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16 Commits

Author SHA1 Message Date
Paul Masurel
d30cafa80a Fixes the behavior of the range doc set, especially on multivalued
cardinality.

We stop scanning from the beginning of the column which is a great
improvement.
This is done by introducing a stateful `SelectCursor`.
2023-02-13 04:02:14 +01:00
PSeitz
36c6138e7f fix: auto downgrade index record option, instead of vint error (#1857)
Prev: thread 'main' panicked at 'called `Result::unwrap()` on an `Err` value: IoError(Custom { kind: InvalidData, error: "Reach end of buffer while reading VInt" })', src/main.rs:46:14
Now: Automatic downgrade to next available level
2023-02-10 13:45:23 +01:00
PSeitz
7a9befd18d fix sort order test for term aggregation (#1858)
fix sort order test for term aggregation
fix invalid request test
2023-02-10 10:26:58 +01:00
Paul Masurel
62c811df2b Added a columnar cli 2023-02-09 19:02:16 +01:00
PSeitz
03345f0aa2 fmt code, update lz4_flex (#1838)
formatting on nightly changed
2023-02-10 01:42:32 +09:00
Paul Masurel
b7bfa20e38 Fixed test performance. 2023-02-09 17:39:55 +01:00
Paul Masurel
db8583db75 Fixing unit test 2023-02-09 16:53:05 +01:00
trinity-1686a
1390834ae8 make Term::as_slice public (#1846) 2023-02-09 15:37:07 +01:00
trinity-1686a
3ac973bea4 fix invalid endianness in documentation (#1845)
* fix doc about term endianness

* rustfmt
2023-02-09 15:36:38 +01:00
Paul Masurel
405e2cf4d9 Merge with main 2023-02-09 14:28:57 +01:00
Paul Masurel
b63c6c27bc adding change from main 2023-02-09 14:18:46 +01:00
Paul Masurel
bd5eea9852 Integrated columnar work. 2023-02-09 13:14:31 +01:00
PSeitz
0f20787917 fix doc store cache docs (#1821)
* fix doc store cache docs

addresses an issue reported in #1820

* rename doc_store_cache_size
2023-01-23 07:06:49 +01:00
Paul Masurel
2874554ee4 Removed the sorting logic that forced column type to be sorted like (#1816)
* Removed the sorting logic that forced column type to be sorted like
ColumnTypes.

* add comments

Co-authored-by: PSeitz <PSeitz@users.noreply.github.com>
2023-01-20 12:43:28 +01:00
PSeitz
cbc70a9eae Cargo.toml cleanup (#1817) 2023-01-20 12:30:35 +01:00
PSeitz
226d0f88bc add columnar to workspace (#1808) 2023-01-20 11:47:10 +01:00
127 changed files with 7901 additions and 6131 deletions

View File

@@ -23,7 +23,7 @@ regex = { version = "1.5.5", default-features = false, features = ["std", "unico
aho-corasick = "0.7"
tantivy-fst = "0.4.0"
memmap2 = { version = "0.5.3", optional = true }
lz4_flex = { version = "0.9.2", default-features = false, features = ["checked-decode"], optional = true }
lz4_flex = { version = "0.10", default-features = false, features = ["checked-decode"], optional = true }
brotli = { version = "3.3.4", optional = true }
zstd = { version = "0.12", optional = true, default-features = false }
snap = { version = "1.0.5", optional = true }
@@ -55,13 +55,12 @@ measure_time = "0.8.2"
async-trait = "0.1.53"
arc-swap = "1.5.0"
columnar = { version="0.1", path="./columnar", package ="tantivy-columnar" }
sstable = { version="0.1", path="./sstable", package ="tantivy-sstable", optional = true }
stacker = { version="0.1", path="./stacker", package ="tantivy-stacker" }
tantivy-query-grammar = { version= "0.19.0", path="./query-grammar" }
tantivy-bitpacker = { version= "0.3", path="./bitpacker" }
columnar = { version= "0.1", path="./columnar", package="tantivy-columnar" }
common = { version= "0.5", path = "./common/", package = "tantivy-common" }
fastfield_codecs = { version= "0.3", path="./fastfield_codecs", default-features = false }
tantivy-bitpacker = { version= "0.3", path="./bitpacker" }
common = { version= "0.5", path = "./common/", package = "tantivy-common" }
tokenizer-api = { version="0.1", path="./tokenizer-api", package="tantivy-tokenizer-api" }
[target.'cfg(windows)'.dependencies]
@@ -108,7 +107,7 @@ unstable = [] # useful for benches.
quickwit = ["sstable"]
[workspace]
members = ["query-grammar", "bitpacker", "common", "fastfield_codecs", "ownedbytes", "stacker", "sstable", "tokenizer-api", "columnar"]
members = ["query-grammar", "bitpacker", "common", "ownedbytes", "stacker", "sstable", "tokenizer-api", "columnar"]
# Following the "fail" crate best practises, we isolate
# tests that define specific behavior in fail check points

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@@ -19,7 +19,7 @@ impl BitPacker {
}
#[inline]
pub fn write<TWrite: io::Write>(
pub fn write<TWrite: io::Write + ?Sized>(
&mut self,
val: u64,
num_bits: u8,
@@ -43,7 +43,7 @@ impl BitPacker {
Ok(())
}
pub fn flush<TWrite: io::Write>(&mut self, output: &mut TWrite) -> io::Result<()> {
pub fn flush<TWrite: io::Write + ?Sized>(&mut self, output: &mut TWrite) -> io::Result<()> {
if self.mini_buffer_written > 0 {
let num_bytes = (self.mini_buffer_written + 7) / 8;
let bytes = self.mini_buffer.to_le_bytes();
@@ -54,7 +54,7 @@ impl BitPacker {
Ok(())
}
pub fn close<TWrite: io::Write>(&mut self, output: &mut TWrite) -> io::Result<()> {
pub fn close<TWrite: io::Write + ?Sized>(&mut self, output: &mut TWrite) -> io::Result<()> {
self.flush(output)?;
Ok(())
}

View File

@@ -5,24 +5,28 @@ edition = "2021"
license = "MIT"
[dependencies]
itertools = "0.10.5"
log = "0.4.17"
fnv = "1.0.7"
fastdivide = "0.4.0"
rand = { version = "0.8.5", optional = true }
measure_time = { version = "0.8.2", optional = true }
prettytable-rs = { version = "0.10.0", optional = true }
stacker = { path = "../stacker", package="tantivy-stacker"}
serde_json = "1"
thiserror = "1"
fnv = "1"
sstable = { path = "../sstable", package = "tantivy-sstable" }
common = { path = "../common", package = "tantivy-common" }
itertools = "0.10"
log = "0.4"
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"
measure_time = { version="0.8.2", optional=true}
[dev-dependencies]
proptest = "1"
more-asserts = "0.3.0"
rand = "0.8.3"
more-asserts = "0.3.1"
rand = "0.8.5"
criterion = "0.4"
[features]
unstable = []
[[bench]]
name = "bench_index"
harness = false

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@@ -1,6 +0,0 @@
test:
echo "Run test only... No examples."
cargo test --tests --lib
fmt:
cargo +nightly fmt --all

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@@ -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 rand::prelude::*;
use tantivy_columnar::*;
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
});
}
}

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@@ -0,0 +1,91 @@
use std::ops::Range;
use criterion::*;
use rand::prelude::*;
use tantivy_columnar::column_index::MultiValueIndex;
use tantivy_columnar::RowId;
const WINDOW: usize = 40;
fn bench_multi_value_index_util(
len_range: Range<u32>,
num_rows: RowId,
select_value_ratio: f64,
b: &mut criterion::Bencher,
) {
let mut start_index: Vec<RowId> = vec![0u32];
let mut cursor: u32 = 0u32;
let mut rng = StdRng::from_seed([16u8; 32]);
for i in 0..num_rows {
let num_vals = rng.gen_range(len_range.clone());
cursor += num_vals;
start_index.push(cursor);
}
let select_rows: Vec<RowId> = (0u32..cursor)
.filter(|i| rng.gen_bool(select_value_ratio))
.collect();
let mv_index = MultiValueIndex::for_test(&start_index);
// mv_index.select_batch_in_place(0, &mut select_rows[..]);
let mut buffer = Vec::new();
b.iter(|| {
let mut start_row = 0u32;
let mut len = 0;
for chunk in select_rows.chunks(WINDOW) {
buffer.clear();
buffer.extend_from_slice(chunk);
mv_index.select_batch_in_place(start_row, &mut buffer);
start_row = buffer.last().copied().unwrap();
len += buffer.len()
}
assert_eq!(len, 4303);
len
});
}
fn bench_multi_value_index_util2(
len_range: Range<u32>,
num_rows: RowId,
select_value_ratio: f64,
b: &mut criterion::Bencher,
) {
let mut start_index: Vec<RowId> = vec![0u32];
let mut cursor: u32 = 0u32;
let mut rng = StdRng::from_seed([16u8; 32]);
for i in 0..num_rows {
let num_vals = rng.gen_range(len_range.clone());
cursor += num_vals;
start_index.push(cursor);
}
let select_rows: Vec<RowId> = (0u32..cursor)
.filter(|i| rng.gen_bool(select_value_ratio))
.collect();
let mv_index = MultiValueIndex::for_test(&start_index);
// mv_index.select_batch_in_place(0, &mut select_rows[..]);
let mut buffer = Vec::new();
b.iter(|| {
let mut mv_index_cursor = mv_index.select_cursor();
let mut len = 0;
for chunk in select_rows.chunks(WINDOW) {
buffer.clear();
buffer.extend_from_slice(chunk);
mv_index_cursor.select_batch_in_place(&mut buffer);
len += buffer.len();
}
assert_eq!(len, 4303);
len
});
}
fn select_benchmark(c: &mut criterion::Criterion) {
c.bench_function("bench_multi_value_index_10_100", |b| {
bench_multi_value_index_util(0..10, 100_000, 0.01f64, b)
});
c.bench_function("bench_multi_value_cursor_index_10_100", |b| {
bench_multi_value_index_util2(0..10, 100_000, 0.01f64, b)
});
}
criterion_group!(benches, select_benchmark);
criterion_main!(benches);

View File

@@ -0,0 +1,124 @@
#![feature(test)]
use std::ops::RangeInclusive;
use std::sync::Arc;
use common::OwnedBytes;
use rand::rngs::StdRng;
use rand::seq::SliceRandom;
use rand::{random, Rng, SeedableRng};
use tantivy_columnar::ColumnValues;
use test::Bencher;
extern crate test;
// TODO does this make sense for IPv6 ?
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
}
fn get_u128_column_random() -> Arc<dyn ColumnValues<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 ColumnValues<u128>> {
let mut out = vec![];
tantivy_columnar::column_values::serialize_column_values_u128(&data, &mut out).unwrap();
let out = OwnedBytes::new(out);
tantivy_columnar::column_values::open_u128_mapped::<u128>(out).unwrap()
}
const FIFTY_PERCENT_RANGE: RangeInclusive<u64> = 1..=50;
const SINGLE_ITEM: u64 = 90;
const SINGLE_ITEM_RANGE: RangeInclusive<u64> = 90..=90;
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
}
#[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
});
}

View File

@@ -0,0 +1,211 @@
#![feature(test)]
extern crate test;
use std::ops::RangeInclusive;
use std::sync::Arc;
use rand::prelude::*;
use tantivy_columnar::column_values::{serialize_and_load_u64_based_column_values, CodecType};
use tantivy_columnar::*;
use test::Bencher;
// 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(column: &[u64], codec_type: CodecType) -> Arc<dyn ColumnValues<u64>> {
serialize_and_load_u64_based_column_values(&column, &[codec_type])
}
#[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_bitpacked(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 = 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
}
// 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 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
});
}

View 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

View 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(())
}

View File

@@ -1,25 +1,21 @@
# 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
* replug facets
* replug range queries
+ mutlivaued range queries restrat frm the beginning all of the time.
* 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`
* 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
@@ -29,7 +25,6 @@ Add alignment?
Consider another codec to bridge the gap between few and 5k elements
# Cleanup and rationalization
remove the 6 bit limitation of columntype. use 4 + 4 bits instead.
in benchmark, unify percent vs ratio, f32 vs f64.
investigate if should have better errors? io::Error is overused at the moment.
rename rank/select in unit tests
@@ -40,16 +35,13 @@ 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
remove old column from the fast field API.
remove the Column traits alias.
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.

View File

@@ -56,18 +56,28 @@ impl BytesColumn {
#[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();

View File

@@ -2,27 +2,46 @@ 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 struct Column<T = u64> {
pub idx: ColumnIndex,
pub values: Arc<dyn ColumnValues<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,
@@ -52,6 +71,15 @@ impl<T: PartialOrd + Copy + Debug + 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,
@@ -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)
}
@@ -105,7 +133,7 @@ impl<T: PartialOrd + Debug + Send + Sync + Copy + 'static> ColumnValues<T>
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(),
}
}
}

View File

@@ -1,4 +1,3 @@
use std::fmt::Debug;
use std::io;
use std::io::Write;
use std::sync::Arc;
@@ -9,43 +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 + Debug>(
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())?;
@@ -62,7 +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,
@@ -88,15 +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))
}

View 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]);
}
}

View 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]);
}
}

View 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]);
}
}

View File

@@ -1,15 +1,98 @@
mod merge;
mod multivalued_index;
mod optional_index;
mod serialize;
use std::ops::Range;
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_index::multivalued_index::MultiValueIndex;
pub use crate::column_index::multivalued_index::{MultiValueIndex, MultiValueIndexCursor};
use crate::column_index::optional_index::OptionalIndexSelectCursor;
use crate::{Cardinality, RowId};
pub struct ColumnIndexSelectCursor {
last_rank: Option<RowId>,
cardinality_specific_impl: CardinalitySpecificSelectCursor,
}
impl From<CardinalitySpecificSelectCursor> for ColumnIndexSelectCursor {
fn from(cardinality_specific_impl: CardinalitySpecificSelectCursor) -> Self {
ColumnIndexSelectCursor {
last_rank: None,
cardinality_specific_impl,
}
}
}
enum CardinalitySpecificSelectCursor {
Full,
Optional(OptionalIndexSelectCursor),
Multivalued(MultiValueIndexCursor),
}
/// This cursor object point is to compute batches of `select` operations.
///
/// Regardless of cardinality, a column index can always be seen as a mapping
/// from row_id -> start_value_row_id. By definition, it is increasing.
/// If `left <= right, column_index[left] <= column_index[right]`.
///
/// The select operation then identifies, given a value row id, which row it
/// belong to: it is the inverse mapping.
///
/// As a more formal definition, `select(rank)` is defined as the only `i` such that
/// mapping[i] <= rank and mapping[i+1] < rank.
/// Another way to define it is to say that it is the last i such that
/// mapping[i] <= rank.
/// Finally it can be defined as the number of `row_id` such that
/// mapping[i] <= rank.
///
/// `select_batch_in_place` is a complex function that copmutes
/// select operation in batches and in place.
///
/// For optimization reasons, it only supports supplying ever striclty increasing
/// values of `rank_ids`, even cross calls.
///
/// It is also required from the caller, to only supply rank_ids lower than max(mapping).
/// Within those condition, the returned `row_ids` are guaranteed to be unique.
///
/// # Panics
///
/// Panics if the supplied rank_ids are not increasing from one call to another.
/// We only check that the `rank_ids` Vec is increasing in debug mode for
/// performance reason.
impl ColumnIndexSelectCursor {
/// Returns a list of
pub fn select_batch_in_place(&mut self, rank_ids: &mut Vec<RowId>) {
// `rank_ids` has to be sorted.
debug_assert!(rank_ids.windows(2).all(|window| window[0] < window[1]));
// Two consecutive calls must pass strictly increasing `rank_ids`.
let (Some(first_rank), Some(new_last_rank)) = (rank_ids.first().copied(), rank_ids.last().copied()) else {
// rank_ids is empty, there is nothing to do.
return;
};
if let Some(last_rank) = self.last_rank {
assert!(last_rank < first_rank);
}
self.last_rank = Some(new_last_rank);
match &mut self.cardinality_specific_impl {
CardinalitySpecificSelectCursor::Full => {
// No need to do anything:
// `value_idx` and `row_idx` are the same.
}
CardinalitySpecificSelectCursor::Optional(optional_index) => {
optional_index.select_batch_in_place(&mut rank_ids[..]);
}
CardinalitySpecificSelectCursor::Multivalued(multivalued_index) => {
// TODO important: avoid using 0u32, and restart from the beginning all of the time.
multivalued_index.select_batch_in_place(rank_ids)
}
}
}
}
#[derive(Clone)]
pub enum ColumnIndex {
Full,
@@ -19,6 +102,18 @@ pub enum ColumnIndex {
Multivalued(MultiValueIndex),
}
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 {
@@ -28,6 +123,17 @@ impl ColumnIndex {
}
}
/// 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,
@@ -42,18 +148,15 @@ impl ColumnIndex {
}
}
pub fn select_batch_in_place(&self, rank_ids: &mut Vec<RowId>) {
pub fn select_cursor(&self) -> ColumnIndexSelectCursor {
match self {
ColumnIndex::Full => {
// No need to do anything:
// value_idx and row_idx are the same.
}
ColumnIndex::Full => CardinalitySpecificSelectCursor::Full.into(),
ColumnIndex::Optional(optional_index) => {
optional_index.select_batch(&mut rank_ids[..]);
CardinalitySpecificSelectCursor::Optional(optional_index.select_cursor()).into()
}
ColumnIndex::Multivalued(multivalued_index) => {
// TODO important: avoid using 0u32, and restart from the beginning all of the time.
multivalued_index.select_batch_in_place(0u32, rank_ids)
CardinalitySpecificSelectCursor::Multivalued(multivalued_index.select_cursor())
.into()
}
}
}

View File

@@ -5,16 +5,18 @@ 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;
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(())
@@ -22,7 +24,7 @@ pub fn serialize_multivalued_index(
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)?;
crate::column_values::u64_based::load_u64_based_column_values(bytes)?;
Ok(MultiValueIndex { start_index_column })
}
@@ -30,16 +32,17 @@ pub fn open_multivalued_index(bytes: OwnedBytes) -> io::Result<MultiValueIndex>
/// Index to resolve value range for given doc_id.
/// Starts at 0.
pub struct MultiValueIndex {
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 }
}
pub start_index_column: Arc<dyn crate::ColumnValues<RowId>>,
}
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]
@@ -55,78 +58,302 @@ impl MultiValueIndex {
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.
pub fn select_cursor(&self) -> MultiValueIndexCursor {
MultiValueIndexCursor {
multivalued_index: self.clone(),
row_cursor: 0u32,
}
}
}
pub struct MultiValueIndexCursor {
multivalued_index: MultiValueIndex,
row_cursor: RowId,
}
impl MultiValueIndexCursor {
/// See contract in `ColumnIndexSelectCursor`.
///
/// Since there is no index for value pos -> docid, but docid -> value pos range, we scan the
/// index.
/// Multi valued cardinality is special for two different
/// ranks `rank_left` and `rank_right`, we can end up with
/// the same `select(rank_left)` and `select(rank_right)`.
///
/// 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>) {
/// For this reason, this function includes extra complexity
/// to prevent the cursor from emitting the same row_id.
/// - From a last call, by skipping ranks mapping to
/// the same row_id
/// - With the batch, by simply deduplicating the output.
pub fn select_batch_in_place(&mut self, ranks: &mut Vec<RowId>) {
if ranks.is_empty() {
return;
}
let mut cur_doc = row_start;
let mut last_doc = None;
let mut row_cursor = self.row_cursor;
assert!(self.start_index_column.get_val(row_start) as u32 <= ranks[0]);
let mut write_cursor_id = usize::MAX;
let mut last_written_row_id = u32::MAX;
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;
// We skip all of the ranks that we already passed.
//
// It is possible in the case of multivalued, for a the first
// few rank to belong to the same row_id as the last rank
// of the previous call.
let start_bound = self
.multivalued_index
.start_index_column
.get_val(row_cursor);
let mut skip = 0;
while ranks[skip] < start_bound {
skip += 1;
if skip == ranks.len() {
ranks.clear();
return;
}
}
ranks.truncate(write_doc_pos);
for i in skip..ranks.len() {
let rank = ranks[i];
let row_id = loop {
// TODO See if we can find a way to introduce a function in
// ColumnValue to remove dynamic dispatch.
// This is tricky however... because it only applies to T=u32.
//
// TODO consider using exponential search.
let end = self
.multivalued_index
.start_index_column
.get_val(row_cursor + 1) as u32;
if end > rank {
break row_cursor;
}
row_cursor += 1;
};
// We remove duplicates in a branchless fashion: we only advance
// the write cursor when we are writing a value different from
// the last written value.
write_cursor_id =
write_cursor_id.wrapping_add(if row_id == last_written_row_id { 0 } else { 1 });
ranks[write_cursor_id] = row_id;
last_written_row_id = row_id;
}
self.row_cursor = row_cursor + 1;
ranks.truncate(write_cursor_id + 1);
}
}
#[cfg(test)]
mod tests {
use std::ops::Range;
use std::sync::Arc;
use super::MultiValueIndex;
use crate::column_values::IterColumn;
use crate::{ColumnValues, RowId};
use proptest::prelude::*;
fn index_to_pos_helper(
index: &MultiValueIndex,
doc_id_range: Range<u32>,
positions: &[u32],
) -> Vec<u32> {
fn index_to_pos_helper(index: &MultiValueIndex, positions: &[u32]) -> Vec<u32> {
let mut positions = positions.to_vec();
index.select_batch_in_place(doc_id_range.start, &mut positions);
let mut cursor = index.select_cursor();
cursor.select_batch_in_place(&mut positions);
positions
}
// Value row id ranges are [0..10, 10..12, 12..15, etc.]
const START_OFFSETS: &[RowId] = &[0, 10, 12, 15, 22, 23];
#[track_caller]
fn test_multivalue_select_cursor_aux(
start_offsets: &'static [RowId],
ranks: &[RowId],
expected: &[RowId],
) {
let column: Arc<dyn ColumnValues<RowId>> =
Arc::new(IterColumn::from(start_offsets.iter().copied()));
let index = MultiValueIndex {
start_index_column: column,
};
assert_eq!(&index_to_pos_helper(&index, &ranks), expected);
}
#[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]);
fn test_multivalue_select_cursor_empty() {
test_multivalue_select_cursor_aux(START_OFFSETS, &[], &[]);
}
#[test]
fn test_multivalue_select_cursor_single() {
test_multivalue_select_cursor_aux(START_OFFSETS, &[9], &[0]);
test_multivalue_select_cursor_aux(START_OFFSETS, &[10], &[1]);
test_multivalue_select_cursor_aux(START_OFFSETS, &[11], &[1]);
test_multivalue_select_cursor_aux(START_OFFSETS, &[11], &[1]);
test_multivalue_select_cursor_aux(START_OFFSETS, &[12], &[2]);
}
#[test]
fn test_multivalue_select_cursor_duplicates() {
test_multivalue_select_cursor_aux(START_OFFSETS, &[12, 14], &[2]);
}
#[test]
fn test_multivalue_select_cursor_complex() {
test_multivalue_select_cursor_aux(START_OFFSETS, &[10, 11, 15, 20, 21, 22], &[1, 3, 4])
}
#[test]
fn test_multivalue_select_corner_case_skip_all() {
let column: Arc<dyn ColumnValues<RowId>> =
Arc::new(IterColumn::from([0, 10].into_iter()));
let index = MultiValueIndex {
start_index_column: column,
};
let mut cursor = index.select_cursor();
{
let mut ranks = vec![0];
cursor.select_batch_in_place(&mut ranks);
assert_eq!(ranks, &[0]);
}
{
let mut ranks = vec![5];
cursor.select_batch_in_place(&mut ranks);
assert_eq!(ranks, &[]);
}
}
#[test]
fn test_multi_value_index_cursor_bug() {
let column: Arc<dyn ColumnValues<RowId>> =
Arc::new(IterColumn::from([0, 10].into_iter()));
let index = MultiValueIndex {
start_index_column: column,
};
let mut cursor = index.select_cursor();
{
let mut ranks = vec![0];
cursor.select_batch_in_place(&mut ranks);
assert_eq!(ranks, &[0]);
}
{
let mut ranks = vec![4];
cursor.select_batch_in_place(&mut ranks);
assert_eq!(ranks, &[]);
}
{
let mut ranks = vec![9];
cursor.select_batch_in_place(&mut ranks);
assert_eq!(ranks, &[]);
}
}
#[test]
fn test_multivalue_select_cursor_skip_already_emitted() {
let column: Arc<dyn ColumnValues<RowId>> =
Arc::new(IterColumn::from(START_OFFSETS.iter().copied()));
let index = MultiValueIndex {
start_index_column: column,
};
let mut cursor = index.select_cursor();
{
let mut ranks = vec![1, 10];
cursor.select_batch_in_place(&mut ranks);
assert_eq!(ranks, &[0, 1]);
}
{
// Here we skip row_id = 1.
let mut ranks = vec![11, 12];
cursor.select_batch_in_place(&mut ranks);
assert_eq!(ranks, &[2]);
}
}
fn start_index_strategy() -> impl Strategy<Value = Vec<RowId>> {
proptest::collection::vec(0u32..3u32, 1..6)
.prop_map(|deltas: Vec<u32>| {
let mut start_offsets: Vec<RowId> = Vec::with_capacity(deltas.len() + 1);
let mut cumul = 0u32;
start_offsets.push(cumul);
for delta in deltas {
cumul += delta;
if cumul >= 10 {
break;
}
start_offsets.push(cumul);
}
start_offsets.push(10);
start_offsets
})
}
fn query_strategy() -> impl Strategy<Value = Vec<Vec<RowId>> > {
proptest::collection::btree_set(0u32..10u32, 1..=10)
.prop_flat_map(|els| {
let els: Vec<RowId> = els.into_iter().collect();
proptest::collection::btree_set(0..els.len(), 0..els.len())
.prop_map(move |mut split_positions| {
split_positions.insert(els.len());
let mut queries: Vec<Vec<RowId>> = Vec::with_capacity(split_positions.len() + 1);
let mut cursor = 0;
for split_position in split_positions {
queries.push(els[cursor..split_position].to_vec());
cursor = split_position;
}
queries
})
})
}
/// Simple inefficient implementation used for reference.
struct SimpleSelectCursor {
start_indexes: Vec<RowId>,
last_emitted_row_id: Option<RowId>,
}
impl SimpleSelectCursor {
fn select(&self, rank: u32) -> RowId {
for i in 0..self.start_indexes.len() - 1 {
if self.start_indexes[i] <= rank && self.start_indexes[i + 1] > rank{
return i as u32;
}
}
panic!();
}
fn select_batch_in_place(&mut self, ranks: &mut Vec<RowId>) {
if ranks.is_empty() {
return;
}
for rank in ranks.iter_mut() {
*rank = self.select(*rank);
}
ranks.dedup();
if ranks.first().copied() == self.last_emitted_row_id {
ranks.remove(0);
}
if let Some(last_emitted) = ranks.last().copied() {
self.last_emitted_row_id = Some(last_emitted);
}
}
}
proptest! {
#[test]
fn test_multi_value_index_cursor_proptest(start_indexes in start_index_strategy(), mut queries in query_strategy()) {
let mut simple_select_cursor = SimpleSelectCursor {
start_indexes: start_indexes.clone(),
last_emitted_row_id: None
};
let column: Arc<dyn ColumnValues<RowId>> =
Arc::new(IterColumn::from(start_indexes.into_iter()));
let index = MultiValueIndex { start_index_column: column };
let mut select_cursor = index.select_cursor();
for query in queries.iter_mut() {
let mut query_clone = query.clone();
select_cursor.select_batch_in_place(query);
simple_select_cursor.select_batch_in_place(&mut query_clone);
assert_eq!(&query[..], &query_clone[..]);
}
}
}
}

View File

@@ -1,5 +1,4 @@
use std::io::{self, Write};
use std::ops::Range;
use std::sync::Arc;
mod set;
@@ -11,6 +10,7 @@ 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
@@ -128,17 +118,17 @@ impl<'a> BlockSelectCursor<'a> {
}
}
}
pub struct OptionalIndexSelectCursor<'a> {
current_block_cursor: BlockSelectCursor<'a>,
pub struct OptionalIndexSelectCursor {
current_block_cursor: BlockSelectCursor<'static>,
current_block_id: u16,
// The current block is guaranteed to contain ranks < end_rank.
current_block_end_rank: RowId,
optional_index: &'a OptionalIndex,
optional_index: OptionalIndex,
block_doc_idx_start: RowId,
num_null_rows_before_block: RowId,
}
impl<'a> OptionalIndexSelectCursor<'a> {
impl OptionalIndexSelectCursor {
fn search_and_load_block(&mut self, rank: RowId) {
if rank < self.current_block_end_rank {
// we are already in the right block
@@ -155,14 +145,23 @@ impl<'a> OptionalIndexSelectCursor<'a> {
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 {
let 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()),
};
// We are building a self-owned `OptionalIndexSelectCursor`.
self.current_block_cursor = unsafe { std::mem::transmute(current_block_cursor) };
}
pub fn select_batch_in_place(&mut self, ranks: &mut [RowId]) {
// TODO see if we can batch at the block level as well for optimization purposes.
for rank in ranks {
*rank = self.select(*rank);
}
}
}
impl<'a> SelectCursor<RowId> for OptionalIndexSelectCursor<'a> {
impl<'a> SelectCursor<RowId> for OptionalIndexSelectCursor {
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;
@@ -171,7 +170,7 @@ impl<'a> SelectCursor<RowId> for OptionalIndexSelectCursor<'a> {
}
impl Set<RowId> for OptionalIndex {
type SelectCursor<'b> = OptionalIndexSelectCursor<'b> where Self: 'b;
type SelectCursor<'a> = OptionalIndexSelectCursor;
// Check if value at position is not null.
#[inline]
fn contains(&self, row_id: RowId) -> bool {
@@ -186,6 +185,21 @@ 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 {
@@ -215,14 +229,14 @@ impl Set<RowId> for OptionalIndex {
block_doc_idx_start + in_block_rank as u32
}
fn select_cursor<'b>(&'b self) -> OptionalIndexSelectCursor<'b> {
fn select_cursor(&self) -> OptionalIndexSelectCursor {
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,
optional_index: self.clone(),
block_doc_idx_start: 0u32,
num_null_rows_before_block: 0u32,
}
@@ -230,6 +244,31 @@ impl Set<RowId> for OptionalIndex {
}
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() {
@@ -300,7 +339,7 @@ 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()])
}
@@ -322,12 +361,13 @@ 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();
@@ -480,19 +520,5 @@ 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;

View File

@@ -28,7 +28,10 @@ pub trait Set<T> {
/// 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>;

View File

@@ -148,6 +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();

View File

@@ -44,6 +44,11 @@ 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;

View File

@@ -17,6 +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]);

View File

@@ -37,7 +37,7 @@ 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();
@@ -66,12 +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()
@@ -84,7 +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()
@@ -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 select_cursor = null_index.select_cursor();
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"))]

View File

@@ -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)?
}

View File

@@ -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");
}
}
}

View File

@@ -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
}
}

View File

@@ -1,6 +1,7 @@
use std::fmt::Debug;
use std::marker::PhantomData;
use std::ops::{Range, RangeInclusive};
use std::sync::Arc;
use tantivy_bitpacker::minmax;
@@ -9,7 +10,7 @@ use crate::column_values::monotonic_mapping::StrictlyMonotonicFn;
/// `ColumnValues` provides access to a dense field column.
///
/// `Column` are just a wrapper over `ColumnValues` and a `ColumnIndex`.
pub trait ColumnValues<T: PartialOrd + Debug = u64>: Send + Sync {
pub trait ColumnValues<T: PartialOrd = u64>: Send + Sync {
/// Return the value associated with the given idx.
///
/// This accessor should return as fast as possible.
@@ -27,7 +28,7 @@ pub trait ColumnValues<T: PartialOrd + Debug = 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);
@@ -37,7 +38,7 @@ pub trait ColumnValues<T: PartialOrd + Debug = 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>,
@@ -78,27 +79,33 @@ pub trait ColumnValues<T: PartialOrd + Debug = u64>: Send + Sync {
}
}
impl<T: Copy + PartialOrd + Debug> 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)
}

View File

@@ -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);
}
}
}

View File

@@ -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);
}
}

View File

@@ -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");
}
}
}

View File

@@ -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))
}

View File

@@ -7,9 +7,6 @@
//! - 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;
@@ -18,72 +15,66 @@ use std::sync::Arc;
use common::{BinarySerializable, OwnedBytes};
use compact_space::CompactSpaceDecompressor;
pub use monotonic_mapping::{MonotonicallyMappableToU64, StrictlyMonotonicFn};
use monotonic_mapping::{
StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal,
StrictlyMonotonicMappingToInternalBaseval, StrictlyMonotonicMappingToInternalGCDBaseval,
};
use monotonic_mapping::{StrictlyMonotonicMappingInverter, StrictlyMonotonicMappingToInternal};
pub use monotonic_mapping_u128::MonotonicallyMappableToU128;
use serialize::{Header, U128Header};
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 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};
#[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)
}
@@ -136,68 +127,6 @@ pub fn open_u128_mapped<T: MonotonicallyMappableToU128 + Debug>(
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 + Debug>(
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 + Debug>(
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;

View File

@@ -1,22 +1,12 @@
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:
@@ -31,53 +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,
@@ -85,7 +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(())
@@ -101,157 +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 + Debug>(
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 {
@@ -267,15 +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);
@@ -284,19 +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));
}
}

View 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,
);
}
}

View 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");
}
}
}

View 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");
}
}
}

View File

@@ -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(())

View 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");
}
}
}

View File

@@ -0,0 +1,183 @@
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_index::MultiValueIndex;
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;

View 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
}
);
}
}

View File

@@ -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);

View File

@@ -4,24 +4,22 @@ use std::net::Ipv6Addr;
use crate::value::NumericalType;
use crate::InvalidData;
/// The column type represents the column type and can fit on 6-bits.
///
/// - bits[0..3]: Column category type.
/// - bits[3..6]: Numerical type if necessary.
#[derive(Hash, Eq, PartialEq, Debug, Clone, Copy)]
/// The column type represents the column type.
/// Any changes need to be propagated to `COLUMN_TYPES`.
#[derive(Hash, Eq, PartialEq, Debug, Clone, Copy, Ord, PartialOrd)]
#[repr(u8)]
pub enum ColumnType {
I64 = 0u8,
U64 = 1u8,
F64 = 2u8,
Bytes = 10u8,
Str = 14u8,
Bool = 18u8,
IpAddr = 22u8,
DateTime = 26u8,
Bytes = 3u8,
Str = 4u8,
Bool = 5u8,
IpAddr = 6u8,
DateTime = 7u8,
}
#[cfg(test)]
// The order needs to match _exactly_ the order in the enum
const COLUMN_TYPES: [ColumnType; 8] = [
ColumnType::I64,
ColumnType::U64,
@@ -39,18 +37,7 @@ impl ColumnType {
}
pub(crate) fn try_from_code(code: u8) -> Result<ColumnType, InvalidData> {
use ColumnType::*;
match code {
0u8 => Ok(I64),
1u8 => Ok(U64),
2u8 => Ok(F64),
10u8 => Ok(Bytes),
14u8 => Ok(Str),
18u8 => Ok(Bool),
22u8 => Ok(IpAddr),
26u8 => Ok(Self::DateTime),
_ => Err(InvalidData),
}
COLUMN_TYPES.get(code as usize).copied().ok_or(InvalidData)
}
}
@@ -65,18 +52,6 @@ impl From<NumericalType> for ColumnType {
}
impl ColumnType {
/// get column type category
pub(crate) fn column_type_category(self) -> ColumnTypeCategory {
match self {
ColumnType::I64 | ColumnType::U64 | 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 numerical_type(&self) -> Option<NumericalType> {
match self {
ColumnType::I64 => Some(NumericalType::I64),
@@ -155,70 +130,20 @@ impl HasAssociatedColumnType for Ipv6Addr {
}
}
/// Column types are grouped into different categories that
/// corresponds to the different types of `JsonValue` types.
///
/// The columnar writer will apply coercion rules to make sure that
/// at most one column exist per `ColumnTypeCategory`.
///
/// See also [README.md].
#[derive(Copy, Clone, Ord, PartialOrd, Eq, PartialEq, Hash, Debug)]
#[repr(u8)]
pub 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,
}
}
}
#[cfg(test)]
mod tests {
use std::collections::HashSet;
use super::*;
use crate::Cardinality;
#[test]
fn test_column_type_to_code() {
let mut column_type_set: HashSet<ColumnType> = HashSet::new();
for code in u8::MIN..=u8::MAX {
if let Ok(column_type) = ColumnType::try_from_code(code) {
assert_eq!(column_type.to_code(), code);
assert!(column_type_set.insert(column_type));
for (code, expected_column_type) in super::COLUMN_TYPES.iter().copied().enumerate() {
if let Ok(column_type) = ColumnType::try_from_code(code as u8) {
assert_eq!(column_type, expected_column_type);
}
}
assert_eq!(column_type_set.len(), super::COLUMN_TYPES.len());
}
#[test]
fn test_column_category_sort_consistent_with_column_type_sort() {
// This is a very important property because we
// we need to serialize colunmn in the right order.
let mut column_types: Vec<ColumnType> = super::COLUMN_TYPES.iter().copied().collect();
column_types.sort_by_key(|col| col.to_code());
let column_categories: Vec<ColumnTypeCategory> = column_types
.into_iter()
.map(ColumnTypeCategory::from)
.collect();
for (prev, next) in column_categories.iter().zip(column_categories.iter()) {
assert!(prev <= next);
for code in COLUMN_TYPES.len() as u8..=u8::MAX {
assert!(ColumnType::try_from_code(code as u8).is_err());
}
}

View File

@@ -1,176 +0,0 @@
use std::collections::HashMap;
use std::io;
use super::column_type::ColumnTypeCategory;
use crate::columnar::ColumnarReader;
use crate::dynamic_column::DynamicColumn;
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!();
}
}
}
pub 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().column_type_category())
.or_default();
columns.push(handle.open()?);
}
}
normalize_columns(&mut field_name_to_group);
Ok(field_name_to_group)
}
/// Cast numerical type columns to the same type
pub(crate) 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()));
}
}

View 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])[..]
}
}

View 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()
}
}

View 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;

View 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()
}
}

View 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");
}

View File

@@ -0,0 +1 @@

View File

@@ -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;

View File

@@ -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()?;
@@ -130,7 +137,7 @@ mod tests {
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, &mut buffer).unwrap();
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);
@@ -146,7 +153,7 @@ mod tests {
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, &mut buffer).unwrap();
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);

View File

@@ -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,
@@ -189,10 +210,12 @@ impl CompatibleNumericalTypes {
}
impl NumericalColumnWriter {
pub fn column_type_and_cardinality(&self, num_docs: RowId) -> (NumericalType, Cardinality) {
let numerical_type = self.compatible_numerical_types.to_numerical_type();
let cardinality = self.column_writer.get_cardinality(num_docs);
(numerical_type, cardinality)
pub fn numerical_type(&self) -> NumericalType {
self.compatible_numerical_types.to_numerical_type()
}
pub fn cardinality(&self, num_docs: RowId) -> Cardinality {
self.column_writer.get_cardinality(num_docs)
}
pub fn record_numerical_value(
@@ -208,9 +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)
}
}
@@ -251,9 +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)
}
}

View File

@@ -7,15 +7,16 @@ 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;
use crate::column_values::{
ColumnValues, MonotonicallyMappableToU128, MonotonicallyMappableToU64, VecColumn,
};
use crate::columnar::column_type::{ColumnType, ColumnTypeCategory};
use crate::columnar::column_type::ColumnType;
use crate::columnar::writer::column_writers::{
ColumnWriter, NumericalColumnWriter, StrOrBytesColumnWriter,
};
@@ -44,7 +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,
@@ -103,6 +104,48 @@ impl ColumnarWriter {
+ self.datetime_field_hash_map.mem_usage()
}
/// 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`.
@@ -277,37 +320,47 @@ 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], ColumnTypeCategory, Addr)> = self
let mut columns: Vec<(&[u8], ColumnType, Addr)> = self
.numerical_field_hash_map
.iter()
.map(|(column_name, addr, _)| (column_name, ColumnTypeCategory::Numerical, addr))
.map(|(column_name, addr, _)| {
let numerical_column_writer: NumericalColumnWriter =
self.numerical_field_hash_map.read(addr);
let column_type = numerical_column_writer.numerical_type().into();
(column_name, column_type, addr)
})
.collect();
columns.extend(
self.bytes_field_hash_map
.iter()
.map(|(term, addr, _)| (term, ColumnTypeCategory::Bytes, addr)),
.map(|(term, addr, _)| (term, ColumnType::Bytes, addr)),
);
columns.extend(
self.str_field_hash_map
.iter()
.map(|(column_name, addr, _)| (column_name, ColumnTypeCategory::Str, addr)),
.map(|(column_name, addr, _)| (column_name, ColumnType::Str, addr)),
);
columns.extend(
self.bool_field_hash_map
.iter()
.map(|(column_name, addr, _)| (column_name, ColumnTypeCategory::Bool, addr)),
.map(|(column_name, addr, _)| (column_name, ColumnType::Bool, addr)),
);
columns.extend(
self.ip_addr_field_hash_map
.iter()
.map(|(column_name, addr, _)| (column_name, ColumnTypeCategory::IpAddr, addr)),
.map(|(column_name, addr, _)| (column_name, ColumnType::IpAddr, addr)),
);
columns.extend(
self.datetime_field_hash_map
.iter()
.map(|(column_name, addr, _)| (column_name, ColumnTypeCategory::DateTime, addr)),
.map(|(column_name, addr, _)| (column_name, ColumnType::DateTime, addr)),
);
columns.sort_unstable_by_key(|(column_name, col_type, _)| (*column_name, *col_type));
@@ -315,20 +368,24 @@ impl ColumnarWriter {
let mut symbol_byte_buffer: Vec<u8> = Vec::new();
for (column_name, column_type, addr) in columns {
match column_type {
ColumnTypeCategory::Bool => {
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,
)?;
}
ColumnTypeCategory::IpAddr => {
ColumnType::IpAddr => {
let column_writer: ColumnWriter = self.ip_addr_field_hash_map.read(addr);
let cardinality = column_writer.get_cardinality(num_docs);
let mut column_serializer =
@@ -336,50 +393,64 @@ 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,
)?;
}
ColumnTypeCategory::Bytes | ColumnTypeCategory::Str => {
let (column_type, str_column_writer): (ColumnType, StrOrBytesColumnWriter) =
if column_type == ColumnTypeCategory::Bytes {
(ColumnType::Bytes, self.bytes_field_hash_map.read(addr))
ColumnType::Bytes | ColumnType::Str => {
let str_or_bytes_column_writer: StrOrBytesColumnWriter =
if column_type == ColumnType::Bytes {
self.bytes_field_hash_map.read(addr)
} else {
(ColumnType::Str, self.str_field_hash_map.read(addr))
self.str_field_hash_map.read(addr)
};
let dictionary_builder =
&dictionaries[str_column_writer.dictionary_id as usize];
let cardinality = str_column_writer.column_writer.get_cardinality(num_docs);
&dictionaries[str_or_bytes_column_writer.dictionary_id as usize];
let cardinality = str_or_bytes_column_writer
.column_writer
.get_cardinality(num_docs);
let mut column_serializer =
serializer.serialize_column(column_name, column_type);
serialize_bytes_or_str_column(
cardinality,
num_docs,
str_column_writer.sort_values_within_row,
str_or_bytes_column_writer.sort_values_within_row,
dictionary_builder,
str_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,
)?;
}
ColumnTypeCategory::Numerical => {
ColumnType::F64 | ColumnType::I64 | ColumnType::U64 => {
let numerical_column_writer: NumericalColumnWriter =
self.numerical_field_hash_map.read(addr);
let (numerical_type, cardinality) =
numerical_column_writer.column_type_and_cardinality(num_docs);
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,
)?;
}
ColumnTypeCategory::DateTime => {
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 =
@@ -388,18 +459,24 @@ impl ColumnarWriter {
cardinality,
num_docs,
NumericalType::I64,
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,
)?;
}
};
}
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,
@@ -547,7 +624,7 @@ fn send_to_serialize_column_mappable_to_u128<
>(
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,
@@ -569,31 +646,30 @@ 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 sort_values_within_row_in_place(
multivalued_index: &impl ColumnValues<RowId>,
values: &mut Vec<u64>,
) {
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() {
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;
@@ -603,7 +679,7 @@ fn sort_values_within_row_in_place(
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<u64>,
@@ -625,22 +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);
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(())
@@ -693,7 +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)));
@@ -722,7 +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)));
@@ -745,7 +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)));
@@ -764,7 +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)));

View File

@@ -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()?;

View File

@@ -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]
);

View File

@@ -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
View 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())
}
}

View File

@@ -10,11 +10,12 @@ extern crate test;
use std::io;
mod column;
mod column_index;
mod column_values;
pub mod column_index;
pub mod column_values;
mod columnar;
mod dictionary;
mod dynamic_column;
mod iterable;
pub(crate) mod utils;
mod value;
@@ -23,7 +24,7 @@ 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};
@@ -31,6 +32,13 @@ 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>;
@@ -39,6 +47,12 @@ 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;
@@ -66,6 +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
}

View File

@@ -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();

View File

@@ -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)

View File

@@ -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])

View File

@@ -1,34 +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/" }
columnar = { version= "0.1", path="../columnar", package="tantivy-columnar" }
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 = []

View File

@@ -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 |
+----------------------------------+-------------------+------------------------+
```

View File

@@ -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
});
}
}

View File

@@ -1,10 +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
pub use columnar::ColumnValues as Column;

View File

@@ -2,9 +2,8 @@
use std::rc::Rc;
use std::sync::atomic::AtomicU32;
use std::sync::Arc;
use fastfield_codecs::Column;
use columnar::{Column, StrColumn};
use super::agg_req::{Aggregation, Aggregations, BucketAggregationType, MetricAggregation};
use super::bucket::{HistogramAggregation, RangeAggregation, TermsAggregation};
@@ -14,9 +13,8 @@ use super::metric::{
};
use super::segment_agg_result::BucketCount;
use super::VecWithNames;
use crate::fastfield::{type_and_cardinality, MultiValuedFastFieldReader};
use crate::schema::Type;
use crate::{InvertedIndexReader, SegmentReader, TantivyError};
use crate::{SegmentReader, TantivyError};
#[derive(Clone, Default)]
pub(crate) struct AggregationsWithAccessor {
@@ -37,38 +35,12 @@ impl AggregationsWithAccessor {
}
}
#[derive(Clone)]
pub(crate) enum FastFieldAccessor {
Multi(MultiValuedFastFieldReader<u64>),
Single(Arc<dyn Column<u64>>),
}
impl FastFieldAccessor {
pub fn as_single(&self) -> Option<&dyn Column<u64>> {
match self {
FastFieldAccessor::Multi(_) => None,
FastFieldAccessor::Single(reader) => Some(&**reader),
}
}
pub fn into_single(self) -> Option<Arc<dyn Column<u64>>> {
match self {
FastFieldAccessor::Multi(_) => None,
FastFieldAccessor::Single(reader) => Some(reader),
}
}
pub fn as_multi(&self) -> Option<&MultiValuedFastFieldReader<u64>> {
match self {
FastFieldAccessor::Multi(reader) => Some(reader),
FastFieldAccessor::Single(_) => None,
}
}
}
#[derive(Clone)]
pub struct BucketAggregationWithAccessor {
/// In general there can be buckets without fast field access, e.g. buckets that are created
/// based on search terms. So eventually this needs to be Option or moved.
pub(crate) accessor: FastFieldAccessor,
pub(crate) inverted_index: Option<Arc<InvertedIndexReader>>,
pub(crate) accessor: Column<u64>,
pub(crate) str_dict_column: Option<StrColumn>,
pub(crate) field_type: Type,
pub(crate) bucket_agg: BucketAggregationType,
pub(crate) sub_aggregation: AggregationsWithAccessor,
@@ -83,20 +55,19 @@ impl BucketAggregationWithAccessor {
bucket_count: Rc<AtomicU32>,
max_bucket_count: u32,
) -> crate::Result<BucketAggregationWithAccessor> {
let mut inverted_index = None;
let mut str_dict_column = None;
let (accessor, field_type) = match &bucket {
BucketAggregationType::Range(RangeAggregation {
field: field_name, ..
}) => get_ff_reader_and_validate(reader, field_name, Cardinality::SingleValue)?,
}) => get_ff_reader_and_validate(reader, field_name)?,
BucketAggregationType::Histogram(HistogramAggregation {
field: field_name, ..
}) => get_ff_reader_and_validate(reader, field_name, Cardinality::SingleValue)?,
}) => get_ff_reader_and_validate(reader, field_name)?,
BucketAggregationType::Terms(TermsAggregation {
field: field_name, ..
}) => {
let field = reader.schema().get_field(field_name)?;
inverted_index = Some(reader.inverted_index(field)?);
get_ff_reader_and_validate(reader, field_name, Cardinality::MultiValues)?
str_dict_column = reader.fast_fields().str(&field_name)?;
get_ff_reader_and_validate(reader, field_name)?
}
};
let sub_aggregation = sub_aggregation.clone();
@@ -110,7 +81,7 @@ impl BucketAggregationWithAccessor {
max_bucket_count,
)?,
bucket_agg: bucket.clone(),
inverted_index,
str_dict_column,
bucket_count: BucketCount {
bucket_count,
max_bucket_count,
@@ -124,7 +95,7 @@ impl BucketAggregationWithAccessor {
pub struct MetricAggregationWithAccessor {
pub metric: MetricAggregation,
pub field_type: Type,
pub accessor: Arc<dyn Column>,
pub accessor: Column<u64>,
}
impl MetricAggregationWithAccessor {
@@ -139,13 +110,10 @@ impl MetricAggregationWithAccessor {
| MetricAggregation::Min(MinAggregation { field: field_name })
| MetricAggregation::Stats(StatsAggregation { field: field_name })
| MetricAggregation::Sum(SumAggregation { field: field_name }) => {
let (accessor, field_type) =
get_ff_reader_and_validate(reader, field_name, Cardinality::SingleValue)?;
let (accessor, field_type) = get_ff_reader_and_validate(reader, field_name)?;
Ok(MetricAggregationWithAccessor {
accessor: accessor
.into_single()
.expect("unexpected fast field cardinality"),
accessor,
field_type,
metric: metric.clone(),
})
@@ -190,32 +158,22 @@ pub(crate) fn get_aggs_with_accessor_and_validate(
fn get_ff_reader_and_validate(
reader: &SegmentReader,
field_name: &str,
cardinality: Cardinality,
) -> crate::Result<(FastFieldAccessor, Type)> {
) -> crate::Result<(columnar::Column<u64>, Type)> {
let field = reader.schema().get_field(field_name)?;
let field_type = reader.schema().get_field_entry(field).field_type();
if let Some((_ff_type, field_cardinality)) = type_and_cardinality(field_type) {
if cardinality != field_cardinality {
return Err(TantivyError::InvalidArgument(format!(
"Invalid field cardinality on field {} expected {:?}, but got {:?}",
field_name, cardinality, field_cardinality
)));
}
} else {
return Err(TantivyError::InvalidArgument(format!(
"Only fast fields of type f64, u64, i64 are supported, but got {:?} ",
field_type.value_type()
)));
};
// TODO we should get type metadata from columnar
let field_type = reader
.schema()
.get_field_entry(field)
.field_type()
.value_type();
// TODO Do validation
let ff_fields = reader.fast_fields();
match cardinality {
Cardinality::SingleValue => ff_fields
.u64_lenient(field_name)
.map(|field| (FastFieldAccessor::Single(field), field_type.value_type())),
Cardinality::MultiValues => ff_fields
.u64s_lenient(field_name)
.map(|field| (FastFieldAccessor::Multi(field), field_type.value_type())),
}
let ff_field = ff_fields.u64_lenient(field_name)?.ok_or_else(|| {
TantivyError::InvalidArgument(format!(
"No numerical fast field found for field: {}",
field_name
))
})?;
Ok((ff_field, field_type))
}

View File

@@ -0,0 +1,126 @@
use serde::{Deserialize, Serialize};
/// DateHistogramAggregation is similar to `HistogramAggregation`, but it can only be used with date
/// type.
///
/// Currently only **fixed time** intervals are supported. Calendar-aware time intervals are not
/// supported.
///
/// Like the histogram, values are rounded down into the closest bucket.
///
/// For this calculation all fastfield values are converted to f64.
///
/// # Limitations/Compatibility
/// Only fixed time intervals are supported.
///
/// # JSON Format
/// ```json
/// {
/// "prices": {
/// "date_histogram": {
/// "field": "price",
/// "fixed_interval": "30d"
/// }
/// }
/// }
/// ```
///
/// Response
/// See [`BucketEntry`](crate::aggregation::agg_result::BucketEntry)
#[derive(Clone, Debug, Default, PartialEq, Serialize, Deserialize)]
pub struct DateHistogramAggregationReq {
/// The field to aggregate on.
pub field: String,
/// The interval to chunk your data range. Each bucket spans a value range of
/// [0..fixed_interval). Accepted values
///
/// Fixed intervals are configured with the `fixed_interval` parameter.
/// In contrast to calendar-aware intervals, fixed intervals are a fixed number of SI units and
/// never deviate, regardless of where they fall on the calendar. One second is always
/// composed of 1000ms. This allows fixed intervals to be specified in any multiple of the
/// supported units. However, it means fixed intervals cannot express other units such as
/// months, since the duration of a month is not a fixed quantity. Attempting to specify a
/// calendar interval like month or quarter will return an Error.
///
/// The accepted units for fixed intervals are:
/// * `ms`: milliseconds
/// * `s`: seconds. Defined as 1000 milliseconds each.
/// * `m`: minutes. Defined as 60 seconds each (60_000 milliseconds).
/// * `h`: hours. Defined as 60 minutes each (3_600_000 milliseconds).
/// * `d`: days. Defined as 24 hours (86_400_000 milliseconds).
///
/// Fractional time values are not supported, but you can address this by shifting to another
/// time unit (e.g., `1.5h` could instead be specified as `90m`).
pub fixed_interval: String,
/// Intervals implicitly defines an absolute grid of buckets `[interval * k, interval * (k +
/// 1))`.
pub offset: Option<String>,
/// Whether to return the buckets as a hash map
#[serde(default)]
pub keyed: bool,
}
impl DateHistogramAggregationReq {
fn validate(&self) -> crate::Result<()> {
Ok(())
}
}
#[derive(Debug, PartialEq, Eq)]
/// Errors when parsing the fixed interval for `DateHistogramAggregationReq`.
pub enum DateHistogramParseError {
/// Unit not recognized in passed String
UnitNotRecognized(String),
/// Number not found in passed String
NumberMissing(String),
/// Unit not found in passed String
UnitMissing(String),
}
fn parse_into_milliseconds(input: &str) -> Result<u64, DateHistogramParseError> {
let split_boundary = input
.char_indices()
.take_while(|(pos, el)| el.is_numeric())
.count();
let (number, unit) = input.split_at(split_boundary);
if number.is_empty() {
return Err(DateHistogramParseError::NumberMissing(input.to_string()));
}
if unit.is_empty() {
return Err(DateHistogramParseError::UnitMissing(input.to_string()));
}
let number: u64 = number.parse().unwrap();
let multiplier_from_unit = match unit {
"ms" => 1,
"s" => 1000,
"m" => 60 * 1000,
"h" => 60 * 60 * 1000,
"d" => 24 * 60 * 60 * 1000,
_ => return Err(DateHistogramParseError::UnitNotRecognized(unit.to_string())),
};
Ok(number * multiplier_from_unit)
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn parser_test() {
assert_eq!(parse_into_milliseconds("1m").unwrap(), 60_000);
assert_eq!(parse_into_milliseconds("2m").unwrap(), 120_000);
assert_eq!(
parse_into_milliseconds("2y").unwrap_err(),
DateHistogramParseError::UnitNotRecognized("y".to_string())
);
assert_eq!(
parse_into_milliseconds("2000").unwrap_err(),
DateHistogramParseError::UnitMissing("2000".to_string())
);
assert_eq!(
parse_into_milliseconds("ms").unwrap_err(),
DateHistogramParseError::NumberMissing("ms".to_string())
);
}
}

View File

@@ -1,7 +1,7 @@
use std::cmp::Ordering;
use std::fmt::Display;
use fastfield_codecs::Column;
use columnar::Column;
use itertools::Itertools;
use serde::{Deserialize, Serialize};
@@ -13,7 +13,9 @@ use crate::aggregation::agg_result::BucketEntry;
use crate::aggregation::intermediate_agg_result::{
IntermediateAggregationResults, IntermediateBucketResult, IntermediateHistogramBucketEntry,
};
use crate::aggregation::segment_agg_result::SegmentAggregationResultsCollector;
use crate::aggregation::segment_agg_result::{
GenericSegmentAggregationResultsCollector, SegmentAggregationCollector,
};
use crate::aggregation::{f64_from_fastfield_u64, format_date};
use crate::schema::{Schema, Type};
use crate::{DocId, TantivyError};
@@ -62,7 +64,6 @@ use crate::{DocId, TantivyError};
///
/// Response
/// See [`BucketEntry`](crate::aggregation::agg_result::BucketEntry)
#[derive(Clone, Debug, Default, PartialEq, Serialize, Deserialize)]
pub struct HistogramAggregation {
/// The field to aggregate on.
@@ -184,7 +185,7 @@ pub(crate) struct SegmentHistogramBucketEntry {
impl SegmentHistogramBucketEntry {
pub(crate) fn into_intermediate_bucket_entry(
self,
sub_aggregation: SegmentAggregationResultsCollector,
sub_aggregation: GenericSegmentAggregationResultsCollector,
agg_with_accessor: &AggregationsWithAccessor,
) -> crate::Result<IntermediateHistogramBucketEntry> {
Ok(IntermediateHistogramBucketEntry {
@@ -198,11 +199,11 @@ impl SegmentHistogramBucketEntry {
/// The collector puts values from the fast field into the correct buckets and does a conversion to
/// the correct datatype.
#[derive(Clone, Debug, PartialEq)]
#[derive(Clone, Debug)]
pub struct SegmentHistogramCollector {
/// The buckets containing the aggregation data.
buckets: Vec<SegmentHistogramBucketEntry>,
sub_aggregations: Option<Vec<SegmentAggregationResultsCollector>>,
sub_aggregations: Option<Vec<GenericSegmentAggregationResultsCollector>>,
field_type: Type,
interval: f64,
offset: f64,
@@ -283,7 +284,7 @@ impl SegmentHistogramCollector {
req: &HistogramAggregation,
sub_aggregation: &AggregationsWithAccessor,
field_type: Type,
accessor: &dyn Column<u64>,
accessor: &Column<u64>,
) -> crate::Result<Self> {
req.validate()?;
let min = f64_from_fastfield_u64(accessor.min_value(), &field_type);
@@ -300,7 +301,7 @@ impl SegmentHistogramCollector {
None
} else {
let sub_aggregation =
SegmentAggregationResultsCollector::from_req_and_validate(sub_aggregation)?;
GenericSegmentAggregationResultsCollector::from_req_and_validate(sub_aggregation)?;
Some(buckets.iter().map(|_| sub_aggregation.clone()).collect())
};
@@ -335,7 +336,7 @@ impl SegmentHistogramCollector {
#[inline]
pub(crate) fn collect_block(
&mut self,
doc: &[DocId],
docs: &[DocId],
bucket_with_accessor: &BucketAggregationWithAccessor,
force_flush: bool,
) -> crate::Result<()> {
@@ -346,64 +347,20 @@ impl SegmentHistogramCollector {
let get_bucket_num =
|val| (get_bucket_num_f64(val, interval, offset) as i64 - first_bucket_num) as usize;
let accessor = bucket_with_accessor
.accessor
.as_single()
.expect("unexpected fast field cardinatility");
let mut iter = doc.chunks_exact(4);
for docs in iter.by_ref() {
let val0 = self.f64_from_fastfield_u64(accessor.get_val(docs[0]));
let val1 = self.f64_from_fastfield_u64(accessor.get_val(docs[1]));
let val2 = self.f64_from_fastfield_u64(accessor.get_val(docs[2]));
let val3 = self.f64_from_fastfield_u64(accessor.get_val(docs[3]));
let accessor = &bucket_with_accessor.accessor;
for doc in docs {
for val in accessor.values(*doc) {
let val = self.f64_from_fastfield_u64(val);
let bucket_pos0 = get_bucket_num(val0);
let bucket_pos1 = get_bucket_num(val1);
let bucket_pos2 = get_bucket_num(val2);
let bucket_pos3 = get_bucket_num(val3);
self.increment_bucket_if_in_bounds(
val0,
&bounds,
bucket_pos0,
docs[0],
&bucket_with_accessor.sub_aggregation,
)?;
self.increment_bucket_if_in_bounds(
val1,
&bounds,
bucket_pos1,
docs[1],
&bucket_with_accessor.sub_aggregation,
)?;
self.increment_bucket_if_in_bounds(
val2,
&bounds,
bucket_pos2,
docs[2],
&bucket_with_accessor.sub_aggregation,
)?;
self.increment_bucket_if_in_bounds(
val3,
&bounds,
bucket_pos3,
docs[3],
&bucket_with_accessor.sub_aggregation,
)?;
}
for &doc in iter.remainder() {
let val = f64_from_fastfield_u64(accessor.get_val(doc), &self.field_type);
if !bounds.contains(val) {
continue;
let bucket_pos = get_bucket_num(val);
self.increment_bucket_if_in_bounds(
val,
&bounds,
bucket_pos,
*doc,
&bucket_with_accessor.sub_aggregation,
)?;
}
let bucket_pos = (get_bucket_num_f64(val, self.interval, self.offset) as i64
- self.first_bucket_num) as usize;
debug_assert_eq!(
self.buckets[bucket_pos].key,
get_bucket_val(val, self.interval, self.offset)
);
self.increment_bucket(bucket_pos, doc, &bucket_with_accessor.sub_aggregation)?;
}
if force_flush {
if let Some(sub_aggregations) = self.sub_aggregations.as_mut() {

View File

@@ -1,2 +1,4 @@
mod date_histogram;
mod histogram;
pub use date_histogram::*;
pub use histogram::*;

View File

@@ -1,7 +1,7 @@
use std::fmt::Debug;
use std::ops::Range;
use fastfield_codecs::MonotonicallyMappableToU64;
use columnar::MonotonicallyMappableToU64;
use rustc_hash::FxHashMap;
use serde::{Deserialize, Serialize};
@@ -11,7 +11,9 @@ use crate::aggregation::agg_req_with_accessor::{
use crate::aggregation::intermediate_agg_result::{
IntermediateBucketResult, IntermediateRangeBucketEntry, IntermediateRangeBucketResult,
};
use crate::aggregation::segment_agg_result::{BucketCount, SegmentAggregationResultsCollector};
use crate::aggregation::segment_agg_result::{
BucketCount, GenericSegmentAggregationResultsCollector, SegmentAggregationCollector,
};
use crate::aggregation::{
f64_from_fastfield_u64, f64_to_fastfield_u64, format_date, Key, SerializedKey,
};
@@ -114,7 +116,7 @@ impl From<Range<u64>> for InternalRangeAggregationRange {
}
}
#[derive(Clone, Debug, PartialEq)]
#[derive(Clone, Debug)]
pub(crate) struct SegmentRangeAndBucketEntry {
range: Range<u64>,
bucket: SegmentRangeBucketEntry,
@@ -122,18 +124,18 @@ pub(crate) struct SegmentRangeAndBucketEntry {
/// The collector puts values from the fast field into the correct buckets and does a conversion to
/// the correct datatype.
#[derive(Clone, Debug, PartialEq)]
#[derive(Clone, Debug)]
pub struct SegmentRangeCollector {
/// The buckets containing the aggregation data.
buckets: Vec<SegmentRangeAndBucketEntry>,
field_type: Type,
}
#[derive(Clone, PartialEq)]
#[derive(Clone)]
pub(crate) struct SegmentRangeBucketEntry {
pub key: Key,
pub doc_count: u64,
pub sub_aggregation: Option<SegmentAggregationResultsCollector>,
pub sub_aggregation: Option<GenericSegmentAggregationResultsCollector>,
/// The from range of the bucket. Equals `f64::MIN` when `None`.
pub from: Option<f64>,
/// The to range of the bucket. Equals `f64::MAX` when `None`. Open interval, `to` is not
@@ -227,9 +229,11 @@ impl SegmentRangeCollector {
let sub_aggregation = if sub_aggregation.is_empty() {
None
} else {
Some(SegmentAggregationResultsCollector::from_req_and_validate(
sub_aggregation,
)?)
Some(
GenericSegmentAggregationResultsCollector::from_req_and_validate(
sub_aggregation,
)?,
)
};
Ok(SegmentRangeAndBucketEntry {
@@ -257,35 +261,18 @@ impl SegmentRangeCollector {
#[inline]
pub(crate) fn collect_block(
&mut self,
doc: &[DocId],
docs: &[DocId],
bucket_with_accessor: &BucketAggregationWithAccessor,
force_flush: bool,
) -> crate::Result<()> {
let mut iter = doc.chunks_exact(4);
let accessor = bucket_with_accessor
.accessor
.as_single()
.expect("unexpected fast field cardinality");
for docs in iter.by_ref() {
let val1 = accessor.get_val(docs[0]);
let val2 = accessor.get_val(docs[1]);
let val3 = accessor.get_val(docs[2]);
let val4 = accessor.get_val(docs[3]);
let bucket_pos1 = self.get_bucket_pos(val1);
let bucket_pos2 = self.get_bucket_pos(val2);
let bucket_pos3 = self.get_bucket_pos(val3);
let bucket_pos4 = self.get_bucket_pos(val4);
let accessor = &bucket_with_accessor.accessor;
for doc in docs {
for val in accessor.values(*doc) {
let bucket_pos = self.get_bucket_pos(val);
self.increment_bucket(bucket_pos, *doc, &bucket_with_accessor.sub_aggregation)?;
}
}
self.increment_bucket(bucket_pos1, docs[0], &bucket_with_accessor.sub_aggregation)?;
self.increment_bucket(bucket_pos2, docs[1], &bucket_with_accessor.sub_aggregation)?;
self.increment_bucket(bucket_pos3, docs[2], &bucket_with_accessor.sub_aggregation)?;
self.increment_bucket(bucket_pos4, docs[3], &bucket_with_accessor.sub_aggregation)?;
}
for &doc in iter.remainder() {
let val = accessor.get_val(doc);
let bucket_pos = self.get_bucket_pos(val);
self.increment_bucket(bucket_pos, doc, &bucket_with_accessor.sub_aggregation)?;
}
if force_flush {
for bucket in &mut self.buckets {
if let Some(sub_aggregation) = &mut bucket.bucket.sub_aggregation {
@@ -434,7 +421,7 @@ pub(crate) fn range_to_key(range: &Range<u64>, field_type: &Type) -> crate::Resu
#[cfg(test)]
mod tests {
use fastfield_codecs::MonotonicallyMappableToU64;
use columnar::MonotonicallyMappableToU64;
use serde_json::Value;
use super::*;

View File

@@ -1,5 +1,6 @@
use std::fmt::Debug;
use columnar::Column;
use itertools::Itertools;
use rustc_hash::FxHashMap;
use serde::{Deserialize, Serialize};
@@ -11,9 +12,11 @@ use crate::aggregation::agg_req_with_accessor::{
use crate::aggregation::intermediate_agg_result::{
IntermediateBucketResult, IntermediateTermBucketEntry, IntermediateTermBucketResult,
};
use crate::aggregation::segment_agg_result::{BucketCount, SegmentAggregationResultsCollector};
use crate::aggregation::segment_agg_result::{
build_segment_agg_collector, GenericSegmentAggregationResultsCollector,
SegmentAggregationCollector,
};
use crate::error::DataCorruption;
use crate::fastfield::MultiValuedFastFieldReader;
use crate::schema::Type;
use crate::{DocId, TantivyError};
@@ -196,17 +199,16 @@ impl TermsAggregationInternal {
}
}
#[derive(Clone, Debug, PartialEq)]
#[derive(Clone, Debug, Default)]
/// Container to store term_ids and their buckets.
struct TermBuckets {
pub(crate) entries: FxHashMap<u32, TermBucketEntry>,
blueprint: Option<SegmentAggregationResultsCollector>,
}
#[derive(Clone, PartialEq, Default)]
#[derive(Clone, Default)]
struct TermBucketEntry {
doc_count: u64,
sub_aggregations: Option<SegmentAggregationResultsCollector>,
sub_aggregations: Option<Box<dyn SegmentAggregationCollector>>,
}
impl Debug for TermBucketEntry {
@@ -218,7 +220,7 @@ impl Debug for TermBucketEntry {
}
impl TermBucketEntry {
fn from_blueprint(blueprint: &Option<SegmentAggregationResultsCollector>) -> Self {
fn from_blueprint(blueprint: &Option<Box<dyn SegmentAggregationCollector>>) -> Self {
Self {
doc_count: 0,
sub_aggregations: blueprint.clone(),
@@ -247,46 +249,11 @@ impl TermBuckets {
sub_aggregation: &AggregationsWithAccessor,
_max_term_id: usize,
) -> crate::Result<Self> {
let has_sub_aggregations = sub_aggregation.is_empty();
let blueprint = if has_sub_aggregations {
let sub_aggregation =
SegmentAggregationResultsCollector::from_req_and_validate(sub_aggregation)?;
Some(sub_aggregation)
} else {
None
};
Ok(TermBuckets {
blueprint,
entries: Default::default(),
})
}
fn increment_bucket(
&mut self,
term_ids: &[u64],
doc: DocId,
sub_aggregation: &AggregationsWithAccessor,
bucket_count: &BucketCount,
blueprint: &Option<SegmentAggregationResultsCollector>,
) -> crate::Result<()> {
for &term_id in term_ids {
let entry = self.entries.entry(term_id as u32).or_insert_with(|| {
bucket_count.add_count(1);
TermBucketEntry::from_blueprint(blueprint)
});
entry.doc_count += 1;
if let Some(sub_aggregations) = entry.sub_aggregations.as_mut() {
sub_aggregations.collect(doc, sub_aggregation)?;
}
}
bucket_count.validate_bucket_count()?;
Ok(())
}
fn force_flush(&mut self, agg_with_accessor: &AggregationsWithAccessor) -> crate::Result<()> {
for entry in &mut self.entries.values_mut() {
if let Some(sub_aggregations) = entry.sub_aggregations.as_mut() {
@@ -299,13 +266,12 @@ impl TermBuckets {
/// The collector puts values from the fast field into the correct buckets and does a conversion to
/// the correct datatype.
#[derive(Clone, Debug, PartialEq)]
#[derive(Clone, Debug)]
pub struct SegmentTermCollector {
/// The buckets containing the aggregation data.
term_buckets: TermBuckets,
req: TermsAggregationInternal,
field_type: Type,
blueprint: Option<SegmentAggregationResultsCollector>,
blueprint: Option<Box<dyn SegmentAggregationCollector>>,
}
pub(crate) fn get_agg_name_and_property(name: &str) -> (&str, &str) {
@@ -317,12 +283,8 @@ impl SegmentTermCollector {
pub(crate) fn from_req_and_validate(
req: &TermsAggregation,
sub_aggregations: &AggregationsWithAccessor,
field_type: Type,
accessor: &MultiValuedFastFieldReader<u64>,
) -> crate::Result<Self> {
let max_term_id = accessor.max_value();
let term_buckets =
TermBuckets::from_req_and_validate(sub_aggregations, max_term_id as usize)?;
let term_buckets = TermBuckets::default();
if let Some(custom_order) = req.order.as_ref() {
// Validate sub aggregtion exists
@@ -340,8 +302,7 @@ impl SegmentTermCollector {
let has_sub_aggregations = !sub_aggregations.is_empty();
let blueprint = if has_sub_aggregations {
let sub_aggregation =
SegmentAggregationResultsCollector::from_req_and_validate(sub_aggregations)?;
let sub_aggregation = build_segment_agg_collector(sub_aggregations)?;
Some(sub_aggregation)
} else {
None
@@ -350,7 +311,6 @@ impl SegmentTermCollector {
Ok(SegmentTermCollector {
req: TermsAggregationInternal::from_req(req),
term_buckets,
field_type,
blueprint,
})
}
@@ -362,13 +322,19 @@ impl SegmentTermCollector {
let mut entries: Vec<(u32, TermBucketEntry)> =
self.term_buckets.entries.into_iter().collect();
let order_by_key = self.req.order.target == OrderTarget::Key;
let order_by_sub_aggregation =
matches!(self.req.order.target, OrderTarget::SubAggregation(_));
match self.req.order.target {
OrderTarget::Key => {
// defer order and cut_off after loading the texts from the dictionary
// We rely on the fact, that term ordinals match the order of the strings
// TODO: We could have a special collector, that keeps only TOP n results at any
// time.
if self.req.order.order == Order::Desc {
entries.sort_unstable_by_key(|bucket| std::cmp::Reverse(bucket.0));
} else {
entries.sort_unstable_by_key(|bucket| bucket.0);
}
}
OrderTarget::SubAggregation(_name) => {
// don't sort and cut off since it's hard to make assumptions on the quality of the
@@ -384,34 +350,40 @@ impl SegmentTermCollector {
}
}
let (term_doc_count_before_cutoff, mut sum_other_doc_count) =
if order_by_key || order_by_sub_aggregation {
(0, 0)
} else {
cut_off_buckets(&mut entries, self.req.segment_size as usize)
};
let (term_doc_count_before_cutoff, sum_other_doc_count) = if order_by_sub_aggregation {
(0, 0)
} else {
cut_off_buckets(&mut entries, self.req.segment_size as usize)
};
let inverted_index = agg_with_accessor
.inverted_index
.str_dict_column
.as_ref()
.expect("internal error: inverted index not loaded for term aggregation");
let term_dict = inverted_index.terms();
let term_dict = inverted_index;
let mut dict: FxHashMap<String, IntermediateTermBucketEntry> = Default::default();
let mut buffer = vec![];
let mut buffer = String::new();
for (term_id, entry) in entries {
term_dict
.ord_to_term(term_id as u64, &mut buffer)
.expect("could not find term");
if !term_dict.ord_to_str(term_id as u64, &mut buffer)? {
return Err(TantivyError::InternalError(format!(
"Couldn't find term_id {} in dict",
term_id
)));
}
dict.insert(
String::from_utf8(buffer.to_vec())
.map_err(|utf8_err| DataCorruption::comment_only(utf8_err.to_string()))?,
buffer.to_string(),
entry.into_intermediate_bucket_entry(&agg_with_accessor.sub_aggregation)?,
);
}
if self.req.min_doc_count == 0 {
let mut stream = term_dict.stream()?;
// TODO: Handle rev streaming for descending sorting by keys
let mut stream = term_dict.dictionary().stream()?;
while let Some((key, _ord)) = stream.next() {
if dict.len() >= self.req.segment_size as usize {
break;
}
let key = std::str::from_utf8(key)
.map_err(|utf8_err| DataCorruption::comment_only(utf8_err.to_string()))?;
if !dict.contains_key(key) {
@@ -420,20 +392,6 @@ impl SegmentTermCollector {
}
}
if order_by_key {
let mut dict_entries = dict.into_iter().collect_vec();
if self.req.order.order == Order::Desc {
dict_entries.sort_unstable_by(|(key1, _), (key2, _)| key1.cmp(key2));
} else {
dict_entries.sort_unstable_by(|(key1, _), (key2, _)| key2.cmp(key1));
}
let (_, sum_other_docs) =
cut_off_buckets(&mut dict_entries, self.req.segment_size as usize);
sum_other_doc_count += sum_other_docs;
dict = dict_entries.into_iter().collect();
}
Ok(IntermediateBucketResult::Terms(
IntermediateTermBucketResult {
entries: dict,
@@ -446,65 +404,26 @@ impl SegmentTermCollector {
#[inline]
pub(crate) fn collect_block(
&mut self,
doc: &[DocId],
docs: &[DocId],
bucket_with_accessor: &BucketAggregationWithAccessor,
force_flush: bool,
) -> crate::Result<()> {
let accessor = bucket_with_accessor
.accessor
.as_multi()
.expect("unexpected fast field cardinatility");
let mut iter = doc.chunks_exact(4);
let mut vals1 = vec![];
let mut vals2 = vec![];
let mut vals3 = vec![];
let mut vals4 = vec![];
for docs in iter.by_ref() {
accessor.get_vals(docs[0], &mut vals1);
accessor.get_vals(docs[1], &mut vals2);
accessor.get_vals(docs[2], &mut vals3);
accessor.get_vals(docs[3], &mut vals4);
let accessor = &bucket_with_accessor.accessor;
self.term_buckets.increment_bucket(
&vals1,
docs[0],
&bucket_with_accessor.sub_aggregation,
&bucket_with_accessor.bucket_count,
&self.blueprint,
)?;
self.term_buckets.increment_bucket(
&vals2,
docs[1],
&bucket_with_accessor.sub_aggregation,
&bucket_with_accessor.bucket_count,
&self.blueprint,
)?;
self.term_buckets.increment_bucket(
&vals3,
docs[2],
&bucket_with_accessor.sub_aggregation,
&bucket_with_accessor.bucket_count,
&self.blueprint,
)?;
self.term_buckets.increment_bucket(
&vals4,
docs[3],
&bucket_with_accessor.sub_aggregation,
&bucket_with_accessor.bucket_count,
&self.blueprint,
)?;
for doc in docs {
for term_id in accessor.values(*doc) {
let entry = self
.term_buckets
.entries
.entry(term_id as u32)
.or_insert_with(|| TermBucketEntry::from_blueprint(&self.blueprint));
entry.doc_count += 1;
if let Some(sub_aggregations) = entry.sub_aggregations.as_mut() {
sub_aggregations.collect(*doc, &bucket_with_accessor.sub_aggregation)?;
}
}
}
for &doc in iter.remainder() {
accessor.get_vals(doc, &mut vals1);
self.term_buckets.increment_bucket(
&vals1,
doc,
&bucket_with_accessor.sub_aggregation,
&bucket_with_accessor.bucket_count,
&self.blueprint,
)?;
}
if force_flush {
self.term_buckets
.force_flush(&bucket_with_accessor.sub_aggregation)?;
@@ -923,14 +842,14 @@ mod tests {
];
let index = get_test_index_from_values_and_terms(merge_segments, &segment_and_terms)?;
// key desc
// key asc
let agg_req: Aggregations = vec![(
"my_texts".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Terms(TermsAggregation {
field: "string_id".to_string(),
order: Some(CustomOrder {
order: Order::Desc,
order: Order::Asc,
target: OrderTarget::Key,
}),
..Default::default()
@@ -957,7 +876,7 @@ mod tests {
bucket_agg: BucketAggregationType::Terms(TermsAggregation {
field: "string_id".to_string(),
order: Some(CustomOrder {
order: Order::Desc,
order: Order::Asc,
target: OrderTarget::Key,
}),
size: Some(2),
@@ -981,14 +900,14 @@ mod tests {
assert_eq!(res["my_texts"]["sum_other_doc_count"], 3);
// key desc and segment_size cut_off
// key asc and segment_size cut_off
let agg_req: Aggregations = vec![(
"my_texts".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Terms(TermsAggregation {
field: "string_id".to_string(),
order: Some(CustomOrder {
order: Order::Desc,
order: Order::Asc,
target: OrderTarget::Key,
}),
size: Some(2),
@@ -1011,14 +930,14 @@ mod tests {
serde_json::Value::Null
);
// key asc
// key desc
let agg_req: Aggregations = vec![(
"my_texts".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Terms(TermsAggregation {
field: "string_id".to_string(),
order: Some(CustomOrder {
order: Order::Asc,
order: Order::Desc,
target: OrderTarget::Key,
}),
..Default::default()
@@ -1038,14 +957,14 @@ mod tests {
assert_eq!(res["my_texts"]["buckets"][2]["doc_count"], 5);
assert_eq!(res["my_texts"]["sum_other_doc_count"], 0);
// key asc, size cut_off
// key desc, size cut_off
let agg_req: Aggregations = vec![(
"my_texts".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Terms(TermsAggregation {
field: "string_id".to_string(),
order: Some(CustomOrder {
order: Order::Asc,
order: Order::Desc,
target: OrderTarget::Key,
}),
size: Some(2),
@@ -1068,14 +987,14 @@ mod tests {
);
assert_eq!(res["my_texts"]["sum_other_doc_count"], 5);
// key asc, segment_size cut_off
// key desc, segment_size cut_off
let agg_req: Aggregations = vec![(
"my_texts".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Terms(TermsAggregation {
field: "string_id".to_string(),
order: Some(CustomOrder {
order: Order::Asc,
order: Order::Desc,
target: OrderTarget::Key,
}),
size: Some(2),
@@ -1207,36 +1126,37 @@ mod tests {
Ok(())
}
#[test]
fn terms_aggregation_term_bucket_limit() -> crate::Result<()> {
let terms: Vec<String> = (0..100_000).map(|el| el.to_string()).collect();
let terms_per_segment = vec![terms.iter().map(|el| el.as_str()).collect()];
// TODO reenable with memory limit
//#[test]
// fn terms_aggregation_term_bucket_limit() -> crate::Result<()> {
// let terms: Vec<String> = (0..100_000).map(|el| el.to_string()).collect();
// let terms_per_segment = vec![terms.iter().map(|el| el.as_str()).collect()];
let index = get_test_index_from_terms(true, &terms_per_segment)?;
// let index = get_test_index_from_terms(true, &terms_per_segment)?;
let agg_req: Aggregations = vec![(
"my_texts".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Terms(TermsAggregation {
field: "string_id".to_string(),
min_doc_count: Some(0),
..Default::default()
}),
sub_aggregation: Default::default(),
}),
)]
.into_iter()
.collect();
// let agg_req: Aggregations = vec![(
//"my_texts".to_string(),
// Aggregation::Bucket(BucketAggregation {
// bucket_agg: BucketAggregationType::Terms(TermsAggregation {
// field: "string_id".to_string(),
// min_doc_count: Some(0),
//..Default::default()
//}),
// sub_aggregation: Default::default(),
//}),
//)]
//.into_iter()
//.collect();
let res = exec_request_with_query(agg_req, &index, None);
// let res = exec_request_with_query(agg_req, &index, None);
assert!(res.is_err());
// assert!(res.is_err());
Ok(())
}
// Ok(())
//}
#[test]
fn terms_aggregation_multi_token_per_doc() -> crate::Result<()> {
fn terms_aggregation_different_tokenizer_on_ff_test() -> crate::Result<()> {
let terms = vec!["Hello Hello", "Hallo Hallo"];
let index = get_test_index_from_terms(true, &[terms])?;
@@ -1256,12 +1176,13 @@ mod tests {
.collect();
let res = exec_request_with_query(agg_req, &index, None).unwrap();
println!("{}", serde_json::to_string_pretty(&res).unwrap());
assert_eq!(res["my_texts"]["buckets"][0]["key"], "hello");
assert_eq!(res["my_texts"]["buckets"][0]["doc_count"], 2);
assert_eq!(res["my_texts"]["buckets"][0]["key"], "Hallo Hallo");
assert_eq!(res["my_texts"]["buckets"][0]["doc_count"], 1);
assert_eq!(res["my_texts"]["buckets"][1]["key"], "hallo");
assert_eq!(res["my_texts"]["buckets"][1]["doc_count"], 2);
assert_eq!(res["my_texts"]["buckets"][1]["key"], "Hello Hello");
assert_eq!(res["my_texts"]["buckets"][1]["doc_count"], 1);
Ok(())
}
@@ -1352,68 +1273,3 @@ mod tests {
Ok(())
}
}
#[cfg(all(test, feature = "unstable"))]
mod bench {
use itertools::Itertools;
use rand::seq::SliceRandom;
use rand::thread_rng;
use super::*;
fn get_collector_with_buckets(num_docs: u64) -> TermBuckets {
TermBuckets::from_req_and_validate(&Default::default(), num_docs as usize).unwrap()
}
fn get_rand_terms(total_terms: u64, num_terms_returned: u64) -> Vec<u64> {
let mut rng = thread_rng();
let all_terms = (0..total_terms - 1).collect_vec();
let mut vals = vec![];
for _ in 0..num_terms_returned {
let val = all_terms.as_slice().choose(&mut rng).unwrap();
vals.push(*val);
}
vals
}
fn bench_term_buckets(b: &mut test::Bencher, num_terms: u64, total_terms: u64) {
let mut collector = get_collector_with_buckets(total_terms);
let vals = get_rand_terms(total_terms, num_terms);
let aggregations_with_accessor: AggregationsWithAccessor = Default::default();
let bucket_count: BucketCount = BucketCount {
bucket_count: Default::default(),
max_bucket_count: 1_000_001u32,
};
b.iter(|| {
for &val in &vals {
collector
.increment_bucket(&[val], 0, &aggregations_with_accessor, &bucket_count, &None)
.unwrap();
}
})
}
#[bench]
fn bench_term_buckets_500_of_1_000_000(b: &mut test::Bencher) {
bench_term_buckets(b, 500u64, 1_000_000u64)
}
#[bench]
fn bench_term_buckets_1_000_000_of_50_000(b: &mut test::Bencher) {
bench_term_buckets(b, 1_000_000u64, 50_000u64)
}
#[bench]
fn bench_term_buckets_1_000_000_of_50(b: &mut test::Bencher) {
bench_term_buckets(b, 1_000_000u64, 50u64)
}
#[bench]
fn bench_term_buckets_1_000_000_of_1_000_000(b: &mut test::Bencher) {
bench_term_buckets(b, 1_000_000u64, 1_000_000u64)
}
}

View File

@@ -4,7 +4,10 @@ use super::agg_req::Aggregations;
use super::agg_req_with_accessor::AggregationsWithAccessor;
use super::agg_result::AggregationResults;
use super::intermediate_agg_result::IntermediateAggregationResults;
use super::segment_agg_result::SegmentAggregationResultsCollector;
use super::segment_agg_result::{
build_segment_agg_collector, GenericSegmentAggregationResultsCollector,
SegmentAggregationCollector,
};
use crate::aggregation::agg_req_with_accessor::get_aggs_with_accessor_and_validate;
use crate::collector::{Collector, SegmentCollector};
use crate::schema::Schema;
@@ -137,7 +140,7 @@ fn merge_fruits(
/// `AggregationSegmentCollector` does the aggregation collection on a segment.
pub struct AggregationSegmentCollector {
aggs_with_accessor: AggregationsWithAccessor,
result: SegmentAggregationResultsCollector,
result: Box<dyn SegmentAggregationCollector>,
error: Option<TantivyError>,
}
@@ -151,8 +154,7 @@ impl AggregationSegmentCollector {
) -> crate::Result<Self> {
let aggs_with_accessor =
get_aggs_with_accessor_and_validate(agg, reader, Rc::default(), max_bucket_count)?;
let result =
SegmentAggregationResultsCollector::from_req_and_validate(&aggs_with_accessor)?;
let result = build_segment_agg_collector(&aggs_with_accessor)?;
Ok(AggregationSegmentCollector {
aggs_with_accessor,
result,

View File

@@ -222,24 +222,23 @@ pub enum IntermediateMetricResult {
impl From<SegmentMetricResultCollector> for IntermediateMetricResult {
fn from(tree: SegmentMetricResultCollector) -> Self {
use super::metric::SegmentStatsType;
match tree {
SegmentMetricResultCollector::Stats(collector) => match collector.collecting_for {
super::metric::SegmentStatsType::Average => IntermediateMetricResult::Average(
SegmentStatsType::Average => IntermediateMetricResult::Average(
IntermediateAverage::from_collector(collector),
),
super::metric::SegmentStatsType::Count => {
SegmentStatsType::Count => {
IntermediateMetricResult::Count(IntermediateCount::from_collector(collector))
}
super::metric::SegmentStatsType::Max => {
SegmentStatsType::Max => {
IntermediateMetricResult::Max(IntermediateMax::from_collector(collector))
}
super::metric::SegmentStatsType::Min => {
SegmentStatsType::Min => {
IntermediateMetricResult::Min(IntermediateMin::from_collector(collector))
}
super::metric::SegmentStatsType::Stats => {
IntermediateMetricResult::Stats(collector.stats)
}
super::metric::SegmentStatsType::Sum => {
SegmentStatsType::Stats => IntermediateMetricResult::Stats(collector.stats),
SegmentStatsType::Sum => {
IntermediateMetricResult::Sum(IntermediateSum::from_collector(collector))
}
},
@@ -499,7 +498,7 @@ impl IntermediateTermBucketResult {
match req.order.target {
OrderTarget::Key => {
buckets.sort_by(|left, right| {
if req.order.order == Order::Desc {
if req.order.order == Order::Asc {
left.key.partial_cmp(&right.key)
} else {
right.key.partial_cmp(&left.key)

View File

@@ -1,7 +1,13 @@
use fastfield_codecs::Column;
use columnar::Column;
use serde::{Deserialize, Serialize};
use crate::aggregation::f64_from_fastfield_u64;
use super::*;
use crate::aggregation::agg_req_with_accessor::AggregationsWithAccessor;
use crate::aggregation::intermediate_agg_result::{
IntermediateAggregationResults, IntermediateMetricResult,
};
use crate::aggregation::segment_agg_result::SegmentAggregationCollector;
use crate::aggregation::{f64_from_fastfield_u64, VecWithNames};
use crate::schema::Type;
use crate::{DocId, TantivyError};
@@ -160,27 +166,74 @@ impl SegmentStatsCollector {
stats: IntermediateStats::default(),
}
}
pub(crate) fn collect_block(&mut self, doc: &[DocId], field: &dyn Column<u64>) {
let mut iter = doc.chunks_exact(4);
for docs in iter.by_ref() {
let val1 = field.get_val(docs[0]);
let val2 = field.get_val(docs[1]);
let val3 = field.get_val(docs[2]);
let val4 = field.get_val(docs[3]);
let val1 = f64_from_fastfield_u64(val1, &self.field_type);
let val2 = f64_from_fastfield_u64(val2, &self.field_type);
let val3 = f64_from_fastfield_u64(val3, &self.field_type);
let val4 = f64_from_fastfield_u64(val4, &self.field_type);
pub(crate) fn collect_block(&mut self, docs: &[DocId], field: &Column<u64>) {
// TODO special case for Required, Optional column type
for doc in docs {
for val in field.values(*doc) {
let val1 = f64_from_fastfield_u64(val, &self.field_type);
self.stats.collect(val1);
}
}
}
}
impl SegmentAggregationCollector for SegmentStatsCollector {
fn into_intermediate_aggregations_result(
self: Box<Self>,
agg_with_accessor: &AggregationsWithAccessor,
) -> crate::Result<IntermediateAggregationResults> {
let name = agg_with_accessor.metrics.keys[0].to_string();
let intermediate_metric_result = match self.collecting_for {
SegmentStatsType::Average => {
IntermediateMetricResult::Average(IntermediateAverage::from_collector(*self))
}
SegmentStatsType::Count => {
IntermediateMetricResult::Count(IntermediateCount::from_collector(*self))
}
SegmentStatsType::Max => {
IntermediateMetricResult::Max(IntermediateMax::from_collector(*self))
}
SegmentStatsType::Min => {
IntermediateMetricResult::Min(IntermediateMin::from_collector(*self))
}
SegmentStatsType::Stats => IntermediateMetricResult::Stats(self.stats),
SegmentStatsType::Sum => {
IntermediateMetricResult::Sum(IntermediateSum::from_collector(*self))
}
};
let metrics = Some(VecWithNames::from_entries(vec![(
name,
intermediate_metric_result,
)]));
Ok(IntermediateAggregationResults {
metrics,
buckets: None,
})
}
fn collect(
&mut self,
doc: crate::DocId,
agg_with_accessor: &AggregationsWithAccessor,
) -> crate::Result<()> {
let accessor = &agg_with_accessor.metrics.values[0].accessor;
for val in accessor.values(doc) {
let val1 = f64_from_fastfield_u64(val, &self.field_type);
self.stats.collect(val1);
self.stats.collect(val2);
self.stats.collect(val3);
self.stats.collect(val4);
}
for &doc in iter.remainder() {
let val = field.get_val(doc);
let val = f64_from_fastfield_u64(val, &self.field_type);
self.stats.collect(val);
}
Ok(())
}
fn flush_staged_docs(
&mut self,
_agg_with_accessor: &AggregationsWithAccessor,
_force_flush: bool,
) -> crate::Result<()> {
Ok(())
}
}

View File

@@ -172,8 +172,8 @@ pub use collector::{
AggregationCollector, AggregationSegmentCollector, DistributedAggregationCollector,
MAX_BUCKET_COUNT,
};
use columnar::MonotonicallyMappableToU64;
pub(crate) use date::format_date;
use fastfield_codecs::MonotonicallyMappableToU64;
use itertools::Itertools;
use serde::{Deserialize, Serialize};
@@ -182,7 +182,7 @@ use crate::schema::Type;
/// Represents an associative array `(key => values)` in a very efficient manner.
#[derive(Clone, PartialEq, Serialize, Deserialize)]
pub(crate) struct VecWithNames<T: Clone> {
values: Vec<T>,
pub(crate) values: Vec<T>,
keys: Vec<String>,
}
@@ -248,9 +248,6 @@ impl<T: Clone> VecWithNames<T> {
fn values_mut(&mut self) -> impl Iterator<Item = &mut T> + '_ {
self.values.iter_mut()
}
fn entries(&self) -> impl Iterator<Item = (&str, &T)> + '_ {
self.keys().zip(self.values.iter())
}
fn is_empty(&self) -> bool {
self.keys.is_empty()
}
@@ -336,8 +333,9 @@ mod tests {
use crate::aggregation::intermediate_agg_result::IntermediateAggregationResults;
use crate::aggregation::segment_agg_result::DOC_BLOCK_SIZE;
use crate::aggregation::DistributedAggregationCollector;
use crate::indexer::NoMergePolicy;
use crate::query::{AllQuery, TermQuery};
use crate::schema::{Cardinality, IndexRecordOption, Schema, TextFieldIndexing, FAST, STRING};
use crate::schema::{IndexRecordOption, Schema, TextFieldIndexing, FAST, STRING};
use crate::{DateTime, Index, Term};
fn get_avg_req(field_name: &str) -> Aggregation {
@@ -432,8 +430,7 @@ mod tests {
let text_field = schema_builder.add_text_field("text", text_fieldtype.clone());
let text_field_id = schema_builder.add_text_field("text_id", text_fieldtype);
let string_field_id = schema_builder.add_text_field("string_id", STRING | FAST);
let score_fieldtype =
crate::schema::NumericOptions::default().set_fast();
let score_fieldtype = crate::schema::NumericOptions::default().set_fast();
let score_field = schema_builder.add_u64_field("score", score_fieldtype.clone());
let score_field_f64 = schema_builder.add_f64_field("score_f64", score_fieldtype.clone());
let score_field_i64 = schema_builder.add_i64_field("score_i64", score_fieldtype);
@@ -445,6 +442,7 @@ mod tests {
{
// let mut index_writer = index.writer_for_tests()?;
let mut index_writer = index.writer_with_num_threads(1, 30_000_000)?;
index_writer.set_merge_policy(Box::new(NoMergePolicy));
for values in segment_and_values {
for (i, term) in values {
let i = *i;
@@ -656,13 +654,11 @@ mod tests {
let text_field = schema_builder.add_text_field("text", text_fieldtype);
let date_field = schema_builder.add_date_field("date", FAST);
schema_builder.add_text_field("dummy_text", STRING);
let score_fieldtype =
crate::schema::NumericOptions::default().set_fast();
let score_fieldtype = crate::schema::NumericOptions::default().set_fast();
let score_field = schema_builder.add_u64_field("score", score_fieldtype.clone());
let score_field_f64 = schema_builder.add_f64_field("score_f64", score_fieldtype.clone());
let multivalue =
crate::schema::NumericOptions::default().set_fast();
let multivalue = crate::schema::NumericOptions::default().set_fast();
let scores_field_i64 = schema_builder.add_i64_field("scores_i64", multivalue);
let score_field_i64 = schema_builder.add_i64_field("score_i64", score_fieldtype);
@@ -1147,7 +1143,7 @@ mod tests {
let agg_res = avg_on_field("dummy_text");
assert_eq!(
format!("{:?}", agg_res),
r#"InvalidArgument("Only fast fields of type f64, u64, i64 are supported, but got Str ")"#
r#"InvalidArgument("No numerical fast field found for field: dummy_text")"#
);
let agg_res = avg_on_field("not_exist_field");
@@ -1156,12 +1152,6 @@ mod tests {
r#"FieldNotFound("not_exist_field")"#
);
let agg_res = avg_on_field("scores_i64");
assert_eq!(
format!("{:?}", agg_res),
r#"InvalidArgument("Invalid field cardinality on field scores_i64 expected SingleValue, but got MultiValues")"#
);
Ok(())
}
@@ -1173,11 +1163,14 @@ mod tests {
use test::{self, Bencher};
use super::*;
use crate::aggregation::bucket::{HistogramAggregation, HistogramBounds, TermsAggregation};
use crate::aggregation::bucket::{
CustomOrder, HistogramAggregation, HistogramBounds, Order, OrderTarget,
TermsAggregation,
};
use crate::aggregation::metric::StatsAggregation;
use crate::query::AllQuery;
fn get_test_index_bench(merge_segments: bool) -> crate::Result<Index> {
fn get_test_index_bench(_merge_segments: bool) -> crate::Result<Index> {
let mut schema_builder = Schema::builder();
let text_fieldtype = crate::schema::TextOptions::default()
.set_indexing_options(
@@ -1189,20 +1182,19 @@ mod tests {
schema_builder.add_text_field("text_many_terms", STRING | FAST);
let text_field_few_terms =
schema_builder.add_text_field("text_few_terms", STRING | FAST);
let score_fieldtype =
crate::schema::NumericOptions::default().set_fast();
let score_fieldtype = crate::schema::NumericOptions::default().set_fast();
let score_field = schema_builder.add_u64_field("score", score_fieldtype.clone());
let score_field_f64 =
schema_builder.add_f64_field("score_f64", score_fieldtype.clone());
let score_field_i64 = schema_builder.add_i64_field("score_i64", score_fieldtype);
let index = Index::create_from_tempdir(schema_builder.build())?;
let few_terms_data = vec!["INFO", "ERROR", "WARN", "DEBUG"];
let many_terms_data = (0..15_000)
let many_terms_data = (0..150_000)
.map(|num| format!("author{}", num))
.collect::<Vec<_>>();
{
let mut rng = thread_rng();
let mut index_writer = index.writer_for_tests()?;
let mut index_writer = index.writer_with_num_threads(1, 100_000_000)?;
// writing the segment
for _ in 0..1_000_000 {
let val: f64 = rng.gen_range(0.0..1_000_000.0);
@@ -1217,14 +1209,6 @@ mod tests {
}
index_writer.commit()?;
}
if merge_segments {
let segment_ids = index
.searchable_segment_ids()
.expect("Searchable segments failed.");
let mut index_writer = index.writer_for_tests()?;
index_writer.merge(&segment_ids).wait()?;
index_writer.wait_merging_threads()?;
}
Ok(index)
}
@@ -1376,7 +1360,42 @@ mod tests {
}
#[bench]
fn bench_aggregation_terms_many(b: &mut Bencher) {
fn bench_aggregation_terms_many_with_sub_agg(b: &mut Bencher) {
let index = get_test_index_bench(false).unwrap();
let reader = index.reader().unwrap();
b.iter(|| {
let sub_agg_req: Aggregations = vec![(
"average_f64".to_string(),
Aggregation::Metric(MetricAggregation::Average(
AverageAggregation::from_field_name("score_f64".to_string()),
)),
)]
.into_iter()
.collect();
let agg_req: Aggregations = vec![(
"my_texts".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Terms(TermsAggregation {
field: "text_many_terms".to_string(),
..Default::default()
}),
sub_aggregation: sub_agg_req,
}),
)]
.into_iter()
.collect();
let collector = AggregationCollector::from_aggs(agg_req, None, index.schema());
let searcher = reader.searcher();
searcher.search(&AllQuery, &collector).unwrap()
});
}
#[bench]
fn bench_aggregation_terms_many2(b: &mut Bencher) {
let index = get_test_index_bench(false).unwrap();
let reader = index.reader().unwrap();
@@ -1401,6 +1420,36 @@ mod tests {
});
}
#[bench]
fn bench_aggregation_terms_many_order_by_term(b: &mut Bencher) {
let index = get_test_index_bench(false).unwrap();
let reader = index.reader().unwrap();
b.iter(|| {
let agg_req: Aggregations = vec![(
"my_texts".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Terms(TermsAggregation {
field: "text_many_terms".to_string(),
order: Some(CustomOrder {
order: Order::Desc,
target: OrderTarget::Key,
}),
..Default::default()
}),
sub_aggregation: Default::default(),
}),
)]
.into_iter()
.collect();
let collector = AggregationCollector::from_aggs(agg_req, None, index.schema());
let searcher = reader.searcher();
searcher.search(&AllQuery, &collector).unwrap()
});
}
#[bench]
fn bench_aggregation_range_only(b: &mut Bencher) {
let index = get_test_index_bench(false).unwrap();

View File

@@ -25,15 +25,89 @@ use crate::{DocId, TantivyError};
pub(crate) const DOC_BLOCK_SIZE: usize = 64;
pub(crate) type DocBlock = [DocId; DOC_BLOCK_SIZE];
#[derive(Clone, PartialEq)]
pub(crate) struct SegmentAggregationResultsCollector {
pub(crate) trait SegmentAggregationCollector: CollectorClone + Debug {
fn into_intermediate_aggregations_result(
self: Box<Self>,
agg_with_accessor: &AggregationsWithAccessor,
) -> crate::Result<IntermediateAggregationResults>;
fn collect(
&mut self,
doc: crate::DocId,
agg_with_accessor: &AggregationsWithAccessor,
) -> crate::Result<()>;
fn flush_staged_docs(
&mut self,
agg_with_accessor: &AggregationsWithAccessor,
force_flush: bool,
) -> crate::Result<()>;
}
pub(crate) trait CollectorClone {
fn clone_box(&self) -> Box<dyn SegmentAggregationCollector>;
}
impl<T> CollectorClone for T
where T: 'static + SegmentAggregationCollector + Clone
{
fn clone_box(&self) -> Box<dyn SegmentAggregationCollector> {
Box::new(self.clone())
}
}
impl Clone for Box<dyn SegmentAggregationCollector> {
fn clone(&self) -> Box<dyn SegmentAggregationCollector> {
self.clone_box()
}
}
pub(crate) fn build_segment_agg_collector(
req: &AggregationsWithAccessor,
) -> crate::Result<Box<dyn SegmentAggregationCollector>> {
// Single metric special case
if req.buckets.is_empty() && req.metrics.len() == 1 {
let req = &req.metrics.values[0];
let stats_collector = match &req.metric {
MetricAggregation::Average(AverageAggregation { .. }) => {
SegmentStatsCollector::from_req(req.field_type, SegmentStatsType::Average)
}
MetricAggregation::Count(CountAggregation { .. }) => {
SegmentStatsCollector::from_req(req.field_type, SegmentStatsType::Count)
}
MetricAggregation::Max(MaxAggregation { .. }) => {
SegmentStatsCollector::from_req(req.field_type, SegmentStatsType::Max)
}
MetricAggregation::Min(MinAggregation { .. }) => {
SegmentStatsCollector::from_req(req.field_type, SegmentStatsType::Min)
}
MetricAggregation::Stats(StatsAggregation { .. }) => {
SegmentStatsCollector::from_req(req.field_type, SegmentStatsType::Stats)
}
MetricAggregation::Sum(SumAggregation { .. }) => {
SegmentStatsCollector::from_req(req.field_type, SegmentStatsType::Sum)
}
};
return Ok(Box::new(stats_collector));
}
let agg = GenericSegmentAggregationResultsCollector::from_req_and_validate(req)?;
Ok(Box::new(agg))
}
#[derive(Clone)]
/// The GenericSegmentAggregationResultsCollector is the generic version of the collector, which
/// can handle arbitrary complexity of sub-aggregations. Ideally we never have to pick this one
/// and can provide specialized versions instead, that remove some of its overhead.
pub(crate) struct GenericSegmentAggregationResultsCollector {
pub(crate) metrics: Option<VecWithNames<SegmentMetricResultCollector>>,
pub(crate) buckets: Option<VecWithNames<SegmentBucketResultCollector>>,
staged_docs: DocBlock,
num_staged_docs: usize,
}
impl Default for SegmentAggregationResultsCollector {
impl Default for GenericSegmentAggregationResultsCollector {
fn default() -> Self {
Self {
metrics: Default::default(),
@@ -44,7 +118,7 @@ impl Default for SegmentAggregationResultsCollector {
}
}
impl Debug for SegmentAggregationResultsCollector {
impl Debug for GenericSegmentAggregationResultsCollector {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("SegmentAggregationResultsCollector")
.field("metrics", &self.metrics)
@@ -55,9 +129,9 @@ impl Debug for SegmentAggregationResultsCollector {
}
}
impl SegmentAggregationResultsCollector {
pub fn into_intermediate_aggregations_result(
self,
impl SegmentAggregationCollector for GenericSegmentAggregationResultsCollector {
fn into_intermediate_aggregations_result(
self: Box<Self>,
agg_with_accessor: &AggregationsWithAccessor,
) -> crate::Result<IntermediateAggregationResults> {
let buckets = if let Some(buckets) = self.buckets {
@@ -75,47 +149,7 @@ impl SegmentAggregationResultsCollector {
Ok(IntermediateAggregationResults { metrics, buckets })
}
pub(crate) fn from_req_and_validate(req: &AggregationsWithAccessor) -> crate::Result<Self> {
let buckets = req
.buckets
.entries()
.map(|(key, req)| {
Ok((
key.to_string(),
SegmentBucketResultCollector::from_req_and_validate(req)?,
))
})
.collect::<crate::Result<Vec<(String, _)>>>()?;
let metrics = req
.metrics
.entries()
.map(|(key, req)| {
Ok((
key.to_string(),
SegmentMetricResultCollector::from_req_and_validate(req)?,
))
})
.collect::<crate::Result<Vec<(String, _)>>>()?;
let metrics = if metrics.is_empty() {
None
} else {
Some(VecWithNames::from_entries(metrics))
};
let buckets = if buckets.is_empty() {
None
} else {
Some(VecWithNames::from_entries(buckets))
};
Ok(SegmentAggregationResultsCollector {
metrics,
buckets,
staged_docs: [0; DOC_BLOCK_SIZE],
num_staged_docs: 0,
})
}
#[inline]
pub(crate) fn collect(
fn collect(
&mut self,
doc: crate::DocId,
agg_with_accessor: &AggregationsWithAccessor,
@@ -128,7 +162,7 @@ impl SegmentAggregationResultsCollector {
Ok(())
}
pub(crate) fn flush_staged_docs(
fn flush_staged_docs(
&mut self,
agg_with_accessor: &AggregationsWithAccessor,
force_flush: bool,
@@ -162,6 +196,66 @@ impl SegmentAggregationResultsCollector {
}
}
impl GenericSegmentAggregationResultsCollector {
pub fn into_intermediate_aggregations_result(
self,
agg_with_accessor: &AggregationsWithAccessor,
) -> crate::Result<IntermediateAggregationResults> {
let buckets = if let Some(buckets) = self.buckets {
let entries = buckets
.into_iter()
.zip(agg_with_accessor.buckets.values())
.map(|((key, bucket), acc)| Ok((key, bucket.into_intermediate_bucket_result(acc)?)))
.collect::<crate::Result<Vec<(String, _)>>>()?;
Some(VecWithNames::from_entries(entries))
} else {
None
};
let metrics = self.metrics.map(VecWithNames::from_other);
Ok(IntermediateAggregationResults { metrics, buckets })
}
pub(crate) fn from_req_and_validate(req: &AggregationsWithAccessor) -> crate::Result<Self> {
let buckets = req
.buckets
.iter()
.map(|(key, req)| {
Ok((
key.to_string(),
SegmentBucketResultCollector::from_req_and_validate(req)?,
))
})
.collect::<crate::Result<Vec<(String, _)>>>()?;
let metrics = req
.metrics
.iter()
.map(|(key, req)| {
Ok((
key.to_string(),
SegmentMetricResultCollector::from_req_and_validate(req)?,
))
})
.collect::<crate::Result<Vec<(String, _)>>>()?;
let metrics = if metrics.is_empty() {
None
} else {
Some(VecWithNames::from_entries(metrics))
};
let buckets = if buckets.is_empty() {
None
} else {
Some(VecWithNames::from_entries(buckets))
};
Ok(GenericSegmentAggregationResultsCollector {
metrics,
buckets,
staged_docs: [0; DOC_BLOCK_SIZE],
num_staged_docs: 0,
})
}
}
#[derive(Clone, Debug, PartialEq)]
pub(crate) enum SegmentMetricResultCollector {
Stats(SegmentStatsCollector),
@@ -205,7 +299,7 @@ impl SegmentMetricResultCollector {
pub(crate) fn collect_block(&mut self, doc: &[DocId], metric: &MetricAggregationWithAccessor) {
match self {
SegmentMetricResultCollector::Stats(stats_collector) => {
stats_collector.collect_block(doc, &*metric.accessor);
stats_collector.collect_block(doc, &metric.accessor);
}
}
}
@@ -215,7 +309,7 @@ impl SegmentMetricResultCollector {
/// segments.
/// The typical structure of Map<Key, Bucket> is not suitable during collection for performance
/// reasons.
#[derive(Clone, Debug, PartialEq)]
#[derive(Clone, Debug)]
pub(crate) enum SegmentBucketResultCollector {
Range(SegmentRangeCollector),
Histogram(Box<SegmentHistogramCollector>),
@@ -243,14 +337,7 @@ impl SegmentBucketResultCollector {
pub fn from_req_and_validate(req: &BucketAggregationWithAccessor) -> crate::Result<Self> {
match &req.bucket_agg {
BucketAggregationType::Terms(terms_req) => Ok(Self::Terms(Box::new(
SegmentTermCollector::from_req_and_validate(
terms_req,
&req.sub_aggregation,
req.field_type,
req.accessor
.as_multi()
.expect("unexpected fast field cardinality"),
)?,
SegmentTermCollector::from_req_and_validate(terms_req, &req.sub_aggregation)?,
))),
BucketAggregationType::Range(range_req) => {
Ok(Self::Range(SegmentRangeCollector::from_req_and_validate(
@@ -265,9 +352,7 @@ impl SegmentBucketResultCollector {
histogram,
&req.sub_aggregation,
req.field_type,
req.accessor
.as_single()
.expect("unexpected fast field cardinality"),
&req.accessor,
)?,
))),
}

View File

@@ -150,7 +150,7 @@ fn facet_depth(facet_bytes: &[u8]) -> usize {
/// }
///
/// {
/// let mut facet_collector = FacetCollector::for_field(facet);
/// let mut facet_collector = FacetCollector::for_field("facet");
/// facet_collector.add_facet("/category/fiction");
/// let facet_counts = searcher.search(&AllQuery, &facet_collector)?;
///
@@ -829,7 +829,7 @@ mod bench {
let reader = index.reader().unwrap();
b.iter(|| {
let searcher = reader.searcher();
let facet_collector = FacetCollector::for_field(facet_field);
let facet_collector = FacetCollector::for_field("facet");
searcher.search(&AllQuery, &facet_collector).unwrap();
});
}

View File

@@ -12,8 +12,7 @@
use std::marker::PhantomData;
use std::sync::Arc;
use columnar::{DynamicColumn, HasAssociatedColumnType};
use fastfield_codecs::Column;
use columnar::{ColumnValues, DynamicColumn, HasAssociatedColumnType};
use crate::collector::{Collector, SegmentCollector};
use crate::schema::Field;
@@ -121,7 +120,7 @@ where
let fast_field_reader = segment_reader
.fast_fields()
.typed_column_first_or_default(schema.get_field_name(self.field))?;
.column_first_or_default(schema.get_field_name(self.field))?;
let segment_collector = self
.collector
@@ -152,7 +151,7 @@ where
TPredicate: 'static,
DynamicColumn: Into<Option<columnar::Column<TPredicateValue>>>,
{
fast_field_reader: Arc<dyn Column<TPredicateValue>>,
fast_field_reader: Arc<dyn ColumnValues<TPredicateValue>>,
segment_collector: TSegmentCollector,
predicate: TPredicate,
t_predicate_value: PhantomData<TPredicateValue>,

View File

@@ -1,7 +1,7 @@
use std::sync::Arc;
use columnar::ColumnValues;
use fastdivide::DividerU64;
use fastfield_codecs::Column;
use crate::collector::{Collector, SegmentCollector};
use crate::fastfield::{FastFieldNotAvailableError, FastValue};
@@ -87,7 +87,7 @@ impl HistogramComputer {
}
pub struct SegmentHistogramCollector {
histogram_computer: HistogramComputer,
column_u64: Arc<dyn Column<u64>>,
column_u64: Arc<dyn ColumnValues<u64>>,
}
impl SegmentCollector for SegmentHistogramCollector {

View File

@@ -1,12 +1,12 @@
use std::sync::Arc;
use fastfield_codecs::Column;
use columnar::{BytesColumn, ColumnValues};
use super::*;
use crate::collector::{Count, FilterCollector, TopDocs};
use crate::core::SegmentReader;
use crate::query::{AllQuery, QueryParser};
use crate::schema::{Field, Schema, FAST, TEXT};
use crate::schema::{Schema, FAST, TEXT};
use crate::time::format_description::well_known::Rfc3339;
use crate::time::OffsetDateTime;
use crate::{doc, DateTime, DocAddress, DocId, Document, Index, Score, Searcher, SegmentOrdinal};
@@ -160,7 +160,7 @@ pub struct FastFieldTestCollector {
pub struct FastFieldSegmentCollector {
vals: Vec<u64>,
reader: Arc<dyn Column<u64>>,
reader: Arc<dyn columnar::ColumnValues>,
}
impl FastFieldTestCollector {
@@ -212,62 +212,73 @@ impl SegmentCollector for FastFieldSegmentCollector {
}
}
// /// Collects in order all of the fast field bytes for all of the
// /// docs in the `DocSet`
// ///
// /// This collector is mainly useful for tests.
// pub struct BytesFastFieldTestCollector {
// field: Field,
// }
/// Collects in order all of the fast field bytes for all of the
/// docs in the `DocSet`
///
/// This collector is mainly useful for tests.
/// It is very slow.
pub struct BytesFastFieldTestCollector {
field: String,
}
// pub struct BytesFastFieldSegmentCollector {
// vals: Vec<u8>,
// reader: BytesFastFieldReader,
// }
pub struct BytesFastFieldSegmentCollector {
vals: Vec<u8>,
column_opt: Option<BytesColumn>,
buffer: Vec<u8>,
}
// impl BytesFastFieldTestCollector {
// pub fn for_field(field: Field) -> BytesFastFieldTestCollector {
// BytesFastFieldTestCollector { field }
// }
// }
impl BytesFastFieldTestCollector {
pub fn for_field(field: impl ToString) -> BytesFastFieldTestCollector {
BytesFastFieldTestCollector {
field: field.to_string(),
}
}
}
// impl Collector for BytesFastFieldTestCollector {
// type Fruit = Vec<u8>;
// type Child = BytesFastFieldSegmentCollector;
impl Collector for BytesFastFieldTestCollector {
type Fruit = Vec<u8>;
type Child = BytesFastFieldSegmentCollector;
// fn for_segment(
// &self,
// _segment_local_id: u32,
// segment_reader: &SegmentReader,
// ) -> crate::Result<BytesFastFieldSegmentCollector> {
// let reader = segment_reader.fast_fields().bytes(self.field)?;
// Ok(BytesFastFieldSegmentCollector {
// vals: Vec::new(),
// reader,
// })
// }
fn for_segment(
&self,
_segment_local_id: u32,
segment_reader: &SegmentReader,
) -> crate::Result<BytesFastFieldSegmentCollector> {
let column_opt = segment_reader.fast_fields().bytes(&self.field)?;
Ok(BytesFastFieldSegmentCollector {
vals: Vec::new(),
column_opt,
buffer: Vec::new(),
})
}
// fn requires_scoring(&self) -> bool {
// false
// }
fn requires_scoring(&self) -> bool {
false
}
// fn merge_fruits(&self, children: Vec<Vec<u8>>) -> crate::Result<Vec<u8>> {
// Ok(children.into_iter().flat_map(|c| c.into_iter()).collect())
// }
// }
fn merge_fruits(&self, children: Vec<Vec<u8>>) -> crate::Result<Vec<u8>> {
Ok(children.into_iter().flat_map(|c| c.into_iter()).collect())
}
}
// impl SegmentCollector for BytesFastFieldSegmentCollector {
// type Fruit = Vec<u8>;
impl SegmentCollector for BytesFastFieldSegmentCollector {
type Fruit = Vec<u8>;
// fn collect(&mut self, doc: u32, _score: Score) {
// let data = self.reader.get_bytes(doc);
// self.vals.extend(data);
// }
fn collect(&mut self, doc: DocId, _score: Score) {
if let Some(column) = self.column_opt.as_ref() {
for term_ord in column.term_ords(doc) {
let (vals, buffer) = (&mut self.vals, &mut self.buffer);
if column.ord_to_bytes(term_ord, buffer).unwrap() {
vals.extend(&buffer[..]);
}
}
}
}
// fn harvest(self) -> <Self as SegmentCollector>::Fruit {
// self.vals
// }
// }
fn harvest(self) -> <Self as SegmentCollector>::Fruit {
self.vals
}
}
fn make_test_searcher() -> crate::Result<Searcher> {
let schema = Schema::builder().build();

View File

@@ -3,7 +3,7 @@ use std::fmt;
use std::marker::PhantomData;
use std::sync::Arc;
use fastfield_codecs::Column;
use columnar::ColumnValues;
use super::Collector;
use crate::collector::custom_score_top_collector::CustomScoreTopCollector;
@@ -14,7 +14,6 @@ use crate::collector::{
};
use crate::fastfield::{FastFieldNotAvailableError, FastValue};
use crate::query::Weight;
use crate::schema::Field;
use crate::{DocAddress, DocId, Score, SegmentOrdinal, SegmentReader, TantivyError};
struct FastFieldConvertCollector<
@@ -133,7 +132,7 @@ impl fmt::Debug for TopDocs {
}
struct ScorerByFastFieldReader {
sort_column: Arc<dyn Column<u64>>,
sort_column: Arc<dyn ColumnValues<u64>>,
}
impl CustomSegmentScorer<u64> for ScorerByFastFieldReader {
@@ -236,7 +235,6 @@ impl TopDocs {
/// # use tantivy::query::{Query, QueryParser};
/// use tantivy::Searcher;
/// use tantivy::collector::TopDocs;
/// use tantivy::schema::Field;
///
/// # fn main() -> tantivy::Result<()> {
/// # let mut schema_builder = Schema::builder();
@@ -253,7 +251,7 @@ impl TopDocs {
/// # index_writer.commit()?;
/// # let reader = index.reader()?;
/// # let query = QueryParser::for_index(&index, vec![title]).parse_query("diary")?;
/// # let top_docs = docs_sorted_by_rating(&reader.searcher(), &query, rating)?;
/// # let top_docs = docs_sorted_by_rating(&reader.searcher(), &query)?;
/// # assert_eq!(top_docs,
/// # vec![(97u64, DocAddress::new(0u32, 1)),
/// # (80u64, DocAddress::new(0u32, 3))]);
@@ -263,8 +261,7 @@ impl TopDocs {
/// /// collects the top 10 documents, order by the u64-`field`
/// /// given in argument.
/// fn docs_sorted_by_rating(searcher: &Searcher,
/// query: &dyn Query,
/// rating_field: Field)
/// query: &dyn Query)
/// -> tantivy::Result<Vec<(u64, DocAddress)>> {
///
/// // This is where we build our topdocs collector
@@ -272,7 +269,7 @@ impl TopDocs {
/// // Note the `rating_field` needs to be a FAST field here.
/// let top_books_by_rating = TopDocs
/// ::with_limit(10)
/// .order_by_u64_field(rating_field);
/// .order_by_u64_field("rating");
///
/// // ... and here are our documents. Note this is a simple vec.
/// // The `u64` in the pair is the value of our fast field for
@@ -324,22 +321,21 @@ impl TopDocs {
/// # use tantivy::query::{Query, AllQuery};
/// use tantivy::Searcher;
/// use tantivy::collector::TopDocs;
/// use tantivy::schema::Field;
///
/// # fn main() -> tantivy::Result<()> {
/// # let mut schema_builder = Schema::builder();
/// # let title = schema_builder.add_text_field("company", TEXT);
/// # let rating = schema_builder.add_i64_field("revenue", FAST);
/// # let revenue = schema_builder.add_i64_field("revenue", FAST);
/// # let schema = schema_builder.build();
/// #
/// # let index = Index::create_in_ram(schema);
/// # let mut index_writer = index.writer_with_num_threads(1, 10_000_000)?;
/// # index_writer.add_document(doc!(title => "MadCow Inc.", rating => 92_000_000i64))?;
/// # index_writer.add_document(doc!(title => "Zozo Cow KKK", rating => 119_000_000i64))?;
/// # index_writer.add_document(doc!(title => "Declining Cow", rating => -63_000_000i64))?;
/// # index_writer.add_document(doc!(title => "MadCow Inc.", revenue => 92_000_000i64))?;
/// # index_writer.add_document(doc!(title => "Zozo Cow KKK", revenue => 119_000_000i64))?;
/// # index_writer.add_document(doc!(title => "Declining Cow", revenue => -63_000_000i64))?;
/// # assert!(index_writer.commit().is_ok());
/// # let reader = index.reader()?;
/// # let top_docs = docs_sorted_by_revenue(&reader.searcher(), &AllQuery, rating)?;
/// # let top_docs = docs_sorted_by_revenue(&reader.searcher(), &AllQuery, "revenue")?;
/// # assert_eq!(top_docs,
/// # vec![(119_000_000i64, DocAddress::new(0, 1)),
/// # (92_000_000i64, DocAddress::new(0, 0))]);
@@ -350,7 +346,7 @@ impl TopDocs {
/// /// given in argument.
/// fn docs_sorted_by_revenue(searcher: &Searcher,
/// query: &dyn Query,
/// revenue_field: Field)
/// revenue_field: &str)
/// -> tantivy::Result<Vec<(i64, DocAddress)>> {
///
/// // This is where we build our topdocs collector
@@ -359,7 +355,7 @@ impl TopDocs {
/// // type `sort_by_field`. revenue_field here is a FAST i64 field.
/// let top_company_by_revenue = TopDocs
/// ::with_limit(2)
/// .order_by_fast_field(revenue_field);
/// .order_by_fast_field("revenue");
///
/// // ... and here are our documents. Note this is a simple vec.
/// // The `i64` in the pair is the value of our fast field for

View File

@@ -905,12 +905,14 @@ mod tests {
let field = schema.get_field("num_likes").unwrap();
let index = Index::create(directory.clone(), schema, IndexSettings::default())?;
let mut writer = index.writer_with_num_threads(8, 24_000_000).unwrap();
for i in 0u64..8_000u64 {
writer.add_document(doc!(field => i))?;
let mut writer = index.writer_with_num_threads(1, 32_000_000).unwrap();
for _seg in 0..8 {
for i in 0u64..1_000u64 {
writer.add_document(doc!(field => i))?;
}
writer.commit()?;
}
writer.commit()?;
let mem_right_after_commit = directory.total_mem_usage();
let reader = index

View File

@@ -135,6 +135,8 @@ impl InvertedIndexReader {
term_info: &TermInfo,
option: IndexRecordOption,
) -> io::Result<SegmentPostings> {
let option = option.downgrade(self.record_option);
let block_postings = self.read_block_postings_from_terminfo(term_info, option)?;
let position_reader = {
if option.has_positions() {

View File

@@ -249,7 +249,7 @@ impl SearcherInner {
index: Index,
segment_readers: Vec<SegmentReader>,
generation: TrackedObject<SearcherGeneration>,
doc_store_cache_size: usize,
doc_store_cache_num_blocks: usize,
) -> io::Result<SearcherInner> {
assert_eq!(
&segment_readers
@@ -261,7 +261,7 @@ impl SearcherInner {
);
let store_readers: Vec<StoreReader> = segment_readers
.iter()
.map(|segment_reader| segment_reader.get_store_reader(doc_store_cache_size))
.map(|segment_reader| segment_reader.get_store_reader(doc_store_cache_num_blocks))
.collect::<io::Result<Vec<_>>>()?;
Ok(SearcherInner {

View File

@@ -9,7 +9,7 @@ use crate::directory::{CompositeFile, FileSlice};
use crate::error::DataCorruption;
use crate::fastfield::{intersect_alive_bitsets, AliveBitSet, FacetReader, FastFieldReaders};
use crate::fieldnorm::{FieldNormReader, FieldNormReaders};
use crate::schema::{Field, FieldType, IndexRecordOption, Schema, Type};
use crate::schema::{Field, IndexRecordOption, Schema, Type};
use crate::space_usage::SegmentSpaceUsage;
use crate::store::StoreReader;
use crate::termdict::TermDictionary;
@@ -99,7 +99,7 @@ impl SegmentReader {
"`{field_name}` is not a facet field.`"
)));
}
let Some(facet_column) = self.fast_fields().str_column_opt(field_name)? else {
let Some(facet_column) = self.fast_fields().str(field_name)? else {
panic!("Facet Field `{field_name}` is missing. This should not happen");
};
Ok(FacetReader::new(facet_column))
@@ -128,9 +128,12 @@ impl SegmentReader {
&self.fieldnorm_readers
}
/// Accessor to the segment's `StoreReader`.
pub fn get_store_reader(&self, cache_size: usize) -> io::Result<StoreReader> {
StoreReader::open(self.store_file.clone(), cache_size)
/// Accessor to the segment's [`StoreReader`](crate::store::StoreReader).
///
/// `cache_num_blocks` sets the number of decompressed blocks to be cached in an LRU.
/// The size of blocks is configurable, this should be reflexted in the
pub fn get_store_reader(&self, cache_num_blocks: usize) -> io::Result<StoreReader> {
StoreReader::open(self.store_file.clone(), cache_num_blocks)
}
/// Open a new segment for reading.

View File

@@ -22,7 +22,7 @@ impl FileAddr {
}
impl BinarySerializable for FileAddr {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
fn serialize<W: Write + ?Sized>(&self, writer: &mut W) -> io::Result<()> {
self.field.serialize(writer)?;
VInt(self.idx as u64).serialize(writer)?;
Ok(())

View File

@@ -21,15 +21,15 @@
use std::net::Ipv6Addr;
pub use columnar::Column;
use columnar::MonotonicallyMappableToU64;
pub use fastfield_codecs::Column;
pub use self::alive_bitset::{intersect_alive_bitsets, write_alive_bitset, AliveBitSet};
pub use self::error::{FastFieldNotAvailableError, Result};
pub use self::facet_reader::FacetReader;
pub use self::readers::FastFieldReaders;
pub use self::writer::FastFieldsWriter;
use crate::schema::{Type, Value};
use crate::schema::Type;
use crate::DateTime;
mod alive_bitset;
@@ -115,33 +115,13 @@ impl columnar::MonotonicallyMappableToU64 for DateTime {
}
}
fn unexpected_value(expected: &str, actual: &Value) -> crate::TantivyError {
crate::TantivyError::SchemaError(format!(
"Expected a {:?} in fast field, but got {:?}",
expected, actual
))
}
fn value_to_u64(value: &Value) -> crate::Result<u64> {
let value = match value {
Value::U64(val) => val.to_u64(),
Value::I64(val) => val.to_u64(),
Value::F64(val) => val.to_u64(),
Value::Bool(val) => val.to_u64(),
Value::Date(val) => val.to_u64(),
_ => return Err(unexpected_value("u64/i64/f64/bool/date", value)),
};
Ok(value)
}
#[cfg(test)]
mod tests {
use std::collections::HashMap;
use std::ops::{Range, RangeInclusive};
use std::path::Path;
use std::sync::Arc;
use columnar::Column;
use common::{HasLen, TerminatingWrite};
use once_cell::sync::Lazy;
use rand::prelude::SliceRandom;
@@ -149,7 +129,7 @@ mod tests {
use rand::{Rng, SeedableRng};
use super::*;
use crate::directory::{CompositeFile, Directory, RamDirectory, WritePtr};
use crate::directory::{Directory, RamDirectory, WritePtr};
use crate::merge_policy::NoMergePolicy;
use crate::schema::{
Document, Facet, FacetOptions, Field, Schema, SchemaBuilder, FAST, INDEXED, STRING, TEXT,
@@ -191,7 +171,7 @@ mod tests {
}
let file = directory.open_read(path).unwrap();
assert_eq!(file.len(), 157);
assert_eq!(file.len(), 161);
let fast_field_readers = FastFieldReaders::open(file).unwrap();
let column = fast_field_readers.u64("field").unwrap();
assert_eq!(column.get_val(0), 13u64);
@@ -238,7 +218,7 @@ mod tests {
write.terminate().unwrap();
}
let file = directory.open_read(path).unwrap();
assert_eq!(file.len(), 185);
assert_eq!(file.len(), 189);
let fast_field_readers = FastFieldReaders::open(file).unwrap();
let col = fast_field_readers.u64("field").unwrap();
assert_eq!(col.get_val(0), 4u64);
@@ -268,7 +248,7 @@ mod tests {
write.terminate().unwrap();
}
let file = directory.open_read(path).unwrap();
assert_eq!(file.len(), 158);
assert_eq!(file.len(), 162);
let fast_field_readers = FastFieldReaders::open(file).unwrap();
let fast_field_reader = fast_field_readers.u64("field").unwrap();
for doc in 0..10_000 {
@@ -297,7 +277,7 @@ mod tests {
write.terminate().unwrap();
}
let file = directory.open_read(path).unwrap();
assert_eq!(file.len(), 80166);
assert_eq!(file.len(), 4557);
{
let fast_field_readers = FastFieldReaders::open(file).unwrap();
let col = fast_field_readers.u64("field").unwrap();
@@ -327,7 +307,7 @@ mod tests {
write.terminate().unwrap();
}
let file = directory.open_read(path).unwrap();
assert_eq!(file.len(), 329_usize);
assert_eq!(file.len(), 333_usize);
{
let fast_field_readers = FastFieldReaders::open(file).unwrap();
@@ -438,39 +418,38 @@ mod tests {
test_intfastfield_permutation_with_data(permutation);
}
// TODO reenable when merge is here.
// #[test]
// fn test_merge_missing_date_fast_field() {
// let mut schema_builder = Schema::builder();
// let date_field = schema_builder.add_date_field("date", FAST);
// let schema = schema_builder.build();
// let index = Index::create_in_ram(schema);
// let mut index_writer = index.writer_for_tests().unwrap();
// index_writer.set_merge_policy(Box::new(NoMergePolicy));
// index_writer
// .add_document(doc!(date_field =>DateTime::from_utc(OffsetDateTime::now_utc())))
// .unwrap();
// index_writer.commit().unwrap();
// index_writer.add_document(doc!()).unwrap();
// index_writer.commit().unwrap();
// let reader = index.reader().unwrap();
// let segment_ids: Vec<SegmentId> = reader
// .searcher()
// .segment_readers()
// .iter()
// .map(SegmentReader::segment_id)
// .collect();
// assert_eq!(segment_ids.len(), 2);
// index_writer.merge(&segment_ids[..]).wait().unwrap();
// reader.reload().unwrap();
// assert_eq!(reader.searcher().segment_readers().len(), 1);
// }
#[test]
fn test_merge_missing_date_fast_field() {
let mut schema_builder = Schema::builder();
let date_field = schema_builder.add_date_field("date", FAST);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut index_writer = index.writer_for_tests().unwrap();
index_writer.set_merge_policy(Box::new(NoMergePolicy));
index_writer
.add_document(doc!(date_field =>DateTime::from_utc(OffsetDateTime::now_utc())))
.unwrap();
index_writer.commit().unwrap();
index_writer.add_document(doc!()).unwrap();
index_writer.commit().unwrap();
let reader = index.reader().unwrap();
let segment_ids: Vec<SegmentId> = reader
.searcher()
.segment_readers()
.iter()
.map(SegmentReader::segment_id)
.collect();
assert_eq!(segment_ids.len(), 2);
index_writer.merge(&segment_ids[..]).wait().unwrap();
reader.reload().unwrap();
assert_eq!(reader.searcher().segment_readers().len(), 1);
}
// fn get_vals_for_docs(column: &columnar::Column<u64>, docs: Range<u32>) -> Vec<u64> {
// docs.into_iter()
// .flat_map(|doc| column.values(doc))
// .collect()
// }
fn get_vals_for_docs(column: &Column<u64>, docs: Range<u32>) -> Vec<u64> {
docs.into_iter()
.flat_map(|doc| column.values(doc))
.collect()
}
#[test]
fn test_text_fastfield() {
@@ -526,57 +505,61 @@ mod tests {
assert!(str_column.ord_to_str(0, &mut str_term).unwrap());
assert_eq!("AAAAA", &str_term);
// let inverted_index = segment_reader.inverted_index(text_field)?;
// assert_eq!(inverted_index.terms().num_terms(), 3);
// let mut bytes = vec![];
// assert!(inverted_index.terms().ord_to_term(0, &mut bytes)?);
// assert_eq!(bytes, "aaaaa".as_bytes());
// }
let inverted_index = segment_reader.inverted_index(text_field).unwrap();
assert_eq!(inverted_index.terms().num_terms(), 3);
let mut bytes = vec![];
assert!(inverted_index.terms().ord_to_term(0, &mut bytes).unwrap());
assert_eq!(bytes, "aaaaa".as_bytes());
}
// {
// // second segment
// let mut index_writer = index.writer_for_tests()?;
{
// second segment
let mut index_writer = index.writer_for_tests().unwrap();
// index_writer.add_document(doc!(
// text_field => "AAAAA", // term_ord 0
// ))?;
index_writer
.add_document(doc!(
text_field => "AAAAA", // term_ord 0
))
.unwrap();
// index_writer.add_document(doc!(
// text_field => "CCCCC AAAAA", // term_ord 1, after merge 2
// ))?;
index_writer
.add_document(doc!(
text_field => "CCCCC AAAAA", // term_ord 1, after merge 2
))
.unwrap();
// index_writer.add_document(doc!())?;
// index_writer.commit()?;
index_writer.add_document(doc!()).unwrap();
index_writer.commit().unwrap();
// let reader = index.reader()?;
// let searcher = reader.searcher();
// assert_eq!(searcher.segment_readers().len(), 2);
// let segment_reader = searcher.segment_reader(1);
// let fast_fields = segment_reader.fast_fields();
// let text_fast_field = fast_fields.u64s("text").unwrap();
let reader = index.reader().unwrap();
let searcher = reader.searcher();
assert_eq!(searcher.segment_readers().len(), 2);
let segment_reader = searcher.segment_reader(1);
let fast_fields = segment_reader.fast_fields();
let text_fast_field = fast_fields.str("text").unwrap().unwrap();
// assert_eq!(get_vals_for_docs(&text_fast_field, 0..3), vec![0, 1, 0]);
assert_eq!(&get_vals_for_docs(&text_fast_field.ords(), 0..2), &[0, 1]);
}
// TODO uncomment once merging is available
// Merging the segments
// {
// let segment_ids = index.searchable_segment_ids()?;
// let mut index_writer = index.writer_for_tests()?;
// index_writer.merge(&segment_ids).wait()?;
// index_writer.wait_merging_threads()?;
// }
//
// let reader = index.reader()?;
// let searcher = reader.searcher();
// let segment_reader = searcher.segment_reader(0);
// let fast_fields = segment_reader.fast_fields();
// let text_fast_field = fast_fields.u64s("text").unwrap();
//
// assert_eq!(
// get_vals_for_docs(&text_fast_field, 0..8),
// vec![1, 0, 0, 0, 1, 3 /* next segment */, 0, 2, 0]
// );
{
let segment_ids = index.searchable_segment_ids().unwrap();
let mut index_writer = index.writer_for_tests().unwrap();
index_writer.merge(&segment_ids).wait().unwrap();
index_writer.wait_merging_threads().unwrap();
}
let reader = index.reader().unwrap();
let searcher = reader.searcher();
let segment_reader = searcher.segment_reader(0);
let fast_fields = segment_reader.fast_fields();
let text_column = fast_fields.str("text").unwrap().unwrap();
assert_eq!(
get_vals_for_docs(text_column.ords(), 0..8),
vec![1, 0, 0, 0, 1, 3 /* next segment */, 0, 2]
);
}
#[test]
@@ -590,11 +573,7 @@ mod tests {
writer.commit().unwrap();
let searcher = index.reader().unwrap().searcher();
let segment_reader = searcher.segment_reader(0);
let str_column = segment_reader
.fast_fields()
.str_column_opt("text")
.unwrap()
.unwrap();
let str_column = segment_reader.fast_fields().str("text").unwrap().unwrap();
// The string values are not sorted here.
let term_ords: Vec<u64> = str_column.term_ords(0u32).collect();
assert_eq!(&term_ords, &[1, 0]);
@@ -620,155 +599,162 @@ mod tests {
assert_eq!(&facet_ords, &[0, 1]);
}
// #[test]
// fn test_string_fastfield() -> crate::Result<()> {
// let mut schema_builder = Schema::builder();
// let text_field = schema_builder.add_text_field("text", STRING | FAST);
// let schema = schema_builder.build();
// let index = Index::create_in_ram(schema);
#[test]
fn test_string_fastfield() -> crate::Result<()> {
let mut schema_builder = Schema::builder();
let text_field = schema_builder.add_text_field("text", STRING | FAST);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
// {
// // first segment
// let mut index_writer = index.writer_for_tests()?;
// index_writer.set_merge_policy(Box::new(NoMergePolicy));
// index_writer.add_document(doc!(
// text_field => "BBBBB", // term_ord 1
// ))?;
// index_writer.add_document(doc!())?;
// index_writer.add_document(doc!(
// text_field => "AAAAA", // term_ord 0
// ))?;
// index_writer.add_document(doc!(
// text_field => "AAAAA", // term_ord 0
// ))?;
// index_writer.add_document(doc!(
// text_field => "zumberthree", // term_ord 2, after merge term_ord 3
// ))?;
{
// first segment
let mut index_writer = index.writer_for_tests()?;
index_writer.set_merge_policy(Box::new(NoMergePolicy));
index_writer.add_document(doc!(
text_field => "BBBBB", // term_ord 1
))?;
index_writer.add_document(doc!())?;
index_writer.add_document(doc!(
text_field => "AAAAA", // term_ord 0
))?;
index_writer.add_document(doc!(
text_field => "AAAAA", // term_ord 0
))?;
index_writer.add_document(doc!(
text_field => "zumberthree", // term_ord 2, after merge term_ord 3
))?;
// index_writer.add_document(doc!())?;
// index_writer.commit()?;
index_writer.add_document(doc!())?;
index_writer.commit()?;
// let reader = index.reader()?;
// let searcher = reader.searcher();
// assert_eq!(searcher.segment_readers().len(), 1);
// let segment_reader = searcher.segment_reader(0);
// let fast_fields = segment_reader.fast_fields();
// let text_fast_field = fast_fields.u64s(text_field).unwrap();
let reader = index.reader()?;
let searcher = reader.searcher();
assert_eq!(searcher.segment_readers().len(), 1);
let segment_reader = searcher.segment_reader(0);
let fast_fields = segment_reader.fast_fields();
let text_col = fast_fields.str("text").unwrap().unwrap();
// assert_eq!(get_vals_for_docs(&text_fast_field, 0..6), vec![1, 0, 0, 2]);
assert_eq!(get_vals_for_docs(&text_col.ords(), 0..6), vec![1, 0, 0, 2]);
// let inverted_index = segment_reader.inverted_index(text_field)?;
// assert_eq!(inverted_index.terms().num_terms(), 3);
// let mut bytes = vec![];
// assert!(inverted_index.terms().ord_to_term(0, &mut bytes)?);
// assert_eq!(bytes, "AAAAA".as_bytes());
// }
let inverted_index = segment_reader.inverted_index(text_field)?;
assert_eq!(inverted_index.terms().num_terms(), 3);
let mut bytes = vec![];
assert!(inverted_index.terms().ord_to_term(0, &mut bytes)?);
assert_eq!(bytes, "AAAAA".as_bytes());
}
// {
// // second segment
// let mut index_writer = index.writer_for_tests()?;
{
// second segment
let mut index_writer = index.writer_for_tests()?;
// index_writer.add_document(doc!(
// text_field => "AAAAA", // term_ord 0
// ))?;
index_writer.add_document(doc!(
text_field => "AAAAA", // term_ord 0
))?;
// index_writer.add_document(doc!(
// text_field => "CCCCC", // term_ord 1, after merge 2
// ))?;
index_writer.add_document(doc!(
text_field => "CCCCC", // term_ord 1, after merge 2
))?;
// index_writer.add_document(doc!())?;
// index_writer.commit()?;
index_writer.add_document(doc!())?;
index_writer.commit()?;
// let reader = index.reader()?;
// let searcher = reader.searcher();
// assert_eq!(searcher.segment_readers().len(), 2);
// let segment_reader = searcher.segment_reader(1);
// let fast_fields = segment_reader.fast_fields();
// let text_fast_field = fast_fields.u64s(text_field).unwrap();
let reader = index.reader()?;
let searcher = reader.searcher();
assert_eq!(searcher.segment_readers().len(), 2);
let segment_reader = searcher.segment_reader(1);
let fast_fields = segment_reader.fast_fields();
let text_fast_field = fast_fields.str("text").unwrap().unwrap();
// assert_eq!(get_vals_for_docs(&text_fast_field, 0..2), vec![0, 1]);
// }
// // Merging the segments
// {
// let segment_ids = index.searchable_segment_ids()?;
// let mut index_writer = index.writer_for_tests()?;
// index_writer.merge(&segment_ids).wait()?;
// index_writer.wait_merging_threads()?;
// }
assert_eq!(&get_vals_for_docs(text_fast_field.ords(), 0..2), &[0, 1]);
}
// Merging the segments
{
let segment_ids = index.searchable_segment_ids()?;
let mut index_writer = index.writer_for_tests()?;
index_writer.merge(&segment_ids).wait()?;
index_writer.wait_merging_threads()?;
}
// let reader = index.reader()?;
// let searcher = reader.searcher();
// let segment_reader = searcher.segment_reader(0);
// let fast_fields = segment_reader.fast_fields();
// let text_fast_field = fast_fields.u64s(text_field).unwrap();
let reader = index.reader()?;
let searcher = reader.searcher();
let segment_reader = searcher.segment_reader(0);
let fast_fields = segment_reader.fast_fields();
let text_fast_field = fast_fields.str("text").unwrap().unwrap();
// assert_eq!(
// get_vals_for_docs(&text_fast_field, 0..9),
// vec![1, 0, 0, 3 /* next segment */, 0, 2]
// );
assert_eq!(
get_vals_for_docs(&text_fast_field.ords(), 0..9),
vec![1, 0, 0, 3 /* next segment */, 0, 2]
);
// Ok(())
// }
Ok(())
}
// #[test]
// fn test_datefastfield() -> crate::Result<()> {
// let mut schema_builder = Schema::builder();
// let date_field = schema_builder.add_date_field(
// "date",
// DateOptions::from(FAST).set_precision(DatePrecision::Microseconds),
// );
// let multi_date_field = schema_builder.add_date_field(
// "multi_date",
// DateOptions::default()
// .set_precision(DatePrecision::Microseconds)
// .set_fast(),
// );
// let schema = schema_builder.build();
// let index = Index::create_in_ram(schema);
// let mut index_writer = index.writer_for_tests()?;
// index_writer.set_merge_policy(Box::new(NoMergePolicy));
// index_writer.add_document(doc!(
// date_field => DateTime::from_u64(1i64.to_u64()),
// multi_date_field => DateTime::from_u64(2i64.to_u64()),
// multi_date_field => DateTime::from_u64(3i64.to_u64())
// ))?;
// index_writer.add_document(doc!(
// date_field => DateTime::from_u64(4i64.to_u64())
// ))?;
// index_writer.add_document(doc!(
// multi_date_field => DateTime::from_u64(5i64.to_u64()),
// multi_date_field => DateTime::from_u64(6i64.to_u64())
// ))?;
// index_writer.commit()?;
// let reader = index.reader()?;
// let searcher = reader.searcher();
// assert_eq!(searcher.segment_readers().len(), 1);
// let segment_reader = searcher.segment_reader(0);
// let fast_fields = segment_reader.fast_fields();
// let date_fast_field = fast_fields.date(date_field).unwrap();
// let dates_fast_field = fast_fields.dates(multi_date_field).unwrap();
// let mut dates = vec![];
// {
// assert_eq!(date_fast_field.get_val(0).into_timestamp_micros(), 1i64);
// dates_fast_field.get_vals(0u32, &mut dates);
// assert_eq!(dates.len(), 2);
// assert_eq!(dates[0].into_timestamp_micros(), 2i64);
// assert_eq!(dates[1].into_timestamp_micros(), 3i64);
// }
// {
// assert_eq!(date_fast_field.get_val(1).into_timestamp_micros(), 4i64);
// dates_fast_field.get_vals(1u32, &mut dates);
// assert!(dates.is_empty());
// }
// {
// assert_eq!(date_fast_field.get_val(2).into_timestamp_micros(), 0i64);
// dates_fast_field.get_vals(2u32, &mut dates);
// assert_eq!(dates.len(), 2);
// assert_eq!(dates[0].into_timestamp_micros(), 5i64);
// assert_eq!(dates[1].into_timestamp_micros(), 6i64);
// }
// Ok(())
// }
#[test]
fn test_datefastfield() -> crate::Result<()> {
let mut schema_builder = Schema::builder();
let date_field = schema_builder.add_date_field(
"date",
DateOptions::from(FAST).set_precision(DatePrecision::Microseconds),
);
let multi_date_field = schema_builder.add_date_field(
"multi_date",
DateOptions::default()
.set_precision(DatePrecision::Microseconds)
.set_fast(),
);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut index_writer = index.writer_for_tests()?;
index_writer.set_merge_policy(Box::new(NoMergePolicy));
index_writer.add_document(doc!(
date_field => DateTime::from_u64(1i64.to_u64()),
multi_date_field => DateTime::from_u64(2i64.to_u64()),
multi_date_field => DateTime::from_u64(3i64.to_u64())
))?;
index_writer.add_document(doc!(
date_field => DateTime::from_u64(4i64.to_u64())
))?;
index_writer.add_document(doc!(
multi_date_field => DateTime::from_u64(5i64.to_u64()),
multi_date_field => DateTime::from_u64(6i64.to_u64())
))?;
index_writer.commit()?;
let reader = index.reader()?;
let searcher = reader.searcher();
assert_eq!(searcher.segment_readers().len(), 1);
let segment_reader = searcher.segment_reader(0);
let fast_fields = segment_reader.fast_fields();
let date_fast_field = fast_fields
.column_opt::<columnar::DateTime>("date")
.unwrap()
.unwrap()
.first_or_default_col(Default::default());
let dates_fast_field = fast_fields
.column_opt::<columnar::DateTime>("multi_date")
.unwrap()
.unwrap();
let mut dates = vec![];
{
assert_eq!(date_fast_field.get_val(0).into_timestamp_micros(), 1i64);
dates_fast_field.fill_vals(0u32, &mut dates);
assert_eq!(dates.len(), 2);
assert_eq!(dates[0].into_timestamp_micros(), 2i64);
assert_eq!(dates[1].into_timestamp_micros(), 3i64);
}
{
assert_eq!(date_fast_field.get_val(1).into_timestamp_micros(), 4i64);
dates_fast_field.fill_vals(1u32, &mut dates);
assert!(dates.is_empty());
}
{
assert_eq!(date_fast_field.get_val(2).into_timestamp_micros(), 0i64);
dates_fast_field.fill_vals(2u32, &mut dates);
assert_eq!(dates.len(), 2);
assert_eq!(dates[0].into_timestamp_micros(), 5i64);
assert_eq!(dates[1].into_timestamp_micros(), 6i64);
}
Ok(())
}
#[test]
pub fn test_fastfield_bool_small() {
@@ -795,7 +781,7 @@ mod tests {
write.terminate().unwrap();
}
let file = directory.open_read(path).unwrap();
assert_eq!(file.len(), 171);
assert_eq!(file.len(), 175);
let fast_field_readers = FastFieldReaders::open(file).unwrap();
let bool_col = fast_field_readers.bool("field_bool").unwrap();
assert_eq!(bool_col.get_val(0), true);
@@ -827,7 +813,7 @@ mod tests {
write.terminate().unwrap();
}
let file = directory.open_read(path).unwrap();
assert_eq!(file.len(), 183);
assert_eq!(file.len(), 187);
let readers = FastFieldReaders::open(file).unwrap();
let bool_col = readers.bool("field_bool").unwrap();
for i in 0..25 {
@@ -852,7 +838,7 @@ mod tests {
write.terminate().unwrap();
}
let file = directory.open_read(path).unwrap();
assert_eq!(file.len(), 173);
assert_eq!(file.len(), 177);
let fastfield_readers = FastFieldReaders::open(file).unwrap();
let col = fastfield_readers.bool("field_bool").unwrap();
assert_eq!(col.get_val(0), false);
@@ -978,8 +964,7 @@ mod tests {
index_writer.commit().unwrap();
let searcher = index.reader().unwrap().searcher();
let fastfields = searcher.segment_reader(0u32).fast_fields();
let column: columnar::Column<Ipv6Addr> =
fastfields.typed_column_opt("ip").unwrap().unwrap();
let column: Column<Ipv6Addr> = fastfields.column_opt("ip").unwrap().unwrap();
assert_eq!(column.num_rows(), 3);
assert_eq!(column.first(0), None);
assert_eq!(column.first(1), Some(ip_addr));

View File

@@ -1,16 +1,14 @@
use std::collections::HashMap;
use std::io;
use std::net::Ipv6Addr;
use std::sync::Arc;
use columnar::{
BytesColumn, ColumnType, ColumnValues, ColumnarReader, DynamicColumn, DynamicColumnHandle,
HasAssociatedColumnType, StrColumn,
BytesColumn, Column, ColumnType, ColumnValues, ColumnarReader, DynamicColumn,
DynamicColumnHandle, HasAssociatedColumnType, StrColumn,
};
use fastfield_codecs::Column;
use crate::directory::FileSlice;
use crate::schema::{Field, Schema};
use crate::schema::Schema;
use crate::space_usage::{FieldUsage, PerFieldSpaceUsage};
/// Provides access to all of the BitpackedFastFieldReader.
@@ -28,6 +26,10 @@ impl FastFieldReaders {
Ok(FastFieldReaders { columnar })
}
pub(crate) fn columnar(&self) -> &ColumnarReader {
self.columnar.as_ref()
}
pub(crate) fn space_usage(&self, schema: &Schema) -> io::Result<PerFieldSpaceUsage> {
let mut per_field_usages: Vec<FieldUsage> = Default::default();
for (field, field_entry) in schema.fields() {
@@ -44,33 +46,18 @@ impl FastFieldReaders {
Ok(PerFieldSpaceUsage::new(per_field_usages))
}
pub fn typed_column_opt<T>(
&self,
field_name: &str,
) -> crate::Result<Option<columnar::Column<T>>>
/// Returns a typed column associated to a given field name.
///
/// If no column associated with that field_name exists,
/// or existing columns do not have the required type,
/// returns `None`.
pub fn column_opt<T>(&self, field_name: &str) -> crate::Result<Option<Column<T>>>
where
T: PartialOrd + Copy + HasAssociatedColumnType + Send + Sync + 'static,
DynamicColumn: Into<Option<columnar::Column<T>>>,
DynamicColumn: Into<Option<Column<T>>>,
{
let column_type = T::column_type();
let Some(dynamic_column_handle) = self.column_handle(field_name, column_type)?
else {
return Ok(None);
};
let dynamic_column = dynamic_column_handle.open()?;
Ok(dynamic_column.into())
}
pub fn bytes_column_opt(&self, field_name: &str) -> crate::Result<Option<BytesColumn>> {
let Some(dynamic_column_handle) = self.column_handle(field_name, ColumnType::Bytes)?
else {
return Ok(None);
};
let dynamic_column = dynamic_column_handle.open()?;
Ok(dynamic_column.into())
}
pub fn str_column_opt(&self, field_name: &str) -> crate::Result<Option<StrColumn>> {
let Some(dynamic_column_handle) = self.column_handle(field_name, ColumnType::Str)?
let Some(dynamic_column_handle) = self.dynamic_column_handle(field_name, column_type)?
else {
return Ok(None);
};
@@ -78,6 +65,7 @@ impl FastFieldReaders {
Ok(dynamic_column.into())
}
/// Returns the number of `bytes` associated with a column.
pub fn column_num_bytes(&self, field: &str) -> crate::Result<usize> {
Ok(self
.columnar
@@ -87,12 +75,17 @@ impl FastFieldReaders {
.sum())
}
pub fn typed_column_first_or_default<T>(&self, field: &str) -> crate::Result<Arc<dyn Column<T>>>
/// Returns a typed column value object.
///
/// In that column value:
/// - Rows with no value are associated with the default value.
/// - Rows with several values are associated with the first value.
pub fn column_first_or_default<T>(&self, field: &str) -> crate::Result<Arc<dyn ColumnValues<T>>>
where
T: PartialOrd + Copy + HasAssociatedColumnType + Send + Sync + 'static,
DynamicColumn: Into<Option<columnar::Column<T>>>,
DynamicColumn: Into<Option<Column<T>>>,
{
let col_opt: Option<columnar::Column<T>> = self.typed_column_opt(field)?;
let col_opt: Option<Column<T>> = self.column_opt(field)?;
if let Some(col) = col_opt {
Ok(col.first_or_default_col(T::default_value()))
} else {
@@ -106,32 +99,45 @@ impl FastFieldReaders {
///
/// If `field` is not a u64 fast field, this method returns an Error.
pub fn u64(&self, field: &str) -> crate::Result<Arc<dyn ColumnValues<u64>>> {
self.typed_column_first_or_default(field)
self.column_first_or_default(field)
}
/// Returns the `date` fast field reader reader associated with `field`.
///
/// If `field` is not a date fast field, this method returns an Error.
pub fn date(&self, field: &str) -> crate::Result<Arc<dyn ColumnValues<columnar::DateTime>>> {
self.typed_column_first_or_default(field)
self.column_first_or_default(field)
}
/// Returns the `ip` fast field reader reader associated to `field`.
///
/// If `field` is not a u128 fast field, this method returns an Error.
pub fn ip_addr(&self, field: &str) -> crate::Result<Arc<dyn Column<Ipv6Addr>>> {
self.typed_column_first_or_default(field)
pub fn ip_addr(&self, field: &str) -> crate::Result<Arc<dyn ColumnValues<Ipv6Addr>>> {
self.column_first_or_default(field)
}
pub fn str(&self, field: &str) -> crate::Result<Option<columnar::StrColumn>> {
self.str_column_opt(field)
/// Returns a `str` column.
pub fn str(&self, field_name: &str) -> crate::Result<Option<StrColumn>> {
let Some(dynamic_column_handle) = self.dynamic_column_handle(field_name, ColumnType::Str)?
else {
return Ok(None);
};
let dynamic_column = dynamic_column_handle.open()?;
Ok(dynamic_column.into())
}
pub fn bytes(&self, field: &str) -> crate::Result<Option<columnar::BytesColumn>> {
self.bytes_column_opt(field)
/// Returns a `bytes` column.
pub fn bytes(&self, field_name: &str) -> crate::Result<Option<BytesColumn>> {
let Some(dynamic_column_handle) = self.dynamic_column_handle(field_name, ColumnType::Bytes)?
else {
return Ok(None);
};
let dynamic_column = dynamic_column_handle.open()?;
Ok(dynamic_column.into())
}
pub fn column_handle(
/// Returning a `dynamic_column_handle`.
pub fn dynamic_column_handle(
&self,
field_name: &str,
column_type: ColumnType,
@@ -145,7 +151,9 @@ impl FastFieldReaders {
Ok(dynamic_column_handle_opt)
}
pub fn u64_lenient(&self, field_name: &str) -> crate::Result<Option<columnar::Column<u64>>> {
/// Returns the `u64` column used to represent any `u64`-mapped typed (i64, u64, f64, DateTime).
#[doc(hidden)]
pub fn u64_lenient(&self, field_name: &str) -> crate::Result<Option<Column<u64>>> {
for col in self.columnar.read_columns(field_name)? {
if let Some(col_u64) = col.open_u64_lenient()? {
return Ok(Some(col_u64));
@@ -157,21 +165,21 @@ impl FastFieldReaders {
/// Returns the `i64` fast field reader reader associated with `field`.
///
/// If `field` is not a i64 fast field, this method returns an Error.
pub fn i64(&self, field_name: &str) -> crate::Result<Arc<dyn Column<i64>>> {
self.typed_column_first_or_default(field_name)
pub fn i64(&self, field_name: &str) -> crate::Result<Arc<dyn ColumnValues<i64>>> {
self.column_first_or_default(field_name)
}
/// Returns the `f64` fast field reader reader associated with `field`.
///
/// If `field` is not a f64 fast field, this method returns an Error.
pub fn f64(&self, field_name: &str) -> crate::Result<Arc<dyn Column<f64>>> {
self.typed_column_first_or_default(field_name)
pub fn f64(&self, field_name: &str) -> crate::Result<Arc<dyn ColumnValues<f64>>> {
self.column_first_or_default(field_name)
}
/// Returns the `bool` fast field reader reader associated with `field`.
///
/// If `field` is not a bool fast field, this method returns an Error.
pub fn bool(&self, field_name: &str) -> crate::Result<Arc<dyn Column<bool>>> {
self.typed_column_first_or_default(field_name)
pub fn bool(&self, field_name: &str) -> crate::Result<Arc<dyn ColumnValues<bool>>> {
self.column_first_or_default(field_name)
}
}

View File

@@ -67,6 +67,16 @@ impl FastFieldsWriter {
self.columnar_writer.mem_usage()
}
pub(crate) fn sort_order(
&self,
sort_field: &str,
num_docs: DocId,
reversed: bool,
) -> Vec<DocId> {
self.columnar_writer
.sort_order(sort_field, num_docs, reversed)
}
/// Indexes all of the fastfields of a new document.
pub fn add_document(&mut self, doc: &Document) -> crate::Result<()> {
let doc_id = self.num_docs;
@@ -143,11 +153,13 @@ impl FastFieldsWriter {
pub fn serialize(
mut self,
wrt: &mut dyn io::Write,
doc_id_map: Option<&DocIdMapping>,
doc_id_map_opt: Option<&DocIdMapping>,
) -> io::Result<()> {
assert!(doc_id_map.is_none()); // TODO handle doc id map
let num_docs = self.num_docs;
self.columnar_writer.serialize(num_docs, wrt)?;
let old_to_new_row_ids =
doc_id_map_opt.map(|doc_id_mapping| doc_id_mapping.old_to_new_ids());
self.columnar_writer
.serialize(num_docs, old_to_new_row_ids, wrt)?;
Ok(())
}
}

View File

@@ -1,27 +1,44 @@
//! This module is used when sorting the index by a property, e.g.
//! to get mappings from old doc_id to new doc_id and vice versa, after sorting
use std::cmp::Reverse;
use common::ReadOnlyBitSet;
use super::SegmentWriter;
use crate::schema::{Field, Schema};
use crate::{DocAddress, DocId, IndexSortByField, Order, TantivyError};
use crate::{DocAddress, DocId, IndexSortByField, TantivyError};
#[derive(Copy, Clone, Eq, PartialEq)]
pub enum MappingType {
Stacked,
StackedWithDeletes,
Shuffled,
}
/// Struct to provide mapping from new doc_id to old doc_id and segment.
#[derive(Clone)]
pub(crate) struct SegmentDocIdMapping {
new_doc_id_to_old_doc_addr: Vec<DocAddress>,
is_trivial: bool,
pub(crate) new_doc_id_to_old_doc_addr: Vec<DocAddress>,
pub(crate) alive_bitsets: Vec<Option<ReadOnlyBitSet>>,
mapping_type: MappingType,
}
impl SegmentDocIdMapping {
pub(crate) fn new(new_doc_id_to_old_and_segment: Vec<DocAddress>, is_trivial: bool) -> Self {
pub(crate) fn new(
new_doc_id_to_old_doc_addr: Vec<DocAddress>,
mapping_type: MappingType,
alive_bitsets: Vec<Option<ReadOnlyBitSet>>,
) -> Self {
Self {
new_doc_id_to_old_doc_addr: new_doc_id_to_old_and_segment,
is_trivial,
new_doc_id_to_old_doc_addr,
mapping_type,
alive_bitsets,
}
}
pub fn mapping_type(&self) -> MappingType {
self.mapping_type
}
/// Returns an iterator over the old document addresses, ordered by the new document ids.
///
/// In the returned `DocAddress`, the `segment_ord` is the ordinal of targeted segment
@@ -30,16 +47,20 @@ impl SegmentDocIdMapping {
self.new_doc_id_to_old_doc_addr.iter().copied()
}
pub(crate) fn len(&self) -> usize {
self.new_doc_id_to_old_doc_addr.len()
}
/// This flags means the segments are simply stacked in the order of their ordinal.
/// e.g. [(0, 1), .. (n, 1), (0, 2)..., (m, 2)]
///
/// The different segment may present some deletes, in which case it is expressed by skipping a
/// `DocId`. [(0, 1), (0, 3)] <--- here doc_id=0 and doc_id=1 have been deleted
///
/// Being trivial is equivalent to having the `new_doc_id_to_old_doc_addr` array sorted.
///
/// This allows for some optimization.
pub(crate) fn is_trivial(&self) -> bool {
self.is_trivial
match self.mapping_type {
MappingType::Stacked | MappingType::StackedWithDeletes => true,
MappingType::Shuffled => false,
}
}
}
@@ -80,6 +101,11 @@ impl DocIdMapping {
pub fn iter_old_doc_ids(&self) -> impl Iterator<Item = DocId> + Clone + '_ {
self.new_doc_id_to_old.iter().cloned()
}
pub fn old_to_new_ids(&self) -> &[DocId] {
&self.old_doc_id_to_new[..]
}
/// Remaps a given array to the new doc ids.
pub fn remap<T: Copy>(&self, els: &[T]) -> Vec<T> {
self.new_doc_id_to_old
@@ -113,36 +139,15 @@ pub(crate) fn get_doc_id_mapping_from_field(
sort_by_field: IndexSortByField,
segment_writer: &SegmentWriter,
) -> crate::Result<DocIdMapping> {
todo!()
// let schema = segment_writer.segment_serializer.segment().schema();
// let field_id = expect_field_id_for_sort_field(&schema, &sort_by_field)?; // for now expect
// fastfield, but not strictly required
// let fast_field = segment_writer
// .fast_field_writers
// .get_field_writer(field_id)
// .ok_or_else(|| {
// TantivyError::InvalidArgument(format!(
// "sort index by field is required to be a fast field {:?}",
// sort_by_field.field
// ))
// })?;
// // create new doc_id to old doc_id index (used in fast_field_writers)
// let mut doc_id_and_data = fast_field
// .iter()
// .enumerate()
// .map(|el| (el.0 as DocId, el.1))
// .collect::<Vec<_>>();
// if sort_by_field.order == Order::Desc {
// doc_id_and_data.sort_by_key(|k| Reverse(k.1));
// } else {
// doc_id_and_data.sort_by_key(|k| k.1);
// }
// let new_doc_id_to_old = doc_id_and_data
// .into_iter()
// .map(|el| el.0)
// .collect::<Vec<_>>();
// Ok(DocIdMapping::from_new_id_to_old_id(new_doc_id_to_old))
let schema = segment_writer.segment_serializer.segment().schema();
expect_field_id_for_sort_field(&schema, &sort_by_field)?; // for now expect
let new_doc_id_to_old = segment_writer.fast_field_writers.sort_order(
sort_by_field.field.as_str(),
segment_writer.max_doc(),
sort_by_field.order.is_desc(),
);
// create new doc_id to old doc_id index (used in fast_field_writers)
Ok(DocIdMapping::from_new_id_to_old_id(new_doc_id_to_old))
}
#[cfg(test)]

File diff suppressed because it is too large Load Diff

View File

@@ -1,18 +1,19 @@
use std::collections::HashMap;
use std::sync::Arc;
use columnar::{
ColumnValues, ColumnarReader, MergeRowOrder, RowAddr, ShuffleMergeOrder, StackMergeOrder,
};
use common::ReadOnlyBitSet;
use itertools::Itertools;
use measure_time::debug_time;
// use super::sorted_doc_id_multivalue_column::RemappedDocIdMultiValueIndexColumn;
use crate::core::{Segment, SegmentReader};
use crate::directory::WritePtr;
use crate::docset::{DocSet, TERMINATED};
use crate::error::DataCorruption;
use crate::fastfield::{AliveBitSet, Column, FastFieldNotAvailableError};
use crate::fastfield::{AliveBitSet, FastFieldNotAvailableError};
use crate::fieldnorm::{FieldNormReader, FieldNormReaders, FieldNormsSerializer, FieldNormsWriter};
use crate::indexer::doc_id_mapping::SegmentDocIdMapping;
// use crate::indexer::sorted_doc_id_multivalue_column::RemappedDocIdMultiValueColumn;
use crate::indexer::doc_id_mapping::{MappingType, SegmentDocIdMapping};
use crate::indexer::SegmentSerializer;
use crate::postings::{InvertedIndexSerializer, Postings, SegmentPostings};
use crate::schema::{Field, FieldType, Schema};
@@ -85,29 +86,6 @@ pub struct IndexMerger {
max_doc: u32,
}
struct TermOrdinalMapping {
per_segment_new_term_ordinals: Vec<Vec<TermOrdinal>>,
}
impl TermOrdinalMapping {
fn new(max_term_ords: Vec<TermOrdinal>) -> TermOrdinalMapping {
TermOrdinalMapping {
per_segment_new_term_ordinals: max_term_ords
.into_iter()
.map(|max_term_ord| vec![TermOrdinal::default(); max_term_ord as usize])
.collect(),
}
}
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: usize) -> &[TermOrdinal] {
&(self.per_segment_new_term_ordinals[segment_ord])[..]
}
}
struct DeltaComputer {
buffer: Vec<u32>,
}
@@ -132,6 +110,31 @@ impl DeltaComputer {
}
}
fn convert_to_merge_order<'a>(
columnars: &[&'a ColumnarReader],
doc_id_mapping: SegmentDocIdMapping,
) -> MergeRowOrder {
match doc_id_mapping.mapping_type() {
MappingType::Stacked => MergeRowOrder::Stack(StackMergeOrder::stack(columnars)),
MappingType::StackedWithDeletes | MappingType::Shuffled => {
// RUST/LLVM is amazing. The following conversion is actually a no-op:
// no allocation, no copy.
let new_row_id_to_old_row_id: Vec<RowAddr> = doc_id_mapping
.new_doc_id_to_old_doc_addr
.into_iter()
.map(|doc_addr| RowAddr {
segment_ord: doc_addr.segment_ord,
row_id: doc_addr.doc_id,
})
.collect();
MergeRowOrder::Shuffled(ShuffleMergeOrder {
new_row_id_to_old_row_id,
alive_bitsets: doc_id_mapping.alive_bitsets,
})
}
}
}
impl IndexMerger {
pub fn open(
schema: Schema,
@@ -223,7 +226,6 @@ impl IndexMerger {
let mut fieldnorms_data = Vec::with_capacity(self.max_doc as usize);
for field in fields {
fieldnorms_data.clear();
let fieldnorms_readers: Vec<FieldNormReader> = self
.readers
.iter()
@@ -234,7 +236,6 @@ impl IndexMerger {
let fieldnorm_id = fieldnorms_reader.fieldnorm_id(old_doc_addr.doc_id);
fieldnorms_data.push(fieldnorm_id);
}
fieldnorms_serializer.serialize_field(field, &fieldnorms_data[..])?;
}
fieldnorms_serializer.close()?;
@@ -244,67 +245,16 @@ impl IndexMerger {
fn write_fast_fields(
&self,
fast_field_wrt: &mut WritePtr,
mut term_ord_mappings: HashMap<Field, TermOrdinalMapping>,
doc_id_mapping: &SegmentDocIdMapping,
doc_id_mapping: SegmentDocIdMapping,
) -> crate::Result<()> {
debug_time!("wrie-fast-fields");
for (_field, field_entry) in self.schema.fields() {
if field_entry.is_fast() {
todo!();
}
}
// for (field, field_entry) in self.schema.fields() {
// let field_type = field_entry.field_type();
// match field_type {
// FieldType::Facet(_) | FieldType::Str(_) if field_type.is_fast() => {
// let term_ordinal_mapping = term_ord_mappings.remove(&field).expect(
// "Logic Error in Tantivy (Please report). Facet field should have required \
// a`term_ordinal_mapping`.",
// );
// self.write_term_id_fast_field(
// field,
// &term_ordinal_mapping,
// fast_field_serializer,
// doc_id_mapping,
// )?;
// }
// FieldType::U64(ref options)
// | FieldType::I64(ref options)
// | FieldType::F64(ref options)
// | FieldType::Bool(ref options) => {
// todo!()
// }
// FieldType::Date(ref options) => {
// if options.is_fast() {
// todo!();
// }
// Some(Cardinality::SingleValue) => {
// self.write_single_fast_field(field, fast_field_serializer, doc_id_mapping)?;
// }
// Some(Cardinality::MultiValues) => {
// self.write_multi_fast_field(field, fast_field_serializer, doc_id_mapping)?;
// }
// None => {}
// },
// FieldType::Bytes(byte_options) => {
// if byte_options.is_fast() {
// self.write_bytes_fast_field(field, fast_field_serializer, doc_id_mapping)?;
// }
// }
// FieldType::IpAddr(options) => {
// if options.is_fast() {
// todo!();
// }
// },
//
// FieldType::JsonObject(_) | FieldType::Facet(_) | FieldType::Str(_) => {
// We don't handle json fast field for the moment
// They can be implemented using what is done
// for facets in the future
// }
// }
// }
debug_time!("write-fast-fields");
let columnars: Vec<&ColumnarReader> = self
.readers
.iter()
.map(|reader| reader.fast_fields().columnar())
.collect();
let merge_row_order = convert_to_merge_order(&columnars[..], doc_id_mapping);
columnar::merge_columnar(&columnars[..], merge_row_order, fast_field_wrt)?;
Ok(())
}
@@ -334,7 +284,7 @@ impl IndexMerger {
pub(crate) fn get_sort_field_accessor(
reader: &SegmentReader,
sort_by_field: &IndexSortByField,
) -> crate::Result<Arc<dyn Column>> {
) -> crate::Result<Arc<dyn ColumnValues>> {
reader.schema().get_field(&sort_by_field.field)?;
let value_accessor = reader
.fast_fields()
@@ -348,7 +298,7 @@ impl IndexMerger {
pub(crate) fn get_reader_with_sort_field_accessor(
&self,
sort_by_field: &IndexSortByField,
) -> crate::Result<Vec<(SegmentOrdinal, Arc<dyn Column>)>> {
) -> crate::Result<Vec<(SegmentOrdinal, Arc<dyn ColumnValues>)>> {
let reader_ordinal_and_field_accessors = self
.readers
.iter()
@@ -369,7 +319,7 @@ impl IndexMerger {
/// doc_id.
/// ReaderWithOrdinal will include the ordinal position of the
/// reader in self.readers.
pub(crate) fn generate_doc_id_mapping(
pub(crate) fn generate_doc_id_mapping_with_sort_by_field(
&self,
sort_by_field: &IndexSortByField,
) -> crate::Result<SegmentDocIdMapping> {
@@ -414,7 +364,23 @@ impl IndexMerger {
segment_ord,
}),
);
Ok(SegmentDocIdMapping::new(sorted_doc_ids, false))
let alive_bitsets: Vec<Option<ReadOnlyBitSet>> = self
.readers
.iter()
.map(|segment_reader| {
if let Some(alive_bitset) = segment_reader.alive_bitset() {
Some(alive_bitset.bitset().clone())
} else {
None
}
})
.collect();
Ok(SegmentDocIdMapping::new(
sorted_doc_ids,
MappingType::Shuffled,
alive_bitsets,
))
}
/// Creates a mapping if the segments are stacked. this is helpful to merge codelines between
@@ -439,17 +405,39 @@ impl IndexMerger {
})
}),
);
Ok(SegmentDocIdMapping::new(mapping, true))
let has_deletes: bool = self.readers.iter().any(SegmentReader::has_deletes);
let mapping_type = if has_deletes {
MappingType::StackedWithDeletes
} else {
MappingType::Stacked
};
let alive_bitsets: Vec<Option<ReadOnlyBitSet>> = self
.readers
.iter()
.map(|reader| {
if let Some(bitset) = reader.alive_bitset() {
Some(bitset.bitset().clone())
} else {
None
}
})
.collect();
Ok(SegmentDocIdMapping::new(
mapping,
mapping_type,
alive_bitsets,
))
}
fn write_postings_for_field(
&self,
indexed_field: Field,
field_type: &FieldType,
_field_type: &FieldType,
serializer: &mut InvertedIndexSerializer,
fieldnorm_reader: Option<FieldNormReader>,
doc_id_mapping: &SegmentDocIdMapping,
) -> crate::Result<Option<TermOrdinalMapping>> {
) -> crate::Result<()> {
debug_time!("write-postings-for-field");
let mut positions_buffer: Vec<u32> = Vec::with_capacity(1_000);
let mut delta_computer = DeltaComputer::new();
@@ -469,14 +457,6 @@ impl IndexMerger {
max_term_ords.push(terms.num_terms() as u64);
}
let mut term_ord_mapping_opt = match field_type {
FieldType::Facet(_) => Some(TermOrdinalMapping::new(max_term_ords)),
FieldType::Str(options) if options.is_fast() => {
Some(TermOrdinalMapping::new(max_term_ords))
}
_ => None,
};
let mut merged_terms = TermMerger::new(field_term_streams);
// map from segment doc ids to the resulting merged segment doc id.
@@ -559,13 +539,7 @@ impl IndexMerger {
continue;
}
let to_term_ord = field_serializer.new_term(term_bytes, total_doc_freq)?;
if let Some(ref mut term_ord_mapping) = term_ord_mapping_opt {
for (segment_ord, from_term_ord) in merged_terms.matching_segments() {
term_ord_mapping.register_from_to(segment_ord, from_term_ord, to_term_ord);
}
}
field_serializer.new_term(term_bytes, total_doc_freq)?;
// We can now serialize this postings, by pushing each document to the
// postings serializer.
@@ -617,7 +591,7 @@ impl IndexMerger {
field_serializer.close_term()?;
}
field_serializer.close()?;
Ok(term_ord_mapping_opt)
Ok(())
}
fn write_postings(
@@ -625,23 +599,20 @@ impl IndexMerger {
serializer: &mut InvertedIndexSerializer,
fieldnorm_readers: FieldNormReaders,
doc_id_mapping: &SegmentDocIdMapping,
) -> crate::Result<HashMap<Field, TermOrdinalMapping>> {
let mut term_ordinal_mappings = HashMap::new();
) -> crate::Result<()> {
for (field, field_entry) in self.schema.fields() {
let fieldnorm_reader = fieldnorm_readers.get_field(field)?;
if field_entry.is_indexed() {
if let Some(term_ordinal_mapping) = self.write_postings_for_field(
self.write_postings_for_field(
field,
field_entry.field_type(),
serializer,
fieldnorm_reader,
doc_id_mapping,
)? {
term_ordinal_mappings.insert(field, term_ordinal_mapping);
}
)?;
}
}
Ok(term_ordinal_mappings)
Ok(())
}
fn write_storable_fields(
@@ -726,7 +697,7 @@ impl IndexMerger {
if self.is_disjunct_and_sorted_on_sort_property(sort_by_field)? {
self.get_doc_id_from_concatenated_data()?
} else {
self.generate_doc_id_mapping(sort_by_field)?
self.generate_doc_id_mapping_with_sort_by_field(sort_by_field)?
}
} else {
self.get_doc_id_from_concatenated_data()?
@@ -740,19 +711,17 @@ impl IndexMerger {
.segment()
.open_read(SegmentComponent::FieldNorms)?;
let fieldnorm_readers = FieldNormReaders::open(fieldnorm_data)?;
let term_ord_mappings = self.write_postings(
self.write_postings(
serializer.get_postings_serializer(),
fieldnorm_readers,
&doc_id_mapping,
)?;
debug!("write-fastfields");
self.write_fast_fields(
serializer.get_fast_field_write(),
term_ord_mappings,
&doc_id_mapping,
)?;
debug!("write-storagefields");
self.write_storable_fields(serializer.get_store_writer(), &doc_id_mapping)?;
debug!("write-fastfields");
self.write_fast_fields(serializer.get_fast_field_write(), doc_id_mapping)?;
debug!("close-serializer");
serializer.close()?;
Ok(self.max_doc)
@@ -761,11 +730,14 @@ impl IndexMerger {
#[cfg(test)]
mod tests {
use byteorder::{BigEndian, ReadBytesExt};
use columnar::Column;
use schema::FAST;
use crate::collector::tests::{FastFieldTestCollector, TEST_COLLECTOR_WITH_SCORE};
use crate::collector::Count;
use crate::collector::tests::{
BytesFastFieldTestCollector, FastFieldTestCollector, TEST_COLLECTOR_WITH_SCORE,
};
use crate::collector::{Count, FacetCollector};
use crate::core::Index;
use crate::query::{AllQuery, BooleanQuery, EnableScoring, Scorer, TermQuery};
use crate::schema::{
@@ -774,7 +746,7 @@ mod tests {
};
use crate::time::OffsetDateTime;
use crate::{
assert_nearly_equals, schema, DateTime, DocAddress, DocSet, IndexSettings,
assert_nearly_equals, schema, DateTime, DocAddress, DocId, DocSet, IndexSettings,
IndexSortByField, IndexWriter, Order, Searcher, SegmentId,
};
@@ -907,27 +879,27 @@ mod tests {
);
}
// {
// let get_fast_vals = |terms: Vec<Term>| {
// let query = BooleanQuery::new_multiterms_query(terms);
// searcher.search(&query, &FastFieldTestCollector::for_field(score_field))
// };
// let get_fast_vals_bytes = |terms: Vec<Term>| {
// let query = BooleanQuery::new_multiterms_query(terms);
// searcher.search(
// &query,
// &BytesFastFieldTestCollector::for_field(bytes_score_field),
// )
// };
// assert_eq!(
// get_fast_vals(vec![Term::from_field_text(text_field, "a")])?,
// vec![5, 7, 13]
// );
// assert_eq!(
// get_fast_vals_bytes(vec![Term::from_field_text(text_field, "a")])?,
// vec![0, 0, 0, 5, 0, 0, 0, 7, 0, 0, 0, 13]
// );
// }
{
let get_fast_vals = |terms: Vec<Term>| {
let query = BooleanQuery::new_multiterms_query(terms);
searcher.search(&query, &FastFieldTestCollector::for_field("score"))
};
let get_fast_vals_bytes = |terms: Vec<Term>| {
let query = BooleanQuery::new_multiterms_query(terms);
searcher.search(
&query,
&BytesFastFieldTestCollector::for_field("score_bytes"),
)
};
assert_eq!(
get_fast_vals(vec![Term::from_field_text(text_field, "a")])?,
vec![5, 7, 13]
);
assert_eq!(
get_fast_vals_bytes(vec![Term::from_field_text(text_field, "a")])?,
vec![0, 0, 0, 5, 0, 0, 0, 7, 0, 0, 0, 13]
);
}
}
Ok(())
}
@@ -1240,209 +1212,206 @@ mod tests {
Ok(())
}
// TODO re-enable
// #[test]
// fn test_merge_facets_sort_none() {
// test_merge_facets(None, true)
// }
#[test]
fn test_merge_facets_sort_none() {
test_merge_facets(None, true)
}
// #[test]
// fn test_merge_facets_sort_asc() {
// // In the merge case this will go through the doc_id mapping code
// test_merge_facets(
// Some(IndexSettings {
// sort_by_field: Some(IndexSortByField {
// field: "intval".to_string(),
// order: Order::Desc,
// }),
// ..Default::default()
// }),
// true,
// );
// // In the merge case this will not go through the doc_id mapping code, because the data
// is // sorted and disjunct
// test_merge_facets(
// Some(IndexSettings {
// sort_by_field: Some(IndexSortByField {
// field: "intval".to_string(),
// order: Order::Desc,
// }),
// ..Default::default()
// }),
// false,
// );
// }
#[test]
fn test_merge_facets_sort_asc() {
// In the merge case this will go through the doc_id mapping code
test_merge_facets(
Some(IndexSettings {
sort_by_field: Some(IndexSortByField {
field: "intval".to_string(),
order: Order::Desc,
}),
..Default::default()
}),
true,
);
// In the merge case this will not go through the doc_id mapping code, because the data
// sorted and disjunct
test_merge_facets(
Some(IndexSettings {
sort_by_field: Some(IndexSortByField {
field: "intval".to_string(),
order: Order::Desc,
}),
..Default::default()
}),
false,
);
}
// #[test]
// fn test_merge_facets_sort_desc() {
// // In the merge case this will go through the doc_id mapping code
// test_merge_facets(
// Some(IndexSettings {
// sort_by_field: Some(IndexSortByField {
// field: "intval".to_string(),
// order: Order::Desc,
// }),
// ..Default::default()
// }),
// true,
// );
// // In the merge case this will not go through the doc_id mapping code, because the data
// is // sorted and disjunct
// test_merge_facets(
// Some(IndexSettings {
// sort_by_field: Some(IndexSortByField {
// field: "intval".to_string(),
// order: Order::Desc,
// }),
// ..Default::default()
// }),
// false,
// );
// }
#[test]
fn test_merge_facets_sort_desc() {
// In the merge case this will go through the doc_id mapping code
test_merge_facets(
Some(IndexSettings {
sort_by_field: Some(IndexSortByField {
field: "intval".to_string(),
order: Order::Desc,
}),
..Default::default()
}),
true,
);
// In the merge case this will not go through the doc_id mapping code, because the data
// sorted and disjunct
test_merge_facets(
Some(IndexSettings {
sort_by_field: Some(IndexSortByField {
field: "intval".to_string(),
order: Order::Desc,
}),
..Default::default()
}),
false,
);
}
// force_segment_value_overlap forces the int value for sorting to have overlapping min and max
// ranges between segments so that merge algorithm can't apply certain optimizations
// fn test_merge_facets(index_settings: Option<IndexSettings>, force_segment_value_overlap:
// bool) { let mut schema_builder = schema::Schema::builder();
// let facet_field = schema_builder.add_facet_field("facet", FacetOptions::default());
// let int_options = NumericOptions::default()
// .set_fast()
// .set_indexed();
// let int_field = schema_builder.add_u64_field("intval", int_options);
// let mut index_builder = Index::builder().schema(schema_builder.build());
// if let Some(settings) = index_settings {
// index_builder = index_builder.settings(settings);
// }
// let index = index_builder.create_in_ram().unwrap();
// // let index = Index::create_in_ram(schema_builder.build());
// let reader = index.reader().unwrap();
// let mut int_val = 0;
// {
// let mut index_writer = index.writer_for_tests().unwrap();
// let index_doc =
// |index_writer: &mut IndexWriter, doc_facets: &[&str], int_val: &mut u64| {
// let mut doc = Document::default();
// for facet in doc_facets {
// doc.add_facet(facet_field, Facet::from(facet));
// }
// doc.add_u64(int_field, *int_val);
// *int_val += 1;
// index_writer.add_document(doc).unwrap();
// };
fn test_merge_facets(index_settings: Option<IndexSettings>, force_segment_value_overlap: bool) {
let mut schema_builder = schema::Schema::builder();
let facet_field = schema_builder.add_facet_field("facet", FacetOptions::default());
let int_options = NumericOptions::default().set_fast().set_indexed();
let int_field = schema_builder.add_u64_field("intval", int_options);
let mut index_builder = Index::builder().schema(schema_builder.build());
if let Some(settings) = index_settings {
index_builder = index_builder.settings(settings);
}
let index = index_builder.create_in_ram().unwrap();
// let index = Index::create_in_ram(schema_builder.build());
let reader = index.reader().unwrap();
let mut int_val = 0;
{
let mut index_writer = index.writer_for_tests().unwrap();
let index_doc =
|index_writer: &mut IndexWriter, doc_facets: &[&str], int_val: &mut u64| {
let mut doc = Document::default();
for facet in doc_facets {
doc.add_facet(facet_field, Facet::from(facet));
}
doc.add_u64(int_field, *int_val);
*int_val += 1;
index_writer.add_document(doc).unwrap();
};
// index_doc(
// &mut index_writer,
// &["/top/a/firstdoc", "/top/b"],
// &mut int_val,
// );
// index_doc(
// &mut index_writer,
// &["/top/a/firstdoc", "/top/b", "/top/c"],
// &mut int_val,
// );
// index_doc(&mut index_writer, &["/top/a", "/top/b"], &mut int_val);
// index_doc(&mut index_writer, &["/top/a"], &mut int_val);
index_doc(
&mut index_writer,
&["/top/a/firstdoc", "/top/b"],
&mut int_val,
);
index_doc(
&mut index_writer,
&["/top/a/firstdoc", "/top/b", "/top/c"],
&mut int_val,
);
index_doc(&mut index_writer, &["/top/a", "/top/b"], &mut int_val);
index_doc(&mut index_writer, &["/top/a"], &mut int_val);
// index_doc(&mut index_writer, &["/top/b", "/top/d"], &mut int_val);
// if force_segment_value_overlap {
// index_doc(&mut index_writer, &["/top/d"], &mut 0);
// index_doc(&mut index_writer, &["/top/e"], &mut 10);
// index_writer.commit().expect("committed");
// index_doc(&mut index_writer, &["/top/a"], &mut 5); // 5 is between 0 - 10 so the
// // segments don' have disjunct
// // ranges
// } else {
// index_doc(&mut index_writer, &["/top/d"], &mut int_val);
// index_doc(&mut index_writer, &["/top/e"], &mut int_val);
// index_writer.commit().expect("committed");
// index_doc(&mut index_writer, &["/top/a"], &mut int_val);
// }
// index_doc(&mut index_writer, &["/top/b"], &mut int_val);
// index_doc(&mut index_writer, &["/top/c"], &mut int_val);
// index_writer.commit().expect("committed");
index_doc(&mut index_writer, &["/top/b", "/top/d"], &mut int_val);
if force_segment_value_overlap {
index_doc(&mut index_writer, &["/top/d"], &mut 0);
index_doc(&mut index_writer, &["/top/e"], &mut 10);
index_writer.commit().expect("committed");
index_doc(&mut index_writer, &["/top/a"], &mut 5); // 5 is between 0 - 10 so the
// segments don' have disjunct
// ranges
} else {
index_doc(&mut index_writer, &["/top/d"], &mut int_val);
index_doc(&mut index_writer, &["/top/e"], &mut int_val);
index_writer.commit().expect("committed");
index_doc(&mut index_writer, &["/top/a"], &mut int_val);
}
index_doc(&mut index_writer, &["/top/b"], &mut int_val);
index_doc(&mut index_writer, &["/top/c"], &mut int_val);
index_writer.commit().expect("committed");
// index_doc(&mut index_writer, &["/top/e", "/top/f"], &mut int_val);
// index_writer.commit().expect("committed");
// }
index_doc(&mut index_writer, &["/top/e", "/top/f"], &mut int_val);
index_writer.commit().expect("committed");
}
// reader.reload().unwrap();
// let test_searcher = |expected_num_docs: usize, expected: &[(&str, u64)]| {
// let searcher = reader.searcher();
// let mut facet_collector = FacetCollector::for_field(facet_field);
// facet_collector.add_facet(Facet::from("/top"));
// let (count, facet_counts) = searcher
// .search(&AllQuery, &(Count, facet_collector))
// .unwrap();
// assert_eq!(count, expected_num_docs);
// let facets: Vec<(String, u64)> = facet_counts
// .get("/top")
// .map(|(facet, count)| (facet.to_string(), count))
// .collect();
// assert_eq!(
// facets,
// expected
// .iter()
// .map(|&(facet_str, count)| (String::from(facet_str), count))
// .collect::<Vec<_>>()
// );
// };
// test_searcher(
// 11,
// &[
// ("/top/a", 5),
// ("/top/b", 5),
// ("/top/c", 2),
// ("/top/d", 2),
// ("/top/e", 2),
// ("/top/f", 1),
// ],
// );
// // Merging the segments
// {
// let segment_ids = index
// .searchable_segment_ids()
// .expect("Searchable segments failed.");
// let mut index_writer = index.writer_for_tests().unwrap();
// index_writer
// .merge(&segment_ids)
// .wait()
// .expect("Merging failed");
// index_writer.wait_merging_threads().unwrap();
// reader.reload().unwrap();
// test_searcher(
// 11,
// &[
// ("/top/a", 5),
// ("/top/b", 5),
// ("/top/c", 2),
// ("/top/d", 2),
// ("/top/e", 2),
// ("/top/f", 1),
// ],
// );
// }
reader.reload().unwrap();
let test_searcher = |expected_num_docs: usize, expected: &[(&str, u64)]| {
let searcher = reader.searcher();
let mut facet_collector = FacetCollector::for_field("facet");
facet_collector.add_facet(Facet::from("/top"));
let (count, facet_counts) = searcher
.search(&AllQuery, &(Count, facet_collector))
.unwrap();
assert_eq!(count, expected_num_docs);
let facets: Vec<(String, u64)> = facet_counts
.get("/top")
.map(|(facet, count)| (facet.to_string(), count))
.collect();
assert_eq!(
facets,
expected
.iter()
.map(|&(facet_str, count)| (String::from(facet_str), count))
.collect::<Vec<_>>()
);
};
test_searcher(
11,
&[
("/top/a", 5),
("/top/b", 5),
("/top/c", 2),
("/top/d", 2),
("/top/e", 2),
("/top/f", 1),
],
);
// Merging the segments
{
let segment_ids = index
.searchable_segment_ids()
.expect("Searchable segments failed.");
let mut index_writer = index.writer_for_tests().unwrap();
index_writer
.merge(&segment_ids)
.wait()
.expect("Merging failed");
index_writer.wait_merging_threads().unwrap();
reader.reload().unwrap();
test_searcher(
11,
&[
("/top/a", 5),
("/top/b", 5),
("/top/c", 2),
("/top/d", 2),
("/top/e", 2),
("/top/f", 1),
],
);
}
// // Deleting one term
// {
// let mut index_writer = index.writer_for_tests().unwrap();
// let facet = Facet::from_path(vec!["top", "a", "firstdoc"]);
// let facet_term = Term::from_facet(facet_field, &facet);
// index_writer.delete_term(facet_term);
// index_writer.commit().unwrap();
// reader.reload().unwrap();
// test_searcher(
// 9,
// &[
// ("/top/a", 3),
// ("/top/b", 3),
// ("/top/c", 1),
// ("/top/d", 2),
// ("/top/e", 2),
// ("/top/f", 1),
// ],
// );
// }
// }
// Deleting one term
{
let mut index_writer = index.writer_for_tests().unwrap();
let facet = Facet::from_path(vec!["top", "a", "firstdoc"]);
let facet_term = Term::from_facet(facet_field, &facet);
index_writer.delete_term(facet_term);
index_writer.commit().unwrap();
reader.reload().unwrap();
test_searcher(
9,
&[
("/top/a", 3),
("/top/b", 3),
("/top/c", 1),
("/top/d", 2),
("/top/e", 2),
("/top/f", 1),
],
);
}
}
#[test]
fn test_bug_merge() -> crate::Result<()> {
@@ -1511,6 +1480,13 @@ mod tests {
let int_field = schema_builder.add_u64_field("intvals", int_options);
let index = Index::create_in_ram(schema_builder.build());
let mut vals: Vec<u64> = Vec::new();
let mut test_vals = move |col: &Column<u64>, doc: DocId, expected: &[u64]| {
vals.clear();
vals.extend(col.values(doc));
assert_eq!(&vals[..], expected);
};
{
let mut index_writer = index.writer_for_tests()?;
let index_doc = |index_writer: &mut IndexWriter, int_vals: &[u64]| {
@@ -1536,49 +1512,42 @@ mod tests {
}
let reader = index.reader()?;
let searcher = reader.searcher();
let mut vals: Vec<u64> = Vec::new();
{
let segment = searcher.segment_reader(0u32);
// let ff_reader = segment.fast_fields().u64s(int_field).unwrap();
// ff_reader.get_vals(0, &mut vals);
// assert_eq!(&vals, &[1, 2]);
// ff_reader.get_vals(1, &mut vals);
// assert_eq!(&vals, &[1, 2, 3]);
// ff_reader.get_vals(2, &mut vals);
// assert_eq!(&vals, &[4, 5]);
// ff_reader.get_vals(3, &mut vals);
// assert_eq!(&vals, &[1, 2]);
// ff_reader.get_vals(4, &mut vals);
// assert_eq!(&vals, &[1, 5]);
// ff_reader.get_vals(5, &mut vals);
// assert_eq!(&vals, &[3]);
// ff_reader.get_vals(6, &mut vals);
// assert_eq!(&vals, &[17]);
let column = segment
.fast_fields()
.column_opt::<u64>("intvals")
.unwrap()
.unwrap();
test_vals(&column, 0, &[1, 2]);
test_vals(&column, 1, &[1, 2, 3]);
test_vals(&column, 2, &[4, 5]);
test_vals(&column, 3, &[1, 2]);
test_vals(&column, 4, &[1, 5]);
test_vals(&column, 5, &[3]);
test_vals(&column, 6, &[17]);
}
{
let segment = searcher.segment_reader(1u32);
// let ff_reader = segment.fast_fields().u64s(int_field).unwrap();
// ff_reader.get_vals(0, &mut vals);
// assert_eq!(&vals, &[28, 27]);
// ff_reader.get_vals(1, &mut vals);
// assert_eq!(&vals, &[1_000]);
let col = segment
.fast_fields()
.column_opt::<u64>("intvals")
.unwrap()
.unwrap();
test_vals(&col, 0, &[28, 27]);
test_vals(&col, 1, &[1000]);
}
{
let segment = searcher.segment_reader(2u32);
// let ff_reader = segment.fast_fields().u64s(int_field).unwrap();
// ff_reader.get_vals(0, &mut vals);
// assert_eq!(&vals, &[20]);
let col = segment
.fast_fields()
.column_opt::<u64>("intvals")
.unwrap()
.unwrap();
test_vals(&col, 0, &[20]);
}
// Merging the segments
@@ -1593,37 +1562,21 @@ mod tests {
{
let searcher = reader.searcher();
let segment = searcher.segment_reader(0u32);
// let ff_reader = segment.fast_fields().u64s(int_field).unwrap();
// ff_reader.get_vals(0, &mut vals);
// assert_eq!(&vals, &[1, 2]);
// ff_reader.get_vals(1, &mut vals);
// assert_eq!(&vals, &[1, 2, 3]);
// ff_reader.get_vals(2, &mut vals);
// assert_eq!(&vals, &[4, 5]);
// ff_reader.get_vals(3, &mut vals);
// assert_eq!(&vals, &[1, 2]);
// ff_reader.get_vals(4, &mut vals);
// assert_eq!(&vals, &[1, 5]);
// ff_reader.get_vals(5, &mut vals);
// assert_eq!(&vals, &[3]);
// ff_reader.get_vals(6, &mut vals);
// assert_eq!(&vals, &[17]);
// ff_reader.get_vals(7, &mut vals);
// assert_eq!(&vals, &[28, 27]);
// ff_reader.get_vals(8, &mut vals);
// assert_eq!(&vals, &[1_000]);
// ff_reader.get_vals(9, &mut vals);
// assert_eq!(&vals, &[20]);
let col = segment
.fast_fields()
.column_opt::<u64>("intvals")
.unwrap()
.unwrap();
test_vals(&col, 0, &[1, 2]);
test_vals(&col, 1, &[1, 2, 3]);
test_vals(&col, 2, &[4, 5]);
test_vals(&col, 3, &[1, 2]);
test_vals(&col, 4, &[1, 5]);
test_vals(&col, 5, &[3]);
test_vals(&col, 6, &[17]);
test_vals(&col, 7, &[28, 27]);
test_vals(&col, 8, &[1000]);
test_vals(&col, 9, &[20]);
}
Ok(())
}

View File

@@ -155,6 +155,7 @@ mod tests {
fn test_merge_sorted_index_desc_not_disjunct() {
test_merge_sorted_index_desc_(false);
}
#[test]
fn test_merge_sorted_index_desc_disjunct() {
test_merge_sorted_index_desc_(true);
@@ -476,12 +477,11 @@ mod bench_sorted_index_merge {
use std::sync::Arc;
use fastfield_codecs::Column;
use test::{self, Bencher};
use crate::core::Index;
use crate::indexer::merger::IndexMerger;
use crate::schema::{Cardinality, NumericOptions, Schema};
use crate::schema::{NumericOptions, Schema};
use crate::{IndexSettings, IndexSortByField, IndexWriter, Order};
fn create_index(sort_by_field: Option<IndexSortByField>) -> Index {
let mut schema_builder = Schema::builder();
@@ -512,42 +512,42 @@ mod bench_sorted_index_merge {
index
}
#[bench]
fn create_sorted_index_walk_overkmerge_on_merge_fastfield(
b: &mut Bencher,
) -> crate::Result<()> {
let sort_by_field = IndexSortByField {
field: "intval".to_string(),
order: Order::Desc,
};
let index = create_index(Some(sort_by_field.clone()));
let segments = index.searchable_segments().unwrap();
let merger: IndexMerger =
IndexMerger::open(index.schema(), index.settings().clone(), &segments[..])?;
let doc_id_mapping = merger.generate_doc_id_mapping(&sort_by_field).unwrap();
b.iter(|| {
let sorted_doc_ids = doc_id_mapping.iter_old_doc_addrs().map(|doc_addr| {
let reader = &merger.readers[doc_addr.segment_ord as usize];
let u64_reader: Arc<dyn Column<u64>> = reader
.fast_fields()
.typed_fast_field_reader("intval")
.expect(
"Failed to find a reader for single fast field. This is a tantivy bug and \
it should never happen.",
);
(doc_addr.doc_id, reader, u64_reader)
});
// add values in order of the new doc_ids
let mut val = 0;
for (doc_id, _reader, field_reader) in sorted_doc_ids {
val = field_reader.get_val(doc_id);
}
//#[bench]
// fn create_sorted_index_walk_overkmerge_on_merge_fastfield(
// b: &mut Bencher,
//) -> crate::Result<()> {
// let sort_by_field = IndexSortByField {
// field: "intval".to_string(),
// order: Order::Desc,
//};
// let index = create_index(Some(sort_by_field.clone()));
// let segments = index.searchable_segments().unwrap();
// let merger: IndexMerger =
// IndexMerger::open(index.schema(), index.settings().clone(), &segments[..])?;
// let doc_id_mapping = merger.generate_doc_id_mapping(&sort_by_field).unwrap();
// b.iter(|| {
// let sorted_doc_ids = doc_id_mapping.iter_old_doc_addrs().map(|doc_addr| {
// let reader = &merger.readers[doc_addr.segment_ord as usize];
// let u64_reader: Arc<dyn Column<u64>> = reader
//.fast_fields()
//.typed_fast_field_reader("intval")
//.expect(
//"Failed to find a reader for single fast field. This is a tantivy bug and \
// it should never happen.",
//);
//(doc_addr.doc_id, reader, u64_reader)
//});
//// add values in order of the new doc_ids
// let mut val = 0;
// for (doc_id, _reader, field_reader) in sorted_doc_ids {
// val = field_reader.get_val(doc_id);
//}
val
});
// val
//});
Ok(())
}
// Ok(())
//}
#[bench]
fn create_sorted_index_create_doc_id_mapping(b: &mut Bencher) -> crate::Result<()> {
let sort_by_field = IndexSortByField {

View File

@@ -19,8 +19,6 @@ mod segment_register;
pub mod segment_serializer;
pub mod segment_updater;
mod segment_writer;
// mod sorted_doc_id_column;
// mod sorted_doc_id_multivalue_column;
mod stamper;
use crossbeam_channel as channel;
@@ -57,9 +55,9 @@ type AddBatchReceiver = channel::Receiver<AddBatch>;
#[cfg(feature = "mmap")]
#[cfg(test)]
mod tests_mmap {
use crate::collector::Count;
// use crate::query::QueryParser;
use crate::schema::{JsonObjectOptions, Schema, TEXT};
use crate::schema::{Schema, TEXT};
use crate::{Index, Term};
#[test]

View File

@@ -819,20 +819,23 @@ mod tests {
// This is a bit of a contrived example.
let tokens = PreTokenizedString {
text: "contrived-example".to_string(), //< I can't think of a use case where this corner case happens in real life.
tokens: vec![Token { // Not the last token, yet ends after the last token.
offset_from: 0,
offset_to: 14,
position: 0,
text: "long_token".to_string(),
position_length: 3,
},
Token {
offset_from: 0,
offset_to: 14,
position: 1,
text: "short".to_string(),
position_length: 1,
}],
tokens: vec![
Token {
// Not the last token, yet ends after the last token.
offset_from: 0,
offset_to: 14,
position: 0,
text: "long_token".to_string(),
position_length: 3,
},
Token {
offset_from: 0,
offset_to: 14,
position: 1,
text: "short".to_string(),
position_length: 1,
},
],
};
doc.add_pre_tokenized_text(text, tokens);
doc.add_text(text, "hello");

View File

@@ -1,169 +0,0 @@
use std::cmp;
use fastfield_codecs::Column;
use super::flat_map_with_buffer::FlatMapWithBufferIter;
use crate::fastfield::{MultiValueIndex, MultiValuedFastFieldReader};
use crate::indexer::doc_id_mapping::SegmentDocIdMapping;
use crate::{DocAddress, SegmentReader};
pub(crate) struct RemappedDocIdMultiValueColumn<'a> {
doc_id_mapping: &'a SegmentDocIdMapping,
fast_field_readers: Vec<MultiValuedFastFieldReader<u64>>,
min_value: u64,
max_value: u64,
num_vals: u32,
}
impl<'a> RemappedDocIdMultiValueColumn<'a> {
pub(crate) fn new(
readers: &'a [SegmentReader],
doc_id_mapping: &'a SegmentDocIdMapping,
field: &str,
) -> Self {
// Our values are bitpacked and we need to know what should be
// our bitwidth and our minimum value before serializing any values.
//
// Computing those is non-trivial if some documents are deleted.
// We go through a complete first pass to compute the minimum and the
// maximum value and initialize our Serializer.
let mut num_vals = 0;
let mut min_value = u64::MAX;
let mut max_value = u64::MIN;
let mut vals = Vec::new();
let mut fast_field_readers = Vec::with_capacity(readers.len());
for reader in readers {
let ff_reader: MultiValuedFastFieldReader<u64> = reader
.fast_fields()
.typed_fast_field_multi_reader::<u64>(field)
.expect(
"Failed to find multivalued fast field reader. This is a bug in tantivy. \
Please report.",
);
for doc in reader.doc_ids_alive() {
ff_reader.get_vals(doc, &mut vals);
for &val in &vals {
min_value = cmp::min(val, min_value);
max_value = cmp::max(val, max_value);
}
num_vals += vals.len();
}
fast_field_readers.push(ff_reader);
// TODO optimize when no deletes
}
if min_value > max_value {
min_value = 0;
max_value = 0;
}
RemappedDocIdMultiValueColumn {
doc_id_mapping,
fast_field_readers,
min_value,
max_value,
num_vals: num_vals as u32,
}
}
}
impl<'a> Column for RemappedDocIdMultiValueColumn<'a> {
fn get_val(&self, _pos: u32) -> u64 {
unimplemented!()
}
fn iter(&self) -> Box<dyn Iterator<Item = u64> + '_> {
Box::new(
self.doc_id_mapping
.iter_old_doc_addrs()
.flat_map_with_buffer(|old_doc_addr: DocAddress, buffer| {
let ff_reader = &self.fast_field_readers[old_doc_addr.segment_ord as usize];
ff_reader.get_vals(old_doc_addr.doc_id, buffer);
}),
)
}
fn min_value(&self) -> u64 {
self.min_value
}
fn max_value(&self) -> u64 {
self.max_value
}
fn num_vals(&self) -> u32 {
self.num_vals
}
}
pub(crate) struct RemappedDocIdMultiValueIndexColumn<'a> {
doc_id_mapping: &'a SegmentDocIdMapping,
multi_value_length_readers: Vec<&'a MultiValueIndex>,
min_value: u64,
max_value: u64,
num_vals: u32,
}
impl<'a> RemappedDocIdMultiValueIndexColumn<'a> {
pub(crate) fn new(
segment_and_ff_readers: &'a [(&'a SegmentReader, &'a MultiValueIndex)],
doc_id_mapping: &'a SegmentDocIdMapping,
) -> Self {
// We go through a complete first pass to compute the minimum and the
// maximum value and initialize our Column.
let mut num_vals = 0;
let min_value = 0;
let mut max_value = 0;
let mut multi_value_length_readers = Vec::with_capacity(segment_and_ff_readers.len());
for segment_and_ff_reader in segment_and_ff_readers {
let segment_reader = segment_and_ff_reader.0;
let multi_value_length_reader = segment_and_ff_reader.1;
if !segment_reader.has_deletes() {
max_value += multi_value_length_reader.total_num_vals() as u64;
} else {
for doc in segment_reader.doc_ids_alive() {
max_value += multi_value_length_reader.num_vals_for_doc(doc) as u64;
}
}
num_vals += segment_reader.num_docs();
multi_value_length_readers.push(multi_value_length_reader);
}
// The value range is always get_val(doc)..get_val(doc + 1)
num_vals += 1;
Self {
doc_id_mapping,
multi_value_length_readers,
min_value,
max_value,
num_vals,
}
}
}
impl<'a> Column for RemappedDocIdMultiValueIndexColumn<'a> {
fn get_val(&self, _pos: u32) -> u64 {
unimplemented!()
}
fn iter(&self) -> Box<dyn Iterator<Item = u64> + '_> {
let mut offset = 0;
Box::new(
std::iter::once(0).chain(self.doc_id_mapping.iter_old_doc_addrs().map(
move |old_doc_addr| {
let ff_reader =
&self.multi_value_length_readers[old_doc_addr.segment_ord as usize];
offset += ff_reader.num_vals_for_doc(old_doc_addr.doc_id);
offset as u64
},
)),
)
}
fn min_value(&self) -> u64 {
self.min_value
}
fn max_value(&self) -> u64 {
self.max_value
}
fn num_vals(&self) -> u32 {
self.num_vals
}
}

View File

@@ -279,7 +279,7 @@ mod indexer;
pub mod error;
pub mod tokenizer;
// pub mod aggregation;
pub mod aggregation;
pub mod collector;
pub mod directory;
pub mod fastfield;

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