||
//
|
||||
// We drain one collected blank unconditionally, so the empty case is reserved for empty
|
||||
// data, and therefore empty compact_space means the data is empty and no data is covered
|
||||
// (conversely to all data) and we can assign null to it.
|
||||
if compact_space_builder.is_empty() {
|
||||
compact_space_builder.add_blanks(
|
||||
blank_collector
|
||||
.drain()
|
||||
.map(|blank| blank.blank_range())
|
||||
.take(1),
|
||||
);
|
||||
}
|
||||
|
||||
let compact_space = compact_space_builder.finish();
|
||||
if max_value - min_value != u128::MAX {
|
||||
debug_assert_eq!(
|
||||
compact_space.amplitude_compact_space(),
|
||||
amplitude_compact_space
|
||||
);
|
||||
}
|
||||
compact_space
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Eq, PartialEq)]
|
||||
struct CompactSpaceBuilder {
|
||||
blanks: Vec<RangeInclusive<u128>>,
|
||||
}
|
||||
|
||||
impl CompactSpaceBuilder {
|
||||
/// Creates a new compact space builder which will initially cover the whole space.
|
||||
fn new() -> Self {
|
||||
Self { blanks: Vec::new() }
|
||||
}
|
||||
|
||||
/// Assumes that repeated add_blank calls don't overlap and are not adjacent,
|
||||
/// e.g. [3..=5, 5..=10] is not allowed
|
||||
///
|
||||
/// Both of those assumptions are true when blanks are produced from sorted values.
|
||||
fn add_blanks(&mut self, blank: impl Iterator<Item = RangeInclusive<u128>>) {
|
||||
self.blanks.extend(blank);
|
||||
}
|
||||
|
||||
fn is_empty(&self) -> bool {
|
||||
self.blanks.is_empty()
|
||||
}
|
||||
|
||||
/// Convert blanks to covered space and assign null value
|
||||
fn finish(mut self) -> CompactSpace {
|
||||
// sort by start. ranges are not allowed to overlap
|
||||
self.blanks.sort_unstable_by_key(|blank| *blank.start());
|
||||
|
||||
let mut covered_space = Vec::with_capacity(self.blanks.len());
|
||||
|
||||
// begining of the blanks
|
||||
if let Some(first_blank_start) = self.blanks.first().map(RangeInclusive::start) {
|
||||
if *first_blank_start != 0 {
|
||||
covered_space.push(0..=first_blank_start - 1);
|
||||
}
|
||||
}
|
||||
|
||||
// Between the blanks
|
||||
let between_blanks = self.blanks.iter().tuple_windows().map(|(left, right)| {
|
||||
assert!(
|
||||
left.end() < right.start(),
|
||||
"overlapping or adjacent ranges detected"
|
||||
);
|
||||
*left.end() + 1..=*right.start() - 1
|
||||
});
|
||||
covered_space.extend(between_blanks);
|
||||
|
||||
// end of the blanks
|
||||
if let Some(last_blank_end) = self.blanks.last().map(RangeInclusive::end) {
|
||||
if *last_blank_end != u128::MAX {
|
||||
covered_space.push(last_blank_end + 1..=u128::MAX);
|
||||
}
|
||||
}
|
||||
|
||||
if covered_space.is_empty() {
|
||||
covered_space.push(0..=0); // empty data case
|
||||
};
|
||||
|
||||
let mut compact_start: u64 = 1; // 0 is reserved for `null`
|
||||
let mut ranges_mapping: Vec<RangeMapping> = Vec::with_capacity(covered_space.len());
|
||||
for cov in covered_space {
|
||||
let range_mapping = super::RangeMapping {
|
||||
value_range: cov,
|
||||
compact_start,
|
||||
};
|
||||
let covered_range_len = range_mapping.range_length();
|
||||
ranges_mapping.push(range_mapping);
|
||||
compact_start += covered_range_len;
|
||||
}
|
||||
// println!("num ranges {}", ranges_mapping.len());
|
||||
CompactSpace { ranges_mapping }
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_binary_heap_pop_order() {
|
||||
let mut blanks: BinaryHeap<BlankRange> = BinaryHeap::new();
|
||||
blanks.push((0..=10).try_into().unwrap());
|
||||
blanks.push((100..=200).try_into().unwrap());
|
||||
blanks.push((100..=110).try_into().unwrap());
|
||||
assert_eq!(blanks.pop().unwrap().blank_size(), 101);
|
||||
assert_eq!(blanks.pop().unwrap().blank_size(), 11);
|
||||
}
|
||||
}
|
||||
@@ -1,815 +0,0 @@
|
||||
/// This codec takes a large number space (u128) and reduces it to a compact number space.
|
||||
///
|
||||
/// It will find spaces in the number range. For example:
|
||||
///
|
||||
/// 100, 101, 102, 103, 104, 50000, 50001
|
||||
/// could be mapped to
|
||||
/// 100..104 -> 0..4
|
||||
/// 50000..50001 -> 5..6
|
||||
///
|
||||
/// Compact space 0..=6 requires much less bits than 100..=50001
|
||||
///
|
||||
/// The codec is created to compress ip addresses, but may be employed in other use cases.
|
||||
use std::{
|
||||
cmp::Ordering,
|
||||
collections::BTreeSet,
|
||||
io::{self, Write},
|
||||
ops::{Range, RangeInclusive},
|
||||
};
|
||||
|
||||
use common::{BinarySerializable, CountingWriter, OwnedBytes, VInt, VIntU128};
|
||||
use tantivy_bitpacker::{self, BitPacker, BitUnpacker};
|
||||
|
||||
use crate::compact_space::build_compact_space::get_compact_space;
|
||||
use crate::Column;
|
||||
|
||||
mod blank_range;
|
||||
mod build_compact_space;
|
||||
|
||||
/// The cost per blank is quite hard actually, since blanks are delta encoded, the actual cost of
|
||||
/// blanks depends on the number of blanks.
|
||||
///
|
||||
/// The number is taken by looking at a real dataset. It is optimized for larger datasets.
|
||||
const COST_PER_BLANK_IN_BITS: usize = 36;
|
||||
|
||||
#[derive(Debug, Clone, Eq, PartialEq)]
|
||||
pub struct CompactSpace {
|
||||
ranges_mapping: Vec<RangeMapping>,
|
||||
}
|
||||
|
||||
/// Maps the range from the original space to compact_start + range.len()
|
||||
#[derive(Debug, Clone, Eq, PartialEq)]
|
||||
struct RangeMapping {
|
||||
value_range: RangeInclusive<u128>,
|
||||
compact_start: u64,
|
||||
}
|
||||
impl RangeMapping {
|
||||
fn range_length(&self) -> u64 {
|
||||
(self.value_range.end() - self.value_range.start()) as u64 + 1
|
||||
}
|
||||
|
||||
// The last value of the compact space in this range
|
||||
fn compact_end(&self) -> u64 {
|
||||
self.compact_start + self.range_length() - 1
|
||||
}
|
||||
}
|
||||
|
||||
impl BinarySerializable for CompactSpace {
|
||||
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
VInt(self.ranges_mapping.len() as u64).serialize(writer)?;
|
||||
|
||||
let mut prev_value = 0;
|
||||
for value_range in self
|
||||
.ranges_mapping
|
||||
.iter()
|
||||
.map(|range_mapping| &range_mapping.value_range)
|
||||
{
|
||||
let blank_delta_start = value_range.start() - prev_value;
|
||||
VIntU128(blank_delta_start).serialize(writer)?;
|
||||
prev_value = *value_range.start();
|
||||
|
||||
let blank_delta_end = value_range.end() - prev_value;
|
||||
VIntU128(blank_delta_end).serialize(writer)?;
|
||||
prev_value = *value_range.end();
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let num_ranges = VInt::deserialize(reader)?.0;
|
||||
let mut ranges_mapping: Vec<RangeMapping> = vec![];
|
||||
let mut value = 0u128;
|
||||
let mut compact_start = 1u64; // 0 is reserved for `null`
|
||||
for _ in 0..num_ranges {
|
||||
let blank_delta_start = VIntU128::deserialize(reader)?.0;
|
||||
value += blank_delta_start;
|
||||
let blank_start = value;
|
||||
|
||||
let blank_delta_end = VIntU128::deserialize(reader)?.0;
|
||||
value += blank_delta_end;
|
||||
let blank_end = value;
|
||||
|
||||
let range_mapping = RangeMapping {
|
||||
value_range: blank_start..=blank_end,
|
||||
compact_start,
|
||||
};
|
||||
let range_length = range_mapping.range_length();
|
||||
ranges_mapping.push(range_mapping);
|
||||
compact_start += range_length;
|
||||
}
|
||||
|
||||
Ok(Self { ranges_mapping })
|
||||
}
|
||||
}
|
||||
|
||||
impl CompactSpace {
|
||||
/// Amplitude is the value range of the compact space including the sentinel value used to
|
||||
/// identify null values. The compact space is 0..=amplitude .
|
||||
///
|
||||
/// It's only used to verify we don't exceed u64 number space, which would indicate a bug.
|
||||
fn amplitude_compact_space(&self) -> u128 {
|
||||
self.ranges_mapping
|
||||
.last()
|
||||
.map(|last_range| last_range.compact_end() as u128)
|
||||
.unwrap_or(1) // compact space starts at 1, 0 == null
|
||||
}
|
||||
|
||||
fn get_range_mapping(&self, pos: usize) -> &RangeMapping {
|
||||
&self.ranges_mapping[pos]
|
||||
}
|
||||
|
||||
/// Returns either Ok(the value in the compact space) or if it is outside the compact space the
|
||||
/// Err(position where it would be inserted)
|
||||
fn u128_to_compact(&self, value: u128) -> Result<u64, usize> {
|
||||
self.ranges_mapping
|
||||
.binary_search_by(|probe| {
|
||||
let value_range = &probe.value_range;
|
||||
if value < *value_range.start() {
|
||||
Ordering::Greater
|
||||
} else if value > *value_range.end() {
|
||||
Ordering::Less
|
||||
} else {
|
||||
Ordering::Equal
|
||||
}
|
||||
})
|
||||
.map(|pos| {
|
||||
let range_mapping = &self.ranges_mapping[pos];
|
||||
let pos_in_range = (value - range_mapping.value_range.start()) as u64;
|
||||
range_mapping.compact_start + pos_in_range
|
||||
})
|
||||
}
|
||||
|
||||
/// Unpacks a value from compact space u64 to u128 space
|
||||
fn compact_to_u128(&self, compact: u64) -> u128 {
|
||||
let pos = self
|
||||
.ranges_mapping
|
||||
.binary_search_by_key(&compact, |range_mapping| range_mapping.compact_start)
|
||||
// Correctness: Overflow. The first range starts at compact space 0, the error from
|
||||
// binary search can never be 0
|
||||
.map_or_else(|e| e - 1, |v| v);
|
||||
|
||||
let range_mapping = &self.ranges_mapping[pos];
|
||||
let diff = compact - range_mapping.compact_start;
|
||||
range_mapping.value_range.start() + diff as u128
|
||||
}
|
||||
}
|
||||
|
||||
pub struct CompactSpaceCompressor {
|
||||
params: IPCodecParams,
|
||||
}
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct IPCodecParams {
|
||||
compact_space: CompactSpace,
|
||||
bit_unpacker: BitUnpacker,
|
||||
min_value: u128,
|
||||
max_value: u128,
|
||||
num_vals: u32,
|
||||
num_bits: u8,
|
||||
}
|
||||
|
||||
impl CompactSpaceCompressor {
|
||||
/// Taking the vals as Vec may cost a lot of memory. It is used to sort the vals.
|
||||
pub fn train_from(iter: impl Iterator<Item = u128>, num_vals: u32) -> Self {
|
||||
let mut values_sorted = BTreeSet::new();
|
||||
values_sorted.extend(iter);
|
||||
let total_num_values = num_vals;
|
||||
|
||||
let compact_space =
|
||||
get_compact_space(&values_sorted, total_num_values, COST_PER_BLANK_IN_BITS);
|
||||
let amplitude_compact_space = compact_space.amplitude_compact_space();
|
||||
|
||||
assert!(
|
||||
amplitude_compact_space <= u64::MAX as u128,
|
||||
"case unsupported."
|
||||
);
|
||||
|
||||
let num_bits = tantivy_bitpacker::compute_num_bits(amplitude_compact_space as u64);
|
||||
let min_value = *values_sorted.iter().next().unwrap_or(&0);
|
||||
let max_value = *values_sorted.iter().last().unwrap_or(&0);
|
||||
assert_eq!(
|
||||
compact_space
|
||||
.u128_to_compact(max_value)
|
||||
.expect("could not convert max value to compact space"),
|
||||
amplitude_compact_space as u64
|
||||
);
|
||||
CompactSpaceCompressor {
|
||||
params: IPCodecParams {
|
||||
compact_space,
|
||||
bit_unpacker: BitUnpacker::new(num_bits),
|
||||
min_value,
|
||||
max_value,
|
||||
num_vals: total_num_values,
|
||||
num_bits,
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
fn write_footer(self, writer: &mut impl Write) -> io::Result<()> {
|
||||
let writer = &mut CountingWriter::wrap(writer);
|
||||
self.params.serialize(writer)?;
|
||||
|
||||
let footer_len = writer.written_bytes() as u32;
|
||||
footer_len.serialize(writer)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn compress_into(
|
||||
self,
|
||||
vals: impl Iterator<Item = u128>,
|
||||
write: &mut impl Write,
|
||||
) -> io::Result<()> {
|
||||
let mut bitpacker = BitPacker::default();
|
||||
for val in vals {
|
||||
let compact = self
|
||||
.params
|
||||
.compact_space
|
||||
.u128_to_compact(val)
|
||||
.map_err(|_| {
|
||||
io::Error::new(
|
||||
io::ErrorKind::InvalidData,
|
||||
"Could not convert value to compact_space. This is a bug.",
|
||||
)
|
||||
})?;
|
||||
bitpacker.write(compact, self.params.num_bits, write)?;
|
||||
}
|
||||
bitpacker.close(write)?;
|
||||
self.write_footer(write)?;
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct CompactSpaceDecompressor {
|
||||
data: OwnedBytes,
|
||||
params: IPCodecParams,
|
||||
}
|
||||
|
||||
impl BinarySerializable for IPCodecParams {
|
||||
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
// header flags for future optional dictionary encoding
|
||||
let footer_flags = 0u64;
|
||||
footer_flags.serialize(writer)?;
|
||||
|
||||
VIntU128(self.min_value).serialize(writer)?;
|
||||
VIntU128(self.max_value).serialize(writer)?;
|
||||
VIntU128(self.num_vals as u128).serialize(writer)?;
|
||||
self.num_bits.serialize(writer)?;
|
||||
|
||||
self.compact_space.serialize(writer)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let _header_flags = u64::deserialize(reader)?;
|
||||
let min_value = VIntU128::deserialize(reader)?.0;
|
||||
let max_value = VIntU128::deserialize(reader)?.0;
|
||||
let num_vals = VIntU128::deserialize(reader)?.0 as u32;
|
||||
let num_bits = u8::deserialize(reader)?;
|
||||
let compact_space = CompactSpace::deserialize(reader)?;
|
||||
|
||||
Ok(Self {
|
||||
compact_space,
|
||||
bit_unpacker: BitUnpacker::new(num_bits),
|
||||
min_value,
|
||||
max_value,
|
||||
num_vals,
|
||||
num_bits,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
impl Column<u128> for CompactSpaceDecompressor {
|
||||
#[inline]
|
||||
fn get_val(&self, doc: u32) -> u128 {
|
||||
self.get(doc)
|
||||
}
|
||||
|
||||
fn min_value(&self) -> u128 {
|
||||
self.min_value()
|
||||
}
|
||||
|
||||
fn max_value(&self) -> u128 {
|
||||
self.max_value()
|
||||
}
|
||||
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.params.num_vals
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn iter(&self) -> Box<dyn Iterator<Item = u128> + '_> {
|
||||
Box::new(self.iter())
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn get_docids_for_value_range(
|
||||
&self,
|
||||
value_range: RangeInclusive<u128>,
|
||||
positions_range: Range<u32>,
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
self.get_positions_for_value_range(value_range, positions_range, positions)
|
||||
}
|
||||
}
|
||||
|
||||
impl CompactSpaceDecompressor {
|
||||
pub fn open(data: OwnedBytes) -> io::Result<CompactSpaceDecompressor> {
|
||||
let (data_slice, footer_len_bytes) = data.split_at(data.len() - 4);
|
||||
let footer_len = u32::deserialize(&mut &footer_len_bytes[..])?;
|
||||
|
||||
let data_footer = &data_slice[data_slice.len() - footer_len as usize..];
|
||||
let params = IPCodecParams::deserialize(&mut &data_footer[..])?;
|
||||
let decompressor = CompactSpaceDecompressor { data, params };
|
||||
|
||||
Ok(decompressor)
|
||||
}
|
||||
|
||||
/// Converting to compact space for the decompressor is more complex, since we may get values
|
||||
/// which are outside the compact space. e.g. if we map
|
||||
/// 1000 => 5
|
||||
/// 2000 => 6
|
||||
///
|
||||
/// and we want a mapping for 1005, there is no equivalent compact space. We instead return an
|
||||
/// error with the index of the next range.
|
||||
fn u128_to_compact(&self, value: u128) -> Result<u64, usize> {
|
||||
self.params.compact_space.u128_to_compact(value)
|
||||
}
|
||||
|
||||
fn compact_to_u128(&self, compact: u64) -> u128 {
|
||||
self.params.compact_space.compact_to_u128(compact)
|
||||
}
|
||||
|
||||
/// Comparing on compact space: Random dataset 0,24 (50% random hit) - 1.05 GElements/s
|
||||
/// Comparing on compact space: Real dataset 1.08 GElements/s
|
||||
///
|
||||
/// Comparing on original space: Real dataset .06 GElements/s (not completely optimized)
|
||||
#[inline]
|
||||
pub fn get_positions_for_value_range(
|
||||
&self,
|
||||
value_range: RangeInclusive<u128>,
|
||||
position_range: Range<u32>,
|
||||
positions: &mut Vec<u32>,
|
||||
) {
|
||||
if value_range.start() > value_range.end() {
|
||||
return;
|
||||
}
|
||||
let position_range = position_range.start..position_range.end.min(self.num_vals());
|
||||
let from_value = *value_range.start();
|
||||
let to_value = *value_range.end();
|
||||
assert!(to_value >= from_value);
|
||||
let compact_from = self.u128_to_compact(from_value);
|
||||
let compact_to = self.u128_to_compact(to_value);
|
||||
|
||||
// Quick return, if both ranges fall into the same non-mapped space, the range can't cover
|
||||
// any values, so we can early exit
|
||||
match (compact_to, compact_from) {
|
||||
(Err(pos1), Err(pos2)) if pos1 == pos2 => return,
|
||||
_ => {}
|
||||
}
|
||||
|
||||
let compact_from = compact_from.unwrap_or_else(|pos| {
|
||||
// Correctness: Out of bounds, if this value is Err(last_index + 1), we early exit,
|
||||
// since the to_value also mapps into the same non-mapped space
|
||||
let range_mapping = self.params.compact_space.get_range_mapping(pos);
|
||||
range_mapping.compact_start
|
||||
});
|
||||
// If there is no compact space, we go to the closest upperbound compact space
|
||||
let compact_to = compact_to.unwrap_or_else(|pos| {
|
||||
// Correctness: Overflow, if this value is Err(0), we early exit,
|
||||
// since the from_value also mapps into the same non-mapped space
|
||||
|
||||
// Get end of previous range
|
||||
let pos = pos - 1;
|
||||
let range_mapping = self.params.compact_space.get_range_mapping(pos);
|
||||
range_mapping.compact_end()
|
||||
});
|
||||
|
||||
let range = compact_from..=compact_to;
|
||||
|
||||
let scan_num_docs = position_range.end - position_range.start;
|
||||
|
||||
let step_size = 4;
|
||||
let cutoff = position_range.start + scan_num_docs - scan_num_docs % step_size;
|
||||
|
||||
let mut push_if_in_range = |idx, val| {
|
||||
if range.contains(&val) {
|
||||
positions.push(idx);
|
||||
}
|
||||
};
|
||||
let get_val = |idx| self.params.bit_unpacker.get(idx, &self.data);
|
||||
// unrolled loop
|
||||
for idx in (position_range.start..cutoff).step_by(step_size as usize) {
|
||||
let idx1 = idx;
|
||||
let idx2 = idx + 1;
|
||||
let idx3 = idx + 2;
|
||||
let idx4 = idx + 3;
|
||||
let val1 = get_val(idx1);
|
||||
let val2 = get_val(idx2);
|
||||
let val3 = get_val(idx3);
|
||||
let val4 = get_val(idx4);
|
||||
push_if_in_range(idx1, val1);
|
||||
push_if_in_range(idx2, val2);
|
||||
push_if_in_range(idx3, val3);
|
||||
push_if_in_range(idx4, val4);
|
||||
}
|
||||
|
||||
// handle rest
|
||||
for idx in cutoff..position_range.end {
|
||||
push_if_in_range(idx, get_val(idx));
|
||||
}
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn iter_compact(&self) -> impl Iterator<Item = u64> + '_ {
|
||||
(0..self.params.num_vals).map(move |idx| self.params.bit_unpacker.get(idx, &self.data))
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn iter(&self) -> impl Iterator<Item = u128> + '_ {
|
||||
// TODO: Performance. It would be better to iterate on the ranges and check existence via
|
||||
// the bit_unpacker.
|
||||
self.iter_compact()
|
||||
.map(|compact| self.compact_to_u128(compact))
|
||||
}
|
||||
|
||||
#[inline]
|
||||
pub fn get(&self, idx: u32) -> u128 {
|
||||
let compact = self.params.bit_unpacker.get(idx, &self.data);
|
||||
self.compact_to_u128(compact)
|
||||
}
|
||||
|
||||
pub fn min_value(&self) -> u128 {
|
||||
self.params.min_value
|
||||
}
|
||||
|
||||
pub fn max_value(&self) -> u128 {
|
||||
self.params.max_value
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
|
||||
use std::fmt;
|
||||
|
||||
use super::*;
|
||||
use crate::format_version::read_format_version;
|
||||
use crate::null_index_footer::read_null_index_footer;
|
||||
use crate::serialize::U128Header;
|
||||
use crate::{open_u128, serialize_u128};
|
||||
|
||||
#[test]
|
||||
fn compact_space_test() {
|
||||
let ips = &[
|
||||
2u128, 4u128, 1000, 1001, 1002, 1003, 1004, 1005, 1008, 1010, 1012, 1260,
|
||||
]
|
||||
.into_iter()
|
||||
.collect();
|
||||
let compact_space = get_compact_space(ips, ips.len() as u32, 11);
|
||||
let amplitude = compact_space.amplitude_compact_space();
|
||||
assert_eq!(amplitude, 17);
|
||||
assert_eq!(1, compact_space.u128_to_compact(2).unwrap());
|
||||
assert_eq!(2, compact_space.u128_to_compact(3).unwrap());
|
||||
assert_eq!(compact_space.u128_to_compact(100).unwrap_err(), 1);
|
||||
|
||||
for (num1, num2) in (0..3).tuple_windows() {
|
||||
assert_eq!(
|
||||
compact_space.get_range_mapping(num1).compact_end() + 1,
|
||||
compact_space.get_range_mapping(num2).compact_start
|
||||
);
|
||||
}
|
||||
|
||||
let mut output: Vec<u8> = Vec::new();
|
||||
compact_space.serialize(&mut output).unwrap();
|
||||
|
||||
assert_eq!(
|
||||
compact_space,
|
||||
CompactSpace::deserialize(&mut &output[..]).unwrap()
|
||||
);
|
||||
|
||||
for ip in ips {
|
||||
let compact = compact_space.u128_to_compact(*ip).unwrap();
|
||||
assert_eq!(compact_space.compact_to_u128(compact), *ip);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn compact_space_amplitude_test() {
|
||||
let ips = &[100000u128, 1000000].into_iter().collect();
|
||||
let compact_space = get_compact_space(ips, ips.len() as u32, 1);
|
||||
let amplitude = compact_space.amplitude_compact_space();
|
||||
assert_eq!(amplitude, 2);
|
||||
}
|
||||
|
||||
fn test_all(mut data: OwnedBytes, expected: &[u128]) {
|
||||
let _header = U128Header::deserialize(&mut data);
|
||||
let decompressor = CompactSpaceDecompressor::open(data).unwrap();
|
||||
for (idx, expected_val) in expected.iter().cloned().enumerate() {
|
||||
let val = decompressor.get(idx as u32);
|
||||
assert_eq!(val, expected_val);
|
||||
|
||||
let test_range = |range: RangeInclusive<u128>| {
|
||||
let expected_positions = expected
|
||||
.iter()
|
||||
.positions(|val| range.contains(val))
|
||||
.map(|pos| pos as u32)
|
||||
.collect::<Vec<_>>();
|
||||
let mut positions = Vec::new();
|
||||
decompressor.get_positions_for_value_range(
|
||||
range,
|
||||
0..decompressor.num_vals(),
|
||||
&mut positions,
|
||||
);
|
||||
assert_eq!(positions, expected_positions);
|
||||
};
|
||||
|
||||
test_range(expected_val.saturating_sub(1)..=expected_val);
|
||||
test_range(expected_val..=expected_val);
|
||||
test_range(expected_val..=expected_val.saturating_add(1));
|
||||
test_range(expected_val.saturating_sub(1)..=expected_val.saturating_add(1));
|
||||
}
|
||||
}
|
||||
|
||||
fn test_aux_vals(u128_vals: &[u128]) -> OwnedBytes {
|
||||
let mut out = Vec::new();
|
||||
serialize_u128(
|
||||
|| u128_vals.iter().cloned(),
|
||||
u128_vals.len() as u32,
|
||||
&mut out,
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
let data = OwnedBytes::new(out);
|
||||
let (data, _format_version) = read_format_version(data).unwrap();
|
||||
let (data, _null_index_footer) = read_null_index_footer(data).unwrap();
|
||||
test_all(data.clone(), u128_vals);
|
||||
|
||||
data
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_range_1() {
|
||||
let vals = &[
|
||||
1u128,
|
||||
100u128,
|
||||
3u128,
|
||||
99999u128,
|
||||
100000u128,
|
||||
100001u128,
|
||||
4_000_211_221u128,
|
||||
4_000_211_222u128,
|
||||
333u128,
|
||||
];
|
||||
let mut data = test_aux_vals(vals);
|
||||
|
||||
let _header = U128Header::deserialize(&mut data);
|
||||
let decomp = CompactSpaceDecompressor::open(data).unwrap();
|
||||
let complete_range = 0..vals.len() as u32;
|
||||
for (pos, val) in vals.iter().enumerate() {
|
||||
let val = *val;
|
||||
let pos = pos as u32;
|
||||
let mut positions = Vec::new();
|
||||
decomp.get_positions_for_value_range(val..=val, pos..pos + 1, &mut positions);
|
||||
assert_eq!(positions, vec![pos]);
|
||||
}
|
||||
|
||||
// handle docid range out of bounds
|
||||
let positions = get_positions_for_value_range_helper(&decomp, 0..=1, 1..u32::MAX);
|
||||
assert_eq!(positions, vec![]);
|
||||
|
||||
let positions =
|
||||
get_positions_for_value_range_helper(&decomp, 0..=1, complete_range.clone());
|
||||
assert_eq!(positions, vec![0]);
|
||||
let positions =
|
||||
get_positions_for_value_range_helper(&decomp, 0..=2, complete_range.clone());
|
||||
assert_eq!(positions, vec![0]);
|
||||
let positions =
|
||||
get_positions_for_value_range_helper(&decomp, 0..=3, complete_range.clone());
|
||||
assert_eq!(positions, vec![0, 2]);
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
99999u128..=99999u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
vec![3]
|
||||
);
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
99999u128..=100000u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
vec![3, 4]
|
||||
);
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
99998u128..=100000u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
vec![3, 4]
|
||||
);
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
99998u128..=99999u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
vec![3]
|
||||
);
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
99998u128..=99998u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
vec![]
|
||||
);
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
333u128..=333u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
vec![8]
|
||||
);
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
332u128..=333u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
vec![8]
|
||||
);
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
332u128..=334u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
vec![8]
|
||||
);
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
333u128..=334u128,
|
||||
complete_range.clone()
|
||||
),
|
||||
vec![8]
|
||||
);
|
||||
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(
|
||||
&decomp,
|
||||
4_000_211_221u128..=5_000_000_000u128,
|
||||
complete_range
|
||||
),
|
||||
vec![6, 7]
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_empty() {
|
||||
let vals = &[];
|
||||
let data = test_aux_vals(vals);
|
||||
let _decomp = CompactSpaceDecompressor::open(data).unwrap();
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_range_2() {
|
||||
let vals = &[
|
||||
100u128,
|
||||
99999u128,
|
||||
100000u128,
|
||||
100001u128,
|
||||
4_000_211_221u128,
|
||||
4_000_211_222u128,
|
||||
333u128,
|
||||
];
|
||||
let mut data = test_aux_vals(vals);
|
||||
let _header = U128Header::deserialize(&mut data);
|
||||
let decomp = CompactSpaceDecompressor::open(data).unwrap();
|
||||
let complete_range = 0..vals.len() as u32;
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(&decomp, 0..=5, complete_range.clone()),
|
||||
vec![]
|
||||
);
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(&decomp, 0..=100, complete_range.clone()),
|
||||
vec![0]
|
||||
);
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(&decomp, 0..=105, complete_range),
|
||||
vec![0]
|
||||
);
|
||||
}
|
||||
|
||||
fn get_positions_for_value_range_helper<C: Column<T> + ?Sized, T: PartialOrd + fmt::Debug>(
|
||||
column: &C,
|
||||
value_range: RangeInclusive<T>,
|
||||
doc_id_range: Range<u32>,
|
||||
) -> Vec<u32> {
|
||||
let mut positions = Vec::new();
|
||||
column.get_docids_for_value_range(value_range, doc_id_range, &mut positions);
|
||||
positions
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_range_3() {
|
||||
let vals = &[
|
||||
200u128,
|
||||
201,
|
||||
202,
|
||||
203,
|
||||
204,
|
||||
204,
|
||||
206,
|
||||
207,
|
||||
208,
|
||||
209,
|
||||
210,
|
||||
1_000_000,
|
||||
5_000_000_000,
|
||||
];
|
||||
let mut out = Vec::new();
|
||||
serialize_u128(|| vals.iter().cloned(), vals.len() as u32, &mut out).unwrap();
|
||||
let decomp = open_u128::<u128>(OwnedBytes::new(out)).unwrap();
|
||||
let complete_range = 0..vals.len() as u32;
|
||||
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(&*decomp, 199..=200, complete_range.clone()),
|
||||
vec![0]
|
||||
);
|
||||
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(&*decomp, 199..=201, complete_range.clone()),
|
||||
vec![0, 1]
|
||||
);
|
||||
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(&*decomp, 200..=200, complete_range.clone()),
|
||||
vec![0]
|
||||
);
|
||||
|
||||
assert_eq!(
|
||||
get_positions_for_value_range_helper(&*decomp, 1_000_000..=1_000_000, complete_range),
|
||||
vec![11]
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_bug1() {
|
||||
let vals = &[9223372036854775806];
|
||||
let _data = test_aux_vals(vals);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_bug2() {
|
||||
let vals = &[340282366920938463463374607431768211455u128];
|
||||
let _data = test_aux_vals(vals);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_bug3() {
|
||||
let vals = &[340282366920938463463374607431768211454];
|
||||
let _data = test_aux_vals(vals);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_bug4() {
|
||||
let vals = &[340282366920938463463374607431768211455, 0];
|
||||
let _data = test_aux_vals(vals);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_first_large_gaps() {
|
||||
let vals = &[1_000_000_000u128; 100];
|
||||
let _data = test_aux_vals(vals);
|
||||
}
|
||||
use itertools::Itertools;
|
||||
use proptest::prelude::*;
|
||||
|
||||
fn num_strategy() -> impl Strategy<Value = u128> {
|
||||
prop_oneof![
|
||||
1 => prop::num::u128::ANY.prop_map(|num| u128::MAX - (num % 10) ),
|
||||
1 => prop::num::u128::ANY.prop_map(|num| i64::MAX as u128 + 5 - (num % 10) ),
|
||||
1 => prop::num::u128::ANY.prop_map(|num| i128::MAX as u128 + 5 - (num % 10) ),
|
||||
1 => prop::num::u128::ANY.prop_map(|num| num % 10 ),
|
||||
20 => prop::num::u128::ANY,
|
||||
]
|
||||
}
|
||||
|
||||
proptest! {
|
||||
#![proptest_config(ProptestConfig::with_cases(10))]
|
||||
|
||||
#[test]
|
||||
fn compress_decompress_random(vals in proptest::collection::vec(num_strategy()
|
||||
, 1..1000)) {
|
||||
let _data = test_aux_vals(&vals);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,38 +0,0 @@
|
||||
use std::io;
|
||||
|
||||
use common::{BinarySerializable, OwnedBytes};
|
||||
|
||||
const MAGIC_NUMBER: u16 = 4335u16;
|
||||
const FASTFIELD_FORMAT_VERSION: u8 = 1;
|
||||
|
||||
pub(crate) fn append_format_version(output: &mut impl io::Write) -> io::Result<()> {
|
||||
FASTFIELD_FORMAT_VERSION.serialize(output)?;
|
||||
MAGIC_NUMBER.serialize(output)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub(crate) fn read_format_version(data: OwnedBytes) -> io::Result<(OwnedBytes, u8)> {
|
||||
let (data, magic_number_bytes) = data.rsplit(2);
|
||||
|
||||
let magic_number = u16::deserialize(&mut magic_number_bytes.as_slice())?;
|
||||
if magic_number != MAGIC_NUMBER {
|
||||
return Err(io::Error::new(
|
||||
io::ErrorKind::InvalidData,
|
||||
format!("magic number mismatch {} != {}", magic_number, MAGIC_NUMBER),
|
||||
));
|
||||
}
|
||||
let (data, format_version_bytes) = data.rsplit(1);
|
||||
let format_version = u8::deserialize(&mut format_version_bytes.as_slice())?;
|
||||
if format_version > FASTFIELD_FORMAT_VERSION {
|
||||
return Err(io::Error::new(
|
||||
io::ErrorKind::InvalidData,
|
||||
format!(
|
||||
"Unsupported fastfield format version: {}. Max supported version: {}",
|
||||
format_version, FASTFIELD_FORMAT_VERSION
|
||||
),
|
||||
));
|
||||
}
|
||||
|
||||
Ok((data, format_version))
|
||||
}
|
||||
@@ -1,170 +0,0 @@
|
||||
use std::num::NonZeroU64;
|
||||
|
||||
use fastdivide::DividerU64;
|
||||
|
||||
/// Compute the gcd of two non null numbers.
|
||||
///
|
||||
/// It is recommended, but not required, to feed values such that `large >= small`.
|
||||
fn compute_gcd(mut large: NonZeroU64, mut small: NonZeroU64) -> NonZeroU64 {
|
||||
loop {
|
||||
let rem: u64 = large.get() % small;
|
||||
if let Some(new_small) = NonZeroU64::new(rem) {
|
||||
(large, small) = (small, new_small);
|
||||
} else {
|
||||
return small;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Find GCD for iterator of numbers
|
||||
pub fn find_gcd(numbers: impl Iterator<Item = u64>) -> Option<NonZeroU64> {
|
||||
let mut numbers = numbers.flat_map(NonZeroU64::new);
|
||||
let mut gcd: NonZeroU64 = numbers.next()?;
|
||||
if gcd.get() == 1 {
|
||||
return Some(gcd);
|
||||
}
|
||||
|
||||
let mut gcd_divider = DividerU64::divide_by(gcd.get());
|
||||
for val in numbers {
|
||||
let remainder = val.get() - (gcd_divider.divide(val.get())) * gcd.get();
|
||||
if remainder == 0 {
|
||||
continue;
|
||||
}
|
||||
gcd = compute_gcd(val, gcd);
|
||||
if gcd.get() == 1 {
|
||||
return Some(gcd);
|
||||
}
|
||||
|
||||
gcd_divider = DividerU64::divide_by(gcd.get());
|
||||
}
|
||||
Some(gcd)
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use std::io;
|
||||
use std::num::NonZeroU64;
|
||||
|
||||
use common::OwnedBytes;
|
||||
|
||||
use crate::gcd::{compute_gcd, find_gcd};
|
||||
use crate::{FastFieldCodecType, VecColumn};
|
||||
|
||||
fn test_fastfield_gcd_i64_with_codec(
|
||||
codec_type: FastFieldCodecType,
|
||||
num_vals: usize,
|
||||
) -> io::Result<()> {
|
||||
let mut vals: Vec<i64> = (-4..=(num_vals as i64) - 5).map(|val| val * 1000).collect();
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
crate::serialize(VecColumn::from(&vals), &mut buffer, &[codec_type])?;
|
||||
let buffer = OwnedBytes::new(buffer);
|
||||
let column = crate::open::<i64>(buffer.clone())?;
|
||||
assert_eq!(column.get_val(0), -4000i64);
|
||||
assert_eq!(column.get_val(1), -3000i64);
|
||||
assert_eq!(column.get_val(2), -2000i64);
|
||||
assert_eq!(column.max_value(), (num_vals as i64 - 5) * 1000);
|
||||
assert_eq!(column.min_value(), -4000i64);
|
||||
|
||||
// Can't apply gcd
|
||||
let mut buffer_without_gcd = Vec::new();
|
||||
vals.pop();
|
||||
vals.push(1001i64);
|
||||
crate::serialize(
|
||||
VecColumn::from(&vals),
|
||||
&mut buffer_without_gcd,
|
||||
&[codec_type],
|
||||
)?;
|
||||
let buffer_without_gcd = OwnedBytes::new(buffer_without_gcd);
|
||||
assert!(buffer_without_gcd.len() > buffer.len());
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_fastfield_gcd_i64() -> io::Result<()> {
|
||||
for &codec_type in &[
|
||||
FastFieldCodecType::Bitpacked,
|
||||
FastFieldCodecType::BlockwiseLinear,
|
||||
FastFieldCodecType::Linear,
|
||||
] {
|
||||
test_fastfield_gcd_i64_with_codec(codec_type, 5500)?;
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn test_fastfield_gcd_u64_with_codec(
|
||||
codec_type: FastFieldCodecType,
|
||||
num_vals: usize,
|
||||
) -> io::Result<()> {
|
||||
let mut vals: Vec<u64> = (1..=num_vals).map(|i| i as u64 * 1000u64).collect();
|
||||
let mut buffer: Vec<u8> = Vec::new();
|
||||
crate::serialize(VecColumn::from(&vals), &mut buffer, &[codec_type])?;
|
||||
let buffer = OwnedBytes::new(buffer);
|
||||
let column = crate::open::<u64>(buffer.clone())?;
|
||||
assert_eq!(column.get_val(0), 1000u64);
|
||||
assert_eq!(column.get_val(1), 2000u64);
|
||||
assert_eq!(column.get_val(2), 3000u64);
|
||||
assert_eq!(column.max_value(), num_vals as u64 * 1000);
|
||||
assert_eq!(column.min_value(), 1000u64);
|
||||
|
||||
// Can't apply gcd
|
||||
let mut buffer_without_gcd = Vec::new();
|
||||
vals.pop();
|
||||
vals.push(1001u64);
|
||||
crate::serialize(
|
||||
VecColumn::from(&vals),
|
||||
&mut buffer_without_gcd,
|
||||
&[codec_type],
|
||||
)?;
|
||||
let buffer_without_gcd = OwnedBytes::new(buffer_without_gcd);
|
||||
assert!(buffer_without_gcd.len() > buffer.len());
|
||||
Ok(())
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_fastfield_gcd_u64() -> io::Result<()> {
|
||||
for &codec_type in &[
|
||||
FastFieldCodecType::Bitpacked,
|
||||
FastFieldCodecType::BlockwiseLinear,
|
||||
FastFieldCodecType::Linear,
|
||||
] {
|
||||
test_fastfield_gcd_u64_with_codec(codec_type, 5500)?;
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
#[test]
|
||||
pub fn test_fastfield2() {
|
||||
let test_fastfield = crate::serialize_and_load(&[100u64, 200u64, 300u64]);
|
||||
assert_eq!(test_fastfield.get_val(0), 100);
|
||||
assert_eq!(test_fastfield.get_val(1), 200);
|
||||
assert_eq!(test_fastfield.get_val(2), 300);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_compute_gcd() {
|
||||
let test_compute_gcd_aux = |large, small, expected| {
|
||||
let large = NonZeroU64::new(large).unwrap();
|
||||
let small = NonZeroU64::new(small).unwrap();
|
||||
let expected = NonZeroU64::new(expected).unwrap();
|
||||
assert_eq!(compute_gcd(small, large), expected);
|
||||
assert_eq!(compute_gcd(large, small), expected);
|
||||
};
|
||||
test_compute_gcd_aux(1, 4, 1);
|
||||
test_compute_gcd_aux(2, 4, 2);
|
||||
test_compute_gcd_aux(10, 25, 5);
|
||||
test_compute_gcd_aux(25, 25, 25);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn find_gcd_test() {
|
||||
assert_eq!(find_gcd([0].into_iter()), None);
|
||||
assert_eq!(find_gcd([0, 10].into_iter()), NonZeroU64::new(10));
|
||||
assert_eq!(find_gcd([10, 0].into_iter()), NonZeroU64::new(10));
|
||||
assert_eq!(find_gcd([].into_iter()), None);
|
||||
assert_eq!(find_gcd([15, 30, 5, 10].into_iter()), NonZeroU64::new(5));
|
||||
assert_eq!(find_gcd([15, 16, 10].into_iter()), NonZeroU64::new(1));
|
||||
assert_eq!(find_gcd([0, 5, 5, 5].into_iter()), NonZeroU64::new(5));
|
||||
assert_eq!(find_gcd([0, 0].into_iter()), None);
|
||||
}
|
||||
}
|
||||
@@ -1,568 +0,0 @@
|
||||
#![warn(missing_docs)]
|
||||
#![cfg_attr(all(feature = "unstable", test), feature(test))]
|
||||
|
||||
//! # `fastfield_codecs`
|
||||
//!
|
||||
//! - Columnar storage of data for tantivy [`Column`].
|
||||
//! - Encode data in different codecs.
|
||||
//! - Monotonically map values to u64/u128
|
||||
|
||||
#[cfg(test)]
|
||||
#[macro_use]
|
||||
extern crate more_asserts;
|
||||
|
||||
#[cfg(all(test, feature = "unstable"))]
|
||||
extern crate test;
|
||||
|
||||
use std::io::Write;
|
||||
use std::sync::Arc;
|
||||
use std::{fmt, io};
|
||||
|
||||
use common::{BinarySerializable, OwnedBytes};
|
||||
use compact_space::CompactSpaceDecompressor;
|
||||
use format_version::read_format_version;
|
||||
use monotonic_mapping::{
|
||||
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
|
||||
StrictlyMonotonicMappingToInternalBaseval, StrictlyMonotonicMappingToInternalGCDBaseval,
|
||||
};
|
||||
use null_index_footer::read_null_index_footer;
|
||||
use serialize::{Header, U128Header};
|
||||
|
||||
mod bitpacked;
|
||||
mod blockwise_linear;
|
||||
mod compact_space;
|
||||
mod format_version;
|
||||
mod line;
|
||||
mod linear;
|
||||
mod monotonic_mapping;
|
||||
mod monotonic_mapping_u128;
|
||||
#[allow(dead_code)]
|
||||
mod null_index;
|
||||
mod null_index_footer;
|
||||
|
||||
mod column;
|
||||
mod gcd;
|
||||
pub mod serialize;
|
||||
|
||||
use self::bitpacked::BitpackedCodec;
|
||||
use self::blockwise_linear::BlockwiseLinearCodec;
|
||||
pub use self::column::{monotonic_map_column, Column, IterColumn, VecColumn};
|
||||
use self::linear::LinearCodec;
|
||||
pub use self::monotonic_mapping::{MonotonicallyMappableToU64, StrictlyMonotonicFn};
|
||||
pub use self::monotonic_mapping_u128::MonotonicallyMappableToU128;
|
||||
pub use self::serialize::{
|
||||
estimate, serialize, serialize_and_load, serialize_u128, NormalizedHeader,
|
||||
};
|
||||
|
||||
#[derive(PartialEq, Eq, PartialOrd, Ord, Debug, Clone, Copy)]
|
||||
#[repr(u8)]
|
||||
/// Available codecs to use to encode the u64 (via [`MonotonicallyMappableToU64`]) converted data.
|
||||
pub enum FastFieldCodecType {
|
||||
/// Bitpack all values in the value range. The number of bits is defined by the amplitude
|
||||
/// `column.max_value() - column.min_value()`
|
||||
Bitpacked = 1,
|
||||
/// Linear interpolation puts a line between the first and last value and then bitpacks the
|
||||
/// values by the offset from the line. The number of bits is defined by the max deviation from
|
||||
/// the line.
|
||||
Linear = 2,
|
||||
/// Same as [`FastFieldCodecType::Linear`], but encodes in blocks of 512 elements.
|
||||
BlockwiseLinear = 3,
|
||||
}
|
||||
|
||||
impl BinarySerializable for FastFieldCodecType {
|
||||
fn serialize<W: Write>(&self, wrt: &mut W) -> io::Result<()> {
|
||||
self.to_code().serialize(wrt)
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let code = u8::deserialize(reader)?;
|
||||
let codec_type: Self = Self::from_code(code)
|
||||
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "Unknown code `{code}.`"))?;
|
||||
Ok(codec_type)
|
||||
}
|
||||
}
|
||||
|
||||
impl FastFieldCodecType {
|
||||
pub(crate) fn to_code(self) -> u8 {
|
||||
self as u8
|
||||
}
|
||||
|
||||
pub(crate) fn from_code(code: u8) -> Option<Self> {
|
||||
match code {
|
||||
1 => Some(Self::Bitpacked),
|
||||
2 => Some(Self::Linear),
|
||||
3 => Some(Self::BlockwiseLinear),
|
||||
_ => None,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(PartialEq, Eq, PartialOrd, Ord, Debug, Clone, Copy)]
|
||||
#[repr(u8)]
|
||||
/// Available codecs to use to encode the u128 (via [`MonotonicallyMappableToU128`]) converted data.
|
||||
pub enum U128FastFieldCodecType {
|
||||
/// This codec takes a large number space (u128) and reduces it to a compact number space, by
|
||||
/// removing the holes.
|
||||
CompactSpace = 1,
|
||||
}
|
||||
|
||||
impl BinarySerializable for U128FastFieldCodecType {
|
||||
fn serialize<W: Write>(&self, wrt: &mut W) -> io::Result<()> {
|
||||
self.to_code().serialize(wrt)
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let code = u8::deserialize(reader)?;
|
||||
let codec_type: Self = Self::from_code(code)
|
||||
.ok_or_else(|| io::Error::new(io::ErrorKind::InvalidData, "Unknown code `{code}.`"))?;
|
||||
Ok(codec_type)
|
||||
}
|
||||
}
|
||||
|
||||
impl U128FastFieldCodecType {
|
||||
pub(crate) fn to_code(self) -> u8 {
|
||||
self as u8
|
||||
}
|
||||
|
||||
pub(crate) fn from_code(code: u8) -> Option<Self> {
|
||||
match code {
|
||||
1 => Some(Self::CompactSpace),
|
||||
_ => None,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the correct codec reader wrapped in the `Arc` for the data.
|
||||
pub fn open_u128<Item: MonotonicallyMappableToU128 + fmt::Debug>(
|
||||
bytes: OwnedBytes,
|
||||
) -> io::Result<Arc<dyn Column<Item>>> {
|
||||
let (bytes, _format_version) = read_format_version(bytes)?;
|
||||
let (mut bytes, _null_index_footer) = read_null_index_footer(bytes)?;
|
||||
let header = U128Header::deserialize(&mut bytes)?;
|
||||
assert_eq!(header.codec_type, U128FastFieldCodecType::CompactSpace);
|
||||
let reader = CompactSpaceDecompressor::open(bytes)?;
|
||||
let inverted: StrictlyMonotonicMappingInverter<StrictlyMonotonicMappingToInternal<Item>> =
|
||||
StrictlyMonotonicMappingToInternal::<Item>::new().into();
|
||||
Ok(Arc::new(monotonic_map_column(reader, inverted)))
|
||||
}
|
||||
|
||||
/// Returns the correct codec reader wrapped in the `Arc` for the data.
|
||||
pub fn open<T: MonotonicallyMappableToU64 + fmt::Debug>(
|
||||
bytes: OwnedBytes,
|
||||
) -> io::Result<Arc<dyn Column<T>>> {
|
||||
let (bytes, _format_version) = read_format_version(bytes)?;
|
||||
let (mut bytes, _null_index_footer) = read_null_index_footer(bytes)?;
|
||||
let header = Header::deserialize(&mut bytes)?;
|
||||
match header.codec_type {
|
||||
FastFieldCodecType::Bitpacked => open_specific_codec::<BitpackedCodec, _>(bytes, &header),
|
||||
FastFieldCodecType::Linear => open_specific_codec::<LinearCodec, _>(bytes, &header),
|
||||
FastFieldCodecType::BlockwiseLinear => {
|
||||
open_specific_codec::<BlockwiseLinearCodec, _>(bytes, &header)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn open_specific_codec<C: FastFieldCodec, Item: MonotonicallyMappableToU64 + fmt::Debug>(
|
||||
bytes: OwnedBytes,
|
||||
header: &Header,
|
||||
) -> io::Result<Arc<dyn Column<Item>>> {
|
||||
let normalized_header = header.normalized();
|
||||
let reader = C::open_from_bytes(bytes, normalized_header)?;
|
||||
let min_value = header.min_value;
|
||||
if let Some(gcd) = header.gcd {
|
||||
let mapping = StrictlyMonotonicMappingInverter::from(
|
||||
StrictlyMonotonicMappingToInternalGCDBaseval::new(gcd.get(), min_value),
|
||||
);
|
||||
Ok(Arc::new(monotonic_map_column(reader, mapping)))
|
||||
} else {
|
||||
let mapping = StrictlyMonotonicMappingInverter::from(
|
||||
StrictlyMonotonicMappingToInternalBaseval::new(min_value),
|
||||
);
|
||||
Ok(Arc::new(monotonic_map_column(reader, mapping)))
|
||||
}
|
||||
}
|
||||
|
||||
/// The FastFieldSerializerEstimate trait is required on all variants
|
||||
/// of fast field compressions, to decide which one to choose.
|
||||
trait FastFieldCodec: 'static {
|
||||
/// A codex needs to provide a unique name and id, which is
|
||||
/// used for debugging and de/serialization.
|
||||
const CODEC_TYPE: FastFieldCodecType;
|
||||
|
||||
type Reader: Column<u64> + 'static;
|
||||
|
||||
/// Reads the metadata and returns the CodecReader
|
||||
fn open_from_bytes(bytes: OwnedBytes, header: NormalizedHeader) -> io::Result<Self::Reader>;
|
||||
|
||||
/// Serializes the data using the serializer into write.
|
||||
///
|
||||
/// The column iterator should be preferred over using column `get_val` method for
|
||||
/// performance reasons.
|
||||
fn serialize(column: &dyn Column, write: &mut impl Write) -> io::Result<()>;
|
||||
|
||||
/// Returns an estimate of the compression ratio.
|
||||
/// If the codec is not applicable, returns `None`.
|
||||
///
|
||||
/// The baseline is uncompressed 64bit data.
|
||||
///
|
||||
/// It could make sense to also return a value representing
|
||||
/// computational complexity.
|
||||
fn estimate(column: &dyn Column) -> Option<f32>;
|
||||
}
|
||||
|
||||
/// The list of all available codecs for u64 convertible data.
|
||||
pub const ALL_CODEC_TYPES: [FastFieldCodecType; 3] = [
|
||||
FastFieldCodecType::Bitpacked,
|
||||
FastFieldCodecType::BlockwiseLinear,
|
||||
FastFieldCodecType::Linear,
|
||||
];
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
|
||||
use proptest::prelude::*;
|
||||
use proptest::strategy::Strategy;
|
||||
use proptest::{prop_oneof, proptest};
|
||||
|
||||
use crate::bitpacked::BitpackedCodec;
|
||||
use crate::blockwise_linear::BlockwiseLinearCodec;
|
||||
use crate::linear::LinearCodec;
|
||||
use crate::serialize::Header;
|
||||
|
||||
pub(crate) fn create_and_validate<Codec: FastFieldCodec>(
|
||||
data: &[u64],
|
||||
name: &str,
|
||||
) -> Option<(f32, f32)> {
|
||||
let col = &VecColumn::from(data);
|
||||
let header = Header::compute_header(col, &[Codec::CODEC_TYPE])?;
|
||||
let normalized_col = header.normalize_column(col);
|
||||
let estimation = Codec::estimate(&normalized_col)?;
|
||||
|
||||
let mut out = Vec::new();
|
||||
let col = VecColumn::from(data);
|
||||
serialize(col, &mut out, &[Codec::CODEC_TYPE]).unwrap();
|
||||
|
||||
let actual_compression = out.len() as f32 / (data.len() as f32 * 8.0);
|
||||
|
||||
let reader = crate::open::<u64>(OwnedBytes::new(out)).unwrap();
|
||||
assert_eq!(reader.num_vals(), data.len() as u32);
|
||||
for (doc, orig_val) in data.iter().copied().enumerate() {
|
||||
let val = reader.get_val(doc as u32);
|
||||
assert_eq!(
|
||||
val, orig_val,
|
||||
"val `{val}` does not match orig_val {orig_val:?}, in data set {name}, data \
|
||||
`{data:?}`",
|
||||
);
|
||||
}
|
||||
|
||||
if !data.is_empty() {
|
||||
let test_rand_idx = rand::thread_rng().gen_range(0..=data.len() - 1);
|
||||
let expected_positions: Vec<u32> = data
|
||||
.iter()
|
||||
.enumerate()
|
||||
.filter(|(_, el)| **el == data[test_rand_idx])
|
||||
.map(|(pos, _)| pos as u32)
|
||||
.collect();
|
||||
let mut positions = Vec::new();
|
||||
reader.get_docids_for_value_range(
|
||||
data[test_rand_idx]..=data[test_rand_idx],
|
||||
0..data.len() as u32,
|
||||
&mut positions,
|
||||
);
|
||||
assert_eq!(expected_positions, positions);
|
||||
}
|
||||
Some((estimation, actual_compression))
|
||||
}
|
||||
|
||||
proptest! {
|
||||
#![proptest_config(ProptestConfig::with_cases(100))]
|
||||
|
||||
#[test]
|
||||
fn test_proptest_small_bitpacked(data in proptest::collection::vec(num_strategy(), 1..10)) {
|
||||
create_and_validate::<BitpackedCodec>(&data, "proptest bitpacked");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_proptest_small_linear(data in proptest::collection::vec(num_strategy(), 1..10)) {
|
||||
create_and_validate::<LinearCodec>(&data, "proptest linearinterpol");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_proptest_small_blockwise_linear(data in proptest::collection::vec(num_strategy(), 1..10)) {
|
||||
create_and_validate::<BlockwiseLinearCodec>(&data, "proptest multilinearinterpol");
|
||||
}
|
||||
}
|
||||
|
||||
proptest! {
|
||||
#![proptest_config(ProptestConfig::with_cases(10))]
|
||||
|
||||
#[test]
|
||||
fn test_proptest_large_bitpacked(data in proptest::collection::vec(num_strategy(), 1..6000)) {
|
||||
create_and_validate::<BitpackedCodec>(&data, "proptest bitpacked");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_proptest_large_linear(data in proptest::collection::vec(num_strategy(), 1..6000)) {
|
||||
create_and_validate::<LinearCodec>(&data, "proptest linearinterpol");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_proptest_large_blockwise_linear(data in proptest::collection::vec(num_strategy(), 1..6000)) {
|
||||
create_and_validate::<BlockwiseLinearCodec>(&data, "proptest multilinearinterpol");
|
||||
}
|
||||
}
|
||||
|
||||
fn num_strategy() -> impl Strategy<Value = u64> {
|
||||
prop_oneof![
|
||||
1 => prop::num::u64::ANY.prop_map(|num| u64::MAX - (num % 10) ),
|
||||
1 => prop::num::u64::ANY.prop_map(|num| num % 10 ),
|
||||
20 => prop::num::u64::ANY,
|
||||
]
|
||||
}
|
||||
|
||||
pub fn get_codec_test_datasets() -> Vec<(Vec<u64>, &'static str)> {
|
||||
let mut data_and_names = vec![];
|
||||
|
||||
let data = vec![10];
|
||||
data_and_names.push((data, "minimal test"));
|
||||
|
||||
let data = (10..=10_000_u64).collect::<Vec<_>>();
|
||||
data_and_names.push((data, "simple monotonically increasing"));
|
||||
|
||||
data_and_names.push((
|
||||
vec![5, 6, 7, 8, 9, 10, 99, 100],
|
||||
"offset in linear interpol",
|
||||
));
|
||||
|
||||
data_and_names.push((vec![3, 18446744073709551613, 5], "docid range regression"));
|
||||
|
||||
data_and_names.push((vec![5, 50, 3, 13, 1, 1000, 35], "rand small"));
|
||||
data_and_names.push((vec![10], "single value"));
|
||||
|
||||
data_and_names.push((
|
||||
vec![1572656989877777, 1170935903116329, 720575940379279, 0],
|
||||
"overflow error",
|
||||
));
|
||||
|
||||
data_and_names
|
||||
}
|
||||
|
||||
fn test_codec<C: FastFieldCodec>() {
|
||||
let codec_name = format!("{:?}", C::CODEC_TYPE);
|
||||
for (data, dataset_name) in get_codec_test_datasets() {
|
||||
let estimate_actual_opt: Option<(f32, f32)> =
|
||||
crate::tests::create_and_validate::<C>(&data, dataset_name);
|
||||
let result = if let Some((estimate, actual)) = estimate_actual_opt {
|
||||
format!("Estimate `{estimate}` Actual `{actual}`")
|
||||
} else {
|
||||
"Disabled".to_string()
|
||||
};
|
||||
println!("Codec {codec_name}, DataSet {dataset_name}, {result}");
|
||||
}
|
||||
}
|
||||
#[test]
|
||||
fn test_codec_bitpacking() {
|
||||
test_codec::<BitpackedCodec>();
|
||||
}
|
||||
#[test]
|
||||
fn test_codec_interpolation() {
|
||||
test_codec::<LinearCodec>();
|
||||
}
|
||||
#[test]
|
||||
fn test_codec_multi_interpolation() {
|
||||
test_codec::<BlockwiseLinearCodec>();
|
||||
}
|
||||
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn estimation_good_interpolation_case() {
|
||||
let data = (10..=20000_u64).collect::<Vec<_>>();
|
||||
let data: VecColumn = data.as_slice().into();
|
||||
|
||||
let linear_interpol_estimation = LinearCodec::estimate(&data).unwrap();
|
||||
assert_le!(linear_interpol_estimation, 0.01);
|
||||
|
||||
let multi_linear_interpol_estimation = BlockwiseLinearCodec::estimate(&data).unwrap();
|
||||
assert_le!(multi_linear_interpol_estimation, 0.2);
|
||||
assert_lt!(linear_interpol_estimation, multi_linear_interpol_estimation);
|
||||
|
||||
let bitpacked_estimation = BitpackedCodec::estimate(&data).unwrap();
|
||||
assert_lt!(linear_interpol_estimation, bitpacked_estimation);
|
||||
}
|
||||
#[test]
|
||||
fn estimation_test_bad_interpolation_case() {
|
||||
let data: &[u64] = &[200, 10, 10, 10, 10, 1000, 20];
|
||||
|
||||
let data: VecColumn = data.into();
|
||||
let linear_interpol_estimation = LinearCodec::estimate(&data).unwrap();
|
||||
assert_le!(linear_interpol_estimation, 0.34);
|
||||
|
||||
let bitpacked_estimation = BitpackedCodec::estimate(&data).unwrap();
|
||||
assert_lt!(bitpacked_estimation, linear_interpol_estimation);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn estimation_prefer_bitpacked() {
|
||||
let data = VecColumn::from(&[10, 10, 10, 10]);
|
||||
let linear_interpol_estimation = LinearCodec::estimate(&data).unwrap();
|
||||
let bitpacked_estimation = BitpackedCodec::estimate(&data).unwrap();
|
||||
assert_lt!(bitpacked_estimation, linear_interpol_estimation);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn estimation_test_bad_interpolation_case_monotonically_increasing() {
|
||||
let mut data: Vec<u64> = (201..=20000_u64).collect();
|
||||
data.push(1_000_000);
|
||||
let data: VecColumn = data.as_slice().into();
|
||||
|
||||
// in this case the linear interpolation can't in fact not be worse than bitpacking,
|
||||
// but the estimator adds some threshold, which leads to estimated worse behavior
|
||||
let linear_interpol_estimation = LinearCodec::estimate(&data).unwrap();
|
||||
assert_le!(linear_interpol_estimation, 0.35);
|
||||
|
||||
let bitpacked_estimation = BitpackedCodec::estimate(&data).unwrap();
|
||||
assert_le!(bitpacked_estimation, 0.32);
|
||||
assert_le!(bitpacked_estimation, linear_interpol_estimation);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_fast_field_codec_type_to_code() {
|
||||
let mut count_codec = 0;
|
||||
for code in 0..=255 {
|
||||
if let Some(codec_type) = FastFieldCodecType::from_code(code) {
|
||||
assert_eq!(codec_type.to_code(), code);
|
||||
count_codec += 1;
|
||||
}
|
||||
}
|
||||
assert_eq!(count_codec, 3);
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(all(test, feature = "unstable"))]
|
||||
mod bench {
|
||||
use std::sync::Arc;
|
||||
|
||||
use common::OwnedBytes;
|
||||
use rand::rngs::StdRng;
|
||||
use rand::{Rng, SeedableRng};
|
||||
use test::{self, Bencher};
|
||||
|
||||
use super::*;
|
||||
use crate::Column;
|
||||
|
||||
fn get_data() -> Vec<u64> {
|
||||
let mut rng = StdRng::seed_from_u64(2u64);
|
||||
let mut data: Vec<_> = (100..55000_u64)
|
||||
.map(|num| num + rng.gen::<u8>() as u64)
|
||||
.collect();
|
||||
data.push(99_000);
|
||||
data.insert(1000, 2000);
|
||||
data.insert(2000, 100);
|
||||
data.insert(3000, 4100);
|
||||
data.insert(4000, 100);
|
||||
data.insert(5000, 800);
|
||||
data
|
||||
}
|
||||
|
||||
#[inline(never)]
|
||||
fn value_iter() -> impl Iterator<Item = u64> {
|
||||
0..20_000
|
||||
}
|
||||
fn get_reader_for_bench<Codec: FastFieldCodec>(data: &[u64]) -> Codec::Reader {
|
||||
let mut bytes = Vec::new();
|
||||
let min_value = *data.iter().min().unwrap();
|
||||
let data = data.iter().map(|el| *el - min_value).collect::<Vec<_>>();
|
||||
let col = VecColumn::from(&data);
|
||||
let normalized_header = crate::NormalizedHeader {
|
||||
num_vals: col.num_vals(),
|
||||
max_value: col.max_value(),
|
||||
};
|
||||
Codec::serialize(&VecColumn::from(&data), &mut bytes).unwrap();
|
||||
Codec::open_from_bytes(OwnedBytes::new(bytes), normalized_header).unwrap()
|
||||
}
|
||||
fn bench_get<Codec: FastFieldCodec>(b: &mut Bencher, data: &[u64]) {
|
||||
let col = get_reader_for_bench::<Codec>(data);
|
||||
b.iter(|| {
|
||||
let mut sum = 0u64;
|
||||
for pos in value_iter() {
|
||||
let val = col.get_val(pos as u32);
|
||||
sum = sum.wrapping_add(val);
|
||||
}
|
||||
sum
|
||||
});
|
||||
}
|
||||
|
||||
#[inline(never)]
|
||||
fn bench_get_dynamic_helper(b: &mut Bencher, col: Arc<dyn Column>) {
|
||||
b.iter(|| {
|
||||
let mut sum = 0u64;
|
||||
for pos in value_iter() {
|
||||
let val = col.get_val(pos as u32);
|
||||
sum = sum.wrapping_add(val);
|
||||
}
|
||||
sum
|
||||
});
|
||||
}
|
||||
|
||||
fn bench_get_dynamic<Codec: FastFieldCodec>(b: &mut Bencher, data: &[u64]) {
|
||||
let col = Arc::new(get_reader_for_bench::<Codec>(data));
|
||||
bench_get_dynamic_helper(b, col);
|
||||
}
|
||||
fn bench_create<Codec: FastFieldCodec>(b: &mut Bencher, data: &[u64]) {
|
||||
let min_value = *data.iter().min().unwrap();
|
||||
let data = data.iter().map(|el| *el - min_value).collect::<Vec<_>>();
|
||||
|
||||
let mut bytes = Vec::new();
|
||||
b.iter(|| {
|
||||
bytes.clear();
|
||||
Codec::serialize(&VecColumn::from(&data), &mut bytes).unwrap();
|
||||
});
|
||||
}
|
||||
|
||||
#[bench]
|
||||
fn bench_fastfield_bitpack_create(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_create::<BitpackedCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_linearinterpol_create(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_create::<LinearCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_multilinearinterpol_create(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_create::<BlockwiseLinearCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_bitpack_get(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_get::<BitpackedCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_bitpack_get_dynamic(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_get_dynamic::<BitpackedCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_linearinterpol_get(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_get::<LinearCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_linearinterpol_get_dynamic(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_get_dynamic::<LinearCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_multilinearinterpol_get(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_get::<BlockwiseLinearCodec>(b, &data);
|
||||
}
|
||||
#[bench]
|
||||
fn bench_fastfield_multilinearinterpol_get_dynamic(b: &mut Bencher) {
|
||||
let data: Vec<_> = get_data();
|
||||
bench_get_dynamic::<BlockwiseLinearCodec>(b, &data);
|
||||
}
|
||||
}
|
||||
@@ -1,222 +0,0 @@
|
||||
use std::io;
|
||||
use std::num::NonZeroU32;
|
||||
|
||||
use common::{BinarySerializable, VInt};
|
||||
|
||||
use crate::Column;
|
||||
|
||||
const MID_POINT: u64 = (1u64 << 32) - 1u64;
|
||||
|
||||
/// `Line` describes a line function `y: ax + b` using integer
|
||||
/// arithmetics.
|
||||
///
|
||||
/// The slope is in fact a decimal split into a 32 bit integer value,
|
||||
/// and a 32-bit decimal value.
|
||||
///
|
||||
/// The multiplication then becomes.
|
||||
/// `y = m * x >> 32 + b`
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Line {
|
||||
slope: u64,
|
||||
intercept: u64,
|
||||
}
|
||||
|
||||
/// Compute the line slope.
|
||||
///
|
||||
/// This function has the nice property of being
|
||||
/// invariant by translation.
|
||||
/// `
|
||||
/// compute_slope(y0, y1)
|
||||
/// = compute_slope(y0 + X % 2^64, y1 + X % 2^64)
|
||||
/// `
|
||||
fn compute_slope(y0: u64, y1: u64, num_vals: NonZeroU32) -> u64 {
|
||||
let dy = y1.wrapping_sub(y0);
|
||||
let sign = dy <= (1 << 63);
|
||||
let abs_dy = if sign {
|
||||
y1.wrapping_sub(y0)
|
||||
} else {
|
||||
y0.wrapping_sub(y1)
|
||||
};
|
||||
if abs_dy >= 1 << 32 {
|
||||
// This is outside of realm we handle.
|
||||
// Let's just bail.
|
||||
return 0u64;
|
||||
}
|
||||
|
||||
let abs_slope = (abs_dy << 32) / num_vals.get() as u64;
|
||||
if sign {
|
||||
abs_slope
|
||||
} else {
|
||||
// The complement does indeed create the
|
||||
// opposite decreasing slope...
|
||||
//
|
||||
// Intuitively (without the bitshifts and % u64::MAX)
|
||||
// ```
|
||||
// (x + shift)*(u64::MAX - abs_slope)
|
||||
// - (x * (u64::MAX - abs_slope))
|
||||
// = - shift * abs_slope
|
||||
// ```
|
||||
u64::MAX - abs_slope
|
||||
}
|
||||
}
|
||||
|
||||
impl Line {
|
||||
#[inline(always)]
|
||||
pub fn eval(&self, x: u32) -> u64 {
|
||||
let linear_part = ((x as u64).wrapping_mul(self.slope) >> 32) as i32 as u64;
|
||||
self.intercept.wrapping_add(linear_part)
|
||||
}
|
||||
|
||||
// Same as train, but the intercept is only estimated from provided sample positions
|
||||
pub fn estimate(sample_positions_and_values: &[(u64, u64)]) -> Self {
|
||||
let first_val = sample_positions_and_values[0].1;
|
||||
let last_val = sample_positions_and_values[sample_positions_and_values.len() - 1].1;
|
||||
let num_vals = sample_positions_and_values[sample_positions_and_values.len() - 1].0 + 1;
|
||||
Self::train_from(
|
||||
first_val,
|
||||
last_val,
|
||||
num_vals as u32,
|
||||
sample_positions_and_values.iter().cloned(),
|
||||
)
|
||||
}
|
||||
|
||||
// Intercept is only computed from provided positions
|
||||
fn train_from(
|
||||
first_val: u64,
|
||||
last_val: u64,
|
||||
num_vals: u32,
|
||||
positions_and_values: impl Iterator<Item = (u64, u64)>,
|
||||
) -> Self {
|
||||
// TODO replace with let else
|
||||
let idx_last_val = if let Some(idx_last_val) = NonZeroU32::new(num_vals - 1) {
|
||||
idx_last_val
|
||||
} else {
|
||||
return Line::default();
|
||||
};
|
||||
|
||||
let y0 = first_val;
|
||||
let y1 = last_val;
|
||||
|
||||
// We first independently pick our slope.
|
||||
let slope = compute_slope(y0, y1, idx_last_val);
|
||||
|
||||
// We picked our slope. Note that it does not have to be perfect.
|
||||
// Now we need to compute the best intercept.
|
||||
//
|
||||
// Intuitively, the best intercept is such that line passes through one of the
|
||||
// `(i, ys[])`.
|
||||
//
|
||||
// The best intercept therefore has the form
|
||||
// `y[i] - line.eval(i)` (using wrapping arithmetics).
|
||||
// In other words, the best intercept is one of the `y - Line::eval(ys[i])`
|
||||
// and our task is just to pick the one that minimizes our error.
|
||||
//
|
||||
// Without sorting our values, this is a difficult problem.
|
||||
// We however rely on the following trick...
|
||||
//
|
||||
// We only focus on the case where the interpolation is half decent.
|
||||
// If the line interpolation is doing its job on a dataset suited for it,
|
||||
// we can hope that the maximum error won't be larger than `u64::MAX / 2`.
|
||||
//
|
||||
// In other words, even without the intercept the values `y - Line::eval(ys[i])` will all be
|
||||
// within an interval that takes less than half of the modulo space of `u64`.
|
||||
//
|
||||
// Our task is therefore to identify this interval.
|
||||
// Here we simply translate all of our values by `y0 - 2^63` and pick the min.
|
||||
let mut line = Line {
|
||||
slope,
|
||||
intercept: 0,
|
||||
};
|
||||
let heuristic_shift = y0.wrapping_sub(MID_POINT);
|
||||
line.intercept = positions_and_values
|
||||
.map(|(pos, y)| y.wrapping_sub(line.eval(pos as u32)))
|
||||
.min_by_key(|&val| val.wrapping_sub(heuristic_shift))
|
||||
.unwrap_or(0u64); //< Never happens.
|
||||
line
|
||||
}
|
||||
|
||||
/// Returns a line that attemps to approximate a function
|
||||
/// f: i in 0..[ys.num_vals()) -> ys[i].
|
||||
///
|
||||
/// - The approximation is always lower than the actual value.
|
||||
/// Or more rigorously, formally `f(i).wrapping_sub(ys[i])` is small
|
||||
/// for any i in [0..ys.len()).
|
||||
/// - It computes without panicking for any value of it.
|
||||
///
|
||||
/// This function is only invariable by translation if all of the
|
||||
/// `ys` are packaged into half of the space. (See heuristic below)
|
||||
pub fn train(ys: &dyn Column) -> Self {
|
||||
let first_val = ys.iter().next().unwrap();
|
||||
let last_val = ys.iter().nth(ys.num_vals() as usize - 1).unwrap();
|
||||
Self::train_from(
|
||||
first_val,
|
||||
last_val,
|
||||
ys.num_vals(),
|
||||
ys.iter().enumerate().map(|(pos, val)| (pos as u64, val)),
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
impl BinarySerializable for Line {
|
||||
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
VInt(self.slope).serialize(writer)?;
|
||||
VInt(self.intercept).serialize(writer)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let slope = VInt::deserialize(reader)?.0;
|
||||
let intercept = VInt::deserialize(reader)?.0;
|
||||
Ok(Line { slope, intercept })
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::VecColumn;
|
||||
|
||||
/// Test training a line and ensuring that the maximum difference between
|
||||
/// the data points and the line is `expected`.
|
||||
///
|
||||
/// This function operates translation over the data for better coverage.
|
||||
#[track_caller]
|
||||
fn test_line_interpol_with_translation(ys: &[u64], expected: Option<u64>) {
|
||||
let mut translations = vec![0, 100, u64::MAX / 2, u64::MAX, u64::MAX - 1];
|
||||
translations.extend_from_slice(ys);
|
||||
for translation in translations {
|
||||
let translated_ys: Vec<u64> = ys
|
||||
.iter()
|
||||
.copied()
|
||||
.map(|y| y.wrapping_add(translation))
|
||||
.collect();
|
||||
let largest_err = test_eval_max_err(&translated_ys);
|
||||
assert_eq!(largest_err, expected);
|
||||
}
|
||||
}
|
||||
|
||||
fn test_eval_max_err(ys: &[u64]) -> Option<u64> {
|
||||
let line = Line::train(&VecColumn::from(&ys));
|
||||
ys.iter()
|
||||
.enumerate()
|
||||
.map(|(x, y)| y.wrapping_sub(line.eval(x as u32)))
|
||||
.max()
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_train() {
|
||||
test_line_interpol_with_translation(&[11, 11, 11, 12, 12, 13], Some(1));
|
||||
test_line_interpol_with_translation(&[13, 12, 12, 11, 11, 11], Some(1));
|
||||
test_line_interpol_with_translation(&[13, 13, 12, 11, 11, 11], Some(1));
|
||||
test_line_interpol_with_translation(&[13, 13, 12, 11, 11, 11], Some(1));
|
||||
test_line_interpol_with_translation(&[u64::MAX - 1, 0, 0, 1], Some(1));
|
||||
test_line_interpol_with_translation(&[u64::MAX - 1, u64::MAX, 0, 1], Some(0));
|
||||
test_line_interpol_with_translation(&[0, 1, 2, 3, 5], Some(0));
|
||||
test_line_interpol_with_translation(&[1, 2, 3, 4], Some(0));
|
||||
|
||||
let data: Vec<u64> = (0..255).collect();
|
||||
test_line_interpol_with_translation(&data, Some(0));
|
||||
let data: Vec<u64> = (0..255).map(|el| el * 2).collect();
|
||||
test_line_interpol_with_translation(&data, Some(0));
|
||||
}
|
||||
}
|
||||
@@ -1,230 +0,0 @@
|
||||
use std::io::{self, Write};
|
||||
|
||||
use common::{BinarySerializable, OwnedBytes};
|
||||
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
|
||||
|
||||
use crate::line::Line;
|
||||
use crate::serialize::NormalizedHeader;
|
||||
use crate::{Column, FastFieldCodec, FastFieldCodecType};
|
||||
|
||||
/// Depending on the field type, a different
|
||||
/// fast field is required.
|
||||
#[derive(Clone)]
|
||||
pub struct LinearReader {
|
||||
data: OwnedBytes,
|
||||
linear_params: LinearParams,
|
||||
header: NormalizedHeader,
|
||||
}
|
||||
|
||||
impl Column for LinearReader {
|
||||
#[inline]
|
||||
fn get_val(&self, doc: u32) -> u64 {
|
||||
let interpoled_val: u64 = self.linear_params.line.eval(doc);
|
||||
let bitpacked_diff = self.linear_params.bit_unpacker.get(doc, &self.data);
|
||||
interpoled_val.wrapping_add(bitpacked_diff)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn min_value(&self) -> u64 {
|
||||
// The LinearReader assumes a normalized vector.
|
||||
0u64
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn max_value(&self) -> u64 {
|
||||
self.header.max_value
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn num_vals(&self) -> u32 {
|
||||
self.header.num_vals
|
||||
}
|
||||
}
|
||||
|
||||
/// Fastfield serializer, which tries to guess values by linear interpolation
|
||||
/// and stores the difference bitpacked.
|
||||
pub struct LinearCodec;
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
struct LinearParams {
|
||||
line: Line,
|
||||
bit_unpacker: BitUnpacker,
|
||||
}
|
||||
|
||||
impl BinarySerializable for LinearParams {
|
||||
fn serialize<W: io::Write>(&self, writer: &mut W) -> io::Result<()> {
|
||||
self.line.serialize(writer)?;
|
||||
self.bit_unpacker.bit_width().serialize(writer)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn deserialize<R: io::Read>(reader: &mut R) -> io::Result<Self> {
|
||||
let line = Line::deserialize(reader)?;
|
||||
let bit_width = u8::deserialize(reader)?;
|
||||
Ok(Self {
|
||||
line,
|
||||
bit_unpacker: BitUnpacker::new(bit_width),
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
impl FastFieldCodec for LinearCodec {
|
||||
const CODEC_TYPE: FastFieldCodecType = FastFieldCodecType::Linear;
|
||||
|
||||
type Reader = LinearReader;
|
||||
|
||||
/// Opens a fast field given a file.
|
||||
fn open_from_bytes(mut data: OwnedBytes, header: NormalizedHeader) -> io::Result<Self::Reader> {
|
||||
let linear_params = LinearParams::deserialize(&mut data)?;
|
||||
Ok(LinearReader {
|
||||
data,
|
||||
linear_params,
|
||||
header,
|
||||
})
|
||||
}
|
||||
|
||||
/// Creates a new fast field serializer.
|
||||
fn serialize(column: &dyn Column, write: &mut impl Write) -> io::Result<()> {
|
||||
assert_eq!(column.min_value(), 0);
|
||||
let line = Line::train(column);
|
||||
|
||||
let max_offset_from_line = column
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(pos, actual_value)| {
|
||||
let calculated_value = line.eval(pos as u32);
|
||||
actual_value.wrapping_sub(calculated_value)
|
||||
})
|
||||
.max()
|
||||
.unwrap();
|
||||
|
||||
let num_bits = compute_num_bits(max_offset_from_line);
|
||||
let linear_params = LinearParams {
|
||||
line,
|
||||
bit_unpacker: BitUnpacker::new(num_bits),
|
||||
};
|
||||
linear_params.serialize(write)?;
|
||||
|
||||
let mut bit_packer = BitPacker::new();
|
||||
for (pos, actual_value) in column.iter().enumerate() {
|
||||
let calculated_value = line.eval(pos as u32);
|
||||
let offset = actual_value.wrapping_sub(calculated_value);
|
||||
bit_packer.write(offset, num_bits, write)?;
|
||||
}
|
||||
bit_packer.close(write)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// estimation for linear interpolation is hard because, you don't know
|
||||
/// where the local maxima for the deviation of the calculated value are and
|
||||
/// the offset to shift all values to >=0 is also unknown.
|
||||
#[allow(clippy::question_mark)]
|
||||
fn estimate(column: &dyn Column) -> Option<f32> {
|
||||
if column.num_vals() < 3 {
|
||||
return None; // disable compressor for this case
|
||||
}
|
||||
|
||||
let limit_num_vals = column.num_vals().min(100_000);
|
||||
|
||||
let num_samples = 100;
|
||||
let step_size = (limit_num_vals / num_samples).max(1); // 20 samples
|
||||
let mut sample_positions_and_values: Vec<_> = Vec::new();
|
||||
for (pos, val) in column.iter().enumerate().step_by(step_size as usize) {
|
||||
sample_positions_and_values.push((pos as u64, val));
|
||||
}
|
||||
|
||||
let line = Line::estimate(&sample_positions_and_values);
|
||||
|
||||
let estimated_bit_width = sample_positions_and_values
|
||||
.into_iter()
|
||||
.map(|(pos, actual_value)| {
|
||||
let interpolated_val = line.eval(pos as u32);
|
||||
actual_value.wrapping_sub(interpolated_val)
|
||||
})
|
||||
.map(|diff| ((diff as f32 * 1.5) * 2.0) as u64)
|
||||
.map(compute_num_bits)
|
||||
.max()
|
||||
.unwrap_or(0);
|
||||
|
||||
// Extrapolate to whole column
|
||||
let num_bits = (estimated_bit_width as u64 * column.num_vals() as u64) + 64;
|
||||
let num_bits_uncompressed = 64 * column.num_vals();
|
||||
Some(num_bits as f32 / num_bits_uncompressed as f32)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use rand::RngCore;
|
||||
|
||||
use super::*;
|
||||
use crate::tests::get_codec_test_datasets;
|
||||
|
||||
fn create_and_validate(data: &[u64], name: &str) -> Option<(f32, f32)> {
|
||||
crate::tests::create_and_validate::<LinearCodec>(data, name)
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_compression() {
|
||||
let data = (10..=6_000_u64).collect::<Vec<_>>();
|
||||
let (estimate, actual_compression) =
|
||||
create_and_validate(&data, "simple monotonically large").unwrap();
|
||||
|
||||
assert_le!(actual_compression, 0.001);
|
||||
assert_le!(estimate, 0.02);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_with_codec_datasets() {
|
||||
let data_sets = get_codec_test_datasets();
|
||||
for (mut data, name) in data_sets {
|
||||
create_and_validate(&data, name);
|
||||
data.reverse();
|
||||
create_and_validate(&data, name);
|
||||
}
|
||||
}
|
||||
#[test]
|
||||
fn linear_interpol_fast_field_test_large_amplitude() {
|
||||
let data = vec![
|
||||
i64::MAX as u64 / 2,
|
||||
i64::MAX as u64 / 3,
|
||||
i64::MAX as u64 / 2,
|
||||
];
|
||||
|
||||
create_and_validate(&data, "large amplitude");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn overflow_error_test() {
|
||||
let data = vec![1572656989877777, 1170935903116329, 720575940379279, 0];
|
||||
create_and_validate(&data, "overflow test");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn linear_interpol_fast_concave_data() {
|
||||
let data = vec![0, 1, 2, 5, 8, 10, 20, 50];
|
||||
create_and_validate(&data, "concave data");
|
||||
}
|
||||
#[test]
|
||||
fn linear_interpol_fast_convex_data() {
|
||||
let data = vec![0, 40, 60, 70, 75, 77];
|
||||
create_and_validate(&data, "convex data");
|
||||
}
|
||||
#[test]
|
||||
fn linear_interpol_fast_field_test_simple() {
|
||||
let data = (10..=20_u64).collect::<Vec<_>>();
|
||||
create_and_validate(&data, "simple monotonically");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn linear_interpol_fast_field_rand() {
|
||||
let mut rng = rand::thread_rng();
|
||||
for _ in 0..50 {
|
||||
let mut data = (0..10_000).map(|_| rng.next_u64()).collect::<Vec<_>>();
|
||||
create_and_validate(&data, "random");
|
||||
data.reverse();
|
||||
create_and_validate(&data, "random");
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,222 +0,0 @@
|
||||
#[macro_use]
|
||||
extern crate prettytable;
|
||||
use std::collections::HashSet;
|
||||
use std::env;
|
||||
use std::io::BufRead;
|
||||
use std::net::{IpAddr, Ipv6Addr};
|
||||
use std::str::FromStr;
|
||||
|
||||
use common::OwnedBytes;
|
||||
use fastfield_codecs::{open_u128, serialize_u128, Column, FastFieldCodecType, VecColumn};
|
||||
use itertools::Itertools;
|
||||
use measure_time::print_time;
|
||||
use prettytable::{Cell, Row, Table};
|
||||
|
||||
fn print_set_stats(ip_addrs: &[u128]) {
|
||||
println!("NumIps\t{}", ip_addrs.len());
|
||||
let ip_addr_set: HashSet<u128> = ip_addrs.iter().cloned().collect();
|
||||
println!("NumUniqueIps\t{}", ip_addr_set.len());
|
||||
let ratio_unique = ip_addr_set.len() as f64 / ip_addrs.len() as f64;
|
||||
println!("RatioUniqueOverTotal\t{ratio_unique:.4}");
|
||||
|
||||
// histogram
|
||||
let mut ip_addrs = ip_addrs.to_vec();
|
||||
ip_addrs.sort();
|
||||
let mut cnts: Vec<usize> = ip_addrs
|
||||
.into_iter()
|
||||
.dedup_with_count()
|
||||
.map(|(cnt, _)| cnt)
|
||||
.collect();
|
||||
cnts.sort();
|
||||
|
||||
let top_256_cnt: usize = cnts.iter().rev().take(256).sum();
|
||||
let top_128_cnt: usize = cnts.iter().rev().take(128).sum();
|
||||
let top_64_cnt: usize = cnts.iter().rev().take(64).sum();
|
||||
let top_8_cnt: usize = cnts.iter().rev().take(8).sum();
|
||||
let total: usize = cnts.iter().sum();
|
||||
|
||||
println!("{}", total);
|
||||
println!("{}", top_256_cnt);
|
||||
println!("{}", top_128_cnt);
|
||||
println!("Percentage Top8 {:02}", top_8_cnt as f32 / total as f32);
|
||||
println!("Percentage Top64 {:02}", top_64_cnt as f32 / total as f32);
|
||||
println!("Percentage Top128 {:02}", top_128_cnt as f32 / total as f32);
|
||||
println!("Percentage Top256 {:02}", top_256_cnt as f32 / total as f32);
|
||||
|
||||
let mut cnts: Vec<(usize, usize)> = cnts.into_iter().dedup_with_count().collect();
|
||||
cnts.sort_by(|a, b| {
|
||||
if a.1 == b.1 {
|
||||
a.0.cmp(&b.0)
|
||||
} else {
|
||||
b.1.cmp(&a.1)
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
fn ip_dataset() -> Vec<u128> {
|
||||
let mut ip_addr_v4 = 0;
|
||||
|
||||
let stdin = std::io::stdin();
|
||||
let ip_addrs: Vec<u128> = stdin
|
||||
.lock()
|
||||
.lines()
|
||||
.flat_map(|line| {
|
||||
let line = line.unwrap();
|
||||
let line = line.trim();
|
||||
let ip_addr = IpAddr::from_str(line.trim()).ok()?;
|
||||
if ip_addr.is_ipv4() {
|
||||
ip_addr_v4 += 1;
|
||||
}
|
||||
let ip_addr_v6: Ipv6Addr = match ip_addr {
|
||||
IpAddr::V4(v4) => v4.to_ipv6_mapped(),
|
||||
IpAddr::V6(v6) => v6,
|
||||
};
|
||||
Some(ip_addr_v6)
|
||||
})
|
||||
.map(|ip_v6| u128::from_be_bytes(ip_v6.octets()))
|
||||
.collect();
|
||||
|
||||
println!("IpAddrsAny\t{}", ip_addrs.len());
|
||||
println!("IpAddrsV4\t{}", ip_addr_v4);
|
||||
|
||||
ip_addrs
|
||||
}
|
||||
|
||||
fn bench_ip() {
|
||||
let dataset = ip_dataset();
|
||||
print_set_stats(&dataset);
|
||||
|
||||
// Chunks
|
||||
{
|
||||
let mut data = vec![];
|
||||
for dataset in dataset.chunks(500_000) {
|
||||
serialize_u128(|| dataset.iter().cloned(), dataset.len() as u32, &mut data).unwrap();
|
||||
}
|
||||
let compression = data.len() as f64 / (dataset.len() * 16) as f64;
|
||||
println!("Compression 50_000 chunks {:.4}", compression);
|
||||
println!(
|
||||
"Num Bits per elem {:.2}",
|
||||
(data.len() * 8) as f32 / dataset.len() as f32
|
||||
);
|
||||
}
|
||||
|
||||
let mut data = vec![];
|
||||
{
|
||||
print_time!("creation");
|
||||
serialize_u128(|| dataset.iter().cloned(), dataset.len() as u32, &mut data).unwrap();
|
||||
}
|
||||
|
||||
let compression = data.len() as f64 / (dataset.len() * 16) as f64;
|
||||
println!("Compression {:.2}", compression);
|
||||
println!(
|
||||
"Num Bits per elem {:.2}",
|
||||
(data.len() * 8) as f32 / dataset.len() as f32
|
||||
);
|
||||
|
||||
let decompressor = open_u128::<u128>(OwnedBytes::new(data)).unwrap();
|
||||
// Sample some ranges
|
||||
let mut doc_values = Vec::new();
|
||||
for value in dataset.iter().take(1110).skip(1100).cloned() {
|
||||
doc_values.clear();
|
||||
print_time!("get range");
|
||||
decompressor.get_docids_for_value_range(
|
||||
value..=value,
|
||||
0..decompressor.num_vals(),
|
||||
&mut doc_values,
|
||||
);
|
||||
println!("{:?}", doc_values.len());
|
||||
}
|
||||
}
|
||||
|
||||
fn main() {
|
||||
if env::args().nth(1).unwrap() == "bench_ip" {
|
||||
bench_ip();
|
||||
return;
|
||||
}
|
||||
|
||||
let mut table = Table::new();
|
||||
|
||||
// Add a row per time
|
||||
table.add_row(row!["", "Compression Ratio", "Compression Estimation"]);
|
||||
|
||||
for (data, data_set_name) in get_codec_test_data_sets() {
|
||||
let results: Vec<(f32, f32, FastFieldCodecType)> = [
|
||||
serialize_with_codec(&data, FastFieldCodecType::Bitpacked),
|
||||
serialize_with_codec(&data, FastFieldCodecType::Linear),
|
||||
serialize_with_codec(&data, FastFieldCodecType::BlockwiseLinear),
|
||||
]
|
||||
.into_iter()
|
||||
.flatten()
|
||||
.collect();
|
||||
let best_compression_ratio_codec = results
|
||||
.iter()
|
||||
.min_by(|&res1, &res2| res1.partial_cmp(res2).unwrap())
|
||||
.cloned()
|
||||
.unwrap();
|
||||
|
||||
table.add_row(Row::new(vec![Cell::new(data_set_name).style_spec("Bbb")]));
|
||||
for (est, comp, codec_type) in results {
|
||||
let est_cell = est.to_string();
|
||||
let ratio_cell = comp.to_string();
|
||||
let style = if comp == best_compression_ratio_codec.1 {
|
||||
"Fb"
|
||||
} else {
|
||||
""
|
||||
};
|
||||
table.add_row(Row::new(vec![
|
||||
Cell::new(&format!("{codec_type:?}")).style_spec("bFg"),
|
||||
Cell::new(&ratio_cell).style_spec(style),
|
||||
Cell::new(&est_cell).style_spec(""),
|
||||
]));
|
||||
}
|
||||
}
|
||||
|
||||
table.printstd();
|
||||
}
|
||||
|
||||
pub fn get_codec_test_data_sets() -> Vec<(Vec<u64>, &'static str)> {
|
||||
let mut data_and_names = vec![];
|
||||
|
||||
let data = (1000..=200_000_u64).collect::<Vec<_>>();
|
||||
data_and_names.push((data, "Autoincrement"));
|
||||
|
||||
let mut current_cumulative = 0;
|
||||
let data = (1..=200_000_u64)
|
||||
.map(|num| {
|
||||
let num = (num as f32 + num as f32).log10() as u64;
|
||||
current_cumulative += num;
|
||||
current_cumulative
|
||||
})
|
||||
.collect::<Vec<_>>();
|
||||
// let data = (1..=200000_u64).map(|num| num + num).collect::<Vec<_>>();
|
||||
data_and_names.push((data, "Monotonically increasing concave"));
|
||||
|
||||
let mut current_cumulative = 0;
|
||||
let data = (1..=200_000_u64)
|
||||
.map(|num| {
|
||||
let num = (200_000.0 - num as f32).log10() as u64;
|
||||
current_cumulative += num;
|
||||
current_cumulative
|
||||
})
|
||||
.collect::<Vec<_>>();
|
||||
data_and_names.push((data, "Monotonically increasing convex"));
|
||||
|
||||
let data = (1000..=200_000_u64)
|
||||
.map(|num| num + rand::random::<u8>() as u64)
|
||||
.collect::<Vec<_>>();
|
||||
data_and_names.push((data, "Almost monotonically increasing"));
|
||||
|
||||
data_and_names
|
||||
}
|
||||
|
||||
pub fn serialize_with_codec(
|
||||
data: &[u64],
|
||||
codec_type: FastFieldCodecType,
|
||||
) -> Option<(f32, f32, FastFieldCodecType)> {
|
||||
let col = VecColumn::from(data);
|
||||
let estimation = fastfield_codecs::estimate(&col, codec_type)?;
|
||||
let mut out = Vec::new();
|
||||
fastfield_codecs::serialize(&col, &mut out, &[codec_type]).ok()?;
|
||||
let actual_compression = out.len() as f32 / (col.num_vals() * 8) as f32;
|
||||
Some((estimation, actual_compression, codec_type))
|
||||
}
|
||||
@@ -1,320 +0,0 @@
|
||||
use std::fmt;
|
||||
use std::marker::PhantomData;
|
||||
use std::ops::RangeInclusive;
|
||||
|
||||
use fastdivide::DividerU64;
|
||||
|
||||
use crate::MonotonicallyMappableToU128;
|
||||
|
||||
/// Monotonic maps a value to u64 value space.
|
||||
/// Monotonic mapping enables `PartialOrd` on u64 space without conversion to original space.
|
||||
pub trait MonotonicallyMappableToU64:
|
||||
'static + PartialOrd + Copy + Send + Sync + fmt::Debug
|
||||
{
|
||||
/// Converts a value to u64.
|
||||
///
|
||||
/// Internally all fast field values are encoded as u64.
|
||||
fn to_u64(self) -> u64;
|
||||
|
||||
/// Converts a value from u64
|
||||
///
|
||||
/// Internally all fast field values are encoded as u64.
|
||||
/// **Note: To be used for converting encoded Term, Posting values.**
|
||||
fn from_u64(val: u64) -> Self;
|
||||
}
|
||||
|
||||
/// Values need to be strictly monotonic mapped to a `Internal` value (u64 or u128) that can be
|
||||
/// used in fast field codecs.
|
||||
///
|
||||
/// The monotonic mapping is required so that `PartialOrd` can be used on `Internal` without
|
||||
/// converting to `External`.
|
||||
///
|
||||
/// All strictly monotonic functions are invertible because they are guaranteed to have a one-to-one
|
||||
/// mapping from their range to their domain. The `inverse` method is required when opening a codec,
|
||||
/// so a value can be converted back to its original domain (e.g. ip address or f64) from its
|
||||
/// internal representation.
|
||||
pub trait StrictlyMonotonicFn<External: Copy, Internal: Copy> {
|
||||
/// Strictly monotonically maps the value from External to Internal.
|
||||
fn mapping(&self, inp: External) -> Internal;
|
||||
/// Inverse of `mapping`. Maps the value from Internal to External.
|
||||
fn inverse(&self, out: Internal) -> External;
|
||||
|
||||
/// Maps a user provded value from External to Internal.
|
||||
/// It may be necessary to coerce the value if it is outside the value space.
|
||||
/// In that case it tries to find the next greater value in the value space.
|
||||
///
|
||||
/// Returns a bool to mark if a value was outside the value space and had to be coerced _up_.
|
||||
/// With that information we can detect if two values in a range both map outside the same value
|
||||
/// space.
|
||||
///
|
||||
/// coerce_up means the next valid upper value in the value space will be chosen if the value
|
||||
/// has to be coerced.
|
||||
fn mapping_coerce(&self, inp: RangeInclusive<External>) -> RangeInclusive<Internal> {
|
||||
self.mapping(*inp.start())..=self.mapping(*inp.end())
|
||||
}
|
||||
/// Inverse of `mapping_coerce`.
|
||||
fn inverse_coerce(&self, out: RangeInclusive<Internal>) -> RangeInclusive<External> {
|
||||
self.inverse(*out.start())..=self.inverse(*out.end())
|
||||
}
|
||||
}
|
||||
|
||||
/// Inverts a strictly monotonic mapping from `StrictlyMonotonicFn<A, B>` to
|
||||
/// `StrictlyMonotonicFn<B, A>`.
|
||||
///
|
||||
/// # Warning
|
||||
///
|
||||
/// This type comes with a footgun. A type being strictly monotonic does not impose that the inverse
|
||||
/// mapping is strictly monotonic over the entire space External. e.g. a -> a * 2. Use at your own
|
||||
/// risks.
|
||||
pub(crate) struct StrictlyMonotonicMappingInverter<T> {
|
||||
orig_mapping: T,
|
||||
}
|
||||
impl<T> From<T> for StrictlyMonotonicMappingInverter<T> {
|
||||
fn from(orig_mapping: T) -> Self {
|
||||
Self { orig_mapping }
|
||||
}
|
||||
}
|
||||
|
||||
impl<From, To, T> StrictlyMonotonicFn<To, From> for StrictlyMonotonicMappingInverter<T>
|
||||
where
|
||||
T: StrictlyMonotonicFn<From, To>,
|
||||
From: Copy,
|
||||
To: Copy,
|
||||
{
|
||||
#[inline(always)]
|
||||
fn mapping(&self, val: To) -> From {
|
||||
self.orig_mapping.inverse(val)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn inverse(&self, val: From) -> To {
|
||||
self.orig_mapping.mapping(val)
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn mapping_coerce(&self, inp: RangeInclusive<To>) -> RangeInclusive<From> {
|
||||
self.orig_mapping.inverse_coerce(inp)
|
||||
}
|
||||
#[inline]
|
||||
fn inverse_coerce(&self, out: RangeInclusive<From>) -> RangeInclusive<To> {
|
||||
self.orig_mapping.mapping_coerce(out)
|
||||
}
|
||||
}
|
||||
|
||||
/// Applies the strictly monotonic mapping from `T` without any additional changes.
|
||||
pub(crate) struct StrictlyMonotonicMappingToInternal<T> {
|
||||
_phantom: PhantomData<T>,
|
||||
}
|
||||
|
||||
impl<T> StrictlyMonotonicMappingToInternal<T> {
|
||||
pub(crate) fn new() -> StrictlyMonotonicMappingToInternal<T> {
|
||||
Self {
|
||||
_phantom: PhantomData,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<External: MonotonicallyMappableToU128, T: MonotonicallyMappableToU128>
|
||||
StrictlyMonotonicFn<External, u128> for StrictlyMonotonicMappingToInternal<T>
|
||||
where T: MonotonicallyMappableToU128
|
||||
{
|
||||
#[inline(always)]
|
||||
fn mapping(&self, inp: External) -> u128 {
|
||||
External::to_u128(inp)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn inverse(&self, out: u128) -> External {
|
||||
External::from_u128(out)
|
||||
}
|
||||
}
|
||||
|
||||
impl<External: MonotonicallyMappableToU64, T: MonotonicallyMappableToU64>
|
||||
StrictlyMonotonicFn<External, u64> for StrictlyMonotonicMappingToInternal<T>
|
||||
where T: MonotonicallyMappableToU64
|
||||
{
|
||||
#[inline(always)]
|
||||
fn mapping(&self, inp: External) -> u64 {
|
||||
External::to_u64(inp)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn inverse(&self, out: u64) -> External {
|
||||
External::from_u64(out)
|
||||
}
|
||||
}
|
||||
|
||||
/// Mapping dividing by gcd and a base value.
|
||||
///
|
||||
/// The function is assumed to be only called on values divided by passed
|
||||
/// gcd value. (It is necessary for the function to be monotonic.)
|
||||
pub(crate) struct StrictlyMonotonicMappingToInternalGCDBaseval {
|
||||
gcd_divider: DividerU64,
|
||||
gcd: u64,
|
||||
min_value: u64,
|
||||
}
|
||||
impl StrictlyMonotonicMappingToInternalGCDBaseval {
|
||||
pub(crate) fn new(gcd: u64, min_value: u64) -> Self {
|
||||
let gcd_divider = DividerU64::divide_by(gcd);
|
||||
Self {
|
||||
gcd_divider,
|
||||
gcd,
|
||||
min_value,
|
||||
}
|
||||
}
|
||||
}
|
||||
impl<External: MonotonicallyMappableToU64> StrictlyMonotonicFn<External, u64>
|
||||
for StrictlyMonotonicMappingToInternalGCDBaseval
|
||||
{
|
||||
#[inline(always)]
|
||||
fn mapping(&self, inp: External) -> u64 {
|
||||
self.gcd_divider
|
||||
.divide(External::to_u64(inp) - self.min_value)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn inverse(&self, out: u64) -> External {
|
||||
External::from_u64(self.min_value + out * self.gcd)
|
||||
}
|
||||
|
||||
#[inline]
|
||||
#[allow(clippy::reversed_empty_ranges)]
|
||||
fn mapping_coerce(&self, inp: RangeInclusive<External>) -> RangeInclusive<u64> {
|
||||
let end = External::to_u64(*inp.end());
|
||||
if end < self.min_value || inp.end() < inp.start() {
|
||||
return 1..=0;
|
||||
}
|
||||
let map_coerce = |mut inp, coerce_up| {
|
||||
let inp_lower_bound = self.inverse(0);
|
||||
if inp < inp_lower_bound {
|
||||
inp = inp_lower_bound;
|
||||
}
|
||||
let val = External::to_u64(inp);
|
||||
let need_coercion = coerce_up && (val - self.min_value) % self.gcd != 0;
|
||||
let mut mapped_val = self.mapping(inp);
|
||||
if need_coercion {
|
||||
mapped_val += 1;
|
||||
}
|
||||
mapped_val
|
||||
};
|
||||
let start = map_coerce(*inp.start(), true);
|
||||
let end = map_coerce(*inp.end(), false);
|
||||
start..=end
|
||||
}
|
||||
}
|
||||
|
||||
/// Strictly monotonic mapping with a base value.
|
||||
pub(crate) struct StrictlyMonotonicMappingToInternalBaseval {
|
||||
min_value: u64,
|
||||
}
|
||||
impl StrictlyMonotonicMappingToInternalBaseval {
|
||||
#[inline(always)]
|
||||
pub(crate) fn new(min_value: u64) -> Self {
|
||||
Self { min_value }
|
||||
}
|
||||
}
|
||||
|
||||
impl<External: MonotonicallyMappableToU64> StrictlyMonotonicFn<External, u64>
|
||||
for StrictlyMonotonicMappingToInternalBaseval
|
||||
{
|
||||
#[inline]
|
||||
#[allow(clippy::reversed_empty_ranges)]
|
||||
fn mapping_coerce(&self, inp: RangeInclusive<External>) -> RangeInclusive<u64> {
|
||||
if External::to_u64(*inp.end()) < self.min_value {
|
||||
return 1..=0;
|
||||
}
|
||||
let start = self.mapping(External::to_u64(*inp.start()).max(self.min_value));
|
||||
let end = self.mapping(External::to_u64(*inp.end()));
|
||||
start..=end
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn mapping(&self, val: External) -> u64 {
|
||||
External::to_u64(val) - self.min_value
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn inverse(&self, val: u64) -> External {
|
||||
External::from_u64(self.min_value + val)
|
||||
}
|
||||
}
|
||||
|
||||
impl MonotonicallyMappableToU64 for u64 {
|
||||
#[inline(always)]
|
||||
fn to_u64(self) -> u64 {
|
||||
self
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn from_u64(val: u64) -> Self {
|
||||
val
|
||||
}
|
||||
}
|
||||
|
||||
impl MonotonicallyMappableToU64 for i64 {
|
||||
#[inline(always)]
|
||||
fn to_u64(self) -> u64 {
|
||||
common::i64_to_u64(self)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn from_u64(val: u64) -> Self {
|
||||
common::u64_to_i64(val)
|
||||
}
|
||||
}
|
||||
|
||||
impl MonotonicallyMappableToU64 for bool {
|
||||
#[inline(always)]
|
||||
fn to_u64(self) -> u64 {
|
||||
u64::from(self)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn from_u64(val: u64) -> Self {
|
||||
val > 0
|
||||
}
|
||||
}
|
||||
|
||||
// TODO remove me.
|
||||
// Tantivy should refuse NaN values and work with NotNaN internally.
|
||||
impl MonotonicallyMappableToU64 for f64 {
|
||||
#[inline(always)]
|
||||
fn to_u64(self) -> u64 {
|
||||
common::f64_to_u64(self)
|
||||
}
|
||||
|
||||
#[inline(always)]
|
||||
fn from_u64(val: u64) -> Self {
|
||||
common::u64_to_f64(val)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn strictly_monotonic_test() {
|
||||
// identity mapping
|
||||
test_round_trip(&StrictlyMonotonicMappingToInternal::<u64>::new(), 100u64);
|
||||
// round trip to i64
|
||||
test_round_trip(&StrictlyMonotonicMappingToInternal::<i64>::new(), 100u64);
|
||||
// identity mapping
|
||||
test_round_trip(&StrictlyMonotonicMappingToInternal::<u128>::new(), 100u128);
|
||||
|
||||
// base value to i64 round trip
|
||||
let mapping = StrictlyMonotonicMappingToInternalBaseval::new(100);
|
||||
test_round_trip::<_, _, u64>(&mapping, 100i64);
|
||||
// base value and gcd to u64 round trip
|
||||
let mapping = StrictlyMonotonicMappingToInternalGCDBaseval::new(10, 100);
|
||||
test_round_trip::<_, _, u64>(&mapping, 100u64);
|
||||
}
|
||||
|
||||
fn test_round_trip<T: StrictlyMonotonicFn<K, L>, K: std::fmt::Debug + Eq + Copy, L: Copy>(
|
||||
mapping: &T,
|
||||
test_val: K,
|
||||
) {
|
||||
assert_eq!(mapping.inverse(mapping.mapping(test_val)), test_val);
|
||||
}
|
||||
}
|
||||
@@ -1,43 +0,0 @@
|
||||
use std::fmt;
|
||||
use std::net::Ipv6Addr;
|
||||
|
||||
/// Montonic maps a value to u128 value space
|
||||
/// Monotonic mapping enables `PartialOrd` on u128 space without conversion to original space.
|
||||
pub trait MonotonicallyMappableToU128:
|
||||
'static + PartialOrd + Copy + Send + Sync + fmt::Debug
|
||||
{
|
||||
/// Converts a value to u128.
|
||||
///
|
||||
/// Internally all fast field values are encoded as u64.
|
||||
fn to_u128(self) -> u128;
|
||||
|
||||
/// Converts a value from u128
|
||||
///
|
||||
/// Internally all fast field values are encoded as u64.
|
||||
/// **Note: To be used for converting encoded Term, Posting values.**
|
||||
fn from_u128(val: u128) -> Self;
|
||||
}
|
||||
|
||||
impl MonotonicallyMappableToU128 for u128 {
|
||||
fn to_u128(self) -> u128 {
|
||||
self
|
||||
}
|
||||
|
||||
fn from_u128(val: u128) -> Self {
|
||||
val
|
||||
}
|
||||
}
|
||||
|
||||
impl MonotonicallyMappableToU128 for Ipv6Addr {
|
||||
fn to_u128(self) -> u128 {
|
||||
ip_to_u128(self)
|
||||
}
|
||||
|
||||
fn from_u128(val: u128) -> Self {
|
||||
Ipv6Addr::from(val.to_be_bytes())
|
||||
}
|
||||
}
|
||||
|
||||
fn ip_to_u128(ip_addr: Ipv6Addr) -> u128 {
|
||||
u128::from_be_bytes(ip_addr.octets())
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user