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3 Commits

Author SHA1 Message Date
Paul Masurel
d7973892a2 extra commit 2019-12-27 22:53:04 +09:00
Paul Masurel
cd7484c035 Added ReadOnlyDirectory and implemented Bundle Directory 2019-12-27 12:05:39 +09:00
Paul Masurel
7ed6bc8718 Added serialize to bundle in the RAMDirectory. 2019-12-26 10:06:52 +09:00
289 changed files with 14347 additions and 136544 deletions

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@@ -1,13 +0,0 @@
---
name: Actions
about: Actions not directly related to producing code.
---
# Actions title
Action description.
e.g.
- benchmark
- investigate and report
- etc.

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@@ -1,15 +0,0 @@
version: 2
updates:
- package-ecosystem: cargo
directory: "/"
schedule:
interval: daily
time: "20:00"
open-pull-requests-limit: 10
- package-ecosystem: "github-actions"
directory: "/"
schedule:
interval: daily
time: "20:00"
open-pull-requests-limit: 10

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@@ -1,25 +0,0 @@
name: Coverage
on:
push:
branches: [ main ]
pull_request:
branches: [ main ]
jobs:
coverage:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Install Rust
run: rustup toolchain install nightly --component llvm-tools-preview
- name: Install cargo-llvm-cov
run: curl -LsSf https://github.com/taiki-e/cargo-llvm-cov/releases/latest/download/cargo-llvm-cov-x86_64-unknown-linux-gnu.tar.gz | tar xzf - -C ~/.cargo/bin
- name: Generate code coverage
run: cargo llvm-cov --all-features --workspace --lcov --output-path lcov.info
- name: Upload coverage to Codecov
uses: codecov/codecov-action@v2
with:
token: ${{ secrets.CODECOV_TOKEN }} # not required for public repos
files: lcov.info
fail_ci_if_error: true

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@@ -1,24 +0,0 @@
name: Long running tests
on:
push:
branches: [ main ]
env:
CARGO_TERM_COLOR: always
NUM_FUNCTIONAL_TEST_ITERATIONS: 20000
jobs:
functional_test_unsorted:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Run indexing_unsorted
run: cargo test indexing_unsorted -- --ignored
functional_test_sorted:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Run indexing_sorted
run: cargo test indexing_sorted -- --ignored

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@@ -1,42 +0,0 @@
name: Unit tests
on:
push:
branches: [ main ]
pull_request:
branches: [ main ]
env:
CARGO_TERM_COLOR: always
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Build
run: cargo build --verbose --workspace
- name: Install latest nightly to test also against unstable feature flag
uses: actions-rs/toolchain@v1
with:
toolchain: nightly
override: true
components: rustfmt
- name: Install latest nightly to test also against unstable feature flag
uses: actions-rs/toolchain@v1
with:
toolchain: stable
override: true
components: rustfmt, clippy
- name: Run tests
run: cargo +stable test --features mmap,brotli-compression,lz4-compression,snappy-compression,failpoints --verbose --workspace
- name: Check Formatting
run: cargo +nightly fmt --all -- --check
- uses: actions-rs/clippy-check@v1
with:
toolchain: stable
token: ${{ secrets.GITHUB_TOKEN }}
args: --tests

3
.gitignore vendored
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@@ -1,6 +1,4 @@
tantivy.iml
.cargo
proptest-regressions
*.swp
target
target/debug
@@ -13,4 +11,3 @@ cpp/simdcomp/bitpackingbenchmark
*.bk
.idea
trace.dat
cargo-timing*

92
.travis.yml Normal file
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@@ -0,0 +1,92 @@
# Based on the "trust" template v0.1.2
# https://github.com/japaric/trust/tree/v0.1.2
dist: trusty
language: rust
services: docker
sudo: required
env:
global:
- CRATE_NAME=tantivy
- TRAVIS_CARGO_NIGHTLY_FEATURE=""
# - secure: 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
addons:
apt:
sources:
- ubuntu-toolchain-r-test
- kalakris-cmake
packages:
- gcc-4.8
- g++-4.8
- libcurl4-openssl-dev
- libelf-dev
- libdw-dev
- binutils-dev
- cmake
matrix:
include:
# Android
- env: TARGET=aarch64-linux-android DISABLE_TESTS=1
#- env: TARGET=arm-linux-androideabi DISABLE_TESTS=1
#- env: TARGET=armv7-linux-androideabi DISABLE_TESTS=1
#- env: TARGET=i686-linux-android DISABLE_TESTS=1
#- env: TARGET=x86_64-linux-android DISABLE_TESTS=1
# Linux
#- env: TARGET=aarch64-unknown-linux-gnu
#- env: TARGET=i686-unknown-linux-gnu
- env: TARGET=x86_64-unknown-linux-gnu CODECOV=1 #UPLOAD_DOCS=1
# - env: TARGET=x86_64-unknown-linux-musl CODECOV=1
# OSX
#- env: TARGET=x86_64-apple-darwin
# os: osx
before_install:
- set -e
- rustup self update
- rustup component add rustfmt
install:
- sh ci/install.sh
- source ~/.cargo/env || true
- env | grep "TRAVIS"
before_script:
- export PATH=$HOME/.cargo/bin:$PATH
- cargo install cargo-update || echo "cargo-update already installed"
- cargo install cargo-travis || echo "cargo-travis already installed"
script:
- bash ci/script.sh
- cargo fmt --all -- --check
before_deploy:
- sh ci/before_deploy.sh
after_success:
# Needs GH_TOKEN env var to be set in travis settings
- if [[ -v GH_TOKEN ]]; then echo "GH TOKEN IS SET"; else echo "GH TOKEN NOT SET"; fi
- if [[ -v UPLOAD_DOCS ]]; then cargo doc; cargo doc-upload; else echo "doc upload disabled."; fi
#cache: cargo
#before_cache:
# # Travis can't cache files that are not readable by "others"
# - chmod -R a+r $HOME/.cargo
# - find ./target/debug -type f -maxdepth 1 -delete
# - rm -f ./target/.rustc_info.json
# - rm -fr ./target/debug/{deps,.fingerprint}/tantivy*
# - rm -r target/debug/examples/
# - ls -1 examples/ | sed -e 's/\.rs$//' | xargs -I "{}" find target/* -name "*{}*" -type f -delete
#branches:
# only:
# # release tags
# - /^v\d+\.\d+\.\d+.*$/
# - master
notifications:
email:
on_success: never

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@@ -1,295 +0,0 @@
# Tantivy
## What is tantivy?
Tantivy is a library that is meant to build search engines. Although it is by no means a port of Lucene, its architecture is strongly inspired by it. If you are familiar with Lucene, you may be struck by the overlapping vocabulary.
This is not fortuitous.
Tantivy's bread and butter is to address the problem of full-text search :
Given a large set of textual documents, and a text query, return the K-most relevant documents in a very efficient way. To execute these queries rapidly, the tantivy needs to build an index beforehand. The relevance score implemented in the tantivy is not configurable. Tantivy uses the same score as the default similarity used in Lucene / Elasticsearch, called [BM25](https://en.wikipedia.org/wiki/Okapi_BM25).
But tantivy's scope does not stop there. Numerous features are required to power rich-search applications. For instance, one may want to:
- compute the count of documents matching a query in the different section of an e-commerce website,
- display an average price per meter square for a real estate search engine,
- take into account historical user data to rank documents in a specific way,
- or even use tantivy to power an OLAP database.
A more abstract description of the problem space tantivy is trying to address is the following.
Ingest a large set of documents, create an index that makes it possible to
rapidly select all documents matching a given predicate (also known as a query) and
collect some information about them ([See collector](#collector-define-what-to-do-with-matched-documents)).
Roughly speaking the design is following these guiding principles:
- Search should be O(1) in memory.
- Indexing should be O(1) in memory. (In practice it is just sublinear)
- Search should be as fast as possible
This comes at the cost of the dynamicity of the index: while it is possible to add, and delete documents from our corpus, the tantivy is designed to handle these updates in large batches.
## [core/](src/core): Index, segments, searchers.
Core contains all of the high-level code to make it possible to create an index, add documents, delete documents and commit.
This is both the most high-level part of tantivy, the least performance-sensitive one, the seemingly most mundane code... And paradoxically the most complicated part.
### Index and Segments...
A tantivy index is a collection of smaller independent immutable segments.
Each segment contains its own independent set of data structures.
A segment is identified by a segment id that is in fact a UUID.
The file of a segment has the format
```segment-id . ext ```
The extension signals which data structure (or [`SegmentComponent`](src/core/segment_component.rs)) is stored in the file.
A small `meta.json` file is in charge of keeping track of the list of segments, as well as the schema.
On commit, one segment per indexing thread is written to disk, and the `meta.json` is then updated atomically.
For a better idea of how indexing works, you may read the [following blog post](https://fulmicoton.com/posts/behold-tantivy-part2/).
### Deletes
Deletes happen by deleting a "term". Tantivy does not offer any notion of primary id, so it is up to the user to use a field in their schema as if it was a primary id, and delete the associated term if they want to delete only one specific document.
On commit, tantivy will find all of the segments with documents matching this existing term and create a [tombstone file](src/fastfield/delete.rs) that represents the bitset of the document that are deleted.
Like all segment files, this file is immutable. Because it is possible to have more than one tombstone file at a given instant, the tombstone filename has the format ``` segment_id . commit_opstamp . del```.
An opstamp is simply an incremental id that identifies any operation applied to the index. For instance, performing a commit or adding a document.
### DocId
Within a segment, all documents are identified by a DocId that ranges within `[0, max_doc)`.
where `max_doc` is the number of documents in the segment, (deleted or not). Having such a compact `DocId` space is key to the compression of our data structures.
The DocIds are simply allocated in the order documents are added to the index.
### Merges
In separate threads, tantivy's index writer search for opportunities to merge segments.
The point of segment merge is to:
- eventually get rid of tombstoned documents
- reduce the otherwise ever-growing number of segments.
Indeed, while having several segments instead of one does not hurt search too much, having hundreds can have a measurable impact on the search performance.
### Searcher
The user of the library usually does not need to know about the existence of Segments.
Searching is done through an object called a [`Searcher`](src/core/searcher.rs), that captures a
snapshot of the index at one point of time, by holding a list of [SegmentReader](src/core/segment_reader.rs).
In other words, regardless of commits, file garbage collection, or segment merge that might happen, as long as the user holds and reuse the same [Searcher](src/core/searcher.rs), search will happen on an immutable snapshot of the index.
## [directory/](src/directory): Where should the data be stored?
Tantivy, like Lucene, abstracts the place where the data should be stored in a key-trait
called [`Directory`](src/directory/directory.rs).
Contrary to Lucene however, "files" are quite different from some kind of `io::Read` object.
Check out [`src/directory/directory.rs`](src/directory/directory.rs) trait for more details.
Tantivy ships two main directory implementation: the `MMapDirectory` and the `RAMDirectory`,
but users can extend tantivy with their own implementation.
## [schema/](src/schema): What are documents?
Tantivy's document follows a very strict schema, decided before building any index.
The schema defines all of the fields that the indexes [`Document`](src/schema/document.rs) may and should contain, their types (`text`, `i64`, `u64`, `Date`, ...) as well as how it should be indexed / represented in tantivy.
Depending on the type of the field, you can decide to
- put it in the docstore
- store it as a fast field
- index it
Practically, tantivy will push values associated with this type to up to 3 respective
data structures.
*Limitations*
As of today, tantivy's schema imposes a 1:1 relationship between a field that is being ingested and a field represented in the search index. In sophisticated search application, it is fairly common to want to index a field twice using different tokenizers, or to index the concatenation of several fields together into one field.
This is not something tantivy supports, and it is up to the user to duplicate field / concatenate fields before feeding them to tantivy.
## General information about these data structures.
All data structures in tantivy, have:
- a writer
- a serializer
- a reader
The writer builds an in-memory representation of a batch of documents. This representation is not searchable. It is just meant as an intermediary mutable representation, to which we can sequentially add
the document of a batch. At the end of the batch (or if a memory limit is reached), this representation
is then converted into an on-disk immutable representation, that is extremely compact.
This conversion is done by the serializer.
Finally, the reader is in charge of offering an API to read on this on-disk read-only representation.
In tantivy, readers are designed to require very little anonymous memory. The data is read straight from an mmapped file, and loading an index is as fast as mmapping its files.
## [store/](src/store): Here is my DocId, Gimme my document!
The docstore is a row-oriented storage that, for each document, stores a subset of the fields
that are marked as stored in the schema. The docstore is compressed using a general-purpose algorithm
like LZ4.
**Useful for**
In search engines, it is often used to display search results.
Once the top 10 documents have been identified, we fetch them from the store, and display them or their snippet on the search result page (aka SERP).
**Not useful for**
Fetching a document from the store is typically a "slow" operation. It usually consists in
- searching into a compact tree-like data structure to find the position of the right block.
- decompressing a small block
- returning the document from this block.
It is NOT meant to be called for every document matching a query.
As a rule of thumb, if you hit the docstore more than 100 times per search query, you are probably misusing tantivy.
## [fastfield/](src/fastfield): Here is my DocId, Gimme my value!
Fast fields are stored in a column-oriented storage that allows for random access.
The only compression applied is bitpacking. The column comes with two meta data.
The minimum value in the column and the number of bits per doc.
Fetching a value for a `DocId` is then as simple as computing
```
min_value + fetch_bits(num_bits * doc_id..num_bits * (doc_id+1))
```
This operation just requires one memory fetch.
Because, DocSets are scanned through in order (DocId are iterated in a sorted manner) which
also help locality.
In Lucene's jargon, fast fields are called DocValues.
**Useful for**
They are typically integer values that are useful to either rank or compute aggregate over
all of the documents matching a query (aka [DocSet](src/docset.rs)).
For instance, one could define a function to combine upvotes with tantivy's internal relevancy score.
This can be done by fetching a fast field during scoring.
One could also compute the mean price of the items matching a query in an e-commerce website.
This can be done by fetching a fast field in a collector.
Finally one could decide to post-filter a docset to remove docset with a price within a specific range.
If the ratio of filtered out documents is not too low, an efficient way to do this is to fetch the price and apply the filter on the collector side.
Aside from integer values, it is also possible to store an actual byte payload.
For advanced search engine, it is possible to store all of the features required for learning-to-rank in a byte payload, access it during search, and apply the learning-to-rank model.
Finally facets are a specific kind of fast field, and the associated source code is in [`fastfield/facet_reader.rs`](src/fastfield/facet_reader.rs).
# The inverted search index.
The inverted index is the core part of full-text search.
When presented a new document with the text field "Hello, happy tax payer!", tantivy breaks it into a list of so-called tokens. In addition to just splitting these strings into tokens, it might also do different kinds of operations like dropping the punctuation, converting the character to lowercase, apply stemming, etc. Tantivy makes it possible to configure the operations to be applied in the schema (tokenizer/ is the place where these operations are implemented).
For instance, the default tokenizer of tantivy would break our text into: `[hello, happy, tax, payer]`.
The document will therefore be registered in the inverted index as containing the terms
`[text:hello, text:happy, text:tax, text:payer]`.
The role of the inverted index is, when given a term, gives us in return a very fast iterator over the sorted doc ids that match the term.
Such an iterator is called a posting list. In addition to giving us `DocId`, they can also give us optionally the number of occurrence of the term for each document, also called term frequency or TF.
These iterators being sorted by DocId, one can create an iterator over the document containing `text:tax AND text:payer`, `(text:tax AND text:payer) OR (text:contribuable)` or any boolean expression.
In order to represent the function
```Term ⟶ Posting```
The inverted index actually consists of two data structures chained together.
- [Term](src/schema/term.rs) ⟶ [TermInfo](src/postings/term_info.rs) is addressed by the term dictionary.
- [TermInfo](src/postings/term_info.rs) ⟶ [Posting](src/postings/postings.rs) is addressed by the posting lists.
Where [TermInfo](src/postings/term_info.rs) is an object containing some meta data about a term.
## [termdict/](src/termdict): Here is a term, give me the [TermInfo](src/postings/term_info.rs)!
Tantivy's term dictionary is mainly in charge of supplying the function
[Term](src/schema/term.rs) ⟶ [TermInfo](src/postings/term_info.rs)
It is itself broken into two parts.
- [Term](src/schema/term.rs) ⟶ [TermOrdinal](src/termdict/mod.rs) is addressed by a finite state transducer, implemented by the fst crate.
- [TermOrdinal](src/termdict/mod.rs) ⟶ [TermInfo](src/postings/term_info.rs) is addressed by the term info store.
## [postings/](src/postings): Iterate over documents... very fast!
A posting list makes it possible to store a sorted list of doc ids and for each doc store
a term frequency as well.
The posting lists are stored in a separate file. The [TermInfo](src/postings/term_info.rs) contains an offset into that file and a number of documents for the given posting list. Both are required and sufficient to read the posting list.
The posting list is organized in block of 128 documents.
One block of doc ids is followed by one block of term frequencies.
The doc ids are delta encoded and bitpacked.
The term frequencies are bitpacked.
Because the number of docs is rarely a multiple of 128, the last block may contain an arbitrary number of docs between 1 and 127 documents. We then use variable int encoding instead of bitpacking.
## [positions/](src/positions): Where are my terms within the documents?
Phrase queries make it possible to search for documents containing a specific sequence of terms.
For instance, when the phrase query "the art of war" does not match "the war of art".
To make it possible, it is possible to specify in the schema that a field should store positions in addition to being indexed.
The token positions of all of the terms are then stored in a separate file with the extension `.pos`.
The [TermInfo](src/postings/term_info.rs) gives an offset (expressed in position this time) in this file. As we iterate throught the docset,
we advance the position reader by the number of term frequencies of the current document.
## [fieldnorms/](src/fieldnorms): Here is my doc, how many tokens in this field?
The [BM25](https://en.wikipedia.org/wiki/Okapi_BM25) formula also requires to know the number of tokens stored in a specific field for a given document. We store this information on one byte per document in the fieldnorm.
The fieldnorm is therefore compressed. Values up to 40 are encoded unchanged.
## [tokenizer/](src/tokenizer): How should we process text?
Text processing is key to a good search experience.
Splits or normalize your text too much, and the search results will have a less precision and a higher recall.
Do not normalize, or under split your text, you will end up with a higher precision and a lesser recall.
Text processing can be configured by selecting an off-the-shelf [`Tokenizer`](./src/tokenizer/tokenizer.rs) or implementing your own to first split the text into tokens, and then chain different [`TokenFilter`](src/tokenizer/tokenizer.rs)'s to it.
Tantivy's comes with few tokenizers, but external crates are offering advanced tokenizers, such as [Lindera](https://crates.io/crates/lindera) for Japanese.
## [query/](src/query): Define and compose queries
The [Query](src/query/query.rs) trait defines what a query is.
Due to the necessity for some queries to compute some statistics over the entire index, and because the
index is composed of several `SegmentReader`, the path from transforming a `Query` to an iterator over documents is slightly convoluted, but fundamentally, this is what a Query is.
The iterator over a document comes with some scoring function. The resulting trait is called a
[Scorer](src/query/scorer.rs) and is specific to a segment.
Different queries can be combined using the [BooleanQuery](src/query/boolean_query/).
Tantivy comes with different types of queries and can be extended by implementing
the `Query`, `Weight`, and `Scorer` traits.
## [collector](src/collector): Define what to do with matched documents
Collectors define how to aggregate the documents matching a query, in the broadest sense possible.
The search will push matched documents one by one, calling their
`fn collect(doc: DocId, score: Score);` method.
Users may implement their own collectors by implementing the [Collector](src/collector/mod.rs) trait.
## [query-grammar](query-grammar): Defines the grammar of the query parser
While the [QueryParser](src/query/query_parser/query_parser.rs) struct is located in the `query/` directory, the actual parser combinator used to convert user queries into an AST is in an external crate called `query-grammar`. This part was externalized to lighten the work of the compiler.

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@@ -1,138 +1,3 @@
Tantivy 0.17
================================
- LogMergePolicy now triggers merges if the ratio of deleted documents reaches a threshold (@shikhar @fulmicoton) [#115](https://github.com/quickwit-oss/tantivy/issues/115)
- Adds a searcher Warmer API (@shikhar @fulmicoton)
- Change to non-strict schema. Ignore fields in data which are not defined in schema. Previously this returned an error. #1211
- Facets are necessarily indexed. Existing index with indexed facets should work out of the box. Index without facets that are marked with index: false should be broken (but they were already broken in a sense). (@fulmicoton) #1195 .
- Bugfix that could in theory impact durability in theory on some filesystems [#1224](https://github.com/quickwit-oss/tantivy/issues/1224)
- Schema now offers not indexing fieldnorms (@lpouget) [#922](https://github.com/quickwit-oss/tantivy/issues/922)
- Reduce the number of fsync calls [#1225](https://github.com/quickwit-oss/tantivy/issues/1225)
- Fix opening bytes index with dynamic codec (@PSeitz) [#1278](https://github.com/quickwit-oss/tantivy/issues/1278)
- Added an aggregation collector compatible with Elasticsearch (@PSeitz)
Tantivy 0.16.2
================================
- Bugfix in FuzzyTermQuery. (tranposition_cost_one was not doing anything)
Tantivy 0.16.1
========================
- Major Bugfix on multivalued fastfield. #1151
- Demux operation (@PSeitz)
Tantivy 0.16.0
=========================
- Bugfix in the filesum check. (@evanxg852000) #1127
- Bugfix in positions when the index is sorted by a field. (@appaquet) #1125
Tantivy 0.15.3
=========================
- Major bugfix. Deleting documents was broken when the index was sorted by a field. (@appaquet, @fulmicoton) #1101
Tantivy 0.15.2
========================
- Major bugfix. DocStore still panics when a deleted doc is at the beginning of a block. (@appaquet) #1088
Tantivy 0.15.1
=========================
- Major bugfix. DocStore panics when first block is deleted. (@appaquet) #1077
Tantivy 0.15.0
=========================
- API Changes. Using Range instead of (start, end) in the API and internals (`FileSlice`, `OwnedBytes`, `Snippets`, ...)
This change is breaking but migration is trivial.
- Added an Histogram collector. (@fulmicoton) #994
- Added support for Option<TCollector>. (@fulmicoton)
- DocAddress is now a struct (@scampi) #987
- Bugfix consistent tie break handling in facet's topk (@hardikpnsp) #357
- Date field support for range queries (@rihardsk) #516
- Added lz4-flex as the default compression scheme in tantivy (@PSeitz) #1009
- Renamed a lot of symbols to avoid all uppercasing on acronyms, as per new clippy recommendation. For instance, RAMDirectory -> RamDirectory. (@fulmicoton)
- Simplified positions index format (@fulmicoton) #1022
- Moved bitpacking to bitpacker subcrate and add BlockedBitpacker, which bitpacks blocks of 128 elements (@PSeitz) #1030
- Added support for more-like-this query in tantivy (@evanxg852000) #1011
- Added support for sorting an index, e.g presorting documents in an index by a timestamp field. This can heavily improve performance for certain scenarios, by utilizing the sorted data (Top-n optimizations)(@PSeitz). #1026
- Add iterator over documents in doc store (@PSeitz). #1044
- Fix log merge policy (@PSeitz). #1043
- Add detection to avoid small doc store blocks on merge (@PSeitz). #1054
- Make doc store compression dynamic (@PSeitz). #1060
- Switch to json for footer version handling (@PSeitz). #1060
- Updated TermMerger implementation to rely on the union feature of the FST (@scampi) #469
- Add boolean marking whether position is required in the query_terms API call (@fulmicoton). #1070
Tantivy 0.14.0
=========================
- Remove dependency to atomicwrites #833 .Implemented by @fulmicoton upon suggestion and research from @asafigan).
- Migrated tantivy error from the now deprecated `failure` crate to `thiserror` #760. (@hirevo)
- API Change. Accessing the typed value off a `Schema::Value` now returns an Option instead of panicking if the type does not match.
- Large API Change in the Directory API. Tantivy used to assume that all files could be somehow memory mapped. After this change, Directory return a `FileSlice` that can be reduced and eventually read into an `OwnedBytes` object. Long and blocking io operation are still required by they do not span over the entire file.
- Added support for Brotli compression in the DocStore. (@ppodolsky)
- Added helper for building intersections and unions in BooleanQuery (@guilload)
- Bugfix in `Query::explain`
- Removed dependency on `notify` #924. Replaced with `FileWatcher` struct that polls meta file every 500ms in background thread. (@halvorboe @guilload)
- Added `FilterCollector`, which wraps another collector and filters docs using a predicate over a fast field (@barrotsteindev)
- Simplified the encoding of the skip reader struct. BlockWAND max tf is now encoded over a single byte. (@fulmicoton)
- `FilterCollector` now supports all Fast Field value types (@barrotsteindev)
- FastField are not all loaded when opening the segment reader. (@fulmicoton)
- Added an API to merge segments, see `tantivy::merge_segments` #1005. (@evanxg852000)
This version breaks compatibility and requires users to reindex everything.
Tantivy 0.13.2
===================
Bugfix. Acquiring a facet reader on a segment that does not contain any
doc with this facet returns `None`. (#896)
Tantivy 0.13.1
===================
Made `Query` and `Collector` `Send + Sync`.
Updated misc dependency versions.
Tantivy 0.13.0
======================
Tantivy 0.13 introduce a change in the index format that will require
you to reindex your index (BlockWAND information are added in the skiplist).
The index size increase is minor as this information is only added for
full blocks.
If you have a massive index for which reindexing is not an option, please contact me
so that we can discuss possible solutions.
- Bugfix in `FuzzyTermQuery` not matching terms by prefix when it should (@Peachball)
- Relaxed constraints on the custom/tweak score functions. At the segment level, they can be mut, and they are not required to be Sync + Send.
- `MMapDirectory::open` does not return a `Result` anymore.
- Change in the DocSet and Scorer API. (@fulmicoton).
A freshly created DocSet point directly to their first doc. A sentinel value called TERMINATED marks the end of a DocSet.
`.advance()` returns the new DocId. `Scorer::skip(target)` has been replaced by `Scorer::seek(target)` and returns the resulting DocId.
As a result, iterating through DocSet now looks as follows
```rust
let mut doc = docset.doc();
while doc != TERMINATED {
// ...
doc = docset.advance();
}
```
The change made it possible to greatly simplify a lot of the docset's code.
- Misc internal optimization and introduction of the `Scorer::for_each_pruning` function. (@fulmicoton)
- Added an offset option to the Top(.*)Collectors. (@robyoung)
- Added Block WAND. Performance on TOP-K on term-unions should be greatly increased. (@fulmicoton, and special thanks
to the PISA team for answering all my questions!)
Tantivy 0.12.0
======================
- Removing static dispatch in tokenizers for simplicity. (#762)
- Added backward iteration for `TermDictionary` stream. (@halvorboe)
- Fixed a performance issue when searching for the posting lists of a missing term (@audunhalland)
- Added a configurable maximum number of docs (10M by default) for a segment to be considered for merge (@hntd187, landed by @halvorboe #713)
- Important Bugfix #777, causing tantivy to retain memory mapping. (diagnosed by @poljar)
- Added support for field boosting. (#547, @fulmicoton)
## How to update?
Crates relying on custom tokenizer, or registering tokenizer in the manager will require some
minor changes. Check https://github.com/quickwit-oss/tantivy/blob/main/examples/custom_tokenizer.rs
to check for some code sample.
Tantivy 0.11.3
=======================
- Fixed DateTime as a fast field (#735)
@@ -166,7 +31,7 @@ Tantivy 0.11.0
## How to update?
- The index format is changed. You are required to reindex your data to use tantivy 0.11.
- The index format is changed. You are required to reindex your data to use tantivy 0.11.
- `Box<dyn BoxableTokenizer>` has been replaced by a `BoxedTokenizer` struct.
- Regex are now compiled when the `RegexQuery` instance is built. As a result, it can now return
an error and handling the `Result` is required.
@@ -190,26 +55,26 @@ Tantivy 0.10.0
*Tantivy 0.10.0 index format is compatible with the index format in 0.9.0.*
- Added an API to easily tweak or entirely replace the
default score. See `TopDocs::tweak_score`and `TopScore::custom_score` (@fulmicoton)
- Added an API to easily tweak or entirely replace the
default score. See `TopDocs::tweak_score`and `TopScore::custom_score` (@pmasurel)
- Added an ASCII folding filter (@drusellers)
- Bugfix in `query.count` in presence of deletes (@fulmicoton)
- Added `.explain(...)` in `Query` and `Weight` to (@fulmicoton)
- Added an efficient way to `delete_all_documents` in `IndexWriter` (@petr-tik).
- Bugfix in `query.count` in presence of deletes (@pmasurel)
- Added `.explain(...)` in `Query` and `Weight` to (@pmasurel)
- Added an efficient way to `delete_all_documents` in `IndexWriter` (@petr-tik).
All segments are simply removed.
Minor
---------
- Switched to Rust 2018 (@uvd)
- Small simplification of the code.
- Small simplification of the code.
Calling .freq() or .doc() when .advance() has never been called
on segment postings should panic from now on.
- Tokens exceeding `u16::max_value() - 4` chars are discarded silently instead of panicking.
- Fast fields are now preloaded when the `SegmentReader` is created.
- `IndexMeta` is now public. (@hntd187)
- `IndexWriter` `add_document`, `delete_term`. `IndexWriter` is `Sync`, making it possible to use it with a `
Arc<RwLock<IndexWriter>>`. `add_document` and `delete_term` can
only require a read lock. (@fulmicoton)
Arc<RwLock<IndexWriter>>`. `add_document` and `delete_term` can
only require a read lock. (@pmasurel)
- Introducing `Opstamp` as an expressive type alias for `u64`. (@petr-tik)
- Stamper now relies on `AtomicU64` on all platforms (@petr-tik)
- Bugfix - Files get deleted slightly earlier
@@ -223,7 +88,7 @@ Your program should be usable as is.
Fast fields used to be accessed directly from the `SegmentReader`.
The API changed, you are now required to acquire your fast field reader via the
`segment_reader.fast_fields()`, and use one of the typed method:
`segment_reader.fast_fields()`, and use one of the typed method:
- `.u64()`, `.i64()` if your field is single-valued ;
- `.u64s()`, `.i64s()` if your field is multi-valued ;
- `.bytes()` if your field is bytes fast field.
@@ -232,16 +97,16 @@ The API changed, you are now required to acquire your fast field reader via the
Tantivy 0.9.0
=====================
*0.9.0 index format is not compatible with the
*0.9.0 index format is not compatible with the
previous index format.*
- MAJOR BUGFIX :
- MAJOR BUGFIX :
Some `Mmap` objects were being leaked, and would never get released. (@fulmicoton)
- Removed most unsafe (@fulmicoton)
- Indexer memory footprint improved. (VInt comp, inlining the first block. (@fulmicoton)
- Stemming in other language possible (@pentlander)
- Segments with no docs are deleted earlier (@barrotsteindev)
- Added grouped add and delete operations.
They are guaranteed to happen together (i.e. they cannot be split by a commit).
- Added grouped add and delete operations.
They are guaranteed to happen together (i.e. they cannot be split by a commit).
In addition, adds are guaranteed to happen on the same segment. (@elbow-jason)
- Removed `INT_STORED` and `INT_INDEXED`. It is now possible to use `STORED` and `INDEXED`
for int fields. (@fulmicoton)
@@ -255,26 +120,26 @@ tantivy 0.9 brought some API breaking change.
To update from tantivy 0.8, you will need to go through the following steps.
- `schema::INT_INDEXED` and `schema::INT_STORED` should be replaced by `schema::INDEXED` and `schema::INT_STORED`.
- The index now does not hold the pool of searcher anymore. You are required to create an intermediary object called
`IndexReader` for this.
- The index now does not hold the pool of searcher anymore. You are required to create an intermediary object called
`IndexReader` for this.
```rust
// create the reader. You typically need to create 1 reader for the entire
// lifetime of you program.
let reader = index.reader()?;
// Acquire a searcher (previously `index.searcher()`) is now written:
let searcher = reader.searcher();
// With the default setting of the reader, you are not required to
// With the default setting of the reader, you are not required to
// call `index.load_searchers()` anymore.
//
// The IndexReader will pick up that change automatically, regardless
// of whether the update was done in a different process or not.
// If this behavior is not wanted, you can create your reader with
// If this behavior is not wanted, you can create your reader with
// the `ReloadPolicy::Manual`, and manually decide when to reload the index
// by calling `reader.reload()?`.
```
@@ -289,7 +154,7 @@ Tantivy 0.8.1
=====================
Hotfix of #476.
Merge was reflecting deletes before commit was passed.
Merge was reflecting deletes before commit was passed.
Thanks @barrotsteindev for reporting the bug.
@@ -297,7 +162,7 @@ Tantivy 0.8.0
=====================
*No change in the index format*
- API Breaking change in the collector API. (@jwolfe, @fulmicoton)
- Multithreaded search (@jwolfe, @fulmicoton)
- Multithreaded search (@jwolfe, @fulmicoton)
Tantivy 0.7.1
@@ -325,7 +190,7 @@ Tantivy 0.6.1
- Exclusive `field:{startExcl to endExcl}`
- Mixed `field:[startIncl to endExcl}` and vice versa
- Unbounded `field:[start to *]`, `field:[* to end]`
Tantivy 0.6
==========================
@@ -333,10 +198,10 @@ Tantivy 0.6
Special thanks to @drusellers and @jason-wolfe for their contributions
to this release!
- Removed C code. Tantivy is now pure Rust. (@fulmicoton)
- BM25 (@fulmicoton)
- Approximate field norms encoded over 1 byte. (@fulmicoton)
- Compiles on stable rust (@fulmicoton)
- Removed C code. Tantivy is now pure Rust. (@pmasurel)
- BM25 (@pmasurel)
- Approximate field norms encoded over 1 byte. (@pmasurel)
- Compiles on stable rust (@pmasurel)
- Add &[u8] fastfield for associating arbitrary bytes to each document (@jason-wolfe) (#270)
- Completely uncompressed
- Internally: One u64 fast field for indexes, one fast field for the bytes themselves.
@@ -344,7 +209,7 @@ to this release!
- Add Stopword Filter support (@drusellers)
- Add a FuzzyTermQuery (@drusellers)
- Add a RegexQuery (@drusellers)
- Various performance improvements (@fulmicoton)_
- Various performance improvements (@pmasurel)_
Tantivy 0.5.2

View File

@@ -1,77 +1,69 @@
[package]
name = "tantivy"
version = "0.17.0-dev"
version = "0.11.3"
authors = ["Paul Masurel <paul.masurel@gmail.com>"]
license = "MIT"
categories = ["database-implementations", "data-structures"]
description = """Search engine library"""
documentation = "https://docs.rs/tantivy/"
homepage = "https://github.com/quickwit-oss/tantivy"
repository = "https://github.com/quickwit-oss/tantivy"
documentation = "https://tantivy-search.github.io/tantivy/tantivy/index.html"
homepage = "https://github.com/tantivy-search/tantivy"
repository = "https://github.com/tantivy-search/tantivy"
readme = "README.md"
keywords = ["search", "information", "retrieval"]
edition = "2018"
[dependencies]
base64 = "0.13"
byteorder = "1.4.3"
crc32fast = "1.2.1"
once_cell = "1.7.2"
regex ={ version = "1.5.4", default-features = false, features = ["std"] }
tantivy-fst = "0.3"
memmap2 = {version = "0.5", optional=true}
lz4_flex = { version = "0.9", default-features = false, features = ["checked-decode"], optional = true }
brotli = { version = "3.3", optional = true }
snap = { version = "1.0.5", optional = true }
tempfile = { version = "3.2", optional = true }
log = "0.4.14"
serde = { version = "1.0.126", features = ["derive"] }
serde_json = "1.0.64"
num_cpus = "1.13"
fs2={ version = "0.4.3", optional = true }
levenshtein_automata = "0.2"
uuid = { version = "0.8.2", features = ["v4", "serde"] }
crossbeam = "0.8.1"
futures = { version = "0.3.15", features = ["thread-pool"] }
tantivy-query-grammar = { version="0.15.0", path="./query-grammar" }
tantivy-bitpacker = { version="0.1", path="./bitpacker" }
common = { version = "0.1", path = "./common/", package = "tantivy-common" }
fastfield_codecs = { version="0.1", path="./fastfield_codecs", default-features = false }
ownedbytes = { version="0.2", path="./ownedbytes" }
stable_deref_trait = "1.2"
base64 = "0.11.0"
byteorder = "1.0"
crc32fast = "1.2.0"
once_cell = "1.0"
regex ={version = "1.3.0", default-features = false, features = ["std"]}
tantivy-fst = "0.1"
memmap = {version = "0.7", optional=true}
lz4 = {version="1.20", optional=true}
snap = {version="0.2"}
atomicwrites = {version="0.2.2", optional=true}
tempfile = "3.0"
log = "0.4"
serde = "1.0"
serde_derive = "1.0"
serde_json = "1.0"
num_cpus = "1.2"
fs2={version="0.4", optional=true}
itertools = "0.8"
levenshtein_automata = {version="0.1", features=["fst_automaton"]}
notify = {version="4", optional=true}
uuid = { version = "0.8", features = ["v4", "serde"] }
crossbeam = "0.7"
futures = {version = "0.3", features=["thread-pool"] }
owning_ref = "0.4"
stable_deref_trait = "1.0.0"
rust-stemmers = "1.2"
downcast-rs = "1.2"
bitpacking = { version = "0.8.4", default-features = false, features = ["bitpacker4x"] }
downcast-rs = { version="1.0" }
tantivy-query-grammar = { version="0.11", path="./query-grammar" }
bitpacking = {version="0.8", default-features = false, features=["bitpacker4x"]}
census = "0.4"
fnv = "1.0.7"
thiserror = "1.0.24"
fnv = "1.0.6"
owned-read = "0.4"
failure = "0.1"
htmlescape = "0.3.1"
fail = "0.5"
fail = "0.3"
murmurhash32 = "0.2"
chrono = "0.4.19"
smallvec = "1.6.1"
rayon = "1.5"
lru = "0.7.0"
fastdivide = "0.4"
itertools = "0.10.0"
measure_time = "0.8.0"
pretty_assertions = "1.1.0"
chrono = "0.4"
smallvec = "1.0"
rayon = "1"
[target.'cfg(windows)'.dependencies]
winapi = "0.3.9"
winapi = "0.3"
[dev-dependencies]
rand = "0.8.3"
maplit = "1.0.2"
rand = "0.7"
maplit = "1"
matches = "0.1.8"
proptest = "1.0"
criterion = "0.3.5"
test-log = "0.2.8"
env_logger = "0.9.0"
pprof = {version= "0.6", features=["flamegraph", "criterion"]}
time = "0.1.42"
[dev-dependencies.fail]
version = "0.5"
version = "0.3"
features = ["failpoints"]
[profile.release]
@@ -84,18 +76,18 @@ debug-assertions = true
overflow-checks = true
[features]
default = ["mmap", "lz4-compression" ]
mmap = ["fs2", "tempfile", "memmap2"]
brotli-compression = ["brotli"]
lz4-compression = ["lz4_flex"]
snappy-compression = ["snap"]
default = ["mmap"]
mmap = ["atomicwrites", "fs2", "memmap", "notify"]
lz4-compression = ["lz4"]
failpoints = ["fail/failpoints"]
unstable = [] # useful for benches.
wasm-bindgen = ["uuid/wasm-bindgen"]
[workspace]
members = ["query-grammar", "bitpacker", "common", "fastfield_codecs", "ownedbytes"]
members = ["query-grammar"]
[badges]
travis-ci = { repository = "tantivy-search/tantivy" }
# Following the "fail" crate best practises, we isolate
# tests that define specific behavior in fail check points
@@ -108,12 +100,3 @@ members = ["query-grammar", "bitpacker", "common", "fastfield_codecs", "ownedbyt
name = "failpoints"
path = "tests/failpoints/mod.rs"
required-features = ["fail/failpoints"]
[[bench]]
name = "analyzer"
harness = false
[[bench]]
name = "index-bench"
harness = false

View File

@@ -1,6 +1,3 @@
test:
echo "Run test only... No examples."
cargo test --tests --lib
fmt:
cargo +nightly fmt --all

View File

@@ -1,13 +1,26 @@
[![Docs](https://docs.rs/tantivy/badge.svg)](https://docs.rs/crate/tantivy/)
[![Build Status](https://github.com/quickwit-oss/tantivy/actions/workflows/test.yml/badge.svg)](https://github.com/quickwit-oss/tantivy/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/quickwit-oss/tantivy/branch/main/graph/badge.svg)](https://codecov.io/gh/quickwit-oss/tantivy)
[![Join the chat at https://discord.gg/MT27AG5EVE](https://shields.io/discord/908281611840282624?label=chat%20on%20discord)](https://discord.gg/MT27AG5EVE)
[![Build Status](https://travis-ci.org/tantivy-search/tantivy.svg?branch=master)](https://travis-ci.org/tantivy-search/tantivy)
[![codecov](https://codecov.io/gh/tantivy-search/tantivy/branch/master/graph/badge.svg)](https://codecov.io/gh/tantivy-search/tantivy)
[![Join the chat at https://gitter.im/tantivy-search/tantivy](https://badges.gitter.im/tantivy-search/tantivy.svg)](https://gitter.im/tantivy-search/tantivy?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Build status](https://ci.appveyor.com/api/projects/status/r7nb13kj23u8m9pj/branch/master?svg=true)](https://ci.appveyor.com/project/fulmicoton/tantivy/branch/master)
[![Crates.io](https://img.shields.io/crates/v/tantivy.svg)](https://crates.io/crates/tantivy)
[![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/fulmicoton)
![Tantivy](https://tantivy-search.github.io/logo/tantivy-logo.png)
[![](https://sourcerer.io/fame/fulmicoton/tantivy-search/tantivy/images/0)](https://sourcerer.io/fame/fulmicoton/tantivy-search/tantivy/links/0)
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**Tantivy** is a **full text search engine library** written in Rust.
It is closer to [Apache Lucene](https://lucene.apache.org/) than to [Elasticsearch](https://www.elastic.co/products/elasticsearch) or [Apache Solr](https://lucene.apache.org/solr/) in the sense it is not
@@ -18,15 +31,16 @@ Tantivy is, in fact, strongly inspired by Lucene's design.
# Benchmark
The following [benchmark](https://tantivy-search.github.io/bench/) break downs
performance for different type of queries / collection.
Tantivy is typically faster than Lucene, but the results depend on
the nature of the queries in your workload.
Your mileage WILL vary depending on the nature of queries and their load.
The following [benchmark](https://tantivy-search.github.io/bench/) break downs
performance for different type of queries / collection.
# Features
- Full-text search
- Configurable tokenizer (stemming available for 17 Latin languages with third party support for Chinese ([tantivy-jieba](https://crates.io/crates/tantivy-jieba) and [cang-jie](https://crates.io/crates/cang-jie)), Japanese ([lindera](https://github.com/lindera-morphology/lindera-tantivy), [Vaporetto](https://crates.io/crates/vaporetto_tantivy), and [tantivy-tokenizer-tiny-segmenter](https://crates.io/crates/tantivy-tokenizer-tiny-segmenter)) and Korean ([lindera](https://github.com/lindera-morphology/lindera-tantivy) + [lindera-ko-dic-builder](https://github.com/lindera-morphology/lindera-ko-dic-builder))
- Configurable tokenizer (stemming available for 17 Latin languages with third party support for Chinese ([tantivy-jieba](https://crates.io/crates/tantivy-jieba) and [cang-jie](https://crates.io/crates/cang-jie)) and [Japanese](https://crates.io/crates/tantivy-tokenizer-tiny-segmenter))
- Fast (check out the :racehorse: :sparkles: [benchmark](https://tantivy-search.github.io/bench/) :sparkles: :racehorse:)
- Tiny startup time (<10ms), perfect for command line tools
- BM25 scoring (the same as Lucene)
@@ -45,33 +59,35 @@ Your mileage WILL vary depending on the nature of queries and their load.
- Configurable indexing (optional term frequency and position indexing)
- Cheesy logo with a horse
## Non-features
# Non-features
- Distributed search is out of the scope of Tantivy. That being said, Tantivy is a
library upon which one could build a distributed search. Serializable/mergeable collector state for instance,
library upon which one could build a distributed search. Serializable/mergeable collector state for instance,
are within the scope of Tantivy.
# Getting started
# Supported OS and compiler
Tantivy works on stable Rust (>= 1.27) and supports Linux, MacOS, and Windows.
# Getting started
- [Tantivy's simple search example](https://tantivy-search.github.io/examples/basic_search.html)
- [tantivy-cli and its tutorial](https://github.com/quickwit-oss/tantivy-cli) - `tantivy-cli` is an actual command line interface that makes it easy for you to create a search engine,
- [tantivy-cli and its tutorial](https://github.com/tantivy-search/tantivy-cli) - `tantivy-cli` is an actual command line interface that makes it easy for you to create a search engine,
index documents, and search via the CLI or a small server with a REST API.
It walks you through getting a wikipedia search engine up and running in a few minutes.
- [Reference doc for the last released version](https://docs.rs/tantivy/)
# How can I support this project?
There are many ways to support this project.
There are many ways to support this project.
- Use Tantivy and tell us about your experience on [Discord](https://discord.gg/MT27AG5EVE) or by email (paul.masurel@gmail.com)
- Use Tantivy and tell us about your experience on [Gitter](https://gitter.im/tantivy-search/tantivy) or by email (paul.masurel@gmail.com)
- Report bugs
- Write a blog post
- Help with documentation by asking questions or submitting PRs
- Contribute code (you can join [our Discord server](https://discord.gg/MT27AG5EVE))
- Contribute code (you can join [our Gitter](https://gitter.im/tantivy-search/tantivy))
- Talk about Tantivy around you
- Drop a word on on [![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/fulmicoton) or even [![Become a patron](https://c5.patreon.com/external/logo/become_a_patron_button.png)](https://www.patreon.com/fulmicoton)
# Contributing code
@@ -83,7 +99,7 @@ Tantivy compiles on stable Rust but requires `Rust >= 1.27`.
To check out and run tests, you can simply run:
```bash
git clone https://github.com/quickwit-oss/tantivy.git
git clone https://github.com/tantivy-search/tantivy.git
cd tantivy
cargo build
```

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@@ -18,6 +18,5 @@ install:
build: false
test_script:
- REM SET RUST_LOG=tantivy,test & cargo test --all --verbose --no-default-features --features lz4-compression --features mmap
- REM SET RUST_LOG=tantivy,test & cargo test test_store --verbose --no-default-features --features lz4-compression --features snappy-compression --features brotli-compression --features mmap
- REM SET RUST_LOG=tantivy,test & cargo test --verbose --no-default-features --features mmap
- REM SET RUST_BACKTRACE=1 & cargo build --examples

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@@ -1,22 +0,0 @@
use criterion::{criterion_group, criterion_main, Criterion};
use tantivy::tokenizer::TokenizerManager;
const ALICE_TXT: &str = include_str!("alice.txt");
pub fn criterion_benchmark(c: &mut Criterion) {
let tokenizer_manager = TokenizerManager::default();
let tokenizer = tokenizer_manager.get("default").unwrap();
c.bench_function("default-tokenize-alice", |b| {
b.iter(|| {
let mut word_count = 0;
let mut token_stream = tokenizer.token_stream(ALICE_TXT);
while token_stream.advance() {
word_count += 1;
}
assert_eq!(word_count, 30_731);
})
});
}
criterion_group!(benches, criterion_benchmark);
criterion_main!(benches);

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@@ -1,79 +0,0 @@
use criterion::{criterion_group, criterion_main, Criterion};
use pprof::criterion::{Output, PProfProfiler};
use tantivy::schema::{INDEXED, STORED, STRING, TEXT};
use tantivy::Index;
const HDFS_LOGS: &str = include_str!("hdfs.json");
pub fn hdfs_index_benchmark(c: &mut Criterion) {
let schema = {
let mut schema_builder = tantivy::schema::SchemaBuilder::new();
schema_builder.add_u64_field("timestamp", INDEXED);
schema_builder.add_text_field("body", TEXT);
schema_builder.add_text_field("severity", STRING);
schema_builder.build()
};
let schema_with_store = {
let mut schema_builder = tantivy::schema::SchemaBuilder::new();
schema_builder.add_u64_field("timestamp", INDEXED | STORED);
schema_builder.add_text_field("body", TEXT | STORED);
schema_builder.add_text_field("severity", STRING | STORED);
schema_builder.build()
};
let mut group = c.benchmark_group("index-hdfs");
group.sample_size(20);
group.bench_function("index-hdfs-no-commit", |b| {
b.iter(|| {
let index = Index::create_in_ram(schema.clone());
let index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for _ in 0..10 {
for doc_json in HDFS_LOGS.trim().split("\n") {
let doc = schema.parse_document(doc_json).unwrap();
index_writer.add_document(doc).unwrap();
}
}
})
});
group.bench_function("index-hdfs-with-commit", |b| {
b.iter(|| {
let index = Index::create_in_ram(schema.clone());
let mut index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for _ in 0..10 {
for doc_json in HDFS_LOGS.trim().split("\n") {
let doc = schema.parse_document(doc_json).unwrap();
index_writer.add_document(doc).unwrap();
}
}
index_writer.commit().unwrap();
})
});
group.bench_function("index-hdfs-no-commit-with-docstore", |b| {
b.iter(|| {
let index = Index::create_in_ram(schema_with_store.clone());
let index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for doc_json in HDFS_LOGS.trim().split("\n") {
let doc = schema.parse_document(doc_json).unwrap();
index_writer.add_document(doc).unwrap();
}
})
});
group.bench_function("index-hdfs-with-commit-with-docstore", |b| {
b.iter(|| {
let index = Index::create_in_ram(schema_with_store.clone());
let mut index_writer = index.writer_with_num_threads(1, 100_000_000).unwrap();
for doc_json in HDFS_LOGS.trim().split("\n") {
let doc = schema.parse_document(doc_json).unwrap();
index_writer.add_document(doc).unwrap();
}
index_writer.commit().unwrap();
})
});
}
criterion_group! {
name = benches;
config = Criterion::default().with_profiler(PProfProfiler::new(100, Output::Flamegraph(None)));
targets = hdfs_index_benchmark
}
criterion_main!(benches);

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@@ -1,15 +0,0 @@
[package]
name = "tantivy-bitpacker"
version = "0.1.1"
edition = "2018"
authors = ["Paul Masurel <paul.masurel@gmail.com>"]
license = "MIT"
categories = []
description = """Tantivy-sub crate: bitpacking"""
repository = "https://github.com/quickwit-oss/tantivy"
keywords = []
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[dependencies]

View File

@@ -1,33 +0,0 @@
#![feature(test)]
extern crate test;
#[cfg(test)]
mod tests {
use tantivy_bitpacker::BlockedBitpacker;
use test::Bencher;
#[bench]
fn bench_blockedbitp_read(b: &mut Bencher) {
let mut blocked_bitpacker = BlockedBitpacker::new();
for val in 0..=21500 {
blocked_bitpacker.add(val * val);
}
b.iter(|| {
let mut out = 0;
for val in 0..=21500 {
out = blocked_bitpacker.get(val);
}
out
});
}
#[bench]
fn bench_blockedbitp_create(b: &mut Bencher) {
b.iter(|| {
let mut blocked_bitpacker = BlockedBitpacker::new();
for val in 0..=21500 {
blocked_bitpacker.add(val * val);
}
blocked_bitpacker
});
}
}

View File

@@ -1,177 +0,0 @@
use super::bitpacker::BitPacker;
use super::compute_num_bits;
use crate::{minmax, BitUnpacker};
const BLOCK_SIZE: usize = 128;
/// `BlockedBitpacker` compresses data in blocks of
/// 128 elements, while keeping an index on it
#[derive(Debug, Clone)]
pub struct BlockedBitpacker {
// bitpacked blocks
compressed_blocks: Vec<u8>,
// uncompressed data, collected until BLOCK_SIZE
buffer: Vec<u64>,
offset_and_bits: Vec<BlockedBitpackerEntryMetaData>,
}
impl Default for BlockedBitpacker {
fn default() -> Self {
BlockedBitpacker::new()
}
}
/// `BlockedBitpackerEntryMetaData` encodes the
/// offset and bit_width into a u64 bit field
///
/// This saves some space, since 7byte is more
/// than enough and also keeps the access fast
/// because of alignment
#[derive(Debug, Clone, Default)]
struct BlockedBitpackerEntryMetaData {
encoded: u64,
base_value: u64,
}
impl BlockedBitpackerEntryMetaData {
fn new(offset: u64, num_bits: u8, base_value: u64) -> Self {
let encoded = offset | (num_bits as u64) << (64 - 8);
Self {
encoded,
base_value,
}
}
fn offset(&self) -> u64 {
(self.encoded << 8) >> 8
}
fn num_bits(&self) -> u8 {
(self.encoded >> 56) as u8
}
fn base_value(&self) -> u64 {
self.base_value
}
}
#[test]
fn metadata_test() {
let meta = BlockedBitpackerEntryMetaData::new(50000, 6, 40000);
assert_eq!(meta.offset(), 50000);
assert_eq!(meta.num_bits(), 6);
}
impl BlockedBitpacker {
pub fn new() -> Self {
let mut compressed_blocks = vec![];
compressed_blocks.resize(8, 0);
Self {
compressed_blocks,
buffer: vec![],
offset_and_bits: vec![],
}
}
/// The memory used (inclusive childs)
pub fn mem_usage(&self) -> usize {
std::mem::size_of::<BlockedBitpacker>()
+ self.compressed_blocks.capacity()
+ self.offset_and_bits.capacity()
* std::mem::size_of_val(&self.offset_and_bits.get(0).cloned().unwrap_or_default())
+ self.buffer.capacity()
* std::mem::size_of_val(&self.buffer.get(0).cloned().unwrap_or_default())
}
#[inline]
pub fn add(&mut self, val: u64) {
self.buffer.push(val);
if self.buffer.len() == BLOCK_SIZE as usize {
self.flush();
}
}
pub fn flush(&mut self) {
if let Some((min_value, max_value)) = minmax(self.buffer.iter()) {
let mut bit_packer = BitPacker::new();
let num_bits_block = compute_num_bits(*max_value - min_value);
// todo performance: the padding handling could be done better, e.g. use a slice and
// return num_bytes written from bitpacker
self.compressed_blocks
.resize(self.compressed_blocks.len() - 8, 0); // remove padding for bitpacker
let offset = self.compressed_blocks.len() as u64;
// todo performance: for some bit_width we
// can encode multiple vals into the
// mini_buffer before checking to flush
// (to be done in BitPacker)
for val in self.buffer.iter() {
bit_packer
.write(
*val - min_value,
num_bits_block,
&mut self.compressed_blocks,
)
.expect("cannot write bitpacking to output"); // write to in memory can't fail
}
bit_packer.flush(&mut self.compressed_blocks).unwrap();
self.offset_and_bits
.push(BlockedBitpackerEntryMetaData::new(
offset,
num_bits_block,
*min_value,
));
self.buffer.clear();
self.compressed_blocks
.resize(self.compressed_blocks.len() + 8, 0); // add padding for bitpacker
}
}
#[inline]
pub fn get(&self, idx: usize) -> u64 {
let metadata_pos = idx / BLOCK_SIZE as usize;
let pos_in_block = idx % BLOCK_SIZE as usize;
if let Some(metadata) = self.offset_and_bits.get(metadata_pos) {
let unpacked = BitUnpacker::new(metadata.num_bits()).get(
pos_in_block as u64,
&self.compressed_blocks[metadata.offset() as usize..],
);
unpacked + metadata.base_value()
} else {
self.buffer[pos_in_block]
}
}
pub fn iter(&self) -> impl Iterator<Item = u64> + '_ {
// todo performance: we could decompress a whole block and cache it instead
let bitpacked_elems = self.offset_and_bits.len() * BLOCK_SIZE;
let iter = (0..bitpacked_elems)
.map(move |idx| self.get(idx))
.chain(self.buffer.iter().cloned());
iter
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn blocked_bitpacker_empty() {
let blocked_bitpacker = BlockedBitpacker::new();
assert_eq!(blocked_bitpacker.iter().collect::<Vec<u64>>(), vec![]);
}
#[test]
fn blocked_bitpacker_one() {
let mut blocked_bitpacker = BlockedBitpacker::new();
blocked_bitpacker.add(50000);
assert_eq!(blocked_bitpacker.get(0), 50000);
assert_eq!(blocked_bitpacker.iter().collect::<Vec<u64>>(), vec![50000]);
}
#[test]
fn blocked_bitpacker_test() {
let mut blocked_bitpacker = BlockedBitpacker::new();
for val in 0..21500 {
blocked_bitpacker.add(val);
}
for val in 0..21500 {
assert_eq!(blocked_bitpacker.get(val as usize), val);
}
assert_eq!(blocked_bitpacker.iter().count(), 21500);
assert_eq!(blocked_bitpacker.iter().last().unwrap(), 21499);
}
}

View File

@@ -1,80 +0,0 @@
mod bitpacker;
mod blocked_bitpacker;
pub use crate::bitpacker::{BitPacker, BitUnpacker};
pub use crate::blocked_bitpacker::BlockedBitpacker;
/// Computes the number of bits that will be used for bitpacking.
///
/// In general the target is the minimum number of bits
/// required to express the amplitude given in argument.
///
/// e.g. If the amplitude is 10, we can store all ints on simply 4bits.
///
/// The logic is slightly more convoluted here as for optimization
/// reasons, we want to ensure that a value spawns over at most 8 bytes
/// of aligned bytes.
///
/// Spanning over 9 bytes is possible for instance, if we do
/// bitpacking with an amplitude of 63 bits.
/// In this case, the second int will start on bit
/// 63 (which belongs to byte 7) and ends at byte 15;
/// Hence 9 bytes (from byte 7 to byte 15 included).
///
/// To avoid this, we force the number of bits to 64bits
/// when the result is greater than `64-8 = 56 bits`.
///
/// Note that this only affects rare use cases spawning over
/// a very large range of values. Even in this case, it results
/// in an extra cost of at most 12% compared to the optimal
/// number of bits.
pub fn compute_num_bits(n: u64) -> u8 {
let amplitude = (64u32 - n.leading_zeros()) as u8;
if amplitude <= 64 - 8 {
amplitude
} else {
64
}
}
pub fn minmax<I, T>(mut vals: I) -> Option<(T, T)>
where
I: Iterator<Item = T>,
T: Copy + Ord,
{
if let Some(first_el) = vals.next() {
return Some(vals.fold((first_el, first_el), |(min_val, max_val), el| {
(min_val.min(el), max_val.max(el))
}));
}
None
}
#[test]
fn test_compute_num_bits() {
assert_eq!(compute_num_bits(1), 1u8);
assert_eq!(compute_num_bits(0), 0u8);
assert_eq!(compute_num_bits(2), 2u8);
assert_eq!(compute_num_bits(3), 2u8);
assert_eq!(compute_num_bits(4), 3u8);
assert_eq!(compute_num_bits(255), 8u8);
assert_eq!(compute_num_bits(256), 9u8);
assert_eq!(compute_num_bits(5_000_000_000), 33u8);
}
#[test]
fn test_minmax_empty() {
let vals: Vec<u32> = vec![];
assert_eq!(minmax(vals.into_iter()), None);
}
#[test]
fn test_minmax_one() {
assert_eq!(minmax(vec![1].into_iter()), Some((1, 1)));
}
#[test]
fn test_minmax_two() {
assert_eq!(minmax(vec![1, 2].into_iter()), Some((1, 2)));
assert_eq!(minmax(vec![2, 1].into_iter()), Some((1, 2)));
}

View File

@@ -1,17 +0,0 @@
[package]
name = "tantivy-common"
version = "0.1.0"
authors = ["Paul Masurel <paul@quickwit.io>", "Pascal Seitz <pascal@quickwit.io>"]
license = "MIT"
edition = "2018"
description = "common traits and utility functions used by multiple tantivy subcrates"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[dependencies]
byteorder = "1.4.3"
ownedbytes = { version="0.2", path="../ownedbytes" }
[dev-dependencies]
proptest = "1.0.0"
rand = "0.8.4"

View File

@@ -1,745 +0,0 @@
use std::convert::TryInto;
use std::io::Write;
use std::{fmt, io, u64};
use ownedbytes::OwnedBytes;
#[derive(Clone, Copy, Eq, PartialEq)]
pub struct TinySet(u64);
impl fmt::Debug for TinySet {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
self.into_iter().collect::<Vec<u32>>().fmt(f)
}
}
pub struct TinySetIterator(TinySet);
impl Iterator for TinySetIterator {
type Item = u32;
#[inline]
fn next(&mut self) -> Option<Self::Item> {
self.0.pop_lowest()
}
}
impl IntoIterator for TinySet {
type Item = u32;
type IntoIter = TinySetIterator;
fn into_iter(self) -> Self::IntoIter {
TinySetIterator(self)
}
}
impl TinySet {
pub fn serialize<T: Write>(&self, writer: &mut T) -> io::Result<()> {
writer.write_all(self.0.to_le_bytes().as_ref())
}
pub fn into_bytes(self) -> [u8; 8] {
self.0.to_le_bytes()
}
#[inline]
pub fn deserialize(data: [u8; 8]) -> Self {
let val: u64 = u64::from_le_bytes(data);
TinySet(val)
}
/// Returns an empty `TinySet`.
#[inline]
pub fn empty() -> TinySet {
TinySet(0u64)
}
/// Returns a full `TinySet`.
#[inline]
pub fn full() -> TinySet {
TinySet::empty().complement()
}
pub fn clear(&mut self) {
self.0 = 0u64;
}
/// Returns the complement of the set in `[0, 64[`.
///
/// Careful on making this function public, as it will break the padding handling in the last
/// bucket.
#[inline]
fn complement(self) -> TinySet {
TinySet(!self.0)
}
/// Returns true iff the `TinySet` contains the element `el`.
#[inline]
pub fn contains(self, el: u32) -> bool {
!self.intersect(TinySet::singleton(el)).is_empty()
}
/// Returns the number of elements in the TinySet.
#[inline]
pub fn len(self) -> u32 {
self.0.count_ones()
}
/// Returns the intersection of `self` and `other`
#[inline]
#[must_use]
pub fn intersect(self, other: TinySet) -> TinySet {
TinySet(self.0 & other.0)
}
/// Creates a new `TinySet` containing only one element
/// within `[0; 64[`
#[inline]
pub fn singleton(el: u32) -> TinySet {
TinySet(1u64 << u64::from(el))
}
/// Insert a new element within [0..64)
#[inline]
#[must_use]
pub fn insert(self, el: u32) -> TinySet {
self.union(TinySet::singleton(el))
}
/// Removes an element within [0..64)
#[inline]
#[must_use]
pub fn remove(self, el: u32) -> TinySet {
self.intersect(TinySet::singleton(el).complement())
}
/// Insert a new element within [0..64)
///
/// returns true if the set changed
#[inline]
pub fn insert_mut(&mut self, el: u32) -> bool {
let old = *self;
*self = old.insert(el);
old != *self
}
/// Remove a element within [0..64)
///
/// returns true if the set changed
#[inline]
pub fn remove_mut(&mut self, el: u32) -> bool {
let old = *self;
*self = old.remove(el);
old != *self
}
/// Returns the union of two tinysets
#[inline]
#[must_use]
pub fn union(self, other: TinySet) -> TinySet {
TinySet(self.0 | other.0)
}
/// Returns true iff the `TinySet` is empty.
#[inline]
pub fn is_empty(self) -> bool {
self.0 == 0u64
}
/// Returns the lowest element in the `TinySet`
/// and removes it.
#[inline]
pub fn pop_lowest(&mut self) -> Option<u32> {
if self.is_empty() {
None
} else {
let lowest = self.0.trailing_zeros() as u32;
self.0 ^= TinySet::singleton(lowest).0;
Some(lowest)
}
}
/// Returns a `TinySet` than contains all values up
/// to limit excluded.
///
/// The limit is assumed to be strictly lower than 64.
pub fn range_lower(upper_bound: u32) -> TinySet {
TinySet((1u64 << u64::from(upper_bound % 64u32)) - 1u64)
}
/// Returns a `TinySet` that contains all values greater
/// or equal to the given limit, included. (and up to 63)
///
/// The limit is assumed to be strictly lower than 64.
pub fn range_greater_or_equal(from_included: u32) -> TinySet {
TinySet::range_lower(from_included).complement()
}
}
#[derive(Clone)]
pub struct BitSet {
tinysets: Box<[TinySet]>,
len: u64,
max_value: u32,
}
fn num_buckets(max_val: u32) -> u32 {
(max_val + 63u32) / 64u32
}
impl BitSet {
/// serialize a `BitSet`.
pub fn serialize<T: Write>(&self, writer: &mut T) -> io::Result<()> {
writer.write_all(self.max_value.to_le_bytes().as_ref())?;
for tinyset in self.tinysets.iter().cloned() {
writer.write_all(&tinyset.into_bytes())?;
}
writer.flush()?;
Ok(())
}
/// Create a new `BitSet` that may contain elements
/// within `[0, max_val)`.
pub fn with_max_value(max_value: u32) -> BitSet {
let num_buckets = num_buckets(max_value);
let tinybitsets = vec![TinySet::empty(); num_buckets as usize].into_boxed_slice();
BitSet {
tinysets: tinybitsets,
len: 0,
max_value,
}
}
/// Create a new `BitSet` that may contain elements. Initially all values will be set.
/// within `[0, max_val)`.
pub fn with_max_value_and_full(max_value: u32) -> BitSet {
let num_buckets = num_buckets(max_value);
let mut tinybitsets = vec![TinySet::full(); num_buckets as usize].into_boxed_slice();
// Fix padding
let lower = max_value % 64u32;
if lower != 0 {
tinybitsets[tinybitsets.len() - 1] = TinySet::range_lower(lower);
}
BitSet {
tinysets: tinybitsets,
len: max_value as u64,
max_value,
}
}
/// Removes all elements from the `BitSet`.
pub fn clear(&mut self) {
for tinyset in self.tinysets.iter_mut() {
*tinyset = TinySet::empty();
}
}
/// Intersect with serialized bitset
pub fn intersect_update(&mut self, other: &ReadOnlyBitSet) {
self.intersect_update_with_iter(other.iter_tinysets());
}
/// Intersect with tinysets
fn intersect_update_with_iter(&mut self, other: impl Iterator<Item = TinySet>) {
self.len = 0;
for (left, right) in self.tinysets.iter_mut().zip(other) {
*left = left.intersect(right);
self.len += left.len() as u64;
}
}
/// Returns the number of elements in the `BitSet`.
#[inline]
pub fn len(&self) -> usize {
self.len as usize
}
/// Inserts an element in the `BitSet`
#[inline]
pub fn insert(&mut self, el: u32) {
// we do not check saturated els.
let higher = el / 64u32;
let lower = el % 64u32;
self.len += if self.tinysets[higher as usize].insert_mut(lower) {
1
} else {
0
};
}
/// Inserts an element in the `BitSet`
#[inline]
pub fn remove(&mut self, el: u32) {
// we do not check saturated els.
let higher = el / 64u32;
let lower = el % 64u32;
self.len -= if self.tinysets[higher as usize].remove_mut(lower) {
1
} else {
0
};
}
/// Returns true iff the elements is in the `BitSet`.
#[inline]
pub fn contains(&self, el: u32) -> bool {
self.tinyset(el / 64u32).contains(el % 64)
}
/// Returns the first non-empty `TinySet` associated to a bucket lower
/// or greater than bucket.
///
/// Reminder: the tiny set with the bucket `bucket`, represents the
/// elements from `bucket * 64` to `(bucket+1) * 64`.
pub fn first_non_empty_bucket(&self, bucket: u32) -> Option<u32> {
self.tinysets[bucket as usize..]
.iter()
.cloned()
.position(|tinyset| !tinyset.is_empty())
.map(|delta_bucket| bucket + delta_bucket as u32)
}
#[inline]
pub fn max_value(&self) -> u32 {
self.max_value
}
/// Returns the tiny bitset representing the
/// the set restricted to the number range from
/// `bucket * 64` to `(bucket + 1) * 64`.
pub fn tinyset(&self, bucket: u32) -> TinySet {
self.tinysets[bucket as usize]
}
}
/// Serialized BitSet.
#[derive(Clone)]
pub struct ReadOnlyBitSet {
data: OwnedBytes,
max_value: u32,
}
pub fn intersect_bitsets(left: &ReadOnlyBitSet, other: &ReadOnlyBitSet) -> ReadOnlyBitSet {
assert_eq!(left.max_value(), other.max_value());
assert_eq!(left.data.len(), other.data.len());
let union_tinyset_it = left
.iter_tinysets()
.zip(other.iter_tinysets())
.map(|(left_tinyset, right_tinyset)| left_tinyset.intersect(right_tinyset));
let mut output_dataset: Vec<u8> = Vec::with_capacity(left.data.len());
for tinyset in union_tinyset_it {
output_dataset.extend_from_slice(&tinyset.into_bytes());
}
ReadOnlyBitSet {
data: OwnedBytes::new(output_dataset),
max_value: left.max_value(),
}
}
impl ReadOnlyBitSet {
pub fn open(data: OwnedBytes) -> Self {
let (max_value_data, data) = data.split(4);
assert_eq!(data.len() % 8, 0);
let max_value: u32 = u32::from_le_bytes(max_value_data.as_ref().try_into().unwrap());
ReadOnlyBitSet { data, max_value }
}
/// Number of elements in the bitset.
#[inline]
pub fn len(&self) -> usize {
self.iter_tinysets()
.map(|tinyset| tinyset.len() as usize)
.sum()
}
/// Iterate the tinyset on the fly from serialized data.
#[inline]
fn iter_tinysets(&self) -> impl Iterator<Item = TinySet> + '_ {
self.data.chunks_exact(8).map(move |chunk| {
let tinyset: TinySet = TinySet::deserialize(chunk.try_into().unwrap());
tinyset
})
}
/// Iterate over the positions of the elements.
#[inline]
pub fn iter(&self) -> impl Iterator<Item = u32> + '_ {
self.iter_tinysets()
.enumerate()
.flat_map(move |(chunk_num, tinyset)| {
let chunk_base_val = chunk_num as u32 * 64;
tinyset
.into_iter()
.map(move |val| val + chunk_base_val)
.take_while(move |doc| *doc < self.max_value)
})
}
/// Returns true iff the elements is in the `BitSet`.
#[inline]
pub fn contains(&self, el: u32) -> bool {
let byte_offset = el / 8u32;
let b: u8 = self.data[byte_offset as usize];
let shift = (el % 8) as u8;
b & (1u8 << shift) != 0
}
/// Maximum value the bitset may contain.
/// (Note this is not the maximum value contained in the set.)
///
/// A bitset has an intrinsic capacity.
/// It only stores elements within [0..max_value).
#[inline]
pub fn max_value(&self) -> u32 {
self.max_value
}
/// Number of bytes used in the bitset representation.
pub fn num_bytes(&self) -> usize {
self.data.len()
}
}
impl<'a> From<&'a BitSet> for ReadOnlyBitSet {
fn from(bitset: &'a BitSet) -> ReadOnlyBitSet {
let mut buffer = Vec::with_capacity(bitset.tinysets.len() * 8 + 4);
bitset
.serialize(&mut buffer)
.expect("serializing into a buffer should never fail");
ReadOnlyBitSet::open(OwnedBytes::new(buffer))
}
}
#[cfg(test)]
mod tests {
use std::collections::HashSet;
use ownedbytes::OwnedBytes;
use rand::distributions::Bernoulli;
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};
use super::{BitSet, ReadOnlyBitSet, TinySet};
#[test]
fn test_read_serialized_bitset_full_multi() {
for i in 0..1000 {
let bitset = BitSet::with_max_value_and_full(i);
let mut out = vec![];
bitset.serialize(&mut out).unwrap();
let bitset = ReadOnlyBitSet::open(OwnedBytes::new(out));
assert_eq!(bitset.len() as usize, i as usize);
}
}
#[test]
fn test_read_serialized_bitset_full_block() {
let bitset = BitSet::with_max_value_and_full(64);
let mut out = vec![];
bitset.serialize(&mut out).unwrap();
let bitset = ReadOnlyBitSet::open(OwnedBytes::new(out));
assert_eq!(bitset.len() as usize, 64);
}
#[test]
fn test_read_serialized_bitset_full() {
let mut bitset = BitSet::with_max_value_and_full(5);
bitset.remove(3);
let mut out = vec![];
bitset.serialize(&mut out).unwrap();
let bitset = ReadOnlyBitSet::open(OwnedBytes::new(out));
assert_eq!(bitset.len(), 4);
}
#[test]
fn test_bitset_intersect() {
let bitset_serialized = {
let mut bitset = BitSet::with_max_value_and_full(5);
bitset.remove(1);
bitset.remove(3);
let mut out = vec![];
bitset.serialize(&mut out).unwrap();
ReadOnlyBitSet::open(OwnedBytes::new(out))
};
let mut bitset = BitSet::with_max_value_and_full(5);
bitset.remove(1);
bitset.intersect_update(&bitset_serialized);
assert!(bitset.contains(0));
assert!(!bitset.contains(1));
assert!(bitset.contains(2));
assert!(!bitset.contains(3));
assert!(bitset.contains(4));
bitset.intersect_update_with_iter(vec![TinySet::singleton(0)].into_iter());
assert!(bitset.contains(0));
assert!(!bitset.contains(1));
assert!(!bitset.contains(2));
assert!(!bitset.contains(3));
assert!(!bitset.contains(4));
assert_eq!(bitset.len(), 1);
bitset.intersect_update_with_iter(vec![TinySet::singleton(1)].into_iter());
assert!(!bitset.contains(0));
assert!(!bitset.contains(1));
assert!(!bitset.contains(2));
assert!(!bitset.contains(3));
assert!(!bitset.contains(4));
assert_eq!(bitset.len(), 0);
}
#[test]
fn test_read_serialized_bitset_empty() {
let mut bitset = BitSet::with_max_value(5);
bitset.insert(3);
let mut out = vec![];
bitset.serialize(&mut out).unwrap();
let bitset = ReadOnlyBitSet::open(OwnedBytes::new(out));
assert_eq!(bitset.len(), 1);
{
let bitset = BitSet::with_max_value(5);
let mut out = vec![];
bitset.serialize(&mut out).unwrap();
let bitset = ReadOnlyBitSet::open(OwnedBytes::new(out));
assert_eq!(bitset.len(), 0);
}
}
#[test]
fn test_tiny_set_remove() {
{
let mut u = TinySet::empty().insert(63u32).insert(5).remove(63u32);
assert_eq!(u.pop_lowest(), Some(5u32));
assert!(u.pop_lowest().is_none());
}
{
let mut u = TinySet::empty()
.insert(63u32)
.insert(1)
.insert(5)
.remove(63u32);
assert_eq!(u.pop_lowest(), Some(1u32));
assert_eq!(u.pop_lowest(), Some(5u32));
assert!(u.pop_lowest().is_none());
}
{
let mut u = TinySet::empty().insert(1).remove(63u32);
assert_eq!(u.pop_lowest(), Some(1u32));
assert!(u.pop_lowest().is_none());
}
{
let mut u = TinySet::empty().insert(1).remove(1u32);
assert!(u.pop_lowest().is_none());
}
}
#[test]
fn test_tiny_set() {
assert!(TinySet::empty().is_empty());
{
let mut u = TinySet::empty().insert(1u32);
assert_eq!(u.pop_lowest(), Some(1u32));
assert!(u.pop_lowest().is_none())
}
{
let mut u = TinySet::empty().insert(1u32).insert(1u32);
assert_eq!(u.pop_lowest(), Some(1u32));
assert!(u.pop_lowest().is_none())
}
{
let mut u = TinySet::empty().insert(2u32);
assert_eq!(u.pop_lowest(), Some(2u32));
u.insert_mut(1u32);
assert_eq!(u.pop_lowest(), Some(1u32));
assert!(u.pop_lowest().is_none());
}
{
let mut u = TinySet::empty().insert(63u32);
assert_eq!(u.pop_lowest(), Some(63u32));
assert!(u.pop_lowest().is_none());
}
{
let mut u = TinySet::empty().insert(63u32).insert(5);
assert_eq!(u.pop_lowest(), Some(5u32));
assert_eq!(u.pop_lowest(), Some(63u32));
assert!(u.pop_lowest().is_none());
}
{
let original = TinySet::empty().insert(63u32).insert(5);
let after_serialize_deserialize = TinySet::deserialize(original.into_bytes());
assert_eq!(original, after_serialize_deserialize);
}
}
#[test]
fn test_bitset() {
let test_against_hashset = |els: &[u32], max_value: u32| {
let mut hashset: HashSet<u32> = HashSet::new();
let mut bitset = BitSet::with_max_value(max_value);
for &el in els {
assert!(el < max_value);
hashset.insert(el);
bitset.insert(el);
}
for el in 0..max_value {
assert_eq!(hashset.contains(&el), bitset.contains(el));
}
assert_eq!(bitset.max_value(), max_value);
// test deser
let mut data = vec![];
bitset.serialize(&mut data).unwrap();
let ro_bitset = ReadOnlyBitSet::open(OwnedBytes::new(data));
for el in 0..max_value {
assert_eq!(hashset.contains(&el), ro_bitset.contains(el));
}
assert_eq!(ro_bitset.max_value(), max_value);
assert_eq!(ro_bitset.len(), els.len());
};
test_against_hashset(&[], 0);
test_against_hashset(&[], 1);
test_against_hashset(&[0u32], 1);
test_against_hashset(&[0u32], 100);
test_against_hashset(&[1u32, 2u32], 4);
test_against_hashset(&[99u32], 100);
test_against_hashset(&[63u32], 64);
test_against_hashset(&[62u32, 63u32], 64);
}
#[test]
fn test_bitset_num_buckets() {
use super::num_buckets;
assert_eq!(num_buckets(0u32), 0);
assert_eq!(num_buckets(1u32), 1);
assert_eq!(num_buckets(64u32), 1);
assert_eq!(num_buckets(65u32), 2);
assert_eq!(num_buckets(128u32), 2);
assert_eq!(num_buckets(129u32), 3);
}
#[test]
fn test_tinyset_range() {
assert_eq!(
TinySet::range_lower(3).into_iter().collect::<Vec<u32>>(),
[0, 1, 2]
);
assert!(TinySet::range_lower(0).is_empty());
assert_eq!(
TinySet::range_lower(63).into_iter().collect::<Vec<u32>>(),
(0u32..63u32).collect::<Vec<_>>()
);
assert_eq!(
TinySet::range_lower(1).into_iter().collect::<Vec<u32>>(),
[0]
);
assert_eq!(
TinySet::range_lower(2).into_iter().collect::<Vec<u32>>(),
[0, 1]
);
assert_eq!(
TinySet::range_greater_or_equal(3)
.into_iter()
.collect::<Vec<u32>>(),
(3u32..64u32).collect::<Vec<_>>()
);
}
#[test]
fn test_bitset_len() {
let mut bitset = BitSet::with_max_value(1_000);
assert_eq!(bitset.len(), 0);
bitset.insert(3u32);
assert_eq!(bitset.len(), 1);
bitset.insert(103u32);
assert_eq!(bitset.len(), 2);
bitset.insert(3u32);
assert_eq!(bitset.len(), 2);
bitset.insert(103u32);
assert_eq!(bitset.len(), 2);
bitset.insert(104u32);
assert_eq!(bitset.len(), 3);
bitset.remove(105u32);
assert_eq!(bitset.len(), 3);
bitset.remove(104u32);
assert_eq!(bitset.len(), 2);
bitset.remove(3u32);
assert_eq!(bitset.len(), 1);
bitset.remove(103u32);
assert_eq!(bitset.len(), 0);
}
pub fn sample_with_seed(n: u32, ratio: f64, seed_val: u8) -> Vec<u32> {
StdRng::from_seed([seed_val; 32])
.sample_iter(&Bernoulli::new(ratio).unwrap())
.take(n as usize)
.enumerate()
.filter_map(|(val, keep)| if keep { Some(val as u32) } else { None })
.collect()
}
pub fn sample(n: u32, ratio: f64) -> Vec<u32> {
sample_with_seed(n, ratio, 4)
}
#[test]
fn test_bitset_clear() {
let mut bitset = BitSet::with_max_value(1_000);
let els = sample(1_000, 0.01f64);
for &el in &els {
bitset.insert(el);
}
assert!(els.iter().all(|el| bitset.contains(*el)));
bitset.clear();
for el in 0u32..1000u32 {
assert!(!bitset.contains(el));
}
}
}
#[cfg(all(test, feature = "unstable"))]
mod bench {
use test;
use super::{BitSet, TinySet};
#[bench]
fn bench_tinyset_pop(b: &mut test::Bencher) {
b.iter(|| {
let mut tinyset = TinySet::singleton(test::black_box(31u32));
tinyset.pop_lowest();
tinyset.pop_lowest();
tinyset.pop_lowest();
tinyset.pop_lowest();
tinyset.pop_lowest();
tinyset.pop_lowest();
});
}
#[bench]
fn bench_tinyset_sum(b: &mut test::Bencher) {
let tiny_set = TinySet::empty().insert(10u32).insert(14u32).insert(21u32);
b.iter(|| {
assert_eq!(test::black_box(tiny_set).into_iter().sum::<u32>(), 45u32);
});
}
#[bench]
fn bench_tinyarr_sum(b: &mut test::Bencher) {
let v = [10u32, 14u32, 21u32];
b.iter(|| test::black_box(v).iter().cloned().sum::<u32>());
}
#[bench]
fn bench_bitset_initialize(b: &mut test::Bencher) {
b.iter(|| BitSet::with_max_value(1_000_000));
}
}

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@@ -1,114 +0,0 @@
use std::io::{self, BufWriter, Write};
pub struct CountingWriter<W> {
underlying: W,
written_bytes: u64,
}
impl<W: Write> CountingWriter<W> {
pub fn wrap(underlying: W) -> CountingWriter<W> {
CountingWriter {
underlying,
written_bytes: 0,
}
}
#[inline]
pub fn written_bytes(&self) -> u64 {
self.written_bytes
}
/// Returns the underlying write object.
/// Note that this method does not trigger any flushing.
#[inline]
pub fn finish(self) -> W {
self.underlying
}
}
impl<W: Write> Write for CountingWriter<W> {
#[inline]
fn write(&mut self, buf: &[u8]) -> io::Result<usize> {
let written_size = self.underlying.write(buf)?;
self.written_bytes += written_size as u64;
Ok(written_size)
}
#[inline]
fn write_all(&mut self, buf: &[u8]) -> io::Result<()> {
self.underlying.write_all(buf)?;
self.written_bytes += buf.len() as u64;
Ok(())
}
#[inline]
fn flush(&mut self) -> io::Result<()> {
self.underlying.flush()
}
}
impl<W: TerminatingWrite> TerminatingWrite for CountingWriter<W> {
#[inline]
fn terminate_ref(&mut self, token: AntiCallToken) -> io::Result<()> {
self.underlying.terminate_ref(token)
}
}
/// Struct used to prevent from calling
/// [`terminate_ref`](trait.TerminatingWrite.html#tymethod.terminate_ref) directly
///
/// The point is that while the type is public, it cannot be built by anyone
/// outside of this module.
pub struct AntiCallToken(());
/// Trait used to indicate when no more write need to be done on a writer
pub trait TerminatingWrite: Write {
/// Indicate that the writer will no longer be used. Internally call terminate_ref.
fn terminate(mut self) -> io::Result<()>
where Self: Sized {
self.terminate_ref(AntiCallToken(()))
}
/// You should implement this function to define custom behavior.
/// This function should flush any buffer it may hold.
fn terminate_ref(&mut self, _: AntiCallToken) -> io::Result<()>;
}
impl<W: TerminatingWrite + ?Sized> TerminatingWrite for Box<W> {
fn terminate_ref(&mut self, token: AntiCallToken) -> io::Result<()> {
self.as_mut().terminate_ref(token)
}
}
impl<W: TerminatingWrite> TerminatingWrite for BufWriter<W> {
fn terminate_ref(&mut self, a: AntiCallToken) -> io::Result<()> {
self.flush()?;
self.get_mut().terminate_ref(a)
}
}
impl<'a> TerminatingWrite for &'a mut Vec<u8> {
fn terminate_ref(&mut self, _a: AntiCallToken) -> io::Result<()> {
self.flush()
}
}
#[cfg(test)]
mod test {
use std::io::Write;
use super::CountingWriter;
#[test]
fn test_counting_writer() {
let buffer: Vec<u8> = vec![];
let mut counting_writer = CountingWriter::wrap(buffer);
let bytes = (0u8..10u8).collect::<Vec<u8>>();
counting_writer.write_all(&bytes).unwrap();
let len = counting_writer.written_bytes();
let buffer_restituted: Vec<u8> = counting_writer.finish();
assert_eq!(len, 10u64);
assert_eq!(buffer_restituted.len(), 10);
}
}

View File

@@ -7,10 +7,8 @@
- [Segments](./basis.md)
- [Defining your schema](./schema.md)
- [Facetting](./facetting.md)
- [Index Sorting](./index_sorting.md)
- [Innerworkings](./innerworkings.md)
- [Inverted index](./inverted_index.md)
- [Json](./json.md)
- [Best practise](./inverted_index.md)
[Frequently Asked Questions](./faq.md)

View File

@@ -1,61 +0,0 @@
- [Index Sorting](#index-sorting)
+ [Why Sorting](#why-sorting)
* [Compression](#compression)
* [Top-N Optimization](#top-n-optimization)
* [Pruning](#pruning)
* [Other](#other)
+ [Usage](#usage)
# Index Sorting
Tantivy allows you to sort the index according to a property.
## Why Sorting
Presorting an index has several advantages:
###### Compression
When data is sorted it is easier to compress the data. E.g. the numbers sequence [5, 2, 3, 1, 4] would be sorted to [1, 2, 3, 4, 5].
If we apply delta encoding this list would be unsorted [5, -3, 1, -2, 3] vs. [1, 1, 1, 1, 1].
Compression ratio is mainly affected on the fast field of the sorted property, every thing else is likely unaffected.
###### Top-N Optimization
When data is presorted by a field and search queries request sorting by the same field, we can leverage the natural order of the documents.
E.g. if the data is sorted by timestamp and want the top n newest docs containing a term, we can simply leveraging the order of the docids.
Note: Tantivy 0.16 does not do this optimization yet.
###### Pruning
Let's say we want all documents and want to apply the filter `>= 2010-08-11`. When the data is sorted, we could make a lookup in the fast field to find the docid range and use this as the filter.
Note: Tantivy 0.16 does not do this optimization yet.
###### Other?
In principle there are many algorithms possible that exploit the monotonically increasing nature. (aggregations maybe?)
## Usage
The index sorting can be configured setting [`sort_by_field`](https://github.com/quickwit-oss/tantivy/blob/000d76b11a139a84b16b9b95060a1c93e8b9851c/src/core/index_meta.rs#L238) on `IndexSettings` and passing it to a `IndexBuilder`. As of tantvy 0.16 only fast fields are allowed to be used.
```
let settings = IndexSettings {
sort_by_field: Some(IndexSortByField {
field: "intval".to_string(),
order: Order::Desc,
}),
..Default::default()
};
let mut index_builder = Index::builder().schema(schema);
index_builder = index_builder.settings(settings);
let index = index_builder.create_in_ram().unwrap();
```
## Implementation details
Sorting an index is applied in the serialization step. In general there are two serialization steps: [Finishing a single segment](https://github.com/quickwit-oss/tantivy/blob/000d76b11a139a84b16b9b95060a1c93e8b9851c/src/indexer/segment_writer.rs#L338) and [merging multiple segments](https://github.com/quickwit-oss/tantivy/blob/000d76b11a139a84b16b9b95060a1c93e8b9851c/src/indexer/merger.rs#L1073).
In both cases we generate a docid mapping reflecting the sort. This mapping is used when serializing the different components (doc store, fastfields, posting list, normfield, facets).

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@@ -1,82 +0,0 @@
# Json
As of tantivy 0.17, tantivy supports a json object type.
This type can be used to allow for a schema-less search index.
When indexing a json object, we "flatten" the JSON. This operation emits terms that represent a triplet `(json_path, value_type, value)`
For instance, if user is a json field, the following document:
```json
{
"user": {
"name": "Paul Masurel",
"address": {
"city": "Tokyo",
"country": "Japan"
},
"created_at": "2018-11-12T23:20:50.52Z"
}
}
```
emits the following tokens:
- ("name", Text, "Paul")
- ("name", Text, "Masurel")
- ("address.city", Text, "Tokyo")
- ("address.country", Text, "Japan")
- ("created_at", Date, 15420648505)
# Bytes-encoding and lexicographical sort.
Like any other terms, these triplets are encoded into a binary format as follows.
- `json_path`: the json path is a sequence of "segments". In the example above, `address.city`
is just a debug representation of the json path `["address", "city"]`.
Its representation is done by separating segments by a unicode char `\x01`, and ending the path by `\x00`.
- `value type`: One byte represents the `Value` type.
- `value`: The value representation is just the regular Value representation.
This representation is designed to align the natural sort of Terms with the lexicographical sort
of their binary representation (Tantivy's dictionary (whether fst or sstable) is sorted and does prefix encoding).
In the example above, the terms will be sorted as
- ("address.city", Text, "Tokyo")
- ("address.country", Text, "Japan")
- ("name", Text, "Masurel")
- ("name", Text, "Paul")
- ("created_at", Date, 15420648505)
As seen in "pitfalls", we may end up having to search for a value for a same path in several different fields. Putting the field code after the path makes it maximizes compression opportunities but also increases the chances for the two terms to end up in the actual same term dictionary block.
# Pitfalls and limitation.
Json gives very little information about the type of the literals it stores.
All numeric types end up mapped as a "Number" and there are no types for dates.
At ingestion time, tantivy will try to interpret number and strings as different type with a
priority order.
Numbers will be interpreted as u64, i64 and f64 in that order.
Strings will be interpreted as rfc3999 dates or simple strings.
The first working time is picked and only one type will be emitted for indexing.
Note this interpretation happens on a per-document basis, and there is no effort to try to sniff
a consistent field type at the scale of a segment.
On the query parser side on the other hand, we may end up emitting more than one type.
For instance, we do not even know if the type is a number or string based.
So the query
```
my_path.my_segment:233
```
Should be interpreted as
- `(my_path.my_segment, String, 233)`
- `(my_path.my_segment, u64, 233)`
Likewise, we need to emit two tokens if the query contains an rfc3999 date.
Indeed the date could have been actually a single token inside the text of a document at ingestion time. Generally speaking, we will always at least emit a string token in query parsing, and sometimes more.

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@@ -1,130 +0,0 @@
// # Aggregation example
//
// This example shows how you can use built-in aggregations.
// We will use range buckets and compute the average in each bucket.
//
use serde_json::Value;
use tantivy::aggregation::agg_req::{
Aggregation, Aggregations, BucketAggregation, BucketAggregationType, MetricAggregation,
RangeAggregation,
};
use tantivy::aggregation::agg_result::AggregationResults;
use tantivy::aggregation::metric::AverageAggregation;
use tantivy::aggregation::AggregationCollector;
use tantivy::query::TermQuery;
use tantivy::schema::{self, Cardinality, IndexRecordOption, Schema, TextFieldIndexing};
use tantivy::{doc, Index, Term};
fn main() -> tantivy::Result<()> {
let mut schema_builder = Schema::builder();
let text_fieldtype = schema::TextOptions::default()
.set_indexing_options(
TextFieldIndexing::default()
.set_tokenizer("default")
.set_index_option(IndexRecordOption::WithFreqs),
)
.set_stored();
let text_field = schema_builder.add_text_field("text", text_fieldtype);
let score_fieldtype = crate::schema::IntOptions::default().set_fast(Cardinality::SingleValue);
let highscore_field = schema_builder.add_f64_field("highscore", score_fieldtype.clone());
let price_field = schema_builder.add_f64_field("price", score_fieldtype.clone());
let schema = schema_builder.build();
// # Indexing documents
//
// Lets index a bunch of documents for this example.
let index = Index::create_in_ram(schema);
let mut index_writer = index.writer(50_000_000)?;
// writing the segment
index_writer.add_document(doc!(
text_field => "cool",
highscore_field => 1f64,
price_field => 0f64,
))?;
index_writer.add_document(doc!(
text_field => "cool",
highscore_field => 3f64,
price_field => 1f64,
))?;
index_writer.add_document(doc!(
text_field => "cool",
highscore_field => 5f64,
price_field => 1f64,
))?;
index_writer.add_document(doc!(
text_field => "nohit",
highscore_field => 6f64,
price_field => 2f64,
))?;
index_writer.add_document(doc!(
text_field => "cool",
highscore_field => 7f64,
price_field => 2f64,
))?;
index_writer.commit()?;
index_writer.add_document(doc!(
text_field => "cool",
highscore_field => 11f64,
price_field => 10f64,
))?;
index_writer.add_document(doc!(
text_field => "cool",
highscore_field => 14f64,
price_field => 15f64,
))?;
index_writer.add_document(doc!(
text_field => "cool",
highscore_field => 15f64,
price_field => 20f64,
))?;
index_writer.commit()?;
let reader = index.reader()?;
let text_field = reader.searcher().schema().get_field("text").unwrap();
let term_query = TermQuery::new(
Term::from_field_text(text_field, "cool"),
IndexRecordOption::Basic,
);
let sub_agg_req_1: Aggregations = vec![(
"average_price".to_string(),
Aggregation::Metric(MetricAggregation::Average(
AverageAggregation::from_field_name("price".to_string()),
)),
)]
.into_iter()
.collect();
let agg_req_1: Aggregations = vec![(
"score_ranges".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Range(RangeAggregation {
field: "highscore".to_string(),
ranges: vec![
(-1f64..9f64).into(),
(9f64..14f64).into(),
(14f64..20f64).into(),
],
}),
sub_aggregation: sub_agg_req_1.clone(),
}),
)]
.into_iter()
.collect();
let collector = AggregationCollector::from_aggs(agg_req_1);
let searcher = reader.searcher();
let agg_res: AggregationResults = searcher.search(&term_query, &collector).unwrap();
let res: Value = serde_json::from_str(&serde_json::to_string(&agg_res)?)?;
println!("{}", serde_json::to_string_pretty(&res)?);
Ok(())
}

View File

@@ -73,7 +73,7 @@ fn main() -> tantivy::Result<()> {
// multithreaded.
//
// Here we give tantivy a budget of `50MB`.
// Using a bigger memory_arena for the indexer may increase
// Using a bigger heap for the indexer may increase
// throughput, but 50 MB is already plenty.
let mut index_writer = index.writer(50_000_000)?;
@@ -91,12 +91,12 @@ fn main() -> tantivy::Result<()> {
old_man_doc.add_text(title, "The Old Man and the Sea");
old_man_doc.add_text(
body,
"He was an old man who fished alone in a skiff in the Gulf Stream and he had gone \
eighty-four days now without taking a fish.",
"He was an old man who fished alone in a skiff in the Gulf Stream and \
he had gone eighty-four days now without taking a fish.",
);
// ... and add it to the `IndexWriter`.
index_writer.add_document(old_man_doc)?;
index_writer.add_document(old_man_doc);
// For convenience, tantivy also comes with a macro to
// reduce the boilerplate above.
@@ -110,7 +110,19 @@ fn main() -> tantivy::Result<()> {
fresh and green with every spring, carrying in their lower leaf junctures the \
debris of the winters flooding; and sycamores with mottled, white, recumbent \
limbs and branches that arch over the pool"
))?;
));
index_writer.add_document(doc!(
title => "Of Mice and Men",
body => "A few miles south of Soledad, the Salinas River drops in close to the hillside \
bank and runs deep and green. The water is warm too, for it has slipped twinkling \
over the yellow sands in the sunlight before reaching the narrow pool. On one \
side of the river the golden foothill slopes curve up to the strong and rocky \
Gabilan Mountains, but on the valley side the water is lined with trees—willows \
fresh and green with every spring, carrying in their lower leaf junctures the \
debris of the winters flooding; and sycamores with mottled, white, recumbent \
limbs and branches that arch over the pool"
));
// Multivalued field just need to be repeated.
index_writer.add_document(doc!(
@@ -120,7 +132,7 @@ fn main() -> tantivy::Result<()> {
enterprise which you have regarded with such evil forebodings. I arrived here \
yesterday, and my first task is to assure my dear sister of my welfare and \
increasing confidence in the success of my undertaking."
))?;
));
// This is an example, so we will only index 3 documents
// here. You can check out tantivy's tutorial to index

View File

@@ -10,10 +10,11 @@
// ---
// Importing tantivy...
use tantivy::collector::{Collector, SegmentCollector};
use tantivy::fastfield::{DynamicFastFieldReader, FastFieldReader};
use tantivy::fastfield::FastFieldReader;
use tantivy::query::QueryParser;
use tantivy::schema::{Field, Schema, FAST, INDEXED, TEXT};
use tantivy::{doc, Index, Score, SegmentReader};
use tantivy::schema::Field;
use tantivy::schema::{Schema, FAST, INDEXED, TEXT};
use tantivy::{doc, Index, SegmentReader, TantivyError};
#[derive(Default)]
struct Stats {
@@ -71,7 +72,16 @@ impl Collector for StatsCollector {
_segment_local_id: u32,
segment_reader: &SegmentReader,
) -> tantivy::Result<StatsSegmentCollector> {
let fast_field_reader = segment_reader.fast_fields().u64(self.field)?;
let fast_field_reader = segment_reader
.fast_fields()
.u64(self.field)
.ok_or_else(|| {
let field_name = segment_reader.schema().get_field_name(self.field);
TantivyError::SchemaError(format!(
"Field {:?} is not a u64 fast field.",
field_name
))
})?;
Ok(StatsSegmentCollector {
fast_field_reader,
stats: Stats::default(),
@@ -85,24 +95,26 @@ impl Collector for StatsCollector {
fn merge_fruits(&self, segment_stats: Vec<Option<Stats>>) -> tantivy::Result<Option<Stats>> {
let mut stats = Stats::default();
for segment_stats in segment_stats.into_iter().flatten() {
stats.count += segment_stats.count;
stats.sum += segment_stats.sum;
stats.squared_sum += segment_stats.squared_sum;
for segment_stats_opt in segment_stats {
if let Some(segment_stats) = segment_stats_opt {
stats.count += segment_stats.count;
stats.sum += segment_stats.sum;
stats.squared_sum += segment_stats.squared_sum;
}
}
Ok(stats.non_zero_count())
}
}
struct StatsSegmentCollector {
fast_field_reader: DynamicFastFieldReader<u64>,
fast_field_reader: FastFieldReader<u64>,
stats: Stats,
}
impl SegmentCollector for StatsSegmentCollector {
type Fruit = Option<Stats>;
fn collect(&mut self, doc: u32, _score: Score) {
fn collect(&mut self, doc: u32, _score: f32) {
let value = self.fast_field_reader.get(doc) as f64;
self.stats.count += 1;
self.stats.sum += value;
@@ -136,7 +148,7 @@ fn main() -> tantivy::Result<()> {
//
// Lets index a bunch of fake documents for the sake of
// this example.
let index = Index::create_in_ram(schema);
let index = Index::create_in_ram(schema.clone());
let mut index_writer = index.writer(50_000_000)?;
index_writer.add_document(doc!(
@@ -144,23 +156,23 @@ fn main() -> tantivy::Result<()> {
product_description => "While it is ok for short distance travel, this broom \
was designed quiditch. It will up your game.",
price => 30_200u64
))?;
));
index_writer.add_document(doc!(
product_name => "Turbulobroom",
product_description => "You might have heard of this broom before : it is the sponsor of the Wales team.\
You'll enjoy its sharp turns, and rapid acceleration",
price => 29_240u64
))?;
));
index_writer.add_document(doc!(
product_name => "Broomio",
product_description => "Great value for the price. This broom is a market favorite",
price => 21_240u64
))?;
));
index_writer.add_document(doc!(
product_name => "Whack a Mole",
product_description => "Prime quality bat.",
price => 5_200u64
))?;
));
index_writer.commit()?;
let reader = index.reader()?;

View File

@@ -62,13 +62,13 @@ fn main() -> tantivy::Result<()> {
// multithreaded.
//
// Here we use a buffer of 50MB per thread. Using a bigger
// memory arena for the indexer can increase its throughput.
// heap for the indexer can increase its throughput.
let mut index_writer = index.writer(50_000_000)?;
index_writer.add_document(doc!(
title => "The Old Man and the Sea",
body => "He was an old man who fished alone in a skiff in the Gulf Stream and \
he had gone eighty-four days now without taking a fish."
))?;
));
index_writer.add_document(doc!(
title => "Of Mice and Men",
body => r#"A few miles south of Soledad, the Salinas River drops in close to the hillside
@@ -79,14 +79,14 @@ fn main() -> tantivy::Result<()> {
fresh and green with every spring, carrying in their lower leaf junctures the
debris of the winters flooding; and sycamores with mottled, white, recumbent
limbs and branches that arch over the pool"#
))?;
));
index_writer.add_document(doc!(
title => "Frankenstein",
body => r#"You will rejoice to hear that no disaster has accompanied the commencement of an
enterprise which you have regarded with such evil forebodings. I arrived here
yesterday, and my first task is to assure my dear sister of my welfare and
increasing confidence in the success of my undertaking."#
))?;
));
index_writer.commit()?;
let reader = index.reader()?;

View File

@@ -56,9 +56,8 @@ fn main() -> tantivy::Result<()> {
// If it is `text`, let's make sure to keep it `raw` and let's avoid
// running any text processing on it.
// This is done by associating this field to the tokenizer named `raw`.
// Rather than building our
// [`TextOptions`](//docs.rs/tantivy/~0/tantivy/schema/struct.TextOptions.html) manually, We
// use the `STRING` shortcut. `STRING` stands for indexed (without term frequency or positions)
// Rather than building our [`TextOptions`](//docs.rs/tantivy/~0/tantivy/schema/struct.TextOptions.html) manually,
// We use the `STRING` shortcut. `STRING` stands for indexed (without term frequency or positions)
// and untokenized.
//
// Because we also want to be able to see this `id` in our returned documents,
@@ -77,21 +76,21 @@ fn main() -> tantivy::Result<()> {
index_writer.add_document(doc!(
isbn => "978-0099908401",
title => "The old Man and the see"
))?;
));
index_writer.add_document(doc!(
isbn => "978-0140177398",
title => "Of Mice and Men",
))?;
));
index_writer.add_document(doc!(
title => "Frankentein", //< Oops there is a typo here.
isbn => "978-9176370711",
))?;
));
index_writer.commit()?;
let reader = index.reader()?;
let frankenstein_isbn = Term::from_field_text(isbn, "978-9176370711");
// Oops our frankenstein doc seems misspelled
// Oops our frankenstein doc seems mispelled
let frankenstein_doc_misspelled = extract_doc_given_isbn(&reader, &frankenstein_isbn)?.unwrap();
assert_eq!(
schema.to_json(&frankenstein_doc_misspelled),
@@ -123,7 +122,7 @@ fn main() -> tantivy::Result<()> {
index_writer.add_document(doc!(
title => "Frankenstein",
isbn => "978-9176370711",
))?;
));
// You are guaranteed that your clients will only observe your index in
// the state it was in after a commit.

View File

@@ -23,7 +23,7 @@ fn main() -> tantivy::Result<()> {
let name = schema_builder.add_text_field("felin_name", TEXT | STORED);
// this is our faceted field: its scientific classification
let classification = schema_builder.add_facet_field("classification", FacetOptions::default());
let classification = schema_builder.add_facet_field("classification");
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
@@ -35,35 +35,35 @@ fn main() -> tantivy::Result<()> {
index_writer.add_document(doc!(
name => "Cat",
classification => Facet::from("/Felidae/Felinae/Felis")
))?;
));
index_writer.add_document(doc!(
name => "Canada lynx",
classification => Facet::from("/Felidae/Felinae/Lynx")
))?;
));
index_writer.add_document(doc!(
name => "Cheetah",
classification => Facet::from("/Felidae/Felinae/Acinonyx")
))?;
));
index_writer.add_document(doc!(
name => "Tiger",
classification => Facet::from("/Felidae/Pantherinae/Panthera")
))?;
));
index_writer.add_document(doc!(
name => "Lion",
classification => Facet::from("/Felidae/Pantherinae/Panthera")
))?;
));
index_writer.add_document(doc!(
name => "Jaguar",
classification => Facet::from("/Felidae/Pantherinae/Panthera")
))?;
));
index_writer.add_document(doc!(
name => "Sunda clouded leopard",
classification => Facet::from("/Felidae/Pantherinae/Neofelis")
))?;
));
index_writer.add_document(doc!(
name => "Fossa",
classification => Facet::from("/Eupleridae/Cryptoprocta")
))?;
));
index_writer.commit()?;
let reader = index.reader()?;
@@ -92,7 +92,7 @@ fn main() -> tantivy::Result<()> {
// Check the reference doc for different ways to create a `Facet` object.
{
let facet = Facet::from("/Felidae/Pantherinae");
let facet = Facet::from_text("/Felidae/Pantherinae");
let facet_term = Term::from_facet(classification, &facet);
let facet_term_query = TermQuery::new(facet_term, IndexRecordOption::Basic);
let mut facet_collector = FacetCollector::for_field(classification);

View File

@@ -1,98 +0,0 @@
use std::collections::HashSet;
use tantivy::collector::TopDocs;
use tantivy::query::BooleanQuery;
use tantivy::schema::*;
use tantivy::{doc, DocId, Index, Score, SegmentReader};
fn main() -> tantivy::Result<()> {
let mut schema_builder = Schema::builder();
let title = schema_builder.add_text_field("title", STORED);
let ingredient = schema_builder.add_facet_field("ingredient", FacetOptions::default());
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut index_writer = index.writer(30_000_000)?;
index_writer.add_document(doc!(
title => "Fried egg",
ingredient => Facet::from("/ingredient/egg"),
ingredient => Facet::from("/ingredient/oil"),
))?;
index_writer.add_document(doc!(
title => "Scrambled egg",
ingredient => Facet::from("/ingredient/egg"),
ingredient => Facet::from("/ingredient/butter"),
ingredient => Facet::from("/ingredient/milk"),
ingredient => Facet::from("/ingredient/salt"),
))?;
index_writer.add_document(doc!(
title => "Egg rolls",
ingredient => Facet::from("/ingredient/egg"),
ingredient => Facet::from("/ingredient/garlic"),
ingredient => Facet::from("/ingredient/salt"),
ingredient => Facet::from("/ingredient/oil"),
ingredient => Facet::from("/ingredient/tortilla-wrap"),
ingredient => Facet::from("/ingredient/mushroom"),
))?;
index_writer.commit()?;
let reader = index.reader()?;
let searcher = reader.searcher();
{
let facets = vec![
Facet::from("/ingredient/egg"),
Facet::from("/ingredient/oil"),
Facet::from("/ingredient/garlic"),
Facet::from("/ingredient/mushroom"),
];
let query = BooleanQuery::new_multiterms_query(
facets
.iter()
.map(|key| Term::from_facet(ingredient, key))
.collect(),
);
let top_docs_by_custom_score =
TopDocs::with_limit(2).tweak_score(move |segment_reader: &SegmentReader| {
let ingredient_reader = segment_reader.facet_reader(ingredient).unwrap();
let facet_dict = ingredient_reader.facet_dict();
let query_ords: HashSet<u64> = facets
.iter()
.filter_map(|key| facet_dict.term_ord(key.encoded_str()).unwrap())
.collect();
let mut facet_ords_buffer: Vec<u64> = Vec::with_capacity(20);
move |doc: DocId, original_score: Score| {
ingredient_reader.facet_ords(doc, &mut facet_ords_buffer);
let missing_ingredients = facet_ords_buffer
.iter()
.filter(|ord| !query_ords.contains(ord))
.count();
let tweak = 1.0 / 4_f32.powi(missing_ingredients as i32);
original_score * tweak
}
});
let top_docs = searcher.search(&query, &top_docs_by_custom_score)?;
let titles: Vec<String> = top_docs
.iter()
.map(|(_, doc_id)| {
searcher
.doc(*doc_id)
.unwrap()
.get_first(title)
.unwrap()
.as_text()
.unwrap()
.to_owned()
})
.collect();
assert_eq!(titles, vec!["Fried egg", "Egg rolls"]);
}
Ok(())
}

View File

@@ -7,7 +7,7 @@ use tantivy::query::RangeQuery;
use tantivy::schema::{Schema, INDEXED};
use tantivy::{doc, Index, Result};
fn main() -> Result<()> {
fn run() -> Result<()> {
// For the sake of simplicity, this schema will only have 1 field
let mut schema_builder = Schema::builder();
@@ -19,7 +19,7 @@ fn main() -> Result<()> {
{
let mut index_writer = index.writer_with_num_threads(1, 6_000_000)?;
for year in 1950u64..2019u64 {
index_writer.add_document(doc!(year_field => year))?;
index_writer.add_document(doc!(year_field => year));
}
index_writer.commit()?;
// The index will be a range of years
@@ -33,3 +33,7 @@ fn main() -> Result<()> {
assert_eq!(num_60s_books, 10);
Ok(())
}
fn main() {
run().unwrap()
}

View File

@@ -1,4 +1,4 @@
// # Iterating docs and positions.
// # Iterating docs and positioms.
//
// At its core of tantivy, relies on a data structure
// called an inverted index.
@@ -10,7 +10,7 @@
// ---
// Importing tantivy...
use tantivy::schema::*;
use tantivy::{doc, DocSet, Index, Postings, TERMINATED};
use tantivy::{doc, DocId, DocSet, Index, Postings};
fn main() -> tantivy::Result<()> {
// We first create a schema for the sake of the
@@ -22,12 +22,12 @@ fn main() -> tantivy::Result<()> {
let title = schema_builder.add_text_field("title", TEXT | STORED);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let index = Index::create_in_ram(schema.clone());
let mut index_writer = index.writer_with_num_threads(1, 50_000_000)?;
index_writer.add_document(doc!(title => "The Old Man and the Sea"))?;
index_writer.add_document(doc!(title => "Of Mice and Men"))?;
index_writer.add_document(doc!(title => "The modern Promotheus"))?;
index_writer.add_document(doc!(title => "The Old Man and the Sea"));
index_writer.add_document(doc!(title => "Of Mice and Men"));
index_writer.add_document(doc!(title => "The modern Promotheus"));
index_writer.commit()?;
let reader = index.reader()?;
@@ -45,28 +45,29 @@ fn main() -> tantivy::Result<()> {
// Inverted index stands for the combination of
// - the term dictionary
// - the inverted lists associated to each terms and their positions
let inverted_index = segment_reader.inverted_index(title)?;
let inverted_index = segment_reader.inverted_index(title);
// A `Term` is a text token associated with a field.
// Let's go through all docs containing the term `title:the` and access their position
let term_the = Term::from_field_text(title, "the");
// This segment posting object is like a cursor over the documents matching the term.
// The `IndexRecordOption` arguments tells tantivy we will be interested in both term
// frequencies and positions.
// The `IndexRecordOption` arguments tells tantivy we will be interested in both term frequencies
// and positions.
//
// If you don't need all this information, you may get better performance by decompressing
// less information.
// If you don't need all this information, you may get better performance by decompressing less
// information.
if let Some(mut segment_postings) =
inverted_index.read_postings(&term_the, IndexRecordOption::WithFreqsAndPositions)?
inverted_index.read_postings(&term_the, IndexRecordOption::WithFreqsAndPositions)
{
// this buffer will be used to request for positions
let mut positions: Vec<u32> = Vec::with_capacity(100);
let mut doc_id = segment_postings.doc();
while doc_id != TERMINATED {
while segment_postings.advance() {
// the number of time the term appears in the document.
let doc_id: DocId = segment_postings.doc(); //< do not try to access this before calling advance once.
// This MAY contains deleted documents as well.
if segment_reader.is_deleted(doc_id) {
doc_id = segment_postings.advance();
continue;
}
@@ -85,7 +86,6 @@ fn main() -> tantivy::Result<()> {
// Doc 2: TermFreq 1: [0]
// ```
println!("Doc {}: TermFreq {}: {:?}", doc_id, term_freq, positions);
doc_id = segment_postings.advance();
}
}
}
@@ -106,27 +106,22 @@ fn main() -> tantivy::Result<()> {
// Inverted index stands for the combination of
// - the term dictionary
// - the inverted lists associated to each terms and their positions
let inverted_index = segment_reader.inverted_index(title)?;
let inverted_index = segment_reader.inverted_index(title);
// This segment posting object is like a cursor over the documents matching the term.
// The `IndexRecordOption` arguments tells tantivy we will be interested in both term
// frequencies and positions.
// The `IndexRecordOption` arguments tells tantivy we will be interested in both term frequencies
// and positions.
//
// If you don't need all this information, you may get better performance by decompressing
// less information.
// If you don't need all this information, you may get better performance by decompressing less
// information.
if let Some(mut block_segment_postings) =
inverted_index.read_block_postings(&term_the, IndexRecordOption::Basic)?
inverted_index.read_block_postings(&term_the, IndexRecordOption::Basic)
{
loop {
let docs = block_segment_postings.docs();
if docs.is_empty() {
break;
}
while block_segment_postings.advance() {
// Once again these docs MAY contains deleted documents as well.
let docs = block_segment_postings.docs();
// Prints `Docs [0, 2].`
println!("Docs {:?}", docs);
block_segment_postings.advance();
}
}
}

View File

@@ -28,9 +28,8 @@
use std::sync::{Arc, RwLock};
use std::thread;
use std::time::Duration;
use tantivy::schema::{Schema, STORED, TEXT};
use tantivy::{doc, Index, IndexWriter, Opstamp, TantivyError};
use tantivy::{doc, Index, IndexWriter, Opstamp};
fn main() -> tantivy::Result<()> {
// # Defining the schema
@@ -60,11 +59,10 @@ fn main() -> tantivy::Result<()> {
fresh and green with every spring, carrying in their lower leaf junctures the \
debris of the winters flooding; and sycamores with mottled, white, recumbent \
limbs and branches that arch over the pool"
))?;
));
println!("add doc {} from thread 1 - opstamp {}", i, opstamp);
thread::sleep(Duration::from_millis(20));
}
Result::<(), TantivyError>::Ok(())
});
// # Second indexing thread.
@@ -80,21 +78,19 @@ fn main() -> tantivy::Result<()> {
index_writer_rlock.add_document(doc!(
title => "Manufacturing consent",
body => "Some great book description..."
))?
))
};
println!("add doc {} from thread 2 - opstamp {}", i, opstamp);
thread::sleep(Duration::from_millis(10));
}
Result::<(), TantivyError>::Ok(())
});
// # In the main thread, we commit 10 times, once every 500ms.
for _ in 0..10 {
let opstamp: Opstamp = {
// Committing or rollbacking on the other hand requires write lock. This will block
// other threads.
// Committing or rollbacking on the other hand requires write lock. This will block other threads.
let mut index_writer_wlock = index_writer.write().unwrap();
index_writer_wlock.commit()?
index_writer_wlock.commit().unwrap()
};
println!("committed with opstamp {}", opstamp);
thread::sleep(Duration::from_millis(500));

View File

@@ -1,6 +1,6 @@
// # Pre-tokenized text example
//
// This example shows how to use pre-tokenized text. Sometimes you might
// This example shows how to use pre-tokenized text. Sometimes yout might
// want to index and search through text which is already split into
// tokens by some external tool.
//
@@ -9,10 +9,11 @@
// - import tokenized text straight from json,
// - perform a search on documents with pre-tokenized text
use tantivy::tokenizer::{PreTokenizedString, SimpleTokenizer, Token, TokenStream, Tokenizer};
use tantivy::collector::{Count, TopDocs};
use tantivy::query::TermQuery;
use tantivy::schema::*;
use tantivy::tokenizer::{PreTokenizedString, SimpleTokenizer, Token, Tokenizer};
use tantivy::{doc, Index, ReloadPolicy};
use tempfile::TempDir;
@@ -68,7 +69,7 @@ fn main() -> tantivy::Result<()> {
let old_man_doc = doc!(title => title_tok, body => body_tok);
// ... now let's just add it to the IndexWriter
index_writer.add_document(old_man_doc)?;
index_writer.add_document(old_man_doc);
// Pretokenized text can also be fed as JSON
let short_man_json = r#"{
@@ -82,9 +83,9 @@ fn main() -> tantivy::Result<()> {
}]
}"#;
let short_man_doc = schema.parse_document(short_man_json)?;
let short_man_doc = schema.parse_document(&short_man_json)?;
index_writer.add_document(short_man_doc)?;
index_writer.add_document(short_man_doc);
// Let's commit changes
index_writer.commit()?;
@@ -106,7 +107,9 @@ fn main() -> tantivy::Result<()> {
IndexRecordOption::Basic,
);
let (top_docs, count) = searcher.search(&query, &(TopDocs::with_limit(2), Count))?;
let (top_docs, count) = searcher
.search(&query, &(TopDocs::with_limit(2), Count))
.unwrap();
assert_eq!(count, 2);
@@ -127,7 +130,9 @@ fn main() -> tantivy::Result<()> {
IndexRecordOption::Basic,
);
let (_top_docs, count) = searcher.search(&query, &(TopDocs::with_limit(2), Count))?;
let (_top_docs, count) = searcher
.search(&query, &(TopDocs::with_limit(2), Count))
.unwrap();
assert_eq!(count, 0);

View File

@@ -25,7 +25,7 @@ fn main() -> tantivy::Result<()> {
let schema = schema_builder.build();
// # Indexing documents
let index = Index::create_in_dir(&index_path, schema)?;
let index = Index::create_in_dir(&index_path, schema.clone())?;
let mut index_writer = index.writer(50_000_000)?;
@@ -40,7 +40,7 @@ fn main() -> tantivy::Result<()> {
fresh and green with every spring, carrying in their lower leaf junctures the \
debris of the winters flooding; and sycamores with mottled, white, recumbent \
limbs and branches that arch over the pool"
))?;
));
// ...
index_writer.commit()?;
@@ -57,10 +57,7 @@ fn main() -> tantivy::Result<()> {
let doc = searcher.doc(doc_address)?;
let snippet = snippet_generator.snippet_from_doc(&doc);
println!("Document score {}:", score);
println!(
"title: {}",
doc.get_first(title).unwrap().as_text().unwrap()
);
println!("title: {}", doc.get_first(title).unwrap().text().unwrap());
println!("snippet: {}", snippet.to_html());
println!("custom highlighting: {}", highlight(snippet));
}
@@ -72,14 +69,14 @@ fn highlight(snippet: Snippet) -> String {
let mut result = String::new();
let mut start_from = 0;
for fragment_range in snippet.highlighted() {
result.push_str(&snippet.fragment()[start_from..fragment_range.start]);
for (start, end) in snippet.highlighted().iter().map(|h| h.bounds()) {
result.push_str(&snippet.fragments()[start_from..start]);
result.push_str(" --> ");
result.push_str(&snippet.fragment()[fragment_range.clone()]);
result.push_str(&snippet.fragments()[start..end]);
result.push_str(" <-- ");
start_from = fragment_range.end;
start_from = end;
}
result.push_str(&snippet.fragment()[start_from..]);
result.push_str(&snippet.fragments()[start_from..]);
result
}

View File

@@ -50,7 +50,7 @@ fn main() -> tantivy::Result<()> {
// This tokenizer lowers all of the text (to help with stop word matching)
// then removes all instances of `the` and `and` from the corpus
let tokenizer = TextAnalyzer::from(SimpleTokenizer)
let tokenizer = SimpleTokenizer
.filter(LowerCaser)
.filter(StopWordFilter::remove(vec![
"the".to_string(),
@@ -68,7 +68,7 @@ fn main() -> tantivy::Result<()> {
title => "The Old Man and the Sea",
body => "He was an old man who fished alone in a skiff in the Gulf Stream and \
he had gone eighty-four days now without taking a fish."
))?;
));
index_writer.add_document(doc!(
title => "Of Mice and Men",
@@ -80,7 +80,7 @@ fn main() -> tantivy::Result<()> {
fresh and green with every spring, carrying in their lower leaf junctures the \
debris of the winters flooding; and sycamores with mottled, white, recumbent \
limbs and branches that arch over the pool"
))?;
));
index_writer.add_document(doc!(
title => "Frankenstein",
@@ -88,7 +88,7 @@ fn main() -> tantivy::Result<()> {
enterprise which you have regarded with such evil forebodings. I arrived here \
yesterday, and my first task is to assure my dear sister of my welfare and \
increasing confidence in the success of my undertaking."
))?;
));
index_writer.commit()?;

View File

@@ -1,224 +0,0 @@
use std::cmp::Reverse;
use std::collections::{HashMap, HashSet};
use std::sync::{Arc, RwLock, Weak};
use tantivy::collector::TopDocs;
use tantivy::fastfield::FastFieldReader;
use tantivy::query::QueryParser;
use tantivy::schema::{Field, Schema, FAST, TEXT};
use tantivy::{
doc, DocAddress, DocId, Index, IndexReader, Opstamp, Searcher, SearcherGeneration, SegmentId,
SegmentReader, Warmer,
};
// This example shows how warmers can be used to
// load a values from an external sources using the Warmer API.
//
// In this example, we assume an e-commerce search engine.
type ProductId = u64;
/// Price
type Price = u32;
pub trait PriceFetcher: Send + Sync + 'static {
fn fetch_prices(&self, product_ids: &[ProductId]) -> Vec<Price>;
}
struct DynamicPriceColumn {
field: Field,
price_cache: RwLock<HashMap<(SegmentId, Option<Opstamp>), Arc<Vec<Price>>>>,
price_fetcher: Box<dyn PriceFetcher>,
}
impl DynamicPriceColumn {
pub fn with_product_id_field<T: PriceFetcher>(field: Field, price_fetcher: T) -> Self {
DynamicPriceColumn {
field,
price_cache: Default::default(),
price_fetcher: Box::new(price_fetcher),
}
}
pub fn price_for_segment(&self, segment_reader: &SegmentReader) -> Option<Arc<Vec<Price>>> {
let segment_key = (segment_reader.segment_id(), segment_reader.delete_opstamp());
self.price_cache.read().unwrap().get(&segment_key).cloned()
}
}
impl Warmer for DynamicPriceColumn {
fn warm(&self, searcher: &Searcher) -> tantivy::Result<()> {
for segment in searcher.segment_readers() {
let key = (segment.segment_id(), segment.delete_opstamp());
let product_id_reader = segment.fast_fields().u64(self.field)?;
let product_ids: Vec<ProductId> = segment
.doc_ids_alive()
.map(|doc| product_id_reader.get(doc))
.collect();
let mut prices_it = self.price_fetcher.fetch_prices(&product_ids).into_iter();
let mut price_vals: Vec<Price> = Vec::new();
for doc in 0..segment.max_doc() {
if segment.is_deleted(doc) {
price_vals.push(0);
} else {
price_vals.push(prices_it.next().unwrap())
}
}
self.price_cache
.write()
.unwrap()
.insert(key, Arc::new(price_vals));
}
Ok(())
}
fn garbage_collect(&self, live_generations: &[&SearcherGeneration]) {
let live_segment_id_and_delete_ops: HashSet<(SegmentId, Option<Opstamp>)> =
live_generations
.iter()
.flat_map(|gen| gen.segments())
.map(|(&segment_id, &opstamp)| (segment_id, opstamp))
.collect();
let mut price_cache_wrt = self.price_cache.write().unwrap();
// let price_cache = std::mem::take(&mut *price_cache_wrt);
// Drain would be nicer here.
*price_cache_wrt = std::mem::take(&mut *price_cache_wrt)
.into_iter()
.filter(|(seg_id_and_op, _)| !live_segment_id_and_delete_ops.contains(seg_id_and_op))
.collect();
}
}
/// For the sake of this example, the table is just an editable HashMap behind a RwLock.
/// This map represents a map (ProductId -> Price)
///
/// In practise, it could be fetching things from an external service, like a SQL table.
#[derive(Default, Clone)]
pub struct ExternalPriceTable {
prices: Arc<RwLock<HashMap<ProductId, Price>>>,
}
impl ExternalPriceTable {
pub fn update_price(&self, product_id: ProductId, price: Price) {
let mut prices_wrt = self.prices.write().unwrap();
prices_wrt.insert(product_id, price);
}
}
impl PriceFetcher for ExternalPriceTable {
fn fetch_prices(&self, product_ids: &[ProductId]) -> Vec<Price> {
let prices_read = self.prices.read().unwrap();
product_ids
.iter()
.map(|product_id| prices_read.get(product_id).cloned().unwrap_or(0))
.collect()
}
}
fn main() -> tantivy::Result<()> {
// Declaring our schema.
let mut schema_builder = Schema::builder();
// The product id is assumed to be a primary id for our external price source.
let product_id = schema_builder.add_u64_field("product_id", FAST);
let text = schema_builder.add_text_field("text", TEXT);
let schema: Schema = schema_builder.build();
let price_table = ExternalPriceTable::default();
let price_dynamic_column = Arc::new(DynamicPriceColumn::with_product_id_field(
product_id,
price_table.clone(),
));
price_table.update_price(OLIVE_OIL, 12);
price_table.update_price(GLOVES, 13);
price_table.update_price(SNEAKERS, 80);
const OLIVE_OIL: ProductId = 323423;
const GLOVES: ProductId = 3966623;
const SNEAKERS: ProductId = 23222;
let index = Index::create_in_ram(schema);
let mut writer = index.writer_with_num_threads(1, 10_000_000)?;
writer.add_document(doc!(product_id=>OLIVE_OIL, text=>"cooking olive oil from greece"))?;
writer.add_document(doc!(product_id=>GLOVES, text=>"kitchen gloves, perfect for cooking"))?;
writer.add_document(doc!(product_id=>SNEAKERS, text=>"uber sweet sneakers"))?;
writer.commit()?;
let warmers: Vec<Weak<dyn Warmer>> = vec![Arc::downgrade(
&(price_dynamic_column.clone() as Arc<dyn Warmer>),
)];
let reader: IndexReader = index
.reader_builder()
.warmers(warmers)
.num_searchers(1)
.try_into()?;
reader.reload()?;
let query_parser = QueryParser::for_index(&index, vec![text]);
let query = query_parser.parse_query("cooking")?;
let searcher = reader.searcher();
let score_by_price = move |segment_reader: &SegmentReader| {
let price = price_dynamic_column
.price_for_segment(segment_reader)
.unwrap();
move |doc_id: DocId| Reverse(price[doc_id as usize])
};
let most_expensive_first = TopDocs::with_limit(10).custom_score(score_by_price);
let hits = searcher.search(&query, &most_expensive_first)?;
assert_eq!(
&hits,
&[
(
Reverse(12u32),
DocAddress {
segment_ord: 0,
doc_id: 0u32
}
),
(
Reverse(13u32),
DocAddress {
segment_ord: 0,
doc_id: 1u32
}
),
]
);
// Olive oil just got more expensive!
price_table.update_price(OLIVE_OIL, 15);
// The price update are directly reflected on `reload`.
//
// Be careful here though!...
// You may have spotted that we are still using the same `Searcher`.
//
// It is up to the `Warmer` implementer to decide how
// to control this behavior.
reader.reload()?;
let hits_with_new_prices = searcher.search(&query, &most_expensive_first)?;
assert_eq!(
&hits_with_new_prices,
&[
(
Reverse(13u32),
DocAddress {
segment_ord: 0,
doc_id: 1u32
}
),
(
Reverse(15u32),
DocAddress {
segment_ord: 0,
doc_id: 0u32
}
),
]
);
Ok(())
}

View File

@@ -1,3 +1,4 @@
use tantivy;
use tantivy::schema::*;
// # Document from json
@@ -21,7 +22,7 @@ fn main() -> tantivy::Result<()> {
}"#;
// We can parse our document
let _mice_and_men_doc = schema.parse_document(mice_and_men_doc_json)?;
let _mice_and_men_doc = schema.parse_document(&mice_and_men_doc_json)?;
// Multi-valued field are allowed, they are
// expressed in JSON by an array.
@@ -30,7 +31,7 @@ fn main() -> tantivy::Result<()> {
"title": ["Frankenstein", "The Modern Prometheus"],
"year": 1818
}"#;
let _frankenstein_doc = schema.parse_document(frankenstein_json)?;
let _frankenstein_doc = schema.parse_document(&frankenstein_json)?;
// Note that the schema is saved in your index directory.
//

View File

@@ -1,24 +0,0 @@
[package]
name = "fastfield_codecs"
version = "0.1.0"
authors = ["Pascal Seitz <pascal@quickwit.io>"]
license = "MIT"
edition = "2018"
description = "Fast field codecs used by tantivy"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[dependencies]
common = { version = "0.1", path = "../common/", package = "tantivy-common" }
tantivy-bitpacker = { version="0.1.1", path = "../bitpacker/" }
prettytable-rs = {version="0.8.0", optional= true}
rand = {version="0.8.3", optional= true}
[dev-dependencies]
more-asserts = "0.2.1"
rand = "0.8.3"
[features]
bin = ["prettytable-rs", "rand"]
default = ["bin"]

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

@@ -1,108 +0,0 @@
#![feature(test)]
extern crate test;
#[cfg(test)]
mod tests {
use fastfield_codecs::bitpacked::{BitpackedFastFieldReader, BitpackedFastFieldSerializer};
use fastfield_codecs::linearinterpol::{
LinearInterpolFastFieldReader, LinearInterpolFastFieldSerializer,
};
use fastfield_codecs::multilinearinterpol::{
MultiLinearInterpolFastFieldReader, MultiLinearInterpolFastFieldSerializer,
};
use fastfield_codecs::*;
fn get_data() -> Vec<u64> {
let mut data: Vec<_> = (100..55000_u64)
.map(|num| num + rand::random::<u8>() as u64)
.collect();
data.push(99_000);
data.insert(1000, 2000);
data.insert(2000, 100);
data.insert(3000, 4100);
data.insert(4000, 100);
data.insert(5000, 800);
data
}
fn value_iter() -> impl Iterator<Item = u64> {
0..20_000
}
fn bench_get<S: FastFieldCodecSerializer, R: FastFieldCodecReader>(
b: &mut Bencher,
data: &[u64],
) {
let mut bytes = vec![];
S::serialize(
&mut bytes,
&data,
stats_from_vec(data),
data.iter().cloned(),
data.iter().cloned(),
)
.unwrap();
let reader = R::open_from_bytes(&bytes).unwrap();
b.iter(|| {
for pos in value_iter() {
reader.get_u64(pos as u64, &bytes);
}
});
}
fn bench_create<S: FastFieldCodecSerializer>(b: &mut Bencher, data: &[u64]) {
let mut bytes = vec![];
b.iter(|| {
S::serialize(
&mut bytes,
&data,
stats_from_vec(data),
data.iter().cloned(),
data.iter().cloned(),
)
.unwrap();
});
}
use test::Bencher;
#[bench]
fn bench_fastfield_bitpack_create(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_create::<BitpackedFastFieldSerializer>(b, &data);
}
#[bench]
fn bench_fastfield_linearinterpol_create(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_create::<LinearInterpolFastFieldSerializer>(b, &data);
}
#[bench]
fn bench_fastfield_multilinearinterpol_create(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_create::<MultiLinearInterpolFastFieldSerializer>(b, &data);
}
#[bench]
fn bench_fastfield_bitpack_get(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get::<BitpackedFastFieldSerializer, BitpackedFastFieldReader>(b, &data);
}
#[bench]
fn bench_fastfield_linearinterpol_get(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get::<LinearInterpolFastFieldSerializer, LinearInterpolFastFieldReader>(b, &data);
}
#[bench]
fn bench_fastfield_multilinearinterpol_get(b: &mut Bencher) {
let data: Vec<_> = get_data();
bench_get::<MultiLinearInterpolFastFieldSerializer, MultiLinearInterpolFastFieldReader>(
b, &data,
);
}
pub fn stats_from_vec(data: &[u64]) -> FastFieldStats {
let min_value = data.iter().cloned().min().unwrap_or(0);
let max_value = data.iter().cloned().max().unwrap_or(0);
FastFieldStats {
min_value,
max_value,
num_vals: data.len() as u64,
}
}
}

View File

@@ -1,172 +0,0 @@
use std::io::{self, Write};
use common::BinarySerializable;
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use crate::{FastFieldCodecReader, FastFieldCodecSerializer, FastFieldDataAccess, FastFieldStats};
/// Depending on the field type, a different
/// fast field is required.
#[derive(Clone)]
pub struct BitpackedFastFieldReader {
bit_unpacker: BitUnpacker,
pub min_value_u64: u64,
pub max_value_u64: u64,
}
impl<'data> FastFieldCodecReader for BitpackedFastFieldReader {
/// Opens a fast field given a file.
fn open_from_bytes(bytes: &[u8]) -> io::Result<Self> {
let (_data, mut footer) = bytes.split_at(bytes.len() - 16);
let min_value = u64::deserialize(&mut footer)?;
let amplitude = u64::deserialize(&mut footer)?;
let max_value = min_value + amplitude;
let num_bits = compute_num_bits(amplitude);
let bit_unpacker = BitUnpacker::new(num_bits);
Ok(BitpackedFastFieldReader {
min_value_u64: min_value,
max_value_u64: max_value,
bit_unpacker,
})
}
#[inline]
fn get_u64(&self, doc: u64, data: &[u8]) -> u64 {
self.min_value_u64 + self.bit_unpacker.get(doc, data)
}
#[inline]
fn min_value(&self) -> u64 {
self.min_value_u64
}
#[inline]
fn max_value(&self) -> u64 {
self.max_value_u64
}
}
pub struct BitpackedFastFieldSerializerLegacy<'a, W: 'a + Write> {
bit_packer: BitPacker,
write: &'a mut W,
min_value: u64,
amplitude: u64,
num_bits: u8,
}
impl<'a, W: Write> BitpackedFastFieldSerializerLegacy<'a, W> {
/// Creates a new fast field serializer.
///
/// The serializer in fact encode the values by bitpacking
/// `(val - min_value)`.
///
/// It requires a `min_value` and a `max_value` to compute
/// compute the minimum number of bits required to encode
/// values.
pub fn open(
write: &'a mut W,
min_value: u64,
max_value: u64,
) -> io::Result<BitpackedFastFieldSerializerLegacy<'a, W>> {
assert!(min_value <= max_value);
let amplitude = max_value - min_value;
let num_bits = compute_num_bits(amplitude);
let bit_packer = BitPacker::new();
Ok(BitpackedFastFieldSerializerLegacy {
bit_packer,
write,
min_value,
amplitude,
num_bits,
})
}
/// Pushes a new value to the currently open u64 fast field.
#[inline]
pub fn add_val(&mut self, val: u64) -> io::Result<()> {
let val_to_write: u64 = val - self.min_value;
self.bit_packer
.write(val_to_write, self.num_bits, &mut self.write)?;
Ok(())
}
pub fn close_field(mut self) -> io::Result<()> {
self.bit_packer.close(&mut self.write)?;
self.min_value.serialize(&mut self.write)?;
self.amplitude.serialize(&mut self.write)?;
Ok(())
}
}
pub struct BitpackedFastFieldSerializer {}
impl FastFieldCodecSerializer for BitpackedFastFieldSerializer {
const NAME: &'static str = "Bitpacked";
const ID: u8 = 1;
/// Serializes data with the BitpackedFastFieldSerializer.
///
/// The serializer in fact encode the values by bitpacking
/// `(val - min_value)`.
///
/// It requires a `min_value` and a `max_value` to compute
/// compute the minimum number of bits required to encode
/// values.
fn serialize(
write: &mut impl Write,
_fastfield_accessor: &impl FastFieldDataAccess,
stats: FastFieldStats,
data_iter: impl Iterator<Item = u64>,
_data_iter1: impl Iterator<Item = u64>,
) -> io::Result<()> {
let mut serializer =
BitpackedFastFieldSerializerLegacy::open(write, stats.min_value, stats.max_value)?;
for val in data_iter {
serializer.add_val(val)?;
}
serializer.close_field()?;
Ok(())
}
fn is_applicable(
_fastfield_accessor: &impl FastFieldDataAccess,
_stats: FastFieldStats,
) -> bool {
true
}
fn estimate(_fastfield_accessor: &impl FastFieldDataAccess, stats: FastFieldStats) -> f32 {
let amplitude = stats.max_value - stats.min_value;
let num_bits = compute_num_bits(amplitude);
let num_bits_uncompressed = 64;
num_bits as f32 / num_bits_uncompressed as f32
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::tests::get_codec_test_data_sets;
fn create_and_validate(data: &[u64], name: &str) {
crate::tests::create_and_validate::<BitpackedFastFieldSerializer, BitpackedFastFieldReader>(
data, name,
);
}
#[test]
fn test_with_codec_data_sets() {
let data_sets = get_codec_test_data_sets();
for (mut data, name) in data_sets {
create_and_validate(&data, name);
data.reverse();
create_and_validate(&data, name);
}
}
#[test]
fn bitpacked_fast_field_rand() {
for _ in 0..500 {
let mut data = (0..1 + rand::random::<u8>() as usize)
.map(|_| rand::random::<i64>() as u64 / 2)
.collect::<Vec<_>>();
create_and_validate(&data, "rand");
data.reverse();
create_and_validate(&data, "rand");
}
}
}

View File

@@ -1,224 +0,0 @@
#[cfg(test)]
#[macro_use]
extern crate more_asserts;
use std::io;
use std::io::Write;
pub mod bitpacked;
pub mod linearinterpol;
pub mod multilinearinterpol;
pub trait FastFieldCodecReader: Sized {
/// reads the metadata and returns the CodecReader
fn open_from_bytes(bytes: &[u8]) -> std::io::Result<Self>;
fn get_u64(&self, doc: u64, data: &[u8]) -> u64;
fn min_value(&self) -> u64;
fn max_value(&self) -> u64;
}
/// The FastFieldSerializerEstimate trait is required on all variants
/// of fast field compressions, to decide which one to choose.
pub trait FastFieldCodecSerializer {
/// A codex needs to provide a unique name and id, which is
/// used for debugging and de/serialization.
const NAME: &'static str;
const ID: u8;
/// Check if the Codec is able to compress the data
fn is_applicable(fastfield_accessor: &impl FastFieldDataAccess, stats: FastFieldStats) -> bool;
/// Returns an estimate of the compression ratio.
/// The baseline is uncompressed 64bit data.
///
/// It could make sense to also return a value representing
/// computational complexity.
fn estimate(fastfield_accessor: &impl FastFieldDataAccess, stats: FastFieldStats) -> f32;
/// Serializes the data using the serializer into write.
/// There are multiple iterators, in case the codec needs to read the data multiple times.
/// The iterators should be preferred over using fastfield_accessor for performance reasons.
fn serialize(
write: &mut impl Write,
fastfield_accessor: &impl FastFieldDataAccess,
stats: FastFieldStats,
data_iter: impl Iterator<Item = u64>,
data_iter1: impl Iterator<Item = u64>,
) -> io::Result<()>;
}
/// FastFieldDataAccess is the trait to access fast field data during serialization and estimation.
pub trait FastFieldDataAccess {
/// Return the value associated to the given position.
///
/// Whenever possible use the Iterator passed to the fastfield creation instead, for performance
/// reasons.
///
/// # Panics
///
/// May panic if `position` is greater than the index.
fn get_val(&self, position: u64) -> u64;
}
#[derive(Debug, Clone)]
pub struct FastFieldStats {
pub min_value: u64,
pub max_value: u64,
pub num_vals: u64,
}
impl<'a> FastFieldDataAccess for &'a [u64] {
fn get_val(&self, position: u64) -> u64 {
self[position as usize]
}
}
impl FastFieldDataAccess for Vec<u64> {
fn get_val(&self, position: u64) -> u64 {
self[position as usize]
}
}
#[cfg(test)]
mod tests {
use crate::bitpacked::{BitpackedFastFieldReader, BitpackedFastFieldSerializer};
use crate::linearinterpol::{LinearInterpolFastFieldReader, LinearInterpolFastFieldSerializer};
use crate::multilinearinterpol::{
MultiLinearInterpolFastFieldReader, MultiLinearInterpolFastFieldSerializer,
};
pub fn create_and_validate<S: FastFieldCodecSerializer, R: FastFieldCodecReader>(
data: &[u64],
name: &str,
) -> (f32, f32) {
if !S::is_applicable(&data, crate::tests::stats_from_vec(data)) {
return (f32::MAX, 0.0);
}
let estimation = S::estimate(&data, crate::tests::stats_from_vec(data));
let mut out = vec![];
S::serialize(
&mut out,
&data,
crate::tests::stats_from_vec(data),
data.iter().cloned(),
data.iter().cloned(),
)
.unwrap();
let reader = R::open_from_bytes(&out).unwrap();
for (doc, orig_val) in data.iter().enumerate() {
let val = reader.get_u64(doc as u64, &out);
if val != *orig_val {
panic!(
"val {:?} does not match orig_val {:?}, in data set {}, data {:?}",
val, orig_val, name, data
);
}
}
let actual_compression = out.len() as f32 / (data.len() as f32 * 8.0);
(estimation, actual_compression)
}
pub fn get_codec_test_data_sets() -> Vec<(Vec<u64>, &'static str)> {
let mut data_and_names = vec![];
let data = (10..=20_u64).collect::<Vec<_>>();
data_and_names.push((data, "simple monotonically increasing"));
data_and_names.push((
vec![5, 6, 7, 8, 9, 10, 99, 100],
"offset in linear interpol",
));
data_and_names.push((vec![5, 50, 3, 13, 1, 1000, 35], "rand small"));
data_and_names.push((vec![10], "single value"));
data_and_names
}
fn test_codec<S: FastFieldCodecSerializer, R: FastFieldCodecReader>() {
let codec_name = S::NAME;
for (data, data_set_name) in get_codec_test_data_sets() {
let (estimate, actual) =
crate::tests::create_and_validate::<S, R>(&data, data_set_name);
let result = if estimate == f32::MAX {
"Disabled".to_string()
} else {
format!("Estimate {:?} Actual {:?} ", estimate, actual)
};
println!(
"Codec {}, DataSet {}, {}",
codec_name, data_set_name, result
);
}
}
#[test]
fn test_codec_bitpacking() {
test_codec::<BitpackedFastFieldSerializer, BitpackedFastFieldReader>();
}
#[test]
fn test_codec_interpolation() {
test_codec::<LinearInterpolFastFieldSerializer, LinearInterpolFastFieldReader>();
}
#[test]
fn test_codec_multi_interpolation() {
test_codec::<MultiLinearInterpolFastFieldSerializer, MultiLinearInterpolFastFieldReader>();
}
use super::*;
pub fn stats_from_vec(data: &[u64]) -> FastFieldStats {
let min_value = data.iter().cloned().min().unwrap_or(0);
let max_value = data.iter().cloned().max().unwrap_or(0);
FastFieldStats {
min_value,
max_value,
num_vals: data.len() as u64,
}
}
#[test]
fn estimation_good_interpolation_case() {
let data = (10..=20000_u64).collect::<Vec<_>>();
let linear_interpol_estimation =
LinearInterpolFastFieldSerializer::estimate(&data, stats_from_vec(&data));
assert_le!(linear_interpol_estimation, 0.01);
let multi_linear_interpol_estimation =
MultiLinearInterpolFastFieldSerializer::estimate(&data, stats_from_vec(&data));
assert_le!(multi_linear_interpol_estimation, 0.2);
assert_le!(linear_interpol_estimation, multi_linear_interpol_estimation);
let bitpacked_estimation =
BitpackedFastFieldSerializer::estimate(&data, stats_from_vec(&data));
assert_le!(linear_interpol_estimation, bitpacked_estimation);
}
#[test]
fn estimation_test_bad_interpolation_case() {
let data = vec![200, 10, 10, 10, 10, 1000, 20];
let linear_interpol_estimation =
LinearInterpolFastFieldSerializer::estimate(&data, stats_from_vec(&data));
assert_le!(linear_interpol_estimation, 0.32);
let bitpacked_estimation =
BitpackedFastFieldSerializer::estimate(&data, stats_from_vec(&data));
assert_le!(bitpacked_estimation, linear_interpol_estimation);
}
#[test]
fn estimation_test_bad_interpolation_case_monotonically_increasing() {
let mut data = (200..=20000_u64).collect::<Vec<_>>();
data.push(1_000_000);
// 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 =
LinearInterpolFastFieldSerializer::estimate(&data, stats_from_vec(&data));
assert_le!(linear_interpol_estimation, 0.35);
let bitpacked_estimation =
BitpackedFastFieldSerializer::estimate(&data, stats_from_vec(&data));
assert_le!(bitpacked_estimation, 0.32);
assert_le!(bitpacked_estimation, linear_interpol_estimation);
}
}

View File

@@ -1,300 +0,0 @@
use std::io::{self, Read, Write};
use std::ops::Sub;
use common::{BinarySerializable, FixedSize};
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use crate::{FastFieldCodecReader, FastFieldCodecSerializer, FastFieldDataAccess, FastFieldStats};
/// Depending on the field type, a different
/// fast field is required.
#[derive(Clone)]
pub struct LinearInterpolFastFieldReader {
bit_unpacker: BitUnpacker,
pub footer: LinearInterpolFooter,
pub slope: f32,
}
#[derive(Clone, Debug)]
pub struct LinearInterpolFooter {
pub relative_max_value: u64,
pub offset: u64,
pub first_val: u64,
pub last_val: u64,
pub num_vals: u64,
pub min_value: u64,
pub max_value: u64,
}
impl BinarySerializable for LinearInterpolFooter {
fn serialize<W: Write>(&self, write: &mut W) -> io::Result<()> {
self.relative_max_value.serialize(write)?;
self.offset.serialize(write)?;
self.first_val.serialize(write)?;
self.last_val.serialize(write)?;
self.num_vals.serialize(write)?;
self.min_value.serialize(write)?;
self.max_value.serialize(write)?;
Ok(())
}
fn deserialize<R: Read>(reader: &mut R) -> io::Result<LinearInterpolFooter> {
Ok(LinearInterpolFooter {
relative_max_value: u64::deserialize(reader)?,
offset: u64::deserialize(reader)?,
first_val: u64::deserialize(reader)?,
last_val: u64::deserialize(reader)?,
num_vals: u64::deserialize(reader)?,
min_value: u64::deserialize(reader)?,
max_value: u64::deserialize(reader)?,
})
}
}
impl FixedSize for LinearInterpolFooter {
const SIZE_IN_BYTES: usize = 56;
}
impl FastFieldCodecReader for LinearInterpolFastFieldReader {
/// Opens a fast field given a file.
fn open_from_bytes(bytes: &[u8]) -> io::Result<Self> {
let (_data, mut footer) = bytes.split_at(bytes.len() - LinearInterpolFooter::SIZE_IN_BYTES);
let footer = LinearInterpolFooter::deserialize(&mut footer)?;
let slope = get_slope(footer.first_val, footer.last_val, footer.num_vals);
let num_bits = compute_num_bits(footer.relative_max_value);
let bit_unpacker = BitUnpacker::new(num_bits);
Ok(LinearInterpolFastFieldReader {
bit_unpacker,
footer,
slope,
})
}
#[inline]
fn get_u64(&self, doc: u64, data: &[u8]) -> u64 {
let calculated_value = get_calculated_value(self.footer.first_val, doc, self.slope);
(calculated_value + self.bit_unpacker.get(doc, data)) - self.footer.offset
}
#[inline]
fn min_value(&self) -> u64 {
self.footer.min_value
}
#[inline]
fn max_value(&self) -> u64 {
self.footer.max_value
}
}
/// Fastfield serializer, which tries to guess values by linear interpolation
/// and stores the difference bitpacked.
pub struct LinearInterpolFastFieldSerializer {}
#[inline]
fn get_slope(first_val: u64, last_val: u64, num_vals: u64) -> f32 {
if num_vals <= 1 {
return 0.0;
}
// We calculate the slope with f64 high precision and use the result in lower precision f32
// This is done in order to handle estimations for very large values like i64::MAX
((last_val as f64 - first_val as f64) / (num_vals as u64 - 1) as f64) as f32
}
#[inline]
fn get_calculated_value(first_val: u64, pos: u64, slope: f32) -> u64 {
first_val + (pos as f32 * slope) as u64
}
impl FastFieldCodecSerializer for LinearInterpolFastFieldSerializer {
const NAME: &'static str = "LinearInterpol";
const ID: u8 = 2;
/// Creates a new fast field serializer.
fn serialize(
write: &mut impl Write,
fastfield_accessor: &impl FastFieldDataAccess,
stats: FastFieldStats,
data_iter: impl Iterator<Item = u64>,
data_iter1: impl Iterator<Item = u64>,
) -> io::Result<()> {
assert!(stats.min_value <= stats.max_value);
let first_val = fastfield_accessor.get_val(0);
let last_val = fastfield_accessor.get_val(stats.num_vals as u64 - 1);
let slope = get_slope(first_val, last_val, stats.num_vals);
// calculate offset to ensure all values are positive
let mut offset = 0;
let mut rel_positive_max = 0;
for (pos, actual_value) in data_iter1.enumerate() {
let calculated_value = get_calculated_value(first_val, pos as u64, slope);
if calculated_value > actual_value {
// negative value we need to apply an offset
// we ignore negative values in the max value calculation, because negative values
// will be offset to 0
offset = offset.max(calculated_value - actual_value);
} else {
// positive value no offset reuqired
rel_positive_max = rel_positive_max.max(actual_value - calculated_value);
}
}
// rel_positive_max will be adjusted by offset
let relative_max_value = rel_positive_max + offset;
let num_bits = compute_num_bits(relative_max_value);
let mut bit_packer = BitPacker::new();
for (pos, val) in data_iter.enumerate() {
let calculated_value = get_calculated_value(first_val, pos as u64, slope);
let diff = (val + offset) - calculated_value;
bit_packer.write(diff, num_bits, write)?;
}
bit_packer.close(write)?;
let footer = LinearInterpolFooter {
relative_max_value,
offset,
first_val,
last_val,
num_vals: stats.num_vals,
min_value: stats.min_value,
max_value: stats.max_value,
};
footer.serialize(write)?;
Ok(())
}
fn is_applicable(
_fastfield_accessor: &impl FastFieldDataAccess,
stats: FastFieldStats,
) -> bool {
if stats.num_vals < 3 {
return false; // disable compressor for this case
}
// On serialisation the offset is added to the actual value.
// We need to make sure this won't run into overflow calculation issues.
// For this we take the maximum theroretical offset and add this to the max value.
// If this doesn't overflow the algortihm should be fine
let theorethical_maximum_offset = stats.max_value - stats.min_value;
if stats
.max_value
.checked_add(theorethical_maximum_offset)
.is_none()
{
return false;
}
true
}
/// 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.
fn estimate(fastfield_accessor: &impl FastFieldDataAccess, stats: FastFieldStats) -> f32 {
let first_val = fastfield_accessor.get_val(0);
let last_val = fastfield_accessor.get_val(stats.num_vals as u64 - 1);
let slope = get_slope(first_val, last_val, stats.num_vals);
// let's sample at 0%, 5%, 10% .. 95%, 100%
let num_vals = stats.num_vals as f32 / 100.0;
let sample_positions = (0..20)
.map(|pos| (num_vals * pos as f32 * 5.0) as usize)
.collect::<Vec<_>>();
let max_distance = sample_positions
.iter()
.map(|pos| {
let calculated_value = get_calculated_value(first_val, *pos as u64, slope);
let actual_value = fastfield_accessor.get_val(*pos as u64);
distance(calculated_value, actual_value)
})
.max()
.unwrap_or(0);
// the theory would be that we don't have the actual max_distance, but we are close within
// 50% threshold.
// It is multiplied by 2 because in a log case scenario the line would be as much above as
// below. So the offset would = max_distance
//
let relative_max_value = (max_distance as f32 * 1.5) * 2.0;
let num_bits = compute_num_bits(relative_max_value as u64) as u64 * stats.num_vals as u64
+ LinearInterpolFooter::SIZE_IN_BYTES as u64;
let num_bits_uncompressed = 64 * stats.num_vals;
num_bits as f32 / num_bits_uncompressed as f32
}
}
#[inline]
fn distance<T: Sub<Output = T> + Ord>(x: T, y: T) -> T {
if x < y {
y - x
} else {
x - y
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::tests::get_codec_test_data_sets;
fn create_and_validate(data: &[u64], name: &str) -> (f32, f32) {
crate::tests::create_and_validate::<
LinearInterpolFastFieldSerializer,
LinearInterpolFastFieldReader,
>(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");
assert!(actual_compression < 0.01);
assert!(estimate < 0.01);
}
#[test]
fn test_with_codec_data_sets() {
let data_sets = get_codec_test_data_sets();
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 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() {
for _ in 0..5000 {
let mut data = (0..50).map(|_| rand::random::<u64>()).collect::<Vec<_>>();
create_and_validate(&data, "random");
data.reverse();
create_and_validate(&data, "random");
}
}
}

View File

@@ -1,124 +0,0 @@
#[macro_use]
extern crate prettytable;
use fastfield_codecs::linearinterpol::LinearInterpolFastFieldSerializer;
use fastfield_codecs::multilinearinterpol::MultiLinearInterpolFastFieldSerializer;
use fastfield_codecs::{FastFieldCodecSerializer, FastFieldStats};
use prettytable::{Cell, Row, Table};
fn main() {
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 mut results = vec![];
let res = serialize_with_codec::<LinearInterpolFastFieldSerializer>(&data);
results.push(res);
let res = serialize_with_codec::<MultiLinearInterpolFastFieldSerializer>(&data);
results.push(res);
let res = serialize_with_codec::<fastfield_codecs::bitpacked::BitpackedFastFieldSerializer>(
&data,
);
results.push(res);
// let best_estimation_codec = results
//.iter()
//.min_by(|res1, res2| res1.partial_cmp(&res2).unwrap())
//.unwrap();
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 (is_applicable, est, comp, name) in results {
let (est_cell, ratio_cell) = if !is_applicable {
("Codec Disabled".to_string(), "".to_string())
} else {
(est.to_string(), comp.to_string())
};
let style = if comp == best_compression_ratio_codec.1 {
"Fb"
} else {
""
};
table.add_row(Row::new(vec![
Cell::new(name).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<S: FastFieldCodecSerializer>(
data: &[u64],
) -> (bool, f32, f32, &'static str) {
let is_applicable = S::is_applicable(&data, stats_from_vec(data));
if !is_applicable {
return (false, 0.0, 0.0, S::NAME);
}
let estimation = S::estimate(&data, stats_from_vec(data));
let mut out = vec![];
S::serialize(
&mut out,
&data,
stats_from_vec(data),
data.iter().cloned(),
data.iter().cloned(),
)
.unwrap();
let actual_compression = out.len() as f32 / (data.len() * 8) as f32;
(true, estimation, actual_compression, S::NAME)
}
pub fn stats_from_vec(data: &[u64]) -> FastFieldStats {
let min_value = data.iter().cloned().min().unwrap_or(0);
let max_value = data.iter().cloned().max().unwrap_or(0);
FastFieldStats {
min_value,
max_value,
num_vals: data.len() as u64,
}
}

View File

@@ -1,427 +0,0 @@
//! MultiLinearInterpol compressor uses linear interpolation to guess a values and stores the
//! offset, but in blocks of 512.
//!
//! With a CHUNK_SIZE of 512 and 29 byte metadata per block, we get a overhead for metadata of 232 /
//! 512 = 0,45 bits per element. The additional space required per element in a block is the the
//! maximum deviation of the linear interpolation estimation function.
//!
//! E.g. if the maximum deviation of an element is 12, all elements cost 4bits.
//!
//! Size per block:
//! Num Elements * Maximum Deviation from Interpolation + 29 Byte Metadata
use std::io::{self, Read, Write};
use std::ops::Sub;
use common::{BinarySerializable, CountingWriter, DeserializeFrom};
use tantivy_bitpacker::{compute_num_bits, BitPacker, BitUnpacker};
use crate::{FastFieldCodecReader, FastFieldCodecSerializer, FastFieldDataAccess, FastFieldStats};
const CHUNK_SIZE: u64 = 512;
/// Depending on the field type, a different
/// fast field is required.
#[derive(Clone)]
pub struct MultiLinearInterpolFastFieldReader {
pub footer: MultiLinearInterpolFooter,
}
#[derive(Clone, Debug, Default)]
struct Function {
// The offset in the data is required, because we have diffrent bit_widths per block
data_start_offset: u64,
// start_pos in the block will be CHUNK_SIZE * BLOCK_NUM
start_pos: u64,
// only used during serialization, 0 after deserialization
end_pos: u64,
// only used during serialization, 0 after deserialization
value_start_pos: u64,
// only used during serialization, 0 after deserialization
value_end_pos: u64,
slope: f32,
// The offset so that all values are positive when writing them
positive_val_offset: u64,
num_bits: u8,
bit_unpacker: BitUnpacker,
}
impl Function {
fn calc_slope(&mut self) {
let num_vals = self.end_pos - self.start_pos;
self.slope = get_slope(self.value_start_pos, self.value_end_pos, num_vals);
}
// split the interpolation into two function, change self and return the second split
fn split(&mut self, split_pos: u64, split_pos_value: u64) -> Function {
let mut new_function = Function {
start_pos: split_pos,
end_pos: self.end_pos,
value_start_pos: split_pos_value,
value_end_pos: self.value_end_pos,
..Default::default()
};
new_function.calc_slope();
self.end_pos = split_pos;
self.value_end_pos = split_pos_value;
self.calc_slope();
new_function
}
}
impl BinarySerializable for Function {
fn serialize<W: Write>(&self, write: &mut W) -> io::Result<()> {
self.data_start_offset.serialize(write)?;
self.value_start_pos.serialize(write)?;
self.positive_val_offset.serialize(write)?;
self.slope.serialize(write)?;
self.num_bits.serialize(write)?;
Ok(())
}
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Function> {
let data_start_offset = u64::deserialize(reader)?;
let value_start_pos = u64::deserialize(reader)?;
let offset = u64::deserialize(reader)?;
let slope = f32::deserialize(reader)?;
let num_bits = u8::deserialize(reader)?;
let interpolation = Function {
data_start_offset,
value_start_pos,
positive_val_offset: offset,
num_bits,
bit_unpacker: BitUnpacker::new(num_bits),
slope,
..Default::default()
};
Ok(interpolation)
}
}
#[derive(Clone, Debug)]
pub struct MultiLinearInterpolFooter {
pub num_vals: u64,
pub min_value: u64,
pub max_value: u64,
interpolations: Vec<Function>,
}
impl BinarySerializable for MultiLinearInterpolFooter {
fn serialize<W: Write>(&self, write: &mut W) -> io::Result<()> {
let mut out = vec![];
self.num_vals.serialize(&mut out)?;
self.min_value.serialize(&mut out)?;
self.max_value.serialize(&mut out)?;
self.interpolations.serialize(&mut out)?;
write.write_all(&out)?;
(out.len() as u32).serialize(write)?;
Ok(())
}
fn deserialize<R: Read>(reader: &mut R) -> io::Result<MultiLinearInterpolFooter> {
let mut footer = MultiLinearInterpolFooter {
num_vals: u64::deserialize(reader)?,
min_value: u64::deserialize(reader)?,
max_value: u64::deserialize(reader)?,
interpolations: Vec::<Function>::deserialize(reader)?,
};
for (num, interpol) in footer.interpolations.iter_mut().enumerate() {
interpol.start_pos = CHUNK_SIZE * num as u64;
}
Ok(footer)
}
}
#[inline]
fn get_interpolation_position(doc: u64) -> usize {
let index = doc / CHUNK_SIZE;
index as usize
}
#[inline]
fn get_interpolation_function(doc: u64, interpolations: &[Function]) -> &Function {
&interpolations[get_interpolation_position(doc)]
}
impl FastFieldCodecReader for MultiLinearInterpolFastFieldReader {
/// Opens a fast field given a file.
fn open_from_bytes(bytes: &[u8]) -> io::Result<Self> {
let footer_len: u32 = (&bytes[bytes.len() - 4..]).deserialize()?;
let (_data, mut footer) = bytes.split_at(bytes.len() - (4 + footer_len) as usize);
let footer = MultiLinearInterpolFooter::deserialize(&mut footer)?;
Ok(MultiLinearInterpolFastFieldReader { footer })
}
#[inline]
fn get_u64(&self, doc: u64, data: &[u8]) -> u64 {
let interpolation = get_interpolation_function(doc, &self.footer.interpolations);
let doc = doc - interpolation.start_pos;
let calculated_value =
get_calculated_value(interpolation.value_start_pos, doc, interpolation.slope);
let diff = interpolation
.bit_unpacker
.get(doc, &data[interpolation.data_start_offset as usize..]);
(calculated_value + diff) - interpolation.positive_val_offset
}
#[inline]
fn min_value(&self) -> u64 {
self.footer.min_value
}
#[inline]
fn max_value(&self) -> u64 {
self.footer.max_value
}
}
#[inline]
fn get_slope(first_val: u64, last_val: u64, num_vals: u64) -> f32 {
((last_val as f64 - first_val as f64) / (num_vals as u64 - 1) as f64) as f32
}
#[inline]
fn get_calculated_value(first_val: u64, pos: u64, slope: f32) -> u64 {
(first_val as i64 + (pos as f32 * slope) as i64) as u64
}
/// Same as LinearInterpolFastFieldSerializer, but working on chunks of CHUNK_SIZE elements.
pub struct MultiLinearInterpolFastFieldSerializer {}
impl FastFieldCodecSerializer for MultiLinearInterpolFastFieldSerializer {
const NAME: &'static str = "MultiLinearInterpol";
const ID: u8 = 3;
/// Creates a new fast field serializer.
fn serialize(
write: &mut impl Write,
fastfield_accessor: &impl FastFieldDataAccess,
stats: FastFieldStats,
data_iter: impl Iterator<Item = u64>,
_data_iter1: impl Iterator<Item = u64>,
) -> io::Result<()> {
assert!(stats.min_value <= stats.max_value);
let first_val = fastfield_accessor.get_val(0);
let last_val = fastfield_accessor.get_val(stats.num_vals as u64 - 1);
let mut first_function = Function {
end_pos: stats.num_vals,
value_start_pos: first_val,
value_end_pos: last_val,
..Default::default()
};
first_function.calc_slope();
let mut interpolations = vec![first_function];
// Since we potentially apply multiple passes over the data, the data is cached.
// Multiple iteration can be expensive (merge with index sorting can add lot of overhead per
// iteration)
let data = data_iter.collect::<Vec<_>>();
//// let's split this into chunks of CHUNK_SIZE
for data_pos in (0..data.len() as u64).step_by(CHUNK_SIZE as usize).skip(1) {
let new_fun = {
let current_interpolation = interpolations.last_mut().unwrap();
current_interpolation.split(data_pos, data[data_pos as usize])
};
interpolations.push(new_fun);
}
// calculate offset and max (-> numbits) for each function
for interpolation in &mut interpolations {
let mut offset = 0;
let mut rel_positive_max = 0;
for (pos, actual_value) in data
[interpolation.start_pos as usize..interpolation.end_pos as usize]
.iter()
.cloned()
.enumerate()
{
let calculated_value = get_calculated_value(
interpolation.value_start_pos,
pos as u64,
interpolation.slope,
);
if calculated_value > actual_value {
// negative value we need to apply an offset
// we ignore negative values in the max value calculation, because negative
// values will be offset to 0
offset = offset.max(calculated_value - actual_value);
} else {
// positive value no offset reuqired
rel_positive_max = rel_positive_max.max(actual_value - calculated_value);
}
}
interpolation.positive_val_offset = offset;
interpolation.num_bits = compute_num_bits(rel_positive_max + offset);
}
let mut bit_packer = BitPacker::new();
let write = &mut CountingWriter::wrap(write);
for interpolation in &mut interpolations {
interpolation.data_start_offset = write.written_bytes();
let num_bits = interpolation.num_bits;
for (pos, actual_value) in data
[interpolation.start_pos as usize..interpolation.end_pos as usize]
.iter()
.cloned()
.enumerate()
{
let calculated_value = get_calculated_value(
interpolation.value_start_pos,
pos as u64,
interpolation.slope,
);
let diff = (actual_value + interpolation.positive_val_offset) - calculated_value;
bit_packer.write(diff, num_bits, write)?;
}
bit_packer.flush(write)?;
}
bit_packer.close(write)?;
let footer = MultiLinearInterpolFooter {
num_vals: stats.num_vals,
min_value: stats.min_value,
max_value: stats.max_value,
interpolations,
};
footer.serialize(write)?;
Ok(())
}
fn is_applicable(
_fastfield_accessor: &impl FastFieldDataAccess,
stats: FastFieldStats,
) -> bool {
if stats.num_vals < 5_000 {
return false;
}
// On serialization the offset is added to the actual value.
// We need to make sure this won't run into overflow calculation issues.
// For this we take the maximum theroretical offset and add this to the max value.
// If this doesn't overflow the algortihm should be fine
let theorethical_maximum_offset = stats.max_value - stats.min_value;
if stats
.max_value
.checked_add(theorethical_maximum_offset)
.is_none()
{
return false;
}
true
}
/// estimation for linear interpolation is hard because, you don't know
/// where the local maxima are for the deviation of the calculated value and
/// the offset is also unknown.
fn estimate(fastfield_accessor: &impl FastFieldDataAccess, stats: FastFieldStats) -> f32 {
let first_val_in_first_block = fastfield_accessor.get_val(0);
let last_elem_in_first_chunk = CHUNK_SIZE.min(stats.num_vals);
let last_val_in_first_block =
fastfield_accessor.get_val(last_elem_in_first_chunk as u64 - 1);
let slope = get_slope(
first_val_in_first_block,
last_val_in_first_block,
stats.num_vals,
);
// let's sample at 0%, 5%, 10% .. 95%, 100%, but for the first block only
let sample_positions = (0..20)
.map(|pos| (last_elem_in_first_chunk as f32 / 100.0 * pos as f32 * 5.0) as usize)
.collect::<Vec<_>>();
let max_distance = sample_positions
.iter()
.map(|pos| {
let calculated_value =
get_calculated_value(first_val_in_first_block, *pos as u64, slope);
let actual_value = fastfield_accessor.get_val(*pos as u64);
distance(calculated_value, actual_value)
})
.max()
.unwrap();
// Estimate one block and extrapolate the cost to all blocks.
// the theory would be that we don't have the actual max_distance, but we are close within
// 50% threshold.
// It is multiplied by 2 because in a log case scenario the line would be as much above as
// below. So the offset would = max_distance
//
let relative_max_value = (max_distance as f32 * 1.5) * 2.0;
let num_bits = compute_num_bits(relative_max_value as u64) as u64 * stats.num_vals as u64
// function metadata per block
+ 29 * (stats.num_vals / CHUNK_SIZE);
let num_bits_uncompressed = 64 * stats.num_vals;
num_bits as f32 / num_bits_uncompressed as f32
}
}
fn distance<T: Sub<Output = T> + Ord>(x: T, y: T) -> T {
if x < y {
y - x
} else {
x - y
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::tests::get_codec_test_data_sets;
fn create_and_validate(data: &[u64], name: &str) -> (f32, f32) {
crate::tests::create_and_validate::<
MultiLinearInterpolFastFieldSerializer,
MultiLinearInterpolFastFieldReader,
>(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");
assert!(actual_compression < 0.2);
assert!(estimate < 0.20);
assert!(estimate > 0.15);
assert!(actual_compression > 0.01);
}
#[test]
fn test_with_codec_data_sets() {
let data_sets = get_codec_test_data_sets();
for (mut data, name) in data_sets {
create_and_validate(&data, name);
data.reverse();
create_and_validate(&data, name);
}
}
#[test]
fn test_simple() {
let data = (10..=20_u64).collect::<Vec<_>>();
create_and_validate(&data, "simple monotonically");
}
#[test]
fn border_cases_1() {
let data = (0..1024).collect::<Vec<_>>();
create_and_validate(&data, "border case");
}
#[test]
fn border_case_2() {
let data = (0..1025).collect::<Vec<_>>();
create_and_validate(&data, "border case");
}
#[test]
fn rand() {
for _ in 0..10 {
let mut data = (5_000..20_000)
.map(|_| rand::random::<u32>() as u64)
.collect::<Vec<_>>();
let _ = create_and_validate(&data, "random");
data.reverse();
create_and_validate(&data, "random");
}
}
}

View File

@@ -1,11 +0,0 @@
[package]
authors = ["Paul Masurel <paul@quickwit.io>", "Pascal Seitz <pascal@quickwit.io>"]
name = "ownedbytes"
version = "0.2.0"
edition = "2018"
description = "Expose data as static slice"
license = "MIT"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[dependencies]
stable_deref_trait = "1.2.0"

View File

@@ -1,343 +0,0 @@
use std::convert::TryInto;
use std::ops::{Deref, Range};
use std::sync::Arc;
use std::{fmt, io, mem};
use stable_deref_trait::StableDeref;
/// An OwnedBytes simply wraps an object that owns a slice of data and exposes
/// this data as a static slice.
///
/// The backing object is required to be `StableDeref`.
#[derive(Clone)]
pub struct OwnedBytes {
data: &'static [u8],
box_stable_deref: Arc<dyn Deref<Target = [u8]> + Sync + Send>,
}
impl OwnedBytes {
/// Creates an empty `OwnedBytes`.
pub fn empty() -> OwnedBytes {
OwnedBytes::new(&[][..])
}
/// Creates an `OwnedBytes` intance given a `StableDeref` object.
pub fn new<T: StableDeref + Deref<Target = [u8]> + 'static + Send + Sync>(
data_holder: T,
) -> OwnedBytes {
let box_stable_deref = Arc::new(data_holder);
let bytes: &[u8] = box_stable_deref.as_ref();
let data = unsafe { mem::transmute::<_, &'static [u8]>(bytes.deref()) };
OwnedBytes {
data,
box_stable_deref,
}
}
/// creates a fileslice that is just a view over a slice of the data.
#[must_use]
#[inline]
pub fn slice(&self, range: Range<usize>) -> Self {
OwnedBytes {
data: &self.data[range],
box_stable_deref: self.box_stable_deref.clone(),
}
}
/// Returns the underlying slice of data.
/// `Deref` and `AsRef` are also available.
#[inline]
pub fn as_slice(&self) -> &[u8] {
self.data
}
/// Returns the len of the slice.
#[inline]
pub fn len(&self) -> usize {
self.data.len()
}
/// Splits the OwnedBytes into two OwnedBytes `(left, right)`.
///
/// Left will hold `split_len` bytes.
///
/// This operation is cheap and does not require to copy any memory.
/// On the other hand, both `left` and `right` retain a handle over
/// the entire slice of memory. In other words, the memory will only
/// be released when both left and right are dropped.
#[inline]
#[must_use]
pub fn split(self, split_len: usize) -> (OwnedBytes, OwnedBytes) {
let right_box_stable_deref = self.box_stable_deref.clone();
let left = OwnedBytes {
data: &self.data[..split_len],
box_stable_deref: self.box_stable_deref,
};
let right = OwnedBytes {
data: &self.data[split_len..],
box_stable_deref: right_box_stable_deref,
};
(left, right)
}
/// Splits the right part of the `OwnedBytes` at the given offset.
///
/// `self` is truncated to `split_len`, left with the remaining bytes.
pub fn split_off(&mut self, split_len: usize) -> OwnedBytes {
let right_box_stable_deref = self.box_stable_deref.clone();
let right_piece = OwnedBytes {
data: &self.data[split_len..],
box_stable_deref: right_box_stable_deref,
};
self.data = &self.data[..split_len];
right_piece
}
/// Returns true iff this `OwnedBytes` is empty.
#[inline]
pub fn is_empty(&self) -> bool {
self.as_slice().is_empty()
}
/// Drops the left most `advance_len` bytes.
#[inline]
pub fn advance(&mut self, advance_len: usize) {
self.data = &self.data[advance_len..]
}
/// Reads an `u8` from the `OwnedBytes` and advance by one byte.
#[inline]
pub fn read_u8(&mut self) -> u8 {
assert!(!self.is_empty());
let byte = self.as_slice()[0];
self.advance(1);
byte
}
/// Reads an `u64` encoded as little-endian from the `OwnedBytes` and advance by 8 bytes.
#[inline]
pub fn read_u64(&mut self) -> u64 {
assert!(self.len() > 7);
let octlet: [u8; 8] = self.as_slice()[..8].try_into().unwrap();
self.advance(8);
u64::from_le_bytes(octlet)
}
}
impl fmt::Debug for OwnedBytes {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
// We truncate the bytes in order to make sure the debug string
// is not too long.
let bytes_truncated: &[u8] = if self.len() > 8 {
&self.as_slice()[..10]
} else {
self.as_slice()
};
write!(f, "OwnedBytes({:?}, len={})", bytes_truncated, self.len())
}
}
impl PartialEq for OwnedBytes {
fn eq(&self, other: &OwnedBytes) -> bool {
self.as_slice() == other.as_slice()
}
}
impl Eq for OwnedBytes {}
impl PartialEq<[u8]> for OwnedBytes {
fn eq(&self, other: &[u8]) -> bool {
self.as_slice() == other
}
}
impl PartialEq<str> for OwnedBytes {
fn eq(&self, other: &str) -> bool {
self.as_slice() == other.as_bytes()
}
}
impl<'a, T: ?Sized> PartialEq<&'a T> for OwnedBytes
where OwnedBytes: PartialEq<T>
{
fn eq(&self, other: &&'a T) -> bool {
*self == **other
}
}
impl Deref for OwnedBytes {
type Target = [u8];
#[inline]
fn deref(&self) -> &Self::Target {
self.as_slice()
}
}
impl io::Read for OwnedBytes {
#[inline]
fn read(&mut self, buf: &mut [u8]) -> io::Result<usize> {
let read_len = {
let data = self.as_slice();
if data.len() >= buf.len() {
let buf_len = buf.len();
buf.copy_from_slice(&data[..buf_len]);
buf.len()
} else {
let data_len = data.len();
buf[..data_len].copy_from_slice(data);
data_len
}
};
self.advance(read_len);
Ok(read_len)
}
#[inline]
fn read_to_end(&mut self, buf: &mut Vec<u8>) -> io::Result<usize> {
let read_len = {
let data = self.as_slice();
buf.extend(data);
data.len()
};
self.advance(read_len);
Ok(read_len)
}
#[inline]
fn read_exact(&mut self, buf: &mut [u8]) -> io::Result<()> {
let read_len = self.read(buf)?;
if read_len != buf.len() {
return Err(io::Error::new(
io::ErrorKind::UnexpectedEof,
"failed to fill whole buffer",
));
}
Ok(())
}
}
impl AsRef<[u8]> for OwnedBytes {
#[inline]
fn as_ref(&self) -> &[u8] {
self.as_slice()
}
}
#[cfg(test)]
mod tests {
use std::io::{self, Read};
use super::OwnedBytes;
#[test]
fn test_owned_bytes_debug() {
let short_bytes = OwnedBytes::new(b"abcd".as_ref());
assert_eq!(
format!("{:?}", short_bytes),
"OwnedBytes([97, 98, 99, 100], len=4)"
);
let long_bytes = OwnedBytes::new(b"abcdefghijklmnopq".as_ref());
assert_eq!(
format!("{:?}", long_bytes),
"OwnedBytes([97, 98, 99, 100, 101, 102, 103, 104, 105, 106], len=17)"
);
}
#[test]
fn test_owned_bytes_read() -> io::Result<()> {
let mut bytes = OwnedBytes::new(b"abcdefghiklmnopqrstuvwxyz".as_ref());
{
let mut buf = [0u8; 5];
bytes.read_exact(&mut buf[..]).unwrap();
assert_eq!(&buf, b"abcde");
assert_eq!(bytes.as_slice(), b"fghiklmnopqrstuvwxyz")
}
{
let mut buf = [0u8; 2];
bytes.read_exact(&mut buf[..]).unwrap();
assert_eq!(&buf, b"fg");
assert_eq!(bytes.as_slice(), b"hiklmnopqrstuvwxyz")
}
Ok(())
}
#[test]
fn test_owned_bytes_read_right_at_the_end() -> io::Result<()> {
let mut bytes = OwnedBytes::new(b"abcde".as_ref());
let mut buf = [0u8; 5];
assert_eq!(bytes.read(&mut buf[..]).unwrap(), 5);
assert_eq!(&buf, b"abcde");
assert_eq!(bytes.as_slice(), b"");
assert_eq!(bytes.read(&mut buf[..]).unwrap(), 0);
assert_eq!(&buf, b"abcde");
Ok(())
}
#[test]
fn test_owned_bytes_read_incomplete() -> io::Result<()> {
let mut bytes = OwnedBytes::new(b"abcde".as_ref());
let mut buf = [0u8; 7];
assert_eq!(bytes.read(&mut buf[..]).unwrap(), 5);
assert_eq!(&buf[..5], b"abcde");
assert_eq!(bytes.read(&mut buf[..]).unwrap(), 0);
Ok(())
}
#[test]
fn test_owned_bytes_read_to_end() -> io::Result<()> {
let mut bytes = OwnedBytes::new(b"abcde".as_ref());
let mut buf = Vec::new();
bytes.read_to_end(&mut buf)?;
assert_eq!(buf.as_slice(), b"abcde".as_ref());
Ok(())
}
#[test]
fn test_owned_bytes_read_u8() -> io::Result<()> {
let mut bytes = OwnedBytes::new(b"\xFF".as_ref());
assert_eq!(bytes.read_u8(), 255);
assert_eq!(bytes.len(), 0);
Ok(())
}
#[test]
fn test_owned_bytes_read_u64() -> io::Result<()> {
let mut bytes = OwnedBytes::new(b"\0\xFF\xFF\xFF\xFF\xFF\xFF\xFF".as_ref());
assert_eq!(bytes.read_u64(), u64::MAX - 255);
assert_eq!(bytes.len(), 0);
Ok(())
}
#[test]
fn test_owned_bytes_split() {
let bytes = OwnedBytes::new(b"abcdefghi".as_ref());
let (left, right) = bytes.split(3);
assert_eq!(left.as_slice(), b"abc");
assert_eq!(right.as_slice(), b"defghi");
}
#[test]
fn test_owned_bytes_split_boundary() {
let bytes = OwnedBytes::new(b"abcdefghi".as_ref());
{
let (left, right) = bytes.clone().split(0);
assert_eq!(left.as_slice(), b"");
assert_eq!(right.as_slice(), b"abcdefghi");
}
{
let (left, right) = bytes.split(9);
assert_eq!(left.as_slice(), b"abcdefghi");
assert_eq!(right.as_slice(), b"");
}
}
#[test]
fn test_split_off() {
let mut data = OwnedBytes::new(b"abcdef".as_ref());
assert_eq!(data, "abcdef");
assert_eq!(data.split_off(2), "cdef");
assert_eq!(data, "ab");
assert_eq!(data.split_off(1), "b");
assert_eq!(data, "a");
}
}

View File

@@ -1,17 +1,16 @@
[package]
name = "tantivy-query-grammar"
version = "0.15.0"
version = "0.11.0"
authors = ["Paul Masurel <paul.masurel@gmail.com>"]
license = "MIT"
categories = ["database-implementations", "data-structures"]
description = """Search engine library"""
homepage = "https://github.com/quickwit-oss/tantivy"
repository = "https://github.com/quickwit-oss/tantivy"
documentation = "https://tantivy-search.github.io/tantivy/tantivy/index.html"
homepage = "https://github.com/tantivy-search/tantivy"
repository = "https://github.com/tantivy-search/tantivy"
readme = "README.md"
keywords = ["search", "information", "retrieval"]
edition = "2018"
[dependencies]
combine = {version="4", default-features=false, features=[] }
once_cell = "1.7.2"
regex ={ version = "1.5.4", default-features = false, features = ["std"] }
combine = ">=3.6.0,<4.0.0"

View File

@@ -1,3 +1,5 @@
#![recursion_limit = "100"]
mod occur;
mod query_grammar;
mod user_input_ast;
@@ -5,11 +7,11 @@ use combine::parser::Parser;
pub use crate::occur::Occur;
use crate::query_grammar::parse_to_ast;
pub use crate::user_input_ast::{UserInputAst, UserInputBound, UserInputLeaf, UserInputLiteral};
pub use crate::user_input_ast::{UserInputAST, UserInputBound, UserInputLeaf, UserInputLiteral};
pub struct Error;
pub fn parse_query(query: &str) -> Result<UserInputAst, Error> {
pub fn parse_query(query: &str) -> Result<UserInputAST, Error> {
let (user_input_ast, _remaining) = parse_to_ast().parse(query).map_err(|_| Error)?;
Ok(user_input_ast)
}

View File

@@ -31,12 +31,22 @@ impl Occur {
/// Compose two occur values.
pub fn compose(left: Occur, right: Occur) -> Occur {
match (left, right) {
(Occur::Should, _) => right,
(Occur::Must, Occur::MustNot) => Occur::MustNot,
(Occur::Must, _) => Occur::Must,
(Occur::MustNot, Occur::MustNot) => Occur::Must,
(Occur::MustNot, _) => Occur::MustNot,
match left {
Occur::Should => right,
Occur::Must => {
if right == Occur::MustNot {
Occur::MustNot
} else {
Occur::Must
}
}
Occur::MustNot => {
if right == Occur::MustNot {
Occur::Must
} else {
Occur::MustNot
}
}
}
}
}
@@ -46,27 +56,3 @@ impl fmt::Display for Occur {
f.write_char(self.to_char())
}
}
#[cfg(test)]
mod test {
use crate::Occur;
#[test]
fn test_occur_compose() {
assert_eq!(Occur::compose(Occur::Should, Occur::Should), Occur::Should);
assert_eq!(Occur::compose(Occur::Should, Occur::Must), Occur::Must);
assert_eq!(
Occur::compose(Occur::Should, Occur::MustNot),
Occur::MustNot
);
assert_eq!(Occur::compose(Occur::Must, Occur::Should), Occur::Must);
assert_eq!(Occur::compose(Occur::Must, Occur::Must), Occur::Must);
assert_eq!(Occur::compose(Occur::Must, Occur::MustNot), Occur::MustNot);
assert_eq!(
Occur::compose(Occur::MustNot, Occur::Should),
Occur::MustNot
);
assert_eq!(Occur::compose(Occur::MustNot, Occur::Must), Occur::MustNot);
assert_eq!(Occur::compose(Occur::MustNot, Occur::MustNot), Occur::Must);
}
}

View File

@@ -1,294 +1,171 @@
use combine::error::StringStreamError;
use combine::parser::char::{char, digit, space, spaces, string};
use combine::parser::combinator::recognize;
use combine::parser::range::{take_while, take_while1};
use combine::parser::repeat::escaped;
use combine::parser::Parser;
use combine::{
attempt, choice, eof, many, many1, one_of, optional, parser, satisfy, skip_many1, value,
};
use once_cell::sync::Lazy;
use regex::Regex;
use super::user_input_ast::{UserInputAst, UserInputBound, UserInputLeaf, UserInputLiteral};
use super::user_input_ast::*;
use crate::Occur;
use combine::char::*;
use combine::error::StreamError;
use combine::stream::StreamErrorFor;
use combine::*;
// Note: '-' char is only forbidden at the beginning of a field name, would be clearer to add it to
// special characters.
const SPECIAL_CHARS: &[char] = &[
'+', '^', '`', ':', '{', '}', '"', '[', ']', '(', ')', '~', '!', '\\', '*', ' ',
];
const ESCAPED_SPECIAL_CHARS_PATTERN: &str = r#"\\(\+|\^|`|:|\{|\}|"|\[|\]|\(|\)|\~|!|\\|\*| )"#;
/// Parses a field_name
/// A field name must have at least one character and be followed by a colon.
/// All characters are allowed including special characters `SPECIAL_CHARS`, but these
/// need to be escaped with a backslack character '\'.
fn field_name<'a>() -> impl Parser<&'a str, Output = String> {
static ESCAPED_SPECIAL_CHARS_RE: Lazy<Regex> =
Lazy::new(|| Regex::new(ESCAPED_SPECIAL_CHARS_PATTERN).unwrap());
recognize::<String, _, _>(escaped(
parser! {
fn field[I]()(I) -> String
where [I: Stream<Item = char>] {
(
take_while1(|c| !SPECIAL_CHARS.contains(&c) && c != '-'),
take_while(|c| !SPECIAL_CHARS.contains(&c)),
),
'\\',
satisfy(|c| SPECIAL_CHARS.contains(&c)),
))
.skip(char(':'))
.map(|s| ESCAPED_SPECIAL_CHARS_RE.replace_all(&s, "$1").to_string())
.and_then(|s: String| match s.is_empty() {
true => Err(StringStreamError::UnexpectedParse),
_ => Ok(s),
})
letter(),
many(satisfy(|c: char| c.is_alphanumeric() || c == '_')),
).skip(char(':')).map(|(s1, s2): (char, String)| format!("{}{}", s1, s2))
}
}
fn word<'a>() -> impl Parser<&'a str, Output = String> {
(
satisfy(|c: char| {
!c.is_whitespace()
&& !['-', '^', '`', ':', '{', '}', '"', '[', ']', '(', ')'].contains(&c)
}),
many(satisfy(|c: char| {
!c.is_whitespace() && ![':', '^', '{', '}', '"', '[', ']', '(', ')'].contains(&c)
})),
)
parser! {
fn word[I]()(I) -> String
where [I: Stream<Item = char>] {
(
satisfy(|c: char| !c.is_whitespace() && !['-', '`', ':', '{', '}', '"', '[', ']', '(',')'].contains(&c) ),
many(satisfy(|c: char| !c.is_whitespace() && ![':', '{', '}', '"', '[', ']', '(',')'].contains(&c)))
)
.map(|(s1, s2): (char, String)| format!("{}{}", s1, s2))
.and_then(|s: String| match s.as_str() {
"OR" | "AND " | "NOT" => Err(StringStreamError::UnexpectedParse),
_ => Ok(s),
.and_then(|s: String|
match s.as_str() {
"OR" => Err(StreamErrorFor::<I>::unexpected_static_message("OR")),
"AND" => Err(StreamErrorFor::<I>::unexpected_static_message("AND")),
"NOT" => Err(StreamErrorFor::<I>::unexpected_static_message("NOT")),
_ => Ok(s)
})
}
}
parser! {
fn literal[I]()(I) -> UserInputLeaf
where [I: Stream<Item = char>]
{
let term_val = || {
let phrase = char('"').with(many1(satisfy(|c| c != '"'))).skip(char('"'));
phrase.or(word())
};
let term_val_with_field = negative_number().or(term_val());
let term_query =
(field(), term_val_with_field)
.map(|(field_name, phrase)| UserInputLiteral {
field_name: Some(field_name),
phrase,
});
let term_default_field = term_val().map(|phrase| UserInputLiteral {
field_name: None,
phrase,
});
attempt(term_query)
.or(term_default_field)
.map(UserInputLeaf::from)
}
}
parser! {
fn negative_number[I]()(I) -> String
where [I: Stream<Item = char>]
{
(char('-'), many1(satisfy(char::is_numeric)),
optional((char('.'), many1(satisfy(char::is_numeric)))))
.map(|(s1, s2, s3): (char, String, Option<(char, String)>)| {
if let Some(('.', s3)) = s3 {
format!("{}{}.{}", s1, s2, s3)
} else {
format!("{}{}", s1, s2)
}
})
}
}
parser! {
fn spaces1[I]()(I) -> ()
where [I: Stream<Item = char>] {
skip_many1(space())
}
}
parser! {
/// Function that parses a range out of a Stream
/// Supports ranges like:
/// [5 TO 10], {5 TO 10}, [* TO 10], [10 TO *], {10 TO *], >5, <=10
/// [a TO *], [a TO c], [abc TO bcd}
fn range[I]()(I) -> UserInputLeaf
where [I: Stream<Item = char>] {
let range_term_val = || {
word().or(negative_number()).or(char('*').with(value("*".to_string())))
};
// check for unbounded range in the form of <5, <=10, >5, >=5
let elastic_unbounded_range = (choice([attempt(string(">=")),
attempt(string("<=")),
attempt(string("<")),
attempt(string(">"))])
.skip(spaces()),
range_term_val()).
map(|(comparison_sign, bound): (&str, String)|
match comparison_sign {
">=" => (UserInputBound::Inclusive(bound), UserInputBound::Unbounded),
"<=" => (UserInputBound::Unbounded, UserInputBound::Inclusive(bound)),
"<" => (UserInputBound::Unbounded, UserInputBound::Exclusive(bound)),
">" => (UserInputBound::Exclusive(bound), UserInputBound::Unbounded),
// default case
_ => (UserInputBound::Unbounded, UserInputBound::Unbounded)
});
let lower_bound = (one_of("{[".chars()), range_term_val())
.map(|(boundary_char, lower_bound): (char, String)|
if lower_bound == "*" {
UserInputBound::Unbounded
} else if boundary_char == '{' {
UserInputBound::Exclusive(lower_bound)
} else {
UserInputBound::Inclusive(lower_bound)
});
let upper_bound = (range_term_val(), one_of("}]".chars()))
.map(|(higher_bound, boundary_char): (String, char)|
if higher_bound == "*" {
UserInputBound::Unbounded
} else if boundary_char == '}' {
UserInputBound::Exclusive(higher_bound)
} else {
UserInputBound::Inclusive(higher_bound)
});
// return only lower and upper
let lower_to_upper = (lower_bound.
skip((spaces(),
string("TO"),
spaces())),
upper_bound);
(optional(field()).skip(spaces()),
// try elastic first, if it matches, the range is unbounded
attempt(elastic_unbounded_range).or(lower_to_upper))
.map(|(field, (lower, upper))|
// Construct the leaf from extracted field (optional)
// and bounds
UserInputLeaf::Range {
field,
lower,
upper
})
}
}
/// Parses a date time according to rfc3339
/// 2015-08-02T18:54:42+02
/// 2021-04-13T19:46:26.266051969+00:00
///
/// NOTE: also accepts 999999-99-99T99:99:99.266051969+99:99
/// We delegate rejecting such invalid dates to the logical AST compuation code
/// which invokes chrono::DateTime::parse_from_rfc3339 on the value to actually parse
/// it (instead of merely extracting the datetime value as string as done here).
fn date_time<'a>() -> impl Parser<&'a str, Output = String> {
let two_digits = || recognize::<String, _, _>((digit(), digit()));
// Parses a time zone
// -06:30
// Z
let time_zone = {
let utc = recognize::<String, _, _>(char('Z'));
let offset = recognize((
choice([char('-'), char('+')]),
two_digits(),
char(':'),
two_digits(),
));
utc.or(offset)
};
// Parses a date
// 2010-01-30
let date = {
recognize::<String, _, _>((
many1::<String, _, _>(digit()),
char('-'),
two_digits(),
char('-'),
two_digits(),
))
};
// Parses a time
// 12:30:02
// 19:46:26.266051969
let time = {
recognize::<String, _, _>((
two_digits(),
char(':'),
two_digits(),
char(':'),
two_digits(),
optional((char('.'), many1::<String, _, _>(digit()))),
time_zone,
))
};
recognize((date, char('T'), time))
}
fn term_val<'a>() -> impl Parser<&'a str, Output = String> {
let phrase = char('"').with(many1(satisfy(|c| c != '"'))).skip(char('"'));
phrase.or(word())
}
fn term_query<'a>() -> impl Parser<&'a str, Output = UserInputLiteral> {
let term_val_with_field = negative_number().or(term_val());
(field_name(), term_val_with_field).map(|(field_name, phrase)| UserInputLiteral {
field_name: Some(field_name),
phrase,
})
}
fn literal<'a>() -> impl Parser<&'a str, Output = UserInputLeaf> {
let term_default_field = term_val().map(|phrase| UserInputLiteral {
field_name: None,
phrase,
});
attempt(term_query())
.or(term_default_field)
.map(UserInputLeaf::from)
}
fn negative_number<'a>() -> impl Parser<&'a str, Output = String> {
(
char('-'),
many1(digit()),
optional((char('.'), many1(digit()))),
)
.map(|(s1, s2, s3): (char, String, Option<(char, String)>)| {
if let Some(('.', s3)) = s3 {
format!("{}{}.{}", s1, s2, s3)
} else {
format!("{}{}", s1, s2)
}
})
}
fn spaces1<'a>() -> impl Parser<&'a str, Output = ()> {
skip_many1(space())
}
/// Function that parses a range out of a Stream
/// Supports ranges like:
/// [5 TO 10], {5 TO 10}, [* TO 10], [10 TO *], {10 TO *], >5, <=10
/// [a TO *], [a TO c], [abc TO bcd}
fn range<'a>() -> impl Parser<&'a str, Output = UserInputLeaf> {
let range_term_val = || {
attempt(date_time())
.or(word())
.or(negative_number())
.or(char('*').with(value("*".to_string())))
};
// check for unbounded range in the form of <5, <=10, >5, >=5
let elastic_unbounded_range = (
choice([
attempt(string(">=")),
attempt(string("<=")),
attempt(string("<")),
attempt(string(">")),
])
.skip(spaces()),
range_term_val(),
)
.map(
|(comparison_sign, bound): (&str, String)| match comparison_sign {
">=" => (UserInputBound::Inclusive(bound), UserInputBound::Unbounded),
"<=" => (UserInputBound::Unbounded, UserInputBound::Inclusive(bound)),
"<" => (UserInputBound::Unbounded, UserInputBound::Exclusive(bound)),
">" => (UserInputBound::Exclusive(bound), UserInputBound::Unbounded),
// default case
_ => (UserInputBound::Unbounded, UserInputBound::Unbounded),
},
);
let lower_bound = (one_of("{[".chars()), range_term_val()).map(
|(boundary_char, lower_bound): (char, String)| {
if lower_bound == "*" {
UserInputBound::Unbounded
} else if boundary_char == '{' {
UserInputBound::Exclusive(lower_bound)
} else {
UserInputBound::Inclusive(lower_bound)
}
},
);
let upper_bound = (range_term_val(), one_of("}]".chars())).map(
|(higher_bound, boundary_char): (String, char)| {
if higher_bound == "*" {
UserInputBound::Unbounded
} else if boundary_char == '}' {
UserInputBound::Exclusive(higher_bound)
} else {
UserInputBound::Inclusive(higher_bound)
}
},
);
// return only lower and upper
let lower_to_upper = (
lower_bound.skip((spaces(), string("TO"), spaces())),
upper_bound,
);
(
optional(field_name()).skip(spaces()),
// try elastic first, if it matches, the range is unbounded
attempt(elastic_unbounded_range).or(lower_to_upper),
)
.map(|(field, (lower, upper))|
// Construct the leaf from extracted field (optional)
// and bounds
UserInputLeaf::Range {
field,
lower,
upper
})
}
fn negate(expr: UserInputAst) -> UserInputAst {
fn negate(expr: UserInputAST) -> UserInputAST {
expr.unary(Occur::MustNot)
}
fn leaf<'a>() -> impl Parser<&'a str, Output = UserInputAst> {
parser(|input| {
char('(')
.with(ast())
.skip(char(')'))
.or(char('*').map(|_| UserInputAst::from(UserInputLeaf::All)))
.or(attempt(
string("NOT").skip(spaces1()).with(leaf()).map(negate),
))
.or(attempt(range().map(UserInputAst::from)))
.or(literal().map(UserInputAst::from))
.parse_stream(input)
.into_result()
})
fn must(expr: UserInputAST) -> UserInputAST {
expr.unary(Occur::Must)
}
fn occur_symbol<'a>() -> impl Parser<&'a str, Output = Occur> {
char('-')
.map(|_| Occur::MustNot)
.or(char('+').map(|_| Occur::Must))
}
fn occur_leaf<'a>() -> impl Parser<&'a str, Output = (Option<Occur>, UserInputAst)> {
(optional(occur_symbol()), boosted_leaf())
}
fn positive_float_number<'a>() -> impl Parser<&'a str, Output = f64> {
(many1(digit()), optional((char('.'), many1(digit())))).map(
|(int_part, decimal_part_opt): (String, Option<(char, String)>)| {
let mut float_str = int_part;
if let Some((chr, decimal_str)) = decimal_part_opt {
float_str.push(chr);
float_str.push_str(&decimal_str);
}
float_str.parse::<f64>().unwrap()
},
)
}
fn boost<'a>() -> impl Parser<&'a str, Output = f64> {
(char('^'), positive_float_number()).map(|(_, boost)| boost)
}
fn boosted_leaf<'a>() -> impl Parser<&'a str, Output = UserInputAst> {
(leaf(), optional(boost())).map(|(leaf, boost_opt)| match boost_opt {
Some(boost) if (boost - 1.0).abs() > std::f64::EPSILON => {
UserInputAst::Boost(Box::new(leaf), boost)
}
_ => leaf,
})
parser! {
fn leaf[I]()(I) -> UserInputAST
where [I: Stream<Item = char>] {
char('-').with(leaf()).map(negate)
.or(char('+').with(leaf()).map(must))
.or(char('(').with(ast()).skip(char(')')))
.or(char('*').map(|_| UserInputAST::from(UserInputLeaf::All)))
.or(attempt(string("NOT").skip(spaces1()).with(leaf()).map(negate)))
.or(attempt(range().map(UserInputAST::from)))
.or(literal().map(UserInputAST::from))
}
}
#[derive(Clone, Copy)]
@@ -297,17 +174,20 @@ enum BinaryOperand {
And,
}
fn binary_operand<'a>() -> impl Parser<&'a str, Output = BinaryOperand> {
string("AND")
.with(value(BinaryOperand::And))
.or(string("OR").with(value(BinaryOperand::Or)))
parser! {
fn binary_operand[I]()(I) -> BinaryOperand
where [I: Stream<Item = char>]
{
string("AND").with(value(BinaryOperand::And))
.or(string("OR").with(value(BinaryOperand::Or)))
}
}
fn aggregate_binary_expressions(
left: UserInputAst,
others: Vec<(BinaryOperand, UserInputAst)>,
) -> UserInputAst {
let mut dnf: Vec<Vec<UserInputAst>> = vec![vec![left]];
left: UserInputAST,
others: Vec<(BinaryOperand, UserInputAST)>,
) -> UserInputAST {
let mut dnf: Vec<Vec<UserInputAST>> = vec![vec![left]];
for (operator, operand_ast) in others {
match operator {
BinaryOperand::And => {
@@ -321,109 +201,45 @@ fn aggregate_binary_expressions(
}
}
if dnf.len() == 1 {
UserInputAst::and(dnf.into_iter().next().unwrap()) //< safe
UserInputAST::and(dnf.into_iter().next().unwrap()) //< safe
} else {
let conjunctions = dnf.into_iter().map(UserInputAst::and).collect();
UserInputAst::or(conjunctions)
let conjunctions = dnf.into_iter().map(UserInputAST::and).collect();
UserInputAST::or(conjunctions)
}
}
fn operand_leaf<'a>() -> impl Parser<&'a str, Output = (BinaryOperand, UserInputAst)> {
(
binary_operand().skip(spaces()),
boosted_leaf().skip(spaces()),
)
}
pub fn ast<'a>() -> impl Parser<&'a str, Output = UserInputAst> {
let boolean_expr = (boosted_leaf().skip(spaces()), many1(operand_leaf()))
.map(|(left, right)| aggregate_binary_expressions(left, right));
let whitespace_separated_leaves = many1(occur_leaf().skip(spaces().silent())).map(
|subqueries: Vec<(Option<Occur>, UserInputAst)>| {
parser! {
pub fn ast[I]()(I) -> UserInputAST
where [I: Stream<Item = char>]
{
let operand_leaf = (binary_operand().skip(spaces()), leaf().skip(spaces()));
let boolean_expr = (leaf().skip(spaces().silent()), many1(operand_leaf)).map(
|(left, right)| aggregate_binary_expressions(left,right));
let whitespace_separated_leaves = many1(leaf().skip(spaces().silent()))
.map(|subqueries: Vec<UserInputAST>|
if subqueries.len() == 1 {
let (occur_opt, ast) = subqueries.into_iter().next().unwrap();
match occur_opt.unwrap_or(Occur::Should) {
Occur::Must | Occur::Should => ast,
Occur::MustNot => UserInputAst::Clause(vec![(Some(Occur::MustNot), ast)]),
}
subqueries.into_iter().next().unwrap()
} else {
UserInputAst::Clause(subqueries.into_iter().collect())
}
},
);
let expr = attempt(boolean_expr).or(whitespace_separated_leaves);
spaces().with(expr).skip(spaces())
UserInputAST::Clause(subqueries.into_iter().collect())
});
let expr = attempt(boolean_expr).or(whitespace_separated_leaves);
spaces().with(expr).skip(spaces())
}
}
pub fn parse_to_ast<'a>() -> impl Parser<&'a str, Output = UserInputAst> {
spaces()
.with(optional(ast()).skip(eof()))
.map(|opt_ast| opt_ast.unwrap_or_else(UserInputAst::empty_query))
parser! {
pub fn parse_to_ast[I]()(I) -> UserInputAST
where [I: Stream<Item = char>]
{
spaces().with(optional(ast()).skip(eof())).map(|opt_ast| opt_ast.unwrap_or_else(UserInputAST::empty_query))
}
}
#[cfg(test)]
mod test {
type TestParseResult = Result<(), StringStreamError>;
use combine::parser::Parser;
use super::*;
pub fn nearly_equals(a: f64, b: f64) -> bool {
(a - b).abs() < 0.0005 * (a + b).abs()
}
fn assert_nearly_equals(expected: f64, val: f64) {
assert!(
nearly_equals(val, expected),
"Got {}, expected {}.",
val,
expected
);
}
#[test]
fn test_occur_symbol() -> TestParseResult {
assert_eq!(super::occur_symbol().parse("-")?, (Occur::MustNot, ""));
assert_eq!(super::occur_symbol().parse("+")?, (Occur::Must, ""));
Ok(())
}
#[test]
fn test_positive_float_number() {
fn valid_parse(float_str: &str, expected_val: f64, expected_remaining: &str) {
let (val, remaining) = positive_float_number().parse(float_str).unwrap();
assert_eq!(remaining, expected_remaining);
assert_nearly_equals(val, expected_val);
}
fn error_parse(float_str: &str) {
assert!(positive_float_number().parse(float_str).is_err());
}
valid_parse("1.0", 1.0, "");
valid_parse("1", 1.0, "");
valid_parse("0.234234 aaa", 0.234234f64, " aaa");
error_parse(".3332");
error_parse("1.");
error_parse("-1.");
}
#[test]
fn test_date_time() {
let (val, remaining) = date_time()
.parse("2015-08-02T18:54:42+02:30")
.expect("cannot parse date");
assert_eq!(val, "2015-08-02T18:54:42+02:30");
assert_eq!(remaining, "");
assert!(date_time().parse("2015-08-02T18:54:42+02").is_err());
let (val, remaining) = date_time()
.parse("2021-04-13T19:46:26.266051969+00:00")
.expect("cannot parse fractional date");
assert_eq!(val, "2021-04-13T19:46:26.266051969+00:00");
assert_eq!(remaining, "");
}
fn test_parse_query_to_ast_helper(query: &str, expected: &str) {
let query = parse_to_ast().parse(query).unwrap().0;
let query_str = format!("{:?}", query);
@@ -453,24 +269,15 @@ mod test {
"Err(UnexpectedParse)"
);
test_parse_query_to_ast_helper("NOTa", "\"NOTa\"");
test_parse_query_to_ast_helper("NOT a", "(-\"a\")");
}
#[test]
fn test_boosting() {
assert!(parse_to_ast().parse("a^2^3").is_err());
assert!(parse_to_ast().parse("a^2^").is_err());
test_parse_query_to_ast_helper("a^3", "(\"a\")^3");
test_parse_query_to_ast_helper("a^3 b^2", "(*(\"a\")^3 *(\"b\")^2)");
test_parse_query_to_ast_helper("a^1", "\"a\"");
test_parse_query_to_ast_helper("NOT a", "-(\"a\")");
}
#[test]
fn test_parse_query_to_ast_binary_op() {
test_parse_query_to_ast_helper("a AND b", "(+\"a\" +\"b\")");
test_parse_query_to_ast_helper("a OR b", "(?\"a\" ?\"b\")");
test_parse_query_to_ast_helper("a OR b AND c", "(?\"a\" ?(+\"b\" +\"c\"))");
test_parse_query_to_ast_helper("a AND b AND c", "(+\"a\" +\"b\" +\"c\")");
test_parse_query_to_ast_helper("a AND b", "(+(\"a\") +(\"b\"))");
test_parse_query_to_ast_helper("a OR b", "(?(\"a\") ?(\"b\"))");
test_parse_query_to_ast_helper("a OR b AND c", "(?(\"a\") ?((+(\"b\") +(\"c\"))))");
test_parse_query_to_ast_helper("a AND b AND c", "(+(\"a\") +(\"b\") +(\"c\"))");
assert_eq!(
format!("{:?}", parse_to_ast().parse("a OR b aaa")),
"Err(UnexpectedParse)"
@@ -491,207 +298,83 @@ mod test {
#[test]
fn test_parse_elastic_query_ranges() {
test_parse_query_to_ast_helper("title: >a", "\"title\":{\"a\" TO \"*\"}");
test_parse_query_to_ast_helper("title:>=a", "\"title\":[\"a\" TO \"*\"}");
test_parse_query_to_ast_helper("title: <a", "\"title\":{\"*\" TO \"a\"}");
test_parse_query_to_ast_helper("title:<=a", "\"title\":{\"*\" TO \"a\"]");
test_parse_query_to_ast_helper("title:<=bsd", "\"title\":{\"*\" TO \"bsd\"]");
test_parse_query_to_ast_helper("title: >a", "title:{\"a\" TO \"*\"}");
test_parse_query_to_ast_helper("title:>=a", "title:[\"a\" TO \"*\"}");
test_parse_query_to_ast_helper("title: <a", "title:{\"*\" TO \"a\"}");
test_parse_query_to_ast_helper("title:<=a", "title:{\"*\" TO \"a\"]");
test_parse_query_to_ast_helper("title:<=bsd", "title:{\"*\" TO \"bsd\"]");
test_parse_query_to_ast_helper("weight: >70", "\"weight\":{\"70\" TO \"*\"}");
test_parse_query_to_ast_helper("weight:>=70", "\"weight\":[\"70\" TO \"*\"}");
test_parse_query_to_ast_helper("weight: <70", "\"weight\":{\"*\" TO \"70\"}");
test_parse_query_to_ast_helper("weight:<=70", "\"weight\":{\"*\" TO \"70\"]");
test_parse_query_to_ast_helper("weight: >60.7", "\"weight\":{\"60.7\" TO \"*\"}");
test_parse_query_to_ast_helper("weight: >70", "weight:{\"70\" TO \"*\"}");
test_parse_query_to_ast_helper("weight:>=70", "weight:[\"70\" TO \"*\"}");
test_parse_query_to_ast_helper("weight: <70", "weight:{\"*\" TO \"70\"}");
test_parse_query_to_ast_helper("weight:<=70", "weight:{\"*\" TO \"70\"]");
test_parse_query_to_ast_helper("weight: >60.7", "weight:{\"60.7\" TO \"*\"}");
test_parse_query_to_ast_helper("weight: <= 70", "\"weight\":{\"*\" TO \"70\"]");
test_parse_query_to_ast_helper("weight: <= 70", "weight:{\"*\" TO \"70\"]");
test_parse_query_to_ast_helper("weight: <= 70.5", "\"weight\":{\"*\" TO \"70.5\"]");
}
#[test]
fn test_occur_leaf() {
let ((occur, ast), _) = super::occur_leaf().parse("+abc").unwrap();
assert_eq!(occur, Some(Occur::Must));
assert_eq!(format!("{:?}", ast), "\"abc\"");
}
#[test]
fn test_field_name() -> TestParseResult {
assert_eq!(
super::field_name().parse(".my.field.name:a"),
Ok((".my.field.name".to_string(), "a"))
);
assert_eq!(
super::field_name().parse("my\\ field\\ name:a"),
Ok(("my field name".to_string(), "a"))
);
assert!(super::field_name().parse("my field:a").is_err());
assert_eq!(
super::field_name().parse("\\(1\\+1\\):2"),
Ok(("(1+1)".to_string(), "2"))
);
assert_eq!(
super::field_name().parse("my_field_name:a"),
Ok(("my_field_name".to_string(), "a"))
);
assert!(super::field_name().parse("my_field_name").is_err());
assert!(super::field_name().parse(":a").is_err());
assert!(super::field_name().parse("-my_field:a").is_err());
assert_eq!(
super::field_name().parse("_my_field:a")?,
("_my_field".to_string(), "a")
);
Ok(())
}
#[test]
fn test_field_name_re() {
let escaped_special_chars_re = Regex::new(ESCAPED_SPECIAL_CHARS_PATTERN).unwrap();
for special_char in SPECIAL_CHARS.iter() {
assert_eq!(
escaped_special_chars_re.replace_all(&format!("\\{}", special_char), "$1"),
special_char.to_string()
);
}
test_parse_query_to_ast_helper("weight: <= 70.5", "weight:{\"*\" TO \"70.5\"]");
}
#[test]
fn test_range_parser() {
// testing the range() parser separately
let res = range()
.parse("title: <hello")
.expect("Cannot parse felxible bound word")
.0;
let res = range().parse("title: <hello").unwrap().0;
let expected = UserInputLeaf::Range {
field: Some("title".to_string()),
lower: UserInputBound::Unbounded,
upper: UserInputBound::Exclusive("hello".to_string()),
};
let res2 = range()
.parse("title:{* TO hello}")
.expect("Cannot parse ununbounded to word")
.0;
let res2 = range().parse("title:{* TO hello}").unwrap().0;
assert_eq!(res, expected);
assert_eq!(res2, expected);
let expected_weight = UserInputLeaf::Range {
field: Some("weight".to_string()),
lower: UserInputBound::Inclusive("71.2".to_string()),
upper: UserInputBound::Unbounded,
};
let res3 = range()
.parse("weight: >=71.2")
.expect("Cannot parse flexible bound float")
.0;
let res4 = range()
.parse("weight:[71.2 TO *}")
.expect("Cannot parse float to unbounded")
.0;
let res3 = range().parse("weight: >=71.2").unwrap().0;
let res4 = range().parse("weight:[71.2 TO *}").unwrap().0;
assert_eq!(res3, expected_weight);
assert_eq!(res4, expected_weight);
let expected_dates = UserInputLeaf::Range {
field: Some("date_field".to_string()),
lower: UserInputBound::Exclusive("2015-08-02T18:54:42Z".to_string()),
upper: UserInputBound::Inclusive("2021-08-02T18:54:42+02:30".to_string()),
};
let res5 = range()
.parse("date_field:{2015-08-02T18:54:42Z TO 2021-08-02T18:54:42+02:30]")
.expect("Cannot parse date range")
.0;
assert_eq!(res5, expected_dates);
let expected_flexible_dates = UserInputLeaf::Range {
field: Some("date_field".to_string()),
lower: UserInputBound::Unbounded,
upper: UserInputBound::Inclusive("2021-08-02T18:54:42.12345+02:30".to_string()),
};
let res6 = range()
.parse("date_field: <=2021-08-02T18:54:42.12345+02:30")
.expect("Cannot parse date range")
.0;
assert_eq!(res6, expected_flexible_dates);
}
#[test]
fn test_parse_query_to_triming_spaces() {
test_parse_query_to_ast_helper(" abc", "\"abc\"");
test_parse_query_to_ast_helper("abc ", "\"abc\"");
test_parse_query_to_ast_helper("( a OR abc)", "(?\"a\" ?\"abc\")");
test_parse_query_to_ast_helper("(a OR abc)", "(?\"a\" ?\"abc\")");
test_parse_query_to_ast_helper("(a OR abc)", "(?\"a\" ?\"abc\")");
test_parse_query_to_ast_helper("a OR abc ", "(?\"a\" ?\"abc\")");
test_parse_query_to_ast_helper("(a OR abc )", "(?\"a\" ?\"abc\")");
test_parse_query_to_ast_helper("(a OR abc) ", "(?\"a\" ?\"abc\")");
test_parse_query_to_ast_helper("( a OR abc)", "(?(\"a\") ?(\"abc\"))");
test_parse_query_to_ast_helper("(a OR abc)", "(?(\"a\") ?(\"abc\"))");
test_parse_query_to_ast_helper("(a OR abc)", "(?(\"a\") ?(\"abc\"))");
test_parse_query_to_ast_helper("a OR abc ", "(?(\"a\") ?(\"abc\"))");
test_parse_query_to_ast_helper("(a OR abc )", "(?(\"a\") ?(\"abc\"))");
test_parse_query_to_ast_helper("(a OR abc) ", "(?(\"a\") ?(\"abc\"))");
}
#[test]
fn test_parse_query_single_term() {
fn test_parse_query_to_ast() {
test_parse_query_to_ast_helper("abc", "\"abc\"");
}
#[test]
fn test_parse_query_default_clause() {
test_parse_query_to_ast_helper("a b", "(*\"a\" *\"b\")");
}
#[test]
fn test_parse_query_must_default_clause() {
test_parse_query_to_ast_helper("+(a b)", "(*\"a\" *\"b\")");
}
#[test]
fn test_parse_query_must_single_term() {
test_parse_query_to_ast_helper("+d", "\"d\"");
}
#[test]
fn test_single_term_with_field() {
test_parse_query_to_ast_helper("abc:toto", "\"abc\":\"toto\"");
}
#[test]
fn test_single_term_with_float() {
test_parse_query_to_ast_helper("abc:1.1", "\"abc\":\"1.1\"");
test_parse_query_to_ast_helper("a.b.c:1.1", "\"a.b.c\":\"1.1\"");
test_parse_query_to_ast_helper("a\\ b\\ c:1.1", "\"a b c\":\"1.1\"");
}
#[test]
fn test_must_clause() {
test_parse_query_to_ast_helper("(+a +b)", "(+\"a\" +\"b\")");
}
#[test]
fn test_parse_test_query_plus_a_b_plus_d() {
test_parse_query_to_ast_helper("+(a b) +d", "(+(*\"a\" *\"b\") +\"d\")");
}
#[test]
fn test_parse_test_query_other() {
test_parse_query_to_ast_helper("(+a +b) d", "(*(+\"a\" +\"b\") *\"d\")");
test_parse_query_to_ast_helper("+abc:toto", "\"abc\":\"toto\"");
test_parse_query_to_ast_helper("+a\\+b\\+c:toto", "\"a+b+c\":\"toto\"");
test_parse_query_to_ast_helper("(+abc:toto -titi)", "(+\"abc\":\"toto\" -\"titi\")");
test_parse_query_to_ast_helper("-abc:toto", "(-\"abc\":\"toto\")");
test_is_parse_err("--abc:toto");
test_parse_query_to_ast_helper("abc:a b", "(*\"abc\":\"a\" *\"b\")");
test_parse_query_to_ast_helper("abc:\"a b\"", "\"abc\":\"a b\"");
test_parse_query_to_ast_helper("foo:[1 TO 5]", "\"foo\":[\"1\" TO \"5\"]");
}
#[test]
fn test_parse_query_with_range() {
test_parse_query_to_ast_helper("a b", "(\"a\" \"b\")");
test_parse_query_to_ast_helper("+(a b)", "+((\"a\" \"b\"))");
test_parse_query_to_ast_helper("+d", "+(\"d\")");
test_parse_query_to_ast_helper("+(a b) +d", "(+((\"a\" \"b\")) +(\"d\"))");
test_parse_query_to_ast_helper("(+a +b) d", "((+(\"a\") +(\"b\")) \"d\")");
test_parse_query_to_ast_helper("(+a)", "+(\"a\")");
test_parse_query_to_ast_helper("(+a +b)", "(+(\"a\") +(\"b\"))");
test_parse_query_to_ast_helper("abc:toto", "abc:\"toto\"");
test_parse_query_to_ast_helper("abc:1.1", "abc:\"1.1\"");
test_parse_query_to_ast_helper("+abc:toto", "+(abc:\"toto\")");
test_parse_query_to_ast_helper("(+abc:toto -titi)", "(+(abc:\"toto\") -(\"titi\"))");
test_parse_query_to_ast_helper("-abc:toto", "-(abc:\"toto\")");
test_parse_query_to_ast_helper("abc:a b", "(abc:\"a\" \"b\")");
test_parse_query_to_ast_helper("abc:\"a b\"", "abc:\"a b\"");
test_parse_query_to_ast_helper("foo:[1 TO 5]", "foo:[\"1\" TO \"5\"]");
test_parse_query_to_ast_helper("[1 TO 5]", "[\"1\" TO \"5\"]");
test_parse_query_to_ast_helper("foo:{a TO z}", "\"foo\":{\"a\" TO \"z\"}");
test_parse_query_to_ast_helper("foo:[1 TO toto}", "\"foo\":[\"1\" TO \"toto\"}");
test_parse_query_to_ast_helper("foo:[* TO toto}", "\"foo\":{\"*\" TO \"toto\"}");
test_parse_query_to_ast_helper("foo:[1 TO *}", "\"foo\":[\"1\" TO \"*\"}");
test_parse_query_to_ast_helper(
"1.2.foo.bar:[1.1 TO *}",
"\"1.2.foo.bar\":[\"1.1\" TO \"*\"}",
);
test_parse_query_to_ast_helper("foo:{a TO z}", "foo:{\"a\" TO \"z\"}");
test_parse_query_to_ast_helper("foo:[1 TO toto}", "foo:[\"1\" TO \"toto\"}");
test_parse_query_to_ast_helper("foo:[* TO toto}", "foo:{\"*\" TO \"toto\"}");
test_parse_query_to_ast_helper("foo:[1 TO *}", "foo:[\"1\" TO \"*\"}");
test_parse_query_to_ast_helper("foo:[1.1 TO *}", "foo:[\"1.1\" TO \"*\"}");
test_is_parse_err("abc + ");
}
}

View File

@@ -24,7 +24,7 @@ impl Debug for UserInputLeaf {
ref upper,
} => {
if let Some(ref field) = field {
write!(formatter, "\"{}\":", field)?;
write!(formatter, "{}:", field)?;
}
lower.display_lower(formatter)?;
write!(formatter, " TO ")?;
@@ -45,7 +45,7 @@ pub struct UserInputLiteral {
impl fmt::Debug for UserInputLiteral {
fn fmt(&self, formatter: &mut fmt::Formatter<'_>) -> Result<(), fmt::Error> {
match self.field_name {
Some(ref field_name) => write!(formatter, "\"{}\":\"{}\"", field_name, self.phrase),
Some(ref field_name) => write!(formatter, "{}:\"{}\"", field_name, self.phrase),
None => write!(formatter, "\"{}\"", self.phrase),
}
}
@@ -79,47 +79,46 @@ impl UserInputBound {
match *self {
UserInputBound::Inclusive(ref contents) => contents,
UserInputBound::Exclusive(ref contents) => contents,
UserInputBound::Unbounded => "*",
UserInputBound::Unbounded => &"*",
}
}
}
pub enum UserInputAst {
Clause(Vec<(Option<Occur>, UserInputAst)>),
pub enum UserInputAST {
Clause(Vec<UserInputAST>),
Unary(Occur, Box<UserInputAST>),
Leaf(Box<UserInputLeaf>),
Boost(Box<UserInputAst>, f64),
}
impl UserInputAst {
#[must_use]
pub fn unary(self, occur: Occur) -> UserInputAst {
UserInputAst::Clause(vec![(Some(occur), self)])
impl UserInputAST {
pub fn unary(self, occur: Occur) -> UserInputAST {
UserInputAST::Unary(occur, Box::new(self))
}
fn compose(occur: Occur, asts: Vec<UserInputAst>) -> UserInputAst {
fn compose(occur: Occur, asts: Vec<UserInputAST>) -> UserInputAST {
assert_ne!(occur, Occur::MustNot);
assert!(!asts.is_empty());
if asts.len() == 1 {
asts.into_iter().next().unwrap() //< safe
} else {
UserInputAst::Clause(
UserInputAST::Clause(
asts.into_iter()
.map(|ast: UserInputAst| (Some(occur), ast))
.map(|ast: UserInputAST| ast.unary(occur))
.collect::<Vec<_>>(),
)
}
}
pub fn empty_query() -> UserInputAst {
UserInputAst::Clause(Vec::default())
pub fn empty_query() -> UserInputAST {
UserInputAST::Clause(Vec::default())
}
pub fn and(asts: Vec<UserInputAst>) -> UserInputAst {
UserInputAst::compose(Occur::Must, asts)
pub fn and(asts: Vec<UserInputAST>) -> UserInputAST {
UserInputAST::compose(Occur::Must, asts)
}
pub fn or(asts: Vec<UserInputAst>) -> UserInputAst {
UserInputAst::compose(Occur::Should, asts)
pub fn or(asts: Vec<UserInputAST>) -> UserInputAST {
UserInputAST::compose(Occur::Should, asts)
}
}
@@ -129,44 +128,32 @@ impl From<UserInputLiteral> for UserInputLeaf {
}
}
impl From<UserInputLeaf> for UserInputAst {
fn from(leaf: UserInputLeaf) -> UserInputAst {
UserInputAst::Leaf(Box::new(leaf))
impl From<UserInputLeaf> for UserInputAST {
fn from(leaf: UserInputLeaf) -> UserInputAST {
UserInputAST::Leaf(Box::new(leaf))
}
}
fn print_occur_ast(
occur_opt: Option<Occur>,
ast: &UserInputAst,
formatter: &mut fmt::Formatter,
) -> fmt::Result {
if let Some(occur) = occur_opt {
write!(formatter, "{}{:?}", occur, ast)?;
} else {
write!(formatter, "*{:?}", ast)?;
}
Ok(())
}
impl fmt::Debug for UserInputAst {
fn fmt(&self, formatter: &mut fmt::Formatter) -> fmt::Result {
impl fmt::Debug for UserInputAST {
fn fmt(&self, formatter: &mut fmt::Formatter<'_>) -> Result<(), fmt::Error> {
match *self {
UserInputAst::Clause(ref subqueries) => {
UserInputAST::Clause(ref subqueries) => {
if subqueries.is_empty() {
write!(formatter, "<emptyclause>")?;
} else {
write!(formatter, "(")?;
print_occur_ast(subqueries[0].0, &subqueries[0].1, formatter)?;
write!(formatter, "{:?}", &subqueries[0])?;
for subquery in &subqueries[1..] {
write!(formatter, " ")?;
print_occur_ast(subquery.0, &subquery.1, formatter)?;
write!(formatter, " {:?}", subquery)?;
}
write!(formatter, ")")?;
}
Ok(())
}
UserInputAst::Leaf(ref subquery) => write!(formatter, "{:?}", subquery),
UserInputAst::Boost(ref leaf, boost) => write!(formatter, "({:?})^{}", leaf, boost),
UserInputAST::Unary(ref occur, ref subquery) => {
write!(formatter, "{}({:?})", occur, subquery)
}
UserInputAST::Leaf(ref subquery) => write!(formatter, "{:?}", subquery),
}
}
}

View File

@@ -1,7 +1 @@
comment_width = 120
format_strings = true
group_imports = "StdExternalCrate"
imports_granularity = "Module"
normalize_comments = true
where_single_line = true
wrap_comments = true
use_try_shorthand = true

View File

@@ -1,36 +0,0 @@
# Contributing
When adding new bucket aggregation make sure to extend the "test_aggregation_flushing" test for at least 2 levels.
# Code Organization
Tantivy's aggregations have been designed to mimic the
[aggregations of elasticsearch](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations.html).
The code is organized in submodules:
##bucket
Contains all bucket aggregations, like range aggregation. These bucket aggregations group documents into buckets and can contain sub-aggegations.
##metric
Contains all metric aggregations, like average aggregation. Metric aggregations do not have sub aggregations.
#### agg_req
agg_req contains the users aggregation request. Deserialization from json is compatible with elasticsearch aggregation requests.
#### agg_req_with_accessor
agg_req_with_accessor contains the users aggregation request enriched with fast field accessors etc, which are
used during collection.
#### segment_agg_result
segment_agg_result contains the aggregation result tree, which is used for collection of a segment.
The tree from agg_req_with_accessor is passed during collection.
#### intermediate_agg_result
intermediate_agg_result contains the aggregation tree for merging with other trees.
#### agg_result
agg_result contains the final aggregation tree.

View File

@@ -1,169 +0,0 @@
//! Contains the aggregation request tree. Used to build an
//! [AggregationCollector](super::AggregationCollector).
//!
//! [Aggregations] is the top level entry point to create a request, which is a `HashMap<String,
//! Aggregation>`.
//! Requests are compatible with the json format of elasticsearch.
//!
//! # Example
//!
//! ```
//! use tantivy::aggregation::bucket::RangeAggregation;
//! use tantivy::aggregation::agg_req::BucketAggregationType;
//! use tantivy::aggregation::agg_req::{Aggregation, Aggregations};
//! use tantivy::aggregation::agg_req::BucketAggregation;
//! let agg_req1: Aggregations = vec![
//! (
//! "range".to_string(),
//! Aggregation::Bucket(BucketAggregation {
//! bucket_agg: BucketAggregationType::Range(RangeAggregation{
//! field: "score".to_string(),
//! ranges: vec![(3f64..7f64).into(), (7f64..20f64).into()],
//! }),
//! sub_aggregation: Default::default(),
//! }),
//! ),
//! ]
//! .into_iter()
//! .collect();
//!
//! let elasticsearch_compatible_json_req = r#"
//! {
//! "range": {
//! "range": {
//! "field": "score",
//! "ranges": [
//! { "from": 3.0, "to": 7.0 },
//! { "from": 7.0, "to": 20.0 }
//! ]
//! }
//! }
//! }"#;
//! let agg_req2: Aggregations = serde_json::from_str(elasticsearch_compatible_json_req).unwrap();
//! assert_eq!(agg_req1, agg_req2);
//! ```
use std::collections::HashMap;
use serde::{Deserialize, Serialize};
pub use super::bucket::RangeAggregation;
use super::metric::{AverageAggregation, StatsAggregation};
/// The top-level aggregation request structure, which contains [Aggregation] and their user defined
/// names.
///
/// The key is the user defined name of the aggregation.
pub type Aggregations = HashMap<String, Aggregation>;
/// Aggregation request of [BucketAggregation] or [MetricAggregation].
///
/// An aggregation is either a bucket or a metric.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
#[serde(untagged)]
pub enum Aggregation {
/// Bucket aggregation, see [BucketAggregation] for details.
Bucket(BucketAggregation),
/// Metric aggregation, see [MetricAggregation] for details.
Metric(MetricAggregation),
}
/// BucketAggregations create buckets of documents. Each bucket is associated with a rule which
/// determines whether or not a document in the falls into it. In other words, the buckets
/// effectively define document sets. Buckets are not necessarily disjunct, therefore a document can
/// fall into multiple buckets. In addition to the buckets themselves, the bucket aggregations also
/// compute and return the number of documents for each bucket. Bucket aggregations, as opposed to
/// metric aggregations, can hold sub-aggregations. These sub-aggregations will be aggregated for
/// the buckets created by their "parent" bucket aggregation. There are different bucket
/// aggregators, each with a different "bucketing" strategy. Some define a single bucket, some
/// define fixed number of multiple buckets, and others dynamically create the buckets during the
/// aggregation process.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct BucketAggregation {
/// Bucket aggregation strategy to group documents.
#[serde(flatten)]
pub bucket_agg: BucketAggregationType,
/// The sub_aggregations in the buckets. Each bucket will aggregate on the document set in the
/// bucket.
#[serde(rename = "aggs")]
#[serde(default)]
#[serde(skip_serializing_if = "Aggregations::is_empty")]
pub sub_aggregation: Aggregations,
}
/// The bucket aggregation types.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub enum BucketAggregationType {
/// Put data into buckets of user-defined ranges.
#[serde(rename = "range")]
Range(RangeAggregation),
}
/// The aggregations in this family compute metrics based on values extracted
/// from the documents that are being aggregated. Values are extracted from the fast field of
/// the document.
/// Some aggregations output a single numeric metric (e.g. Average) and are called
/// single-value numeric metrics aggregation, others generate multiple metrics (e.g. Stats) and are
/// called multi-value numeric metrics aggregation.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub enum MetricAggregation {
/// Calculates the average.
#[serde(rename = "avg")]
Average(AverageAggregation),
/// Calculates stats sum, average, min, max, standard_deviation on a field.
#[serde(rename = "stats")]
Stats(StatsAggregation),
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn serialize_to_json_test() {
let agg_req1: Aggregations = vec![(
"range".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Range(RangeAggregation {
field: "score".to_string(),
ranges: vec![
(f64::MIN..3f64).into(),
(3f64..7f64).into(),
(7f64..20f64).into(),
(20f64..f64::MAX).into(),
],
}),
sub_aggregation: Default::default(),
}),
)]
.into_iter()
.collect();
let elasticsearch_compatible_json_req = r#"{
"range": {
"range": {
"field": "score",
"ranges": [
{
"to": 3.0
},
{
"from": 3.0,
"to": 7.0
},
{
"from": 7.0,
"to": 20.0
},
{
"from": 20.0
}
]
}
}
}"#;
let agg_req2: String = serde_json::to_string_pretty(&agg_req1).unwrap();
assert_eq!(agg_req2, elasticsearch_compatible_json_req);
}
}

View File

@@ -1,140 +0,0 @@
//! This will enhance the request tree with access to the fastfield and metadata.
use super::agg_req::{Aggregation, Aggregations, BucketAggregationType, MetricAggregation};
use super::bucket::RangeAggregation;
use super::metric::{AverageAggregation, StatsAggregation};
use super::VecWithNames;
use crate::fastfield::DynamicFastFieldReader;
use crate::schema::Type;
use crate::{SegmentReader, TantivyError};
#[derive(Clone, Default)]
pub(crate) struct AggregationsWithAccessor {
pub metrics: VecWithNames<MetricAggregationWithAccessor>,
pub buckets: VecWithNames<BucketAggregationWithAccessor>,
}
impl AggregationsWithAccessor {
fn from_data(
metrics: VecWithNames<MetricAggregationWithAccessor>,
buckets: VecWithNames<BucketAggregationWithAccessor>,
) -> Self {
Self { metrics, buckets }
}
pub fn is_empty(&self) -> bool {
self.metrics.is_empty() && self.buckets.is_empty()
}
}
#[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: DynamicFastFieldReader<u64>,
pub(crate) field_type: Type,
pub(crate) bucket_agg: BucketAggregationType,
pub(crate) sub_aggregation: AggregationsWithAccessor,
}
impl BucketAggregationWithAccessor {
fn from_bucket(
bucket: &BucketAggregationType,
sub_aggregation: &Aggregations,
reader: &SegmentReader,
) -> crate::Result<BucketAggregationWithAccessor> {
let (accessor, field_type) = match &bucket {
BucketAggregationType::Range(RangeAggregation {
field: field_name,
ranges: _,
}) => get_ff_reader_and_validate(reader, field_name)?,
};
let sub_aggregation = sub_aggregation.clone();
Ok(BucketAggregationWithAccessor {
accessor,
field_type,
sub_aggregation: get_aggregations_with_accessor(&sub_aggregation, reader)?,
bucket_agg: bucket.clone(),
})
}
}
/// Contains the metric request and the fast field accessor.
#[derive(Clone)]
pub struct MetricAggregationWithAccessor {
pub metric: MetricAggregation,
pub field_type: Type,
pub accessor: DynamicFastFieldReader<u64>,
}
impl MetricAggregationWithAccessor {
fn from_metric(
metric: &MetricAggregation,
reader: &SegmentReader,
) -> crate::Result<MetricAggregationWithAccessor> {
match &metric {
MetricAggregation::Average(AverageAggregation { field: field_name })
| MetricAggregation::Stats(StatsAggregation { field: field_name }) => {
let (accessor, field_type) = get_ff_reader_and_validate(reader, field_name)?;
Ok(MetricAggregationWithAccessor {
accessor,
field_type,
metric: metric.clone(),
})
}
}
}
}
pub(crate) fn get_aggregations_with_accessor(
aggs: &Aggregations,
reader: &SegmentReader,
) -> crate::Result<AggregationsWithAccessor> {
let mut metrics = vec![];
let mut buckets = vec![];
for (key, agg) in aggs.iter() {
match agg {
Aggregation::Bucket(bucket) => buckets.push((
key.to_string(),
BucketAggregationWithAccessor::from_bucket(
&bucket.bucket_agg,
&bucket.sub_aggregation,
reader,
)?,
)),
Aggregation::Metric(metric) => metrics.push((
key.to_string(),
MetricAggregationWithAccessor::from_metric(metric, reader)?,
)),
}
}
Ok(AggregationsWithAccessor::from_data(
VecWithNames::from_entries(metrics),
VecWithNames::from_entries(buckets),
))
}
fn get_ff_reader_and_validate(
reader: &SegmentReader,
field_name: &str,
) -> crate::Result<(DynamicFastFieldReader<u64>, Type)> {
let field = reader
.schema()
.get_field(field_name)
.ok_or_else(|| TantivyError::FieldNotFound(field_name.to_string()))?;
let field_type = reader.schema().get_field_entry(field).field_type();
if field_type.value_type() != Type::I64
&& field_type.value_type() != Type::U64
&& field_type.value_type() != Type::F64
{
return Err(TantivyError::InvalidArgument(format!(
"Invalid field type in aggregation {:?}, only f64, u64, i64 is supported",
field_type.value_type()
)));
}
let ff_fields = reader.fast_fields();
ff_fields
.u64_lenient(field)
.map(|field| (field, field_type.value_type()))
}

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@@ -1,142 +0,0 @@
//! Contains the final aggregation tree.
//! This tree can be converted via the `into()` method from `IntermediateAggregationResults`.
//! This conversion computes the final result. For example: The intermediate result contains
//! intermediate average results, which is the sum and the number of values. The actual average is
//! calculated on the step from intermediate to final aggregation result tree.
use std::cmp::Ordering;
use std::collections::HashMap;
use itertools::Itertools;
use serde::{Deserialize, Serialize};
use super::intermediate_agg_result::{
IntermediateAggregationResult, IntermediateAggregationResults, IntermediateBucketResult,
IntermediateMetricResult, IntermediateRangeBucketEntry,
};
use super::metric::{SingleMetricResult, Stats};
use super::Key;
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
/// The final aggegation result.
pub struct AggregationResults(pub HashMap<String, AggregationResult>);
impl From<IntermediateAggregationResults> for AggregationResults {
fn from(tree: IntermediateAggregationResults) -> Self {
Self(
tree.0
.into_iter()
.map(|(key, agg)| (key, agg.into()))
.collect(),
)
}
}
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
#[serde(untagged)]
/// An aggregation is either a bucket or a metric.
pub enum AggregationResult {
/// Bucket result variant.
BucketResult(BucketResult),
/// Metric result variant.
MetricResult(MetricResult),
}
impl From<IntermediateAggregationResult> for AggregationResult {
fn from(tree: IntermediateAggregationResult) -> Self {
match tree {
IntermediateAggregationResult::Bucket(bucket) => {
AggregationResult::BucketResult(bucket.into())
}
IntermediateAggregationResult::Metric(metric) => {
AggregationResult::MetricResult(metric.into())
}
}
}
}
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
#[serde(untagged)]
/// MetricResult
pub enum MetricResult {
/// Average metric result.
Average(SingleMetricResult),
/// Stats metric result.
Stats(Stats),
}
impl From<IntermediateMetricResult> for MetricResult {
fn from(metric: IntermediateMetricResult) -> Self {
match metric {
IntermediateMetricResult::Average(avg_data) => {
MetricResult::Average(avg_data.finalize().into())
}
IntermediateMetricResult::Stats(intermediate_stats) => {
MetricResult::Stats(intermediate_stats.finalize())
}
}
}
}
/// BucketEntry holds bucket aggregation result types.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
#[serde(untagged)]
pub enum BucketResult {
/// This is the default entry for a bucket, which contains a key, count, and optionally
/// sub_aggregations.
Range {
/// The range buckets sorted by range.
buckets: Vec<RangeBucketEntry>,
},
}
impl From<IntermediateBucketResult> for BucketResult {
fn from(result: IntermediateBucketResult) -> Self {
match result {
IntermediateBucketResult::Range(range_map) => {
let mut buckets: Vec<RangeBucketEntry> = range_map
.into_iter()
.map(|(_, bucket)| bucket.into())
.collect_vec();
buckets.sort_by(|a, b| {
a.from
.unwrap_or(f64::MIN)
.partial_cmp(&b.from.unwrap_or(f64::MIN))
.unwrap_or(Ordering::Equal)
});
BucketResult::Range { buckets }
}
}
}
}
/// This is the range entry for a bucket, which contains a key, count, and optionally
/// sub_aggregations.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct RangeBucketEntry {
/// The identifier of the bucket.
pub key: Key,
/// Number of documents in the bucket.
pub doc_count: u64,
#[serde(flatten)]
/// sub-aggregations in this bucket.
pub sub_aggregation: AggregationResults,
/// The from range of the bucket. Equals f64::MIN when None.
#[serde(skip_serializing_if = "Option::is_none")]
pub from: Option<f64>,
/// The to range of the bucket. Equals f64::MAX when None.
#[serde(skip_serializing_if = "Option::is_none")]
pub to: Option<f64>,
}
impl From<IntermediateRangeBucketEntry> for RangeBucketEntry {
fn from(entry: IntermediateRangeBucketEntry) -> Self {
RangeBucketEntry {
key: entry.key,
doc_count: entry.doc_count,
sub_aggregation: entry.sub_aggregation.into(),
to: entry.to,
from: entry.from,
}
}
}

View File

@@ -1,10 +0,0 @@
//! Module for all bucket aggregations.
//!
//! Results of final buckets are [BucketEntry](super::agg_result::BucketEntry).
//! Results of intermediate buckets are
//! [IntermediateBucketEntry](super::intermediate_agg_result::IntermediateBucketEntry)
mod range;
pub use range::RangeAggregation;
pub(crate) use range::SegmentRangeCollector;

View File

@@ -1,536 +0,0 @@
use std::ops::Range;
use itertools::Itertools;
use serde::{Deserialize, Serialize};
use crate::aggregation::agg_req_with_accessor::{
AggregationsWithAccessor, BucketAggregationWithAccessor,
};
use crate::aggregation::intermediate_agg_result::IntermediateBucketResult;
use crate::aggregation::segment_agg_result::{
SegmentAggregationResultsCollector, SegmentRangeBucketEntry,
};
use crate::aggregation::{f64_from_fastfield_u64, f64_to_fastfield_u64, Key};
use crate::fastfield::FastFieldReader;
use crate::schema::Type;
use crate::{DocId, TantivyError};
/// Provide user-defined buckets to aggregate on.
/// Two special buckets will automatically be created to cover the whole range of values.
/// The provided buckets have to be continous.
/// During the aggregation, the values extracted from the fast_field `field_name` will be checked
/// against each bucket range. Note that this aggregation includes the from value and excludes the
/// to value for each range.
///
/// Result type is [BucketResult](crate::aggregation::agg_result::BucketResult) with
/// [BucketEntryKeyCount](crate::aggregation::agg_result::BucketEntryKeyCount) on the
/// AggregationCollector.
///
/// Result type is
/// [crate::aggregation::intermediate_agg_result::IntermediateBucketResult] with
/// [crate::aggregation::intermediate_agg_result::IntermediateBucketEntryKeyCount] on the
/// DistributedAggregationCollector.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct RangeAggregation {
/// The field to aggregate on.
pub field: String,
/// Note that this aggregation includes the from value and excludes the to value for each
/// range. Extra buckets will be created until the first to, and last from, if necessary.
pub ranges: Vec<RangeAggregationRange>,
}
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct RangeAggregationRange {
#[serde(skip_serializing_if = "Option::is_none", default)]
pub from: Option<f64>,
#[serde(skip_serializing_if = "Option::is_none", default)]
pub to: Option<f64>,
}
impl From<Range<f64>> for RangeAggregationRange {
fn from(range: Range<f64>) -> Self {
let from = if range.start == f64::MIN {
None
} else {
Some(range.start)
};
let to = if range.end == f64::MAX {
None
} else {
Some(range.end)
};
RangeAggregationRange { from, to }
}
}
#[derive(Clone, Debug, PartialEq)]
pub struct SegmentRangeAndBucketEntry {
range: Range<u64>,
bucket: SegmentRangeBucketEntry,
}
/// The collector puts values from the fast field into the correct buckets and does a conversion to
/// the correct datatype.
#[derive(Clone, Debug, PartialEq)]
pub struct SegmentRangeCollector {
/// The buckets containing the aggregation data.
buckets: Vec<SegmentRangeAndBucketEntry>,
field_type: Type,
}
impl SegmentRangeCollector {
pub fn into_intermediate_bucket_result(self) -> IntermediateBucketResult {
let field_type = self.field_type;
let buckets = self
.buckets
.into_iter()
.map(move |range_bucket| {
(
range_to_key(&range_bucket.range, &field_type),
range_bucket.bucket.into(),
)
})
.collect();
IntermediateBucketResult::Range(buckets)
}
pub(crate) fn from_req(
req: &RangeAggregation,
sub_aggregation: &AggregationsWithAccessor,
field_type: Type,
) -> crate::Result<Self> {
// The range input on the request is f64.
// We need to convert to u64 ranges, because we read the values as u64.
// The mapping from the conversion is monotonic so ordering is preserved.
let buckets = extend_validate_ranges(&req.ranges, &field_type)?
.iter()
.map(|range| {
let to = if range.end == u64::MAX {
None
} else {
Some(f64_from_fastfield_u64(range.end, &field_type))
};
let from = if range.start == u64::MIN {
None
} else {
Some(f64_from_fastfield_u64(range.start, &field_type))
};
let sub_aggregation = if sub_aggregation.is_empty() {
None
} else {
Some(SegmentAggregationResultsCollector::from_req(
sub_aggregation,
)?)
};
Ok(SegmentRangeAndBucketEntry {
range: range.clone(),
bucket: SegmentRangeBucketEntry {
key: range_to_key(range, &field_type),
doc_count: 0,
sub_aggregation,
from,
to,
},
})
})
.collect::<crate::Result<_>>()?;
Ok(SegmentRangeCollector {
buckets,
field_type,
})
}
#[inline]
pub(crate) fn collect_block(
&mut self,
doc: &[DocId],
bucket_with_accessor: &BucketAggregationWithAccessor,
force_flush: bool,
) {
let mut iter = doc.chunks_exact(4);
for docs in iter.by_ref() {
let val1 = bucket_with_accessor.accessor.get(docs[0]);
let val2 = bucket_with_accessor.accessor.get(docs[1]);
let val3 = bucket_with_accessor.accessor.get(docs[2]);
let val4 = bucket_with_accessor.accessor.get(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);
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 = bucket_with_accessor.accessor.get(*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 {
sub_aggregation
.flush_staged_docs(&bucket_with_accessor.sub_aggregation, force_flush);
}
}
}
}
#[inline]
fn increment_bucket(
&mut self,
bucket_pos: usize,
doc: DocId,
bucket_with_accessor: &AggregationsWithAccessor,
) {
let bucket = &mut self.buckets[bucket_pos];
bucket.bucket.doc_count += 1;
if let Some(sub_aggregation) = &mut bucket.bucket.sub_aggregation {
sub_aggregation.collect(doc, bucket_with_accessor);
}
}
#[inline]
fn get_bucket_pos(&self, val: u64) -> usize {
let pos = self
.buckets
.binary_search_by_key(&val, |probe| probe.range.start)
.unwrap_or_else(|pos| pos - 1);
debug_assert!(self.buckets[pos].range.contains(&val));
pos
}
}
/// Converts the user provided f64 range value to fast field value space.
///
/// Internally fast field values are always stored as u64.
/// If the fast field has u64 [1,2,5], these values are stored as is in the fast field.
/// A fast field with f64 [1.0, 2.0, 5.0] is converted to u64 space, using a
/// monotonic mapping function, so the order is preserved.
///
/// Consequently, a f64 user range 1.0..3.0 needs to be converted to fast field value space using
/// the same monotonic mapping function, so that the provided ranges contain the u64 values in the
/// fast field.
/// The alternative would be that every value read would be converted to the f64 range, but that is
/// more computational expensive when many documents are hit.
fn to_u64_range(range: &RangeAggregationRange, field_type: &Type) -> Range<u64> {
range
.from
.map(|from| f64_to_fastfield_u64(from, field_type))
.unwrap_or(u64::MIN)
..range
.to
.map(|to| f64_to_fastfield_u64(to, field_type))
.unwrap_or(u64::MAX)
}
/// Extends the provided buckets to contain the whole value range, by inserting buckets at the
/// beginning and end.
fn extend_validate_ranges(
buckets: &[RangeAggregationRange],
field_type: &Type,
) -> crate::Result<Vec<Range<u64>>> {
let mut converted_buckets = buckets
.iter()
.map(|range| to_u64_range(range, field_type))
.collect_vec();
converted_buckets.sort_by_key(|bucket| bucket.start);
if converted_buckets[0].start != u64::MIN {
converted_buckets.insert(0, u64::MIN..converted_buckets[0].start);
}
if converted_buckets[converted_buckets.len() - 1].end != u64::MAX {
converted_buckets.push(converted_buckets[converted_buckets.len() - 1].end..u64::MAX);
}
// fill up holes in the ranges
let find_hole = |converted_buckets: &[Range<u64>]| {
for (pos, ranges) in converted_buckets.windows(2).enumerate() {
if ranges[0].end > ranges[1].start {
return Err(TantivyError::InvalidArgument(format!(
"Overlapping ranges not supported range {:?}, range+1 {:?}",
ranges[0], ranges[1]
)));
}
if ranges[0].end != ranges[1].start {
return Ok(Some(pos));
}
}
Ok(None)
};
while let Some(hole_pos) = find_hole(&converted_buckets)? {
let new_range = converted_buckets[hole_pos].end..converted_buckets[hole_pos + 1].start;
converted_buckets.insert(hole_pos + 1, new_range);
}
Ok(converted_buckets)
}
pub fn range_to_string(range: &Range<u64>, field_type: &Type) -> String {
// is_start is there for malformed requests, e.g. ig the user passes the range u64::MIN..0.0,
// it should be rendererd as "*-0" and not "*-*"
let to_str = |val: u64, is_start: bool| {
if (is_start && val == u64::MIN) || (!is_start && val == u64::MAX) {
"*".to_string()
} else {
f64_from_fastfield_u64(val, field_type).to_string()
}
};
format!("{}-{}", to_str(range.start, true), to_str(range.end, false))
}
pub fn range_to_key(range: &Range<u64>, field_type: &Type) -> Key {
Key::Str(range_to_string(range, field_type))
}
#[cfg(test)]
mod tests {
use serde_json::Value;
use super::*;
use crate::aggregation::agg_req::{
Aggregation, Aggregations, BucketAggregation, BucketAggregationType,
};
use crate::aggregation::tests::get_test_index_with_num_docs;
use crate::aggregation::AggregationCollector;
use crate::fastfield::FastValue;
use crate::query::AllQuery;
pub fn get_collector_from_ranges(
ranges: Vec<RangeAggregationRange>,
field_type: Type,
) -> SegmentRangeCollector {
let req = RangeAggregation {
field: "dummy".to_string(),
ranges,
};
SegmentRangeCollector::from_req(&req, &Default::default(), field_type).unwrap()
}
#[test]
fn range_fraction_test() -> crate::Result<()> {
let index = get_test_index_with_num_docs(false, 100)?;
let agg_req: Aggregations = vec![(
"range".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Range(RangeAggregation {
field: "fraction_f64".to_string(),
ranges: vec![(0f64..0.1f64).into(), (0.1f64..0.2f64).into()],
}),
sub_aggregation: Default::default(),
}),
)]
.into_iter()
.collect();
let collector = AggregationCollector::from_aggs(agg_req);
let reader = index.reader()?;
let searcher = reader.searcher();
let agg_res = searcher.search(&AllQuery, &collector).unwrap();
let res: Value = serde_json::from_str(&serde_json::to_string(&agg_res)?)?;
assert_eq!(res["range"]["buckets"][0]["key"], "*-0");
assert_eq!(res["range"]["buckets"][0]["doc_count"], 0);
assert_eq!(res["range"]["buckets"][1]["key"], "0-0.1");
assert_eq!(res["range"]["buckets"][1]["doc_count"], 10);
assert_eq!(res["range"]["buckets"][2]["key"], "0.1-0.2");
assert_eq!(res["range"]["buckets"][2]["doc_count"], 10);
assert_eq!(res["range"]["buckets"][3]["key"], "0.2-*");
assert_eq!(res["range"]["buckets"][3]["doc_count"], 80);
Ok(())
}
#[test]
fn bucket_test_extend_range_hole() {
let buckets = vec![(10f64..20f64).into(), (30f64..40f64).into()];
let collector = get_collector_from_ranges(buckets, Type::F64);
let buckets = collector.buckets;
assert_eq!(buckets[0].range.start, u64::MIN);
assert_eq!(buckets[0].range.end, 10f64.to_u64());
assert_eq!(buckets[1].range.start, 10f64.to_u64());
assert_eq!(buckets[1].range.end, 20f64.to_u64());
// Added bucket to fill hole
assert_eq!(buckets[2].range.start, 20f64.to_u64());
assert_eq!(buckets[2].range.end, 30f64.to_u64());
assert_eq!(buckets[3].range.start, 30f64.to_u64());
assert_eq!(buckets[3].range.end, 40f64.to_u64());
}
#[test]
fn bucket_test_range_conversion_special_case() {
// the monotonic conversion between f64 and u64, does not map f64::MIN.to_u64() ==
// u64::MIN, but the into trait converts f64::MIN/MAX to None
let buckets = vec![
(f64::MIN..10f64).into(),
(10f64..20f64).into(),
(20f64..f64::MAX).into(),
];
let collector = get_collector_from_ranges(buckets, Type::F64);
let buckets = collector.buckets;
assert_eq!(buckets[0].range.start, u64::MIN);
assert_eq!(buckets[0].range.end, 10f64.to_u64());
assert_eq!(buckets[1].range.start, 10f64.to_u64());
assert_eq!(buckets[1].range.end, 20f64.to_u64());
assert_eq!(buckets[2].range.start, 20f64.to_u64());
assert_eq!(buckets[2].range.end, u64::MAX);
assert_eq!(buckets.len(), 3);
}
#[test]
fn bucket_range_test_negative_vals() {
let buckets = vec![(-10f64..-1f64).into()];
let collector = get_collector_from_ranges(buckets, Type::F64);
let buckets = collector.buckets;
assert_eq!(&buckets[0].bucket.key.to_string(), "*--10");
assert_eq!(&buckets[buckets.len() - 1].bucket.key.to_string(), "-1-*");
}
#[test]
fn bucket_range_test_positive_vals() {
let buckets = vec![(0f64..10f64).into()];
let collector = get_collector_from_ranges(buckets, Type::F64);
let buckets = collector.buckets;
assert_eq!(&buckets[0].bucket.key.to_string(), "*-0");
assert_eq!(&buckets[buckets.len() - 1].bucket.key.to_string(), "10-*");
}
#[test]
fn range_binary_search_test_u64() {
let check_ranges = |ranges: Vec<RangeAggregationRange>| {
let collector = get_collector_from_ranges(ranges, Type::U64);
let search = |val: u64| collector.get_bucket_pos(val);
assert_eq!(search(u64::MIN), 0);
assert_eq!(search(9), 0);
assert_eq!(search(10), 1);
assert_eq!(search(11), 1);
assert_eq!(search(99), 1);
assert_eq!(search(100), 2);
assert_eq!(search(u64::MAX - 1), 2); // Since the end range is never included, the max
// value
};
let ranges = vec![(10.0..100.0).into()];
check_ranges(ranges);
let ranges = vec![
RangeAggregationRange {
to: Some(10.0),
from: None,
},
(10.0..100.0).into(),
];
check_ranges(ranges);
let ranges = vec![
RangeAggregationRange {
to: Some(10.0),
from: None,
},
(10.0..100.0).into(),
RangeAggregationRange {
to: None,
from: Some(100.0),
},
];
check_ranges(ranges);
}
#[test]
fn range_binary_search_test_f64() {
let ranges = vec![
//(f64::MIN..10.0).into(),
(10.0..100.0).into(),
//(100.0..f64::MAX).into(),
];
let collector = get_collector_from_ranges(ranges, Type::F64);
let search = |val: u64| collector.get_bucket_pos(val);
assert_eq!(search(u64::MIN), 0);
assert_eq!(search(9f64.to_u64()), 0);
assert_eq!(search(10f64.to_u64()), 1);
assert_eq!(search(11f64.to_u64()), 1);
assert_eq!(search(99f64.to_u64()), 1);
assert_eq!(search(100f64.to_u64()), 2);
assert_eq!(search(u64::MAX - 1), 2); // Since the end range is never included,
// the max value
}
}
#[cfg(all(test, feature = "unstable"))]
mod bench {
use rand::seq::SliceRandom;
use rand::thread_rng;
use super::*;
use crate::aggregation::bucket::range::tests::get_collector_from_ranges;
const TOTAL_DOCS: u64 = 1_000_000u64;
const NUM_DOCS: u64 = 50_000u64;
fn get_collector_with_buckets(num_buckets: u64, num_docs: u64) -> SegmentRangeCollector {
let bucket_size = num_docs / num_buckets;
let mut buckets: Vec<RangeAggregationRange> = vec![];
for i in 0..num_buckets {
let bucket_start = (i * bucket_size) as f64;
buckets.push((bucket_start..bucket_start + bucket_size as f64).into())
}
get_collector_from_ranges(buckets, Type::U64)
}
fn get_rand_docs(total_docs: u64, num_docs_returned: u64) -> Vec<u64> {
let mut rng = thread_rng();
let all_docs = (0..total_docs - 1).collect_vec();
let mut vals = all_docs
.as_slice()
.choose_multiple(&mut rng, num_docs_returned as usize)
.cloned()
.collect_vec();
vals.sort();
vals
}
fn bench_range_binary_search(b: &mut test::Bencher, num_buckets: u64) {
let collector = get_collector_with_buckets(num_buckets, TOTAL_DOCS);
let vals = get_rand_docs(TOTAL_DOCS, NUM_DOCS);
b.iter(|| {
let mut bucket_pos = 0;
for val in &vals {
bucket_pos = collector.get_bucket_pos(*val);
}
bucket_pos
})
}
#[bench]
fn bench_range_100_buckets(b: &mut test::Bencher) {
bench_range_binary_search(b, 100)
}
#[bench]
fn bench_range_10_buckets(b: &mut test::Bencher) {
bench_range_binary_search(b, 10)
}
}

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@@ -1,135 +0,0 @@
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 crate::aggregation::agg_req_with_accessor::get_aggregations_with_accessor;
use crate::collector::{Collector, SegmentCollector};
use crate::TantivyError;
/// Collector for aggregations.
///
/// The collector collects all aggregations by the underlying aggregation request.
pub struct AggregationCollector {
agg: Aggregations,
}
impl AggregationCollector {
/// Create collector from aggregation request.
pub fn from_aggs(agg: Aggregations) -> Self {
Self { agg }
}
}
/// Collector for distributed aggregations.
///
/// The collector collects all aggregations by the underlying aggregation request.
///
/// # Purpose
/// AggregationCollector returns `IntermediateAggregationResults` and not the final
/// `AggregationResults`, so that results from differenct indices can be merged and then converted
/// into the final `AggregationResults` via the `into()` method.
pub struct DistributedAggregationCollector {
agg: Aggregations,
}
impl DistributedAggregationCollector {
/// Create collector from aggregation request.
pub fn from_aggs(agg: Aggregations) -> Self {
Self { agg }
}
}
impl Collector for DistributedAggregationCollector {
type Fruit = IntermediateAggregationResults;
type Child = AggregationSegmentCollector;
fn for_segment(
&self,
_segment_local_id: crate::SegmentOrdinal,
reader: &crate::SegmentReader,
) -> crate::Result<Self::Child> {
let aggs_with_accessor = get_aggregations_with_accessor(&self.agg, reader)?;
let result = SegmentAggregationResultsCollector::from_req(&aggs_with_accessor)?;
Ok(AggregationSegmentCollector {
aggs: aggs_with_accessor,
result,
})
}
fn requires_scoring(&self) -> bool {
false
}
fn merge_fruits(
&self,
segment_fruits: Vec<<Self::Child as SegmentCollector>::Fruit>,
) -> crate::Result<Self::Fruit> {
merge_fruits(segment_fruits)
}
}
impl Collector for AggregationCollector {
type Fruit = AggregationResults;
type Child = AggregationSegmentCollector;
fn for_segment(
&self,
_segment_local_id: crate::SegmentOrdinal,
reader: &crate::SegmentReader,
) -> crate::Result<Self::Child> {
let aggs_with_accessor = get_aggregations_with_accessor(&self.agg, reader)?;
let result = SegmentAggregationResultsCollector::from_req(&aggs_with_accessor)?;
Ok(AggregationSegmentCollector {
aggs: aggs_with_accessor,
result,
})
}
fn requires_scoring(&self) -> bool {
false
}
fn merge_fruits(
&self,
segment_fruits: Vec<<Self::Child as SegmentCollector>::Fruit>,
) -> crate::Result<Self::Fruit> {
merge_fruits(segment_fruits).map(|res| res.into())
}
}
fn merge_fruits(
mut segment_fruits: Vec<IntermediateAggregationResults>,
) -> crate::Result<IntermediateAggregationResults> {
if let Some(mut fruit) = segment_fruits.pop() {
for next_fruit in segment_fruits {
fruit.merge_fruits(&next_fruit);
}
Ok(fruit)
} else {
Err(TantivyError::InvalidArgument(
"no fruits provided in merge_fruits".to_string(),
))
}
}
pub struct AggregationSegmentCollector {
aggs: AggregationsWithAccessor,
result: SegmentAggregationResultsCollector,
}
impl SegmentCollector for AggregationSegmentCollector {
type Fruit = IntermediateAggregationResults;
#[inline]
fn collect(&mut self, doc: crate::DocId, _score: crate::Score) {
self.result.collect(doc, &self.aggs);
}
fn harvest(mut self) -> Self::Fruit {
self.result.flush_staged_docs(&self.aggs, true);
self.result.into()
}
}

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@@ -1,304 +0,0 @@
//! Contains the intermediate aggregation tree, that can be merged.
//! Intermediate aggregation results can be used to merge results between segments or between
//! indices.
use std::collections::HashMap;
use serde::{Deserialize, Serialize};
use super::metric::{IntermediateAverage, IntermediateStats};
use super::segment_agg_result::{
SegmentAggregationResultsCollector, SegmentBucketResultCollector, SegmentMetricResultCollector,
SegmentRangeBucketEntry,
};
use super::{Key, VecWithNames};
/// Contains the intermediate aggregation result, which is optimized to be merged with other
/// intermediate results.
#[derive(Default, Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct IntermediateAggregationResults(pub(crate) VecWithNames<IntermediateAggregationResult>);
impl From<SegmentAggregationResultsCollector> for IntermediateAggregationResults {
fn from(tree: SegmentAggregationResultsCollector) -> Self {
let mut data = vec![];
for (key, bucket) in tree.buckets.into_iter() {
data.push((key, IntermediateAggregationResult::Bucket(bucket.into())));
}
for (key, metric) in tree.metrics.into_iter() {
data.push((key, IntermediateAggregationResult::Metric(metric.into())));
}
Self(VecWithNames::from_entries(data))
}
}
impl IntermediateAggregationResults {
/// Merge an other intermediate aggregation result into this result.
///
/// The order of the values need to be the same on both results. This is ensured when the same
/// (key values) are present on the underlying VecWithNames struct.
pub fn merge_fruits(&mut self, other: &IntermediateAggregationResults) {
for (tree_left, tree_right) in self.0.values_mut().zip(other.0.values()) {
tree_left.merge_fruits(tree_right);
}
}
}
/// An aggregation is either a bucket or a metric.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub enum IntermediateAggregationResult {
/// Bucket variant
Bucket(IntermediateBucketResult),
/// Metric variant
Metric(IntermediateMetricResult),
}
impl IntermediateAggregationResult {
fn merge_fruits(&mut self, other: &IntermediateAggregationResult) {
match (self, other) {
(
IntermediateAggregationResult::Bucket(res_left),
IntermediateAggregationResult::Bucket(res_right),
) => {
res_left.merge_fruits(res_right);
}
(
IntermediateAggregationResult::Metric(res_left),
IntermediateAggregationResult::Metric(res_right),
) => {
res_left.merge_fruits(res_right);
}
_ => {
panic!("incompatible types in aggregation tree on merge fruits");
}
}
}
}
/// Holds the intermediate data for metric results
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub enum IntermediateMetricResult {
/// Average containing intermediate average data result
Average(IntermediateAverage),
/// AverageData variant
Stats(IntermediateStats),
}
impl From<SegmentMetricResultCollector> for IntermediateMetricResult {
fn from(tree: SegmentMetricResultCollector) -> Self {
match tree {
SegmentMetricResultCollector::Average(collector) => {
IntermediateMetricResult::Average(IntermediateAverage::from_collector(collector))
}
SegmentMetricResultCollector::Stats(collector) => {
IntermediateMetricResult::Stats(collector.stats)
}
}
}
}
impl IntermediateMetricResult {
fn merge_fruits(&mut self, other: &IntermediateMetricResult) {
match (self, other) {
(
IntermediateMetricResult::Average(avg_data_left),
IntermediateMetricResult::Average(avg_data_right),
) => {
avg_data_left.merge_fruits(avg_data_right);
}
(
IntermediateMetricResult::Stats(stats_left),
IntermediateMetricResult::Stats(stats_right),
) => {
stats_left.merge_fruits(stats_right);
}
_ => {
panic!("incompatible fruit types in tree {:?}", other);
}
}
}
}
/// The intermediate bucket results. Internally they can be easily merged via the keys of the
/// buckets.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub enum IntermediateBucketResult {
/// This is the range entry for a bucket, which contains a key, count, from, to, and optionally
/// sub_aggregations.
Range(HashMap<Key, IntermediateRangeBucketEntry>),
}
impl From<SegmentBucketResultCollector> for IntermediateBucketResult {
fn from(collector: SegmentBucketResultCollector) -> Self {
match collector {
SegmentBucketResultCollector::Range(range) => range.into_intermediate_bucket_result(),
}
}
}
impl IntermediateBucketResult {
fn merge_fruits(&mut self, other: &IntermediateBucketResult) {
match (self, other) {
(
IntermediateBucketResult::Range(entries_left),
IntermediateBucketResult::Range(entries_right),
) => {
for (name, entry_left) in entries_left.iter_mut() {
if let Some(entry_right) = entries_right.get(name) {
entry_left.merge_fruits(entry_right);
}
}
for (key, res) in entries_right.iter() {
if !entries_left.contains_key(key) {
entries_left.insert(key.clone(), res.clone());
}
}
}
}
}
}
/// This is the range entry for a bucket, which contains a key, count, and optionally
/// sub_aggregations.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct IntermediateRangeBucketEntry {
/// The unique the bucket is identified.
pub key: Key,
/// The number of documents in the bucket.
pub doc_count: u64,
pub(crate) values: Option<Vec<u64>>,
/// The sub_aggregation in this bucket.
pub sub_aggregation: IntermediateAggregationResults,
/// The from range of the bucket. Equals f64::MIN when None.
#[serde(skip_serializing_if = "Option::is_none")]
pub from: Option<f64>,
/// The to range of the bucket. Equals f64::MAX when None.
#[serde(skip_serializing_if = "Option::is_none")]
pub to: Option<f64>,
}
impl From<SegmentRangeBucketEntry> for IntermediateRangeBucketEntry {
fn from(entry: SegmentRangeBucketEntry) -> Self {
let sub_aggregation = if let Some(sub_aggregation) = entry.sub_aggregation {
sub_aggregation.into()
} else {
Default::default()
};
// let sub_aggregation = entry.sub_aggregation.into();
IntermediateRangeBucketEntry {
key: entry.key,
doc_count: entry.doc_count,
values: None,
sub_aggregation,
to: entry.to,
from: entry.from,
}
}
}
impl IntermediateRangeBucketEntry {
fn merge_fruits(&mut self, other: &IntermediateRangeBucketEntry) {
self.doc_count += other.doc_count;
self.sub_aggregation.merge_fruits(&other.sub_aggregation);
}
}
#[cfg(test)]
mod tests {
use pretty_assertions::assert_eq;
use super::*;
fn get_sub_test_tree(data: &[(String, u64)]) -> IntermediateAggregationResults {
let mut map = HashMap::new();
let mut buckets = HashMap::new();
for (key, doc_count) in data {
buckets.insert(
Key::Str(key.to_string()),
IntermediateRangeBucketEntry {
key: Key::Str(key.to_string()),
doc_count: *doc_count,
values: None,
sub_aggregation: Default::default(),
from: None,
to: None,
},
);
}
map.insert(
"my_agg_level2".to_string(),
IntermediateAggregationResult::Bucket(IntermediateBucketResult::Range(buckets)),
);
IntermediateAggregationResults(VecWithNames::from_entries(map.into_iter().collect()))
}
fn get_test_tree(data: &[(String, u64, String, u64)]) -> IntermediateAggregationResults {
let mut map = HashMap::new();
let mut buckets = HashMap::new();
for (key, doc_count, sub_aggregation_key, sub_aggregation_count) in data {
buckets.insert(
Key::Str(key.to_string()),
IntermediateRangeBucketEntry {
key: Key::Str(key.to_string()),
doc_count: *doc_count,
values: None,
from: None,
to: None,
sub_aggregation: get_sub_test_tree(&[(
sub_aggregation_key.to_string(),
*sub_aggregation_count,
)]),
},
);
}
map.insert(
"my_agg_level1".to_string(),
IntermediateAggregationResult::Bucket(IntermediateBucketResult::Range(buckets)),
);
IntermediateAggregationResults(VecWithNames::from_entries(map.into_iter().collect()))
}
#[test]
fn test_merge_fruits_tree_1() {
let mut tree_left = get_test_tree(&[
("red".to_string(), 50, "1900".to_string(), 25),
("blue".to_string(), 30, "1900".to_string(), 30),
]);
let tree_right = get_test_tree(&[
("red".to_string(), 60, "1900".to_string(), 30),
("blue".to_string(), 25, "1900".to_string(), 50),
]);
tree_left.merge_fruits(&tree_right);
let tree_expected = get_test_tree(&[
("red".to_string(), 110, "1900".to_string(), 55),
("blue".to_string(), 55, "1900".to_string(), 80),
]);
assert_eq!(tree_left, tree_expected);
}
#[test]
fn test_merge_fruits_tree_2() {
let mut tree_left = get_test_tree(&[
("red".to_string(), 50, "1900".to_string(), 25),
("blue".to_string(), 30, "1900".to_string(), 30),
]);
let tree_right = get_test_tree(&[
("red".to_string(), 60, "1900".to_string(), 30),
("green".to_string(), 25, "1900".to_string(), 50),
]);
tree_left.merge_fruits(&tree_right);
let tree_expected = get_test_tree(&[
("red".to_string(), 110, "1900".to_string(), 55),
("blue".to_string(), 30, "1900".to_string(), 30),
("green".to_string(), 25, "1900".to_string(), 50),
]);
assert_eq!(tree_left, tree_expected);
}
}

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@@ -1,101 +0,0 @@
use std::fmt::Debug;
use serde::{Deserialize, Serialize};
use crate::aggregation::f64_from_fastfield_u64;
use crate::fastfield::{DynamicFastFieldReader, FastFieldReader};
use crate::schema::Type;
use crate::DocId;
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
/// A single-value metric aggregation that computes the average of numeric values that are
/// extracted from the aggregated documents.
/// Supported field types are u64, i64, and f64.
/// See [super::SingleMetricResult] for return value.
pub struct AverageAggregation {
/// The field name to compute the stats on.
pub field: String,
}
impl AverageAggregation {
/// Create new AverageAggregation from a field.
pub fn from_field_name(field_name: String) -> Self {
AverageAggregation { field: field_name }
}
/// Return the field name.
pub fn field_name(&self) -> &str {
&self.field
}
}
#[derive(Clone, PartialEq)]
pub(crate) struct SegmentAverageCollector {
pub data: IntermediateAverage,
field_type: Type,
}
impl Debug for SegmentAverageCollector {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("AverageCollector")
.field("data", &self.data)
.finish()
}
}
impl SegmentAverageCollector {
pub fn from_req(field_type: Type) -> Self {
Self {
field_type,
data: Default::default(),
}
}
pub(crate) fn collect_block(&mut self, doc: &[DocId], field: &DynamicFastFieldReader<u64>) {
let mut iter = doc.chunks_exact(4);
for docs in iter.by_ref() {
let val1 = field.get(docs[0]);
let val2 = field.get(docs[1]);
let val3 = field.get(docs[2]);
let val4 = field.get(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);
self.data.collect(val1);
self.data.collect(val2);
self.data.collect(val3);
self.data.collect(val4);
}
for doc in iter.remainder() {
let val = field.get(*doc);
let val = f64_from_fastfield_u64(val, &self.field_type);
self.data.collect(val);
}
}
}
/// Contains mergeable version of average data.
#[derive(Default, Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct IntermediateAverage {
pub(crate) sum: f64,
pub(crate) doc_count: u64,
}
impl IntermediateAverage {
pub(crate) fn from_collector(collector: SegmentAverageCollector) -> Self {
collector.data
}
/// Merge average data into this instance.
pub fn merge_fruits(&mut self, other: &IntermediateAverage) {
self.sum += other.sum;
self.doc_count += other.doc_count;
}
/// compute final result
pub fn finalize(&self) -> f64 {
self.sum / self.doc_count as f64
}
#[inline]
fn collect(&mut self, val: f64) {
self.doc_count += 1;
self.sum += val;
}
}

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@@ -1,22 +0,0 @@
//! Module for all metric aggregations.
mod average;
mod stats;
pub use average::*;
use serde::{Deserialize, Serialize};
pub use stats::*;
/// Single-metric aggregations use this common result structure.
///
/// Main reason to wrap it in value is to match elasticsearch output structure.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct SingleMetricResult {
/// The value of the single value metric.
pub value: f64,
}
impl From<f64> for SingleMetricResult {
fn from(value: f64) -> Self {
Self { value }
}
}

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@@ -1,273 +0,0 @@
use serde::{Deserialize, Serialize};
use crate::aggregation::f64_from_fastfield_u64;
use crate::fastfield::{DynamicFastFieldReader, FastFieldReader};
use crate::schema::Type;
use crate::DocId;
/// A multi-value metric aggregation that computes stats of numeric values that are
/// extracted from the aggregated documents.
/// Supported field types are u64, i64, and f64.
/// See [Stats] for returned statistics.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct StatsAggregation {
/// The field name to compute the stats on.
pub field: String,
}
impl StatsAggregation {
/// Create new StatsAggregation from a field.
pub fn from_field_name(field_name: String) -> Self {
StatsAggregation { field: field_name }
}
/// Return the field name.
pub fn field_name(&self) -> &str {
&self.field
}
}
/// Stats contains a collection of statistics.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct Stats {
/// The number of documents.
pub count: usize,
/// The sum of the fast field values.
pub sum: f64,
/// The standard deviation of the fast field values.
pub standard_deviation: f64,
/// The min value of the fast field values.
pub min: f64,
/// The max value of the fast field values.
pub max: f64,
/// The average of the values.
pub avg: f64,
}
/// IntermediateStats contains the mergeable version for stats.
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
pub struct IntermediateStats {
count: usize,
sum: f64,
squared_sum: f64,
min: f64,
max: f64,
}
impl IntermediateStats {
fn new() -> Self {
Self {
count: 0,
sum: 0.0,
squared_sum: 0.0,
min: f64::MAX,
max: f64::MIN,
}
}
pub(crate) fn avg(&self) -> f64 {
self.sum / (self.count as f64)
}
fn square_mean(&self) -> f64 {
self.squared_sum / (self.count as f64)
}
pub(crate) fn standard_deviation(&self) -> f64 {
let average = self.avg();
(self.square_mean() - average * average).sqrt()
}
/// Merge data from other stats into this instance.
pub fn merge_fruits(&mut self, other: &IntermediateStats) {
self.count += other.count;
self.sum += other.sum;
self.squared_sum += other.squared_sum;
self.min = self.min.min(other.min);
self.max = self.max.max(other.max);
}
/// compute final result
pub fn finalize(&self) -> Stats {
Stats {
count: self.count,
sum: self.sum,
standard_deviation: self.standard_deviation(),
min: self.min,
max: self.max,
avg: self.avg(),
}
}
#[inline]
fn collect(&mut self, value: f64) {
self.count += 1;
self.sum += value;
self.squared_sum += value * value;
self.min = self.min.min(value);
self.max = self.max.max(value);
}
}
#[derive(Clone, Debug, PartialEq)]
pub(crate) struct SegmentStatsCollector {
pub(crate) stats: IntermediateStats,
field_type: Type,
}
impl SegmentStatsCollector {
pub fn from_req(field_type: Type) -> Self {
Self {
field_type,
stats: IntermediateStats::new(),
}
}
pub(crate) fn collect_block(&mut self, doc: &[DocId], field: &DynamicFastFieldReader<u64>) {
let mut iter = doc.chunks_exact(4);
for docs in iter.by_ref() {
let val1 = field.get(docs[0]);
let val2 = field.get(docs[1]);
let val3 = field.get(docs[2]);
let val4 = field.get(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);
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(*doc);
let val = f64_from_fastfield_u64(val, &self.field_type);
self.stats.collect(val);
}
}
}
#[cfg(test)]
mod tests {
use std::iter;
use serde_json::Value;
use crate::aggregation::agg_req::{
Aggregation, Aggregations, BucketAggregation, BucketAggregationType, MetricAggregation,
RangeAggregation,
};
use crate::aggregation::agg_result::AggregationResults;
use crate::aggregation::metric::StatsAggregation;
use crate::aggregation::tests::get_test_index_2_segments;
use crate::aggregation::AggregationCollector;
use crate::query::TermQuery;
use crate::schema::IndexRecordOption;
use crate::Term;
#[test]
fn test_aggregation_stats() -> crate::Result<()> {
let index = get_test_index_2_segments(false)?;
let reader = index.reader()?;
let text_field = reader.searcher().schema().get_field("text").unwrap();
let term_query = TermQuery::new(
Term::from_field_text(text_field, "cool"),
IndexRecordOption::Basic,
);
let agg_req_1: Aggregations = vec![
(
"stats_i64".to_string(),
Aggregation::Metric(MetricAggregation::Stats(StatsAggregation::from_field_name(
"score_i64".to_string(),
))),
),
(
"stats_f64".to_string(),
Aggregation::Metric(MetricAggregation::Stats(StatsAggregation::from_field_name(
"score_f64".to_string(),
))),
),
(
"stats".to_string(),
Aggregation::Metric(MetricAggregation::Stats(StatsAggregation::from_field_name(
"score".to_string(),
))),
),
(
"range".to_string(),
Aggregation::Bucket(BucketAggregation {
bucket_agg: BucketAggregationType::Range(RangeAggregation {
field: "score".to_string(),
ranges: vec![(3f64..7f64).into(), (7f64..20f64).into()],
}),
sub_aggregation: iter::once((
"stats".to_string(),
Aggregation::Metric(MetricAggregation::Stats(
StatsAggregation::from_field_name("score".to_string()),
)),
))
.collect(),
}),
),
]
.into_iter()
.collect();
let collector = AggregationCollector::from_aggs(agg_req_1);
let searcher = reader.searcher();
let agg_res: AggregationResults = searcher.search(&term_query, &collector).unwrap();
let res: Value = serde_json::from_str(&serde_json::to_string(&agg_res)?)?;
assert_eq!(
res["stats"],
json!({
"avg": 12.142857142857142,
"count": 7,
"max": 44.0,
"min": 1.0,
"standard_deviation": 13.65313748796613,
"sum": 85.0
})
);
assert_eq!(
res["stats_i64"],
json!({
"avg": 12.142857142857142,
"count": 7,
"max": 44.0,
"min": 1.0,
"standard_deviation": 13.65313748796613,
"sum": 85.0
})
);
assert_eq!(
res["stats_f64"],
json!({
"avg": 12.214285714285714,
"count": 7,
"max": 44.5,
"min": 1.0,
"standard_deviation": 13.819905785437443,
"sum": 85.5
})
);
assert_eq!(
res["range"]["buckets"][2]["stats"],
json!({
"avg": 10.666666666666666,
"count": 3,
"max": 14.0,
"min": 7.0,
"standard_deviation": 2.867441755680877,
"sum": 32.0
})
);
Ok(())
}
}

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@@ -1,195 +0,0 @@
//! Contains aggregation trees which is used during collection in a segment.
//! This tree contains datastructrues optimized for fast collection.
//! The tree can be converted to an intermediate tree, which contains datastructrues optimized for
//! merging.
use std::fmt::Debug;
use itertools::Itertools;
use super::agg_req::MetricAggregation;
use super::agg_req_with_accessor::{
AggregationsWithAccessor, BucketAggregationWithAccessor, MetricAggregationWithAccessor,
};
use super::bucket::SegmentRangeCollector;
use super::metric::{
AverageAggregation, SegmentAverageCollector, SegmentStatsCollector, StatsAggregation,
};
use super::{Key, VecWithNames};
use crate::aggregation::agg_req::BucketAggregationType;
use crate::DocId;
pub(crate) const DOC_BLOCK_SIZE: usize = 256;
pub(crate) type DocBlock = [DocId; DOC_BLOCK_SIZE];
#[derive(Clone, PartialEq)]
pub(crate) struct SegmentAggregationResultsCollector {
pub(crate) metrics: VecWithNames<SegmentMetricResultCollector>,
pub(crate) buckets: VecWithNames<SegmentBucketResultCollector>,
staged_docs: DocBlock,
num_staged_docs: usize,
}
impl Debug for SegmentAggregationResultsCollector {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("SegmentAggregationResultsCollector")
.field("metrics", &self.metrics)
.field("buckets", &self.buckets)
.field("staged_docs", &&self.staged_docs[..self.num_staged_docs])
.field("num_staged_docs", &self.num_staged_docs)
.finish()
}
}
impl SegmentAggregationResultsCollector {
pub(crate) fn from_req(req: &AggregationsWithAccessor) -> crate::Result<Self> {
let buckets = req
.buckets
.entries()
.map(|(key, req)| {
Ok((
key.to_string(),
SegmentBucketResultCollector::from_req(req)?,
))
})
.collect::<crate::Result<_>>()?;
let metrics = req
.metrics
.entries()
.map(|(key, req)| (key.to_string(), SegmentMetricResultCollector::from_req(req)))
.collect_vec();
Ok(SegmentAggregationResultsCollector {
metrics: VecWithNames::from_entries(metrics),
buckets: VecWithNames::from_entries(buckets),
staged_docs: [0; DOC_BLOCK_SIZE],
num_staged_docs: 0,
})
}
#[inline]
pub(crate) fn collect(
&mut self,
doc: crate::DocId,
agg_with_accessor: &AggregationsWithAccessor,
) {
self.staged_docs[self.num_staged_docs] = doc;
self.num_staged_docs += 1;
if self.num_staged_docs == self.staged_docs.len() {
self.flush_staged_docs(agg_with_accessor, false);
}
}
#[inline(never)]
pub(crate) fn flush_staged_docs(
&mut self,
agg_with_accessor: &AggregationsWithAccessor,
force_flush: bool,
) {
for (agg_with_accessor, collector) in agg_with_accessor
.metrics
.values()
.zip(self.metrics.values_mut())
{
collector.collect_block(&self.staged_docs[..self.num_staged_docs], agg_with_accessor);
}
for (agg_with_accessor, collector) in agg_with_accessor
.buckets
.values()
.zip(self.buckets.values_mut())
{
collector.collect_block(
&self.staged_docs[..self.num_staged_docs],
agg_with_accessor,
force_flush,
);
}
self.num_staged_docs = 0;
}
}
#[derive(Clone, Debug, PartialEq)]
pub(crate) enum SegmentMetricResultCollector {
Average(SegmentAverageCollector),
Stats(SegmentStatsCollector),
}
impl SegmentMetricResultCollector {
pub fn from_req(req: &MetricAggregationWithAccessor) -> Self {
match &req.metric {
MetricAggregation::Average(AverageAggregation { field: _ }) => {
SegmentMetricResultCollector::Average(SegmentAverageCollector::from_req(
req.field_type,
))
}
MetricAggregation::Stats(StatsAggregation { field: _ }) => {
SegmentMetricResultCollector::Stats(SegmentStatsCollector::from_req(req.field_type))
}
}
}
pub(crate) fn collect_block(&mut self, doc: &[DocId], metric: &MetricAggregationWithAccessor) {
match self {
SegmentMetricResultCollector::Average(avg_collector) => {
avg_collector.collect_block(doc, &metric.accessor);
}
SegmentMetricResultCollector::Stats(stats_collector) => {
stats_collector.collect_block(doc, &metric.accessor);
}
}
}
}
/// SegmentBucketAggregationResultCollectors will have specialized buckets for collection inside
/// segments.
/// The typical structure of Map<Key, Bucket> is not suitable during collection for performance
/// reasons.
#[derive(Clone, Debug, PartialEq)]
pub(crate) enum SegmentBucketResultCollector {
Range(SegmentRangeCollector),
}
impl SegmentBucketResultCollector {
pub fn from_req(req: &BucketAggregationWithAccessor) -> crate::Result<Self> {
match &req.bucket_agg {
BucketAggregationType::Range(range_req) => Ok(Self::Range(
SegmentRangeCollector::from_req(range_req, &req.sub_aggregation, req.field_type)?,
)),
}
}
#[inline]
pub(crate) fn collect_block(
&mut self,
doc: &[DocId],
bucket_with_accessor: &BucketAggregationWithAccessor,
force_flush: bool,
) {
match self {
SegmentBucketResultCollector::Range(range) => {
range.collect_block(doc, bucket_with_accessor, force_flush);
}
}
}
}
#[derive(Clone, PartialEq)]
pub(crate) struct SegmentRangeBucketEntry {
pub key: Key,
pub doc_count: u64,
pub sub_aggregation: Option<SegmentAggregationResultsCollector>,
/// 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.
pub to: Option<f64>,
}
impl Debug for SegmentRangeBucketEntry {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("SegmentRangeBucketEntry")
.field("key", &self.key)
.field("doc_count", &self.doc_count)
.field("from", &self.from)
.field("to", &self.to)
.finish()
}
}

View File

@@ -1,6 +1,10 @@
use super::Collector;
use crate::collector::SegmentCollector;
use crate::{DocId, Score, SegmentOrdinal, SegmentReader};
use crate::DocId;
use crate::Result;
use crate::Score;
use crate::SegmentLocalId;
use crate::SegmentReader;
/// `CountCollector` collector only counts how many
/// documents match the query.
@@ -17,10 +21,10 @@ use crate::{DocId, Score, SegmentOrdinal, SegmentReader};
/// let index = Index::create_in_ram(schema);
///
/// let mut index_writer = index.writer(3_000_000).unwrap();
/// index_writer.add_document(doc!(title => "The Name of the Wind")).unwrap();
/// index_writer.add_document(doc!(title => "The Diary of Muadib")).unwrap();
/// index_writer.add_document(doc!(title => "A Dairy Cow")).unwrap();
/// index_writer.add_document(doc!(title => "The Diary of a Young Girl")).unwrap();
/// index_writer.add_document(doc!(title => "The Name of the Wind"));
/// index_writer.add_document(doc!(title => "The Diary of Muadib"));
/// index_writer.add_document(doc!(title => "A Dairy Cow"));
/// index_writer.add_document(doc!(title => "The Diary of a Young Girl"));
/// assert!(index_writer.commit().is_ok());
///
/// let reader = index.reader().unwrap();
@@ -40,11 +44,7 @@ impl Collector for Count {
type Child = SegmentCountCollector;
fn for_segment(
&self,
_: SegmentOrdinal,
_: &SegmentReader,
) -> crate::Result<SegmentCountCollector> {
fn for_segment(&self, _: SegmentLocalId, _: &SegmentReader) -> Result<SegmentCountCollector> {
Ok(SegmentCountCollector::default())
}
@@ -52,7 +52,7 @@ impl Collector for Count {
false
}
fn merge_fruits(&self, segment_counts: Vec<usize>) -> crate::Result<usize> {
fn merge_fruits(&self, segment_counts: Vec<usize>) -> Result<usize> {
Ok(segment_counts.into_iter().sum())
}
}
@@ -77,7 +77,8 @@ impl SegmentCollector for SegmentCountCollector {
#[cfg(test)]
mod tests {
use super::{Count, SegmentCountCollector};
use crate::collector::{Collector, SegmentCollector};
use crate::collector::Collector;
use crate::collector::SegmentCollector;
#[test]
fn test_count_collect_does_not_requires_scoring() {
@@ -92,18 +93,18 @@ mod tests {
}
{
let mut count_collector = SegmentCountCollector::default();
count_collector.collect(0u32, 1.0);
count_collector.collect(0u32, 1f32);
assert_eq!(count_collector.harvest(), 1);
}
{
let mut count_collector = SegmentCountCollector::default();
count_collector.collect(0u32, 1.0);
count_collector.collect(0u32, 1f32);
assert_eq!(count_collector.harvest(), 1);
}
{
let mut count_collector = SegmentCountCollector::default();
count_collector.collect(0u32, 1.0);
count_collector.collect(1u32, 1.0);
count_collector.collect(0u32, 1f32);
count_collector.collect(1u32, 1f32);
assert_eq!(count_collector.harvest(), 2);
}
}

View File

@@ -1,5 +1,6 @@
use crate::collector::top_collector::{TopCollector, TopSegmentCollector};
use crate::collector::{Collector, SegmentCollector};
use crate::Result;
use crate::{DocAddress, DocId, Score, SegmentReader};
pub(crate) struct CustomScoreTopCollector<TCustomScorer, TScore = Score> {
@@ -8,15 +9,16 @@ pub(crate) struct CustomScoreTopCollector<TCustomScorer, TScore = Score> {
}
impl<TCustomScorer, TScore> CustomScoreTopCollector<TCustomScorer, TScore>
where TScore: Clone + PartialOrd
where
TScore: Clone + PartialOrd,
{
pub(crate) fn new(
pub fn new(
custom_scorer: TCustomScorer,
collector: TopCollector<TScore>,
limit: usize,
) -> CustomScoreTopCollector<TCustomScorer, TScore> {
CustomScoreTopCollector {
custom_scorer,
collector,
collector: TopCollector::with_limit(limit),
}
}
}
@@ -27,7 +29,7 @@ where TScore: Clone + PartialOrd
/// It is the segment local version of the [`CustomScorer`](./trait.CustomScorer.html).
pub trait CustomSegmentScorer<TScore>: 'static {
/// Computes the score of a specific `doc`.
fn score(&mut self, doc: DocId) -> TScore;
fn score(&self, doc: DocId) -> TScore;
}
/// `CustomScorer` makes it possible to define any kind of score.
@@ -40,12 +42,12 @@ pub trait CustomScorer<TScore>: Sync {
type Child: CustomSegmentScorer<TScore>;
/// Builds a child scorer for a specific segment. The child scorer is associated to
/// a specific segment.
fn segment_scorer(&self, segment_reader: &SegmentReader) -> crate::Result<Self::Child>;
fn segment_scorer(&self, segment_reader: &SegmentReader) -> Result<Self::Child>;
}
impl<TCustomScorer, TScore> Collector for CustomScoreTopCollector<TCustomScorer, TScore>
where
TCustomScorer: CustomScorer<TScore> + Send + Sync,
TCustomScorer: CustomScorer<TScore>,
TScore: 'static + PartialOrd + Clone + Send + Sync,
{
type Fruit = Vec<(TScore, DocAddress)>;
@@ -56,9 +58,11 @@ where
&self,
segment_local_id: u32,
segment_reader: &SegmentReader,
) -> crate::Result<Self::Child> {
let segment_collector = self.collector.for_segment(segment_local_id, segment_reader);
) -> Result<Self::Child> {
let segment_scorer = self.custom_scorer.segment_scorer(segment_reader)?;
let segment_collector = self
.collector
.for_segment(segment_local_id, segment_reader)?;
Ok(CustomScoreTopSegmentCollector {
segment_collector,
segment_scorer,
@@ -69,7 +73,7 @@ where
false
}
fn merge_fruits(&self, segment_fruits: Vec<Self::Fruit>) -> crate::Result<Self::Fruit> {
fn merge_fruits(&self, segment_fruits: Vec<Self::Fruit>) -> Result<Self::Fruit> {
self.collector.merge_fruits(segment_fruits)
}
}
@@ -107,15 +111,16 @@ where
{
type Child = T;
fn segment_scorer(&self, segment_reader: &SegmentReader) -> crate::Result<Self::Child> {
fn segment_scorer(&self, segment_reader: &SegmentReader) -> Result<Self::Child> {
Ok((self)(segment_reader))
}
}
impl<F, TScore> CustomSegmentScorer<TScore> for F
where F: 'static + FnMut(DocId) -> TScore
where
F: 'static + Sync + Send + Fn(DocId) -> TScore,
{
fn score(&mut self, doc: DocId) -> TScore {
fn score(&self, doc: DocId) -> TScore {
(self)(doc)
}
}

View File

@@ -1,60 +0,0 @@
use std::collections::HashSet;
use super::{Collector, SegmentCollector};
use crate::{DocAddress, DocId, Score};
/// Collectors that returns the set of DocAddress that matches the query.
///
/// This collector is mostly useful for tests.
pub struct DocSetCollector;
impl Collector for DocSetCollector {
type Fruit = HashSet<DocAddress>;
type Child = DocSetChildCollector;
fn for_segment(
&self,
segment_local_id: crate::SegmentOrdinal,
_segment: &crate::SegmentReader,
) -> crate::Result<Self::Child> {
Ok(DocSetChildCollector {
segment_local_id,
docs: HashSet::new(),
})
}
fn requires_scoring(&self) -> bool {
false
}
fn merge_fruits(
&self,
segment_fruits: Vec<(u32, HashSet<DocId>)>,
) -> crate::Result<Self::Fruit> {
let len: usize = segment_fruits.iter().map(|(_, docset)| docset.len()).sum();
let mut result = HashSet::with_capacity(len);
for (segment_local_id, docs) in segment_fruits {
for doc in docs {
result.insert(DocAddress::new(segment_local_id, doc));
}
}
Ok(result)
}
}
pub struct DocSetChildCollector {
segment_local_id: u32,
docs: HashSet<DocId>,
}
impl SegmentCollector for DocSetChildCollector {
type Fruit = (u32, HashSet<DocId>);
fn collect(&mut self, doc: crate::DocId, _score: Score) {
self.docs.insert(doc);
}
fn harvest(self) -> (u32, HashSet<DocId>) {
(self.segment_local_id, self.docs)
}
}

View File

@@ -1,13 +1,23 @@
use std::cmp::Ordering;
use std::collections::{btree_map, BTreeMap, BTreeSet, BinaryHeap};
use std::iter::Peekable;
use std::ops::Bound;
use std::{u64, usize};
use crate::collector::{Collector, SegmentCollector};
use crate::collector::Collector;
use crate::collector::SegmentCollector;
use crate::docset::SkipResult;
use crate::fastfield::FacetReader;
use crate::schema::{Facet, Field};
use crate::{DocId, Score, SegmentOrdinal, SegmentReader};
use crate::schema::Facet;
use crate::schema::Field;
use crate::DocId;
use crate::Result;
use crate::Score;
use crate::SegmentLocalId;
use crate::SegmentReader;
use crate::TantivyError;
use std::cmp::Ordering;
use std::collections::btree_map;
use std::collections::BTreeMap;
use std::collections::BTreeSet;
use std::collections::BinaryHeap;
use std::collections::Bound;
use std::iter::Peekable;
use std::{u64, usize};
struct Hit<'a> {
count: u64,
@@ -30,10 +40,7 @@ impl<'a> PartialOrd<Hit<'a>> for Hit<'a> {
impl<'a> Ord for Hit<'a> {
fn cmp(&self, other: &Self) -> Ordering {
other
.count
.cmp(&self.count)
.then(self.facet.cmp(other.facet))
other.count.cmp(&self.count)
}
}
@@ -76,16 +83,16 @@ fn facet_depth(facet_bytes: &[u8]) -> usize {
/// ```rust
/// use tantivy::collector::FacetCollector;
/// use tantivy::query::AllQuery;
/// use tantivy::schema::{Facet, Schema, FacetOptions, TEXT};
/// use tantivy::{doc, Index};
/// use tantivy::schema::{Facet, Schema, TEXT};
/// use tantivy::{doc, Index, Result};
///
/// fn example() -> tantivy::Result<()> {
/// fn example() -> Result<()> {
/// let mut schema_builder = Schema::builder();
///
/// // Facet have their own specific type.
/// // It is not a bad practise to put all of your
/// // facet information in the same field.
/// let facet = schema_builder.add_facet_field("facet", FacetOptions::default());
/// let facet = schema_builder.add_facet_field("facet");
/// let title = schema_builder.add_text_field("title", TEXT);
/// let schema = schema_builder.build();
/// let index = Index::create_in_ram(schema);
@@ -96,23 +103,23 @@ fn facet_depth(facet_bytes: &[u8]) -> usize {
/// title => "The Name of the Wind",
/// facet => Facet::from("/lang/en"),
/// facet => Facet::from("/category/fiction/fantasy")
/// ))?;
/// ));
/// index_writer.add_document(doc!(
/// title => "Dune",
/// facet => Facet::from("/lang/en"),
/// facet => Facet::from("/category/fiction/sci-fi")
/// ))?;
/// ));
/// index_writer.add_document(doc!(
/// title => "La Vénus d'Ille",
/// facet => Facet::from("/lang/fr"),
/// facet => Facet::from("/category/fiction/fantasy"),
/// facet => Facet::from("/category/fiction/horror")
/// ))?;
/// ));
/// index_writer.add_document(doc!(
/// title => "The Diary of a Young Girl",
/// facet => Facet::from("/lang/en"),
/// facet => Facet::from("/category/biography")
/// ))?;
/// ));
/// index_writer.commit()?;
/// }
/// let reader = index.reader()?;
@@ -182,11 +189,6 @@ pub struct FacetSegmentCollector {
collapse_facet_ords: Vec<u64>,
}
enum SkipResult {
Found,
NotFound,
}
fn skip<'a, I: Iterator<Item = &'a Facet>>(
target: &[u8],
collapse_it: &mut Peekable<I>,
@@ -196,14 +198,14 @@ fn skip<'a, I: Iterator<Item = &'a Facet>>(
Some(facet_bytes) => match facet_bytes.encoded_str().as_bytes().cmp(target) {
Ordering::Less => {}
Ordering::Greater => {
return SkipResult::NotFound;
return SkipResult::OverStep;
}
Ordering::Equal => {
return SkipResult::Found;
return SkipResult::Reached;
}
},
None => {
return SkipResult::NotFound;
return SkipResult::End;
}
}
collapse_it.next();
@@ -233,7 +235,9 @@ impl FacetCollector {
/// If you need the correct number of unique documents for two such facets,
/// just add them in separate `FacetCollector`.
pub fn add_facet<T>(&mut self, facet_from: T)
where Facet: From<T> {
where
Facet: From<T>,
{
let facet = Facet::from(facet_from);
for old_facet in &self.facets {
assert!(
@@ -256,10 +260,13 @@ impl Collector for FacetCollector {
fn for_segment(
&self,
_: SegmentOrdinal,
_: SegmentLocalId,
reader: &SegmentReader,
) -> crate::Result<FacetSegmentCollector> {
let facet_reader = reader.facet_reader(self.field)?;
) -> Result<FacetSegmentCollector> {
let field_name = reader.schema().get_field_name(self.field);
let facet_reader = reader.facet_reader(self.field).ok_or_else(|| {
TantivyError::SchemaError(format!("Field {:?} is not a facet field.", field_name))
})?;
let mut collapse_mapping = Vec::new();
let mut counts = Vec::new();
@@ -268,14 +275,14 @@ impl Collector for FacetCollector {
let mut collapse_facet_it = self.facets.iter().peekable();
collapse_facet_ords.push(0);
{
let mut facet_streamer = facet_reader.facet_dict().range().into_stream()?;
let mut facet_streamer = facet_reader.facet_dict().range().into_stream();
if facet_streamer.advance() {
'outer: loop {
// at the begining of this loop, facet_streamer
// is positionned on a term that has not been processed yet.
let skip_result = skip(facet_streamer.key(), &mut collapse_facet_it);
match skip_result {
SkipResult::Found => {
SkipResult::Reached => {
// we reach a facet we decided to collapse.
let collapse_depth = facet_depth(facet_streamer.key());
let mut collapsed_id = 0;
@@ -288,12 +295,14 @@ impl Collector for FacetCollector {
if depth == collapse_depth + 1 {
collapsed_id = collapse_facet_ords.len();
collapse_facet_ords.push(facet_streamer.term_ord());
collapse_mapping.push(collapsed_id);
} else {
collapse_mapping.push(collapsed_id);
}
collapse_mapping.push(collapsed_id);
}
break;
}
SkipResult::NotFound => {
SkipResult::End | SkipResult::OverStep => {
collapse_mapping.push(0);
if !facet_streamer.advance() {
break;
@@ -319,7 +328,7 @@ impl Collector for FacetCollector {
false
}
fn merge_fruits(&self, segments_facet_counts: Vec<FacetCounts>) -> crate::Result<FacetCounts> {
fn merge_fruits(&self, segments_facet_counts: Vec<FacetCounts>) -> Result<FacetCounts> {
let mut facet_counts: BTreeMap<Facet, u64> = BTreeMap::new();
for segment_facet_counts in segments_facet_counts {
for (facet, count) in segment_facet_counts.facet_counts {
@@ -360,12 +369,9 @@ impl SegmentCollector for FacetSegmentCollector {
}
let mut facet = vec![];
let facet_ord = self.collapse_facet_ords[collapsed_facet_ord];
// TODO handle errors.
if facet_dict.ord_to_term(facet_ord as u64, &mut facet).is_ok() {
if let Ok(facet) = Facet::from_encoded(facet) {
facet_counts.insert(facet, count);
}
}
facet_dict.ord_to_term(facet_ord as u64, &mut facet);
// TODO
facet_counts.insert(Facet::from_encoded(facet).unwrap(), count);
}
FacetCounts { facet_counts }
}
@@ -390,10 +396,10 @@ impl<'a> Iterator for FacetChildIterator<'a> {
}
impl FacetCounts {
/// Returns an iterator over all of the facet count pairs inside this result.
/// See the documentation for [FacetCollector] for a usage example.
pub fn get<T>(&self, facet_from: T) -> FacetChildIterator<'_>
where Facet: From<T> {
where
Facet: From<T>,
{
let facet = Facet::from(facet_from);
let left_bound = Bound::Excluded(facet.clone());
let right_bound = if facet.is_root() {
@@ -409,10 +415,10 @@ impl FacetCounts {
FacetChildIterator { underlying }
}
/// Returns a vector of top `k` facets with their counts, sorted highest-to-lowest by counts.
/// See the documentation for [FacetCollector] for a usage example.
pub fn top_k<T>(&self, facet: T, k: usize) -> Vec<(&Facet, u64)>
where Facet: From<T> {
where
Facet: From<T>,
{
let mut heap = BinaryHeap::with_capacity(k);
let mut it = self.get(facet);
@@ -445,27 +451,25 @@ impl FacetCounts {
#[cfg(test)]
mod tests {
use std::iter;
use rand::distributions::Uniform;
use rand::prelude::SliceRandom;
use rand::{thread_rng, Rng};
use super::{FacetCollector, FacetCounts};
use crate::collector::Count;
use crate::core::Index;
use crate::query::{AllQuery, QueryParser, TermQuery};
use crate::schema::{Document, Facet, FacetOptions, Field, IndexRecordOption, Schema};
use crate::schema::{Document, Facet, Field, IndexRecordOption, Schema};
use crate::Term;
use rand::distributions::Uniform;
use rand::prelude::SliceRandom;
use rand::{thread_rng, Rng};
use std::iter;
#[test]
fn test_facet_collector_drilldown() -> crate::Result<()> {
fn test_facet_collector_drilldown() {
let mut schema_builder = Schema::builder();
let facet_field = schema_builder.add_facet_field("facet", FacetOptions::default());
let facet_field = schema_builder.add_facet_field("facet");
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut index_writer = index.writer_for_tests()?;
let mut index_writer = index.writer_with_num_threads(1, 3_000_000).unwrap();
let num_facets: usize = 3 * 4 * 5;
let facets: Vec<Facet> = (0..num_facets)
.map(|mut n| {
@@ -480,14 +484,14 @@ mod tests {
for i in 0..num_facets * 10 {
let mut doc = Document::new();
doc.add_facet(facet_field, facets[i % num_facets].clone());
index_writer.add_document(doc)?;
index_writer.add_document(doc);
}
index_writer.commit()?;
let reader = index.reader()?;
index_writer.commit().unwrap();
let reader = index.reader().unwrap();
let searcher = reader.searcher();
let mut facet_collector = FacetCollector::for_field(facet_field);
facet_collector.add_facet(Facet::from("/top1"));
let counts = searcher.search(&AllQuery, &facet_collector)?;
let counts = searcher.search(&AllQuery, &facet_collector).unwrap();
{
let facets: Vec<(String, u64)> = counts
@@ -507,13 +511,11 @@ mod tests {
.collect::<Vec<_>>()
);
}
Ok(())
}
#[test]
#[should_panic(
expected = "Tried to add a facet which is a descendant of an already added facet."
)]
#[should_panic(expected = "Tried to add a facet which is a descendant of \
an already added facet.")]
fn test_misused_facet_collector() {
let mut facet_collector = FacetCollector::for_field(Field::from_field_id(0));
facet_collector.add_facet(Facet::from("/country"));
@@ -521,56 +523,55 @@ mod tests {
}
#[test]
fn test_doc_unsorted_multifacet() -> crate::Result<()> {
fn test_doc_unsorted_multifacet() {
let mut schema_builder = Schema::builder();
let facet_field = schema_builder.add_facet_field("facets", FacetOptions::default());
let facet_field = schema_builder.add_facet_field("facets");
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut index_writer = index.writer_for_tests()?;
let mut index_writer = index.writer_with_num_threads(1, 3_000_000).unwrap();
index_writer.add_document(doc!(
facet_field => Facet::from_text(&"/subjects/A/a").unwrap(),
facet_field => Facet::from_text(&"/subjects/B/a").unwrap(),
facet_field => Facet::from_text(&"/subjects/A/b").unwrap(),
facet_field => Facet::from_text(&"/subjects/B/b").unwrap(),
))?;
index_writer.commit()?;
let reader = index.reader()?;
facet_field => Facet::from_text(&"/subjects/A/a"),
facet_field => Facet::from_text(&"/subjects/B/a"),
facet_field => Facet::from_text(&"/subjects/A/b"),
facet_field => Facet::from_text(&"/subjects/B/b"),
));
index_writer.commit().unwrap();
let reader = index.reader().unwrap();
let searcher = reader.searcher();
assert_eq!(searcher.num_docs(), 1);
let mut facet_collector = FacetCollector::for_field(facet_field);
facet_collector.add_facet("/subjects");
let counts = searcher.search(&AllQuery, &facet_collector)?;
let counts = searcher.search(&AllQuery, &facet_collector).unwrap();
let facets: Vec<(&Facet, u64)> = counts.get("/subjects").collect();
assert_eq!(facets[0].1, 1);
Ok(())
}
#[test]
fn test_doc_search_by_facet() -> crate::Result<()> {
fn test_doc_search_by_facet() {
let mut schema_builder = Schema::builder();
let facet_field = schema_builder.add_facet_field("facet", FacetOptions::default());
let facet_field = schema_builder.add_facet_field("facet");
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut index_writer = index.writer_for_tests()?;
let mut index_writer = index.writer_with_num_threads(1, 3_000_000).unwrap();
index_writer.add_document(doc!(
facet_field => Facet::from_text(&"/A/A").unwrap(),
))?;
facet_field => Facet::from_text(&"/A/A"),
));
index_writer.add_document(doc!(
facet_field => Facet::from_text(&"/A/B").unwrap(),
))?;
facet_field => Facet::from_text(&"/A/B"),
));
index_writer.add_document(doc!(
facet_field => Facet::from_text(&"/A/C/A").unwrap(),
))?;
facet_field => Facet::from_text(&"/A/C/A"),
));
index_writer.add_document(doc!(
facet_field => Facet::from_text(&"/D/C/A").unwrap(),
))?;
index_writer.commit()?;
let reader = index.reader()?;
facet_field => Facet::from_text(&"/D/C/A"),
));
index_writer.commit().unwrap();
let reader = index.reader().unwrap();
let searcher = reader.searcher();
assert_eq!(searcher.num_docs(), 4);
let count_facet = |facet_str: &str| {
let term = Term::from_facet(facet_field, &Facet::from_text(facet_str).unwrap());
let term = Term::from_facet(facet_field, &Facet::from_text(facet_str));
searcher
.search(&TermQuery::new(term, IndexRecordOption::Basic), &Count)
.unwrap()
@@ -582,17 +583,17 @@ mod tests {
assert_eq!(count_facet("/A/C"), 1);
assert_eq!(count_facet("/A/C/A"), 1);
assert_eq!(count_facet("/C/A"), 0);
let query_parser = QueryParser::for_index(&index, vec![]);
{
let query = query_parser.parse_query("facet:/A/B")?;
assert_eq!(1, searcher.search(&query, &Count).unwrap());
let query_parser = QueryParser::for_index(&index, vec![]);
{
let query = query_parser.parse_query("facet:/A/B").unwrap();
assert_eq!(1, searcher.search(&query, &Count).unwrap());
}
{
let query = query_parser.parse_query("facet:/A").unwrap();
assert_eq!(3, searcher.search(&query, &Count).unwrap());
}
}
{
let query = query_parser.parse_query("facet:/A")?;
assert_eq!(3, searcher.search(&query, &Count)?);
}
Ok(())
}
#[test]
@@ -605,7 +606,7 @@ mod tests {
#[test]
fn test_facet_collector_topk() {
let mut schema_builder = Schema::builder();
let facet_field = schema_builder.add_facet_field("facet", FacetOptions::default());
let facet_field = schema_builder.add_facet_field("facet");
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
@@ -627,9 +628,9 @@ mod tests {
.collect();
docs[..].shuffle(&mut thread_rng());
let mut index_writer = index.writer_for_tests().unwrap();
let mut index_writer = index.writer_with_num_threads(1, 3_000_000).unwrap();
for doc in docs {
index_writer.add_document(doc).unwrap();
index_writer.add_document(doc);
}
index_writer.commit().unwrap();
let searcher = index.reader().unwrap().searcher();
@@ -650,59 +651,23 @@ mod tests {
);
}
}
#[test]
fn test_facet_collector_topk_tie_break() -> crate::Result<()> {
let mut schema_builder = Schema::builder();
let facet_field = schema_builder.add_facet_field("facet", FacetOptions::default());
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let docs: Vec<Document> = vec![("b", 2), ("a", 2), ("c", 4)]
.into_iter()
.flat_map(|(c, count)| {
let facet = Facet::from(&format!("/facet/{}", c));
let doc = doc!(facet_field => facet);
iter::repeat(doc).take(count)
})
.collect();
let mut index_writer = index.writer_for_tests()?;
for doc in docs {
index_writer.add_document(doc)?;
}
index_writer.commit()?;
let searcher = index.reader()?.searcher();
let mut facet_collector = FacetCollector::for_field(facet_field);
facet_collector.add_facet("/facet");
let counts: FacetCounts = searcher.search(&AllQuery, &facet_collector)?;
let facets: Vec<(&Facet, u64)> = counts.top_k("/facet", 2);
assert_eq!(
facets,
vec![(&Facet::from("/facet/c"), 4), (&Facet::from("/facet/a"), 2)]
);
Ok(())
}
}
#[cfg(all(test, feature = "unstable"))]
mod bench {
use crate::collector::FacetCollector;
use crate::query::AllQuery;
use crate::schema::{Facet, Schema};
use crate::Index;
use rand::seq::SliceRandom;
use rand::thread_rng;
use test::Bencher;
use crate::collector::FacetCollector;
use crate::query::AllQuery;
use crate::schema::{Facet, Schema, INDEXED};
use crate::Index;
#[bench]
fn bench_facet_collector(b: &mut Bencher) {
let mut schema_builder = Schema::builder();
let facet_field = schema_builder.add_facet_field("facet", INDEXED);
let facet_field = schema_builder.add_facet_field("facet");
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
@@ -716,9 +681,9 @@ mod bench {
// 40425 docs
docs[..].shuffle(&mut thread_rng());
let mut index_writer = index.writer_for_tests().unwrap();
let mut index_writer = index.writer_with_num_threads(1, 3_000_000).unwrap();
for doc in docs {
index_writer.add_document(doc).unwrap();
index_writer.add_document(doc);
}
index_writer.commit().unwrap();
let reader = index.reader().unwrap();

View File

@@ -1,186 +0,0 @@
// # Custom collector example
//
// This example shows how you can implement your own
// collector. As an example, we will compute a collector
// that computes the standard deviation of a given fast field.
//
// Of course, you can have a look at the tantivy's built-in collectors
// such as the `CountCollector` for more examples.
// ---
// Importing tantivy...
use std::marker::PhantomData;
use crate::collector::{Collector, SegmentCollector};
use crate::fastfield::{DynamicFastFieldReader, FastFieldReader, FastValue};
use crate::schema::Field;
use crate::{Score, SegmentReader, TantivyError};
/// The `FilterCollector` filters docs using a fast field value and a predicate.
/// Only the documents for which the predicate returned "true" will be passed on to the next
/// collector.
///
/// ```rust
/// use tantivy::collector::{TopDocs, FilterCollector};
/// use tantivy::query::QueryParser;
/// use tantivy::schema::{Schema, TEXT, INDEXED, FAST};
/// use tantivy::{doc, DocAddress, Index};
///
/// # fn main() -> tantivy::Result<()> {
/// let mut schema_builder = Schema::builder();
/// let title = schema_builder.add_text_field("title", TEXT);
/// let price = schema_builder.add_u64_field("price", INDEXED | 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 => "The Name of the Wind", price => 30_200u64))?;
/// index_writer.add_document(doc!(title => "The Diary of Muadib", price => 29_240u64))?;
/// index_writer.add_document(doc!(title => "A Dairy Cow", price => 21_240u64))?;
/// index_writer.add_document(doc!(title => "The Diary of a Young Girl", price => 20_120u64))?;
/// index_writer.commit()?;
///
/// let reader = index.reader()?;
/// let searcher = reader.searcher();
///
/// let query_parser = QueryParser::for_index(&index, vec![title]);
/// let query = query_parser.parse_query("diary")?;
/// let no_filter_collector = FilterCollector::new(price, &|value: u64| value > 20_120u64, TopDocs::with_limit(2));
/// let top_docs = searcher.search(&query, &no_filter_collector)?;
///
/// assert_eq!(top_docs.len(), 1);
/// assert_eq!(top_docs[0].1, DocAddress::new(0, 1));
///
/// let filter_all_collector: FilterCollector<_, _, u64> = FilterCollector::new(price, &|value| value < 5u64, TopDocs::with_limit(2));
/// let filtered_top_docs = searcher.search(&query, &filter_all_collector)?;
///
/// assert_eq!(filtered_top_docs.len(), 0);
/// # Ok(())
/// # }
/// ```
pub struct FilterCollector<TCollector, TPredicate, TPredicateValue: FastValue>
where TPredicate: 'static + Clone
{
field: Field,
collector: TCollector,
predicate: TPredicate,
t_predicate_value: PhantomData<TPredicateValue>,
}
impl<TCollector, TPredicate, TPredicateValue: FastValue>
FilterCollector<TCollector, TPredicate, TPredicateValue>
where
TCollector: Collector + Send + Sync,
TPredicate: Fn(TPredicateValue) -> bool + Send + Sync + Clone,
{
/// Create a new FilterCollector.
pub fn new(
field: Field,
predicate: TPredicate,
collector: TCollector,
) -> FilterCollector<TCollector, TPredicate, TPredicateValue> {
FilterCollector {
field,
predicate,
collector,
t_predicate_value: PhantomData,
}
}
}
impl<TCollector, TPredicate, TPredicateValue: FastValue> Collector
for FilterCollector<TCollector, TPredicate, TPredicateValue>
where
TCollector: Collector + Send + Sync,
TPredicate: 'static + Fn(TPredicateValue) -> bool + Send + Sync + Clone,
TPredicateValue: FastValue,
{
// That's the type of our result.
// Our standard deviation will be a float.
type Fruit = TCollector::Fruit;
type Child = FilterSegmentCollector<TCollector::Child, TPredicate, TPredicateValue>;
fn for_segment(
&self,
segment_local_id: u32,
segment_reader: &SegmentReader,
) -> crate::Result<FilterSegmentCollector<TCollector::Child, TPredicate, TPredicateValue>> {
let schema = segment_reader.schema();
let field_entry = schema.get_field_entry(self.field);
if !field_entry.is_fast() {
return Err(TantivyError::SchemaError(format!(
"Field {:?} is not a fast field.",
field_entry.name()
)));
}
let requested_type = TPredicateValue::to_type();
let field_schema_type = field_entry.field_type().value_type();
if requested_type != field_schema_type {
return Err(TantivyError::SchemaError(format!(
"Field {:?} is of type {:?}!={:?}",
field_entry.name(),
requested_type,
field_schema_type
)));
}
let fast_field_reader = segment_reader
.fast_fields()
.typed_fast_field_reader(self.field)?;
let segment_collector = self
.collector
.for_segment(segment_local_id, segment_reader)?;
Ok(FilterSegmentCollector {
fast_field_reader,
segment_collector,
predicate: self.predicate.clone(),
t_predicate_value: PhantomData,
})
}
fn requires_scoring(&self) -> bool {
self.collector.requires_scoring()
}
fn merge_fruits(
&self,
segment_fruits: Vec<<TCollector::Child as SegmentCollector>::Fruit>,
) -> crate::Result<TCollector::Fruit> {
self.collector.merge_fruits(segment_fruits)
}
}
pub struct FilterSegmentCollector<TSegmentCollector, TPredicate, TPredicateValue>
where
TPredicate: 'static,
TPredicateValue: FastValue,
{
fast_field_reader: DynamicFastFieldReader<TPredicateValue>,
segment_collector: TSegmentCollector,
predicate: TPredicate,
t_predicate_value: PhantomData<TPredicateValue>,
}
impl<TSegmentCollector, TPredicate, TPredicateValue> SegmentCollector
for FilterSegmentCollector<TSegmentCollector, TPredicate, TPredicateValue>
where
TSegmentCollector: SegmentCollector,
TPredicate: 'static + Fn(TPredicateValue) -> bool + Send + Sync,
TPredicateValue: FastValue,
{
type Fruit = TSegmentCollector::Fruit;
fn collect(&mut self, doc: u32, score: Score) {
let value = self.fast_field_reader.get(doc);
if (self.predicate)(value) {
self.segment_collector.collect(doc, score)
}
}
fn harvest(self) -> <TSegmentCollector as SegmentCollector>::Fruit {
self.segment_collector.harvest()
}
}

View File

@@ -1,293 +0,0 @@
use fastdivide::DividerU64;
use crate::collector::{Collector, SegmentCollector};
use crate::fastfield::{DynamicFastFieldReader, FastFieldReader, FastValue};
use crate::schema::{Field, Type};
use crate::{DocId, Score};
/// Histogram builds an histogram of the values of a fastfield for the
/// collected DocSet.
///
/// At construction, it is given parameters that define a partition of an interval
/// [min_val, max_val) into N buckets with the same width.
/// The ith bucket is then defined by `[min_val + i * bucket_width, min_val + (i+1) * bucket_width)`
///
/// An histogram is then defined as a `Vec<u64>` of length `num_buckets`, that contains a count of
/// documents for each value bucket.
///
/// See also [`HistogramCollector::new()`].
///
/// # Warning
///
/// f64 fields are not supported.
#[derive(Clone)]
pub struct HistogramCollector {
min_value: u64,
num_buckets: usize,
divider: DividerU64,
field: Field,
}
impl HistogramCollector {
/// Builds a new HistogramCollector.
///
/// The scale/range of the histogram is not dynamic. It is required to
/// define it by supplying following parameter:
/// - `min_value`: the minimum value that can be recorded in the histogram.
/// - `bucket_width`: the length of the interval that is associated to each buckets.
/// - `num_buckets`: The overall number of buckets.
///
/// Together, this parameters define a partition of `[min_value, min_value + num_buckets *
/// bucket_width)` into `num_buckets` intervals of width bucket that we call `bucket`.
///
/// # Disclaimer
/// This function panics if the field given is of type f64.
pub fn new<TFastValue: FastValue>(
field: Field,
min_value: TFastValue,
bucket_width: u64,
num_buckets: usize,
) -> HistogramCollector {
let fast_type = TFastValue::to_type();
assert!(fast_type == Type::U64 || fast_type == Type::I64 || fast_type == Type::Date);
HistogramCollector {
min_value: min_value.to_u64(),
num_buckets,
field,
divider: DividerU64::divide_by(bucket_width),
}
}
}
struct HistogramComputer {
counts: Vec<u64>,
min_value: u64,
divider: DividerU64,
}
impl HistogramComputer {
#[inline]
pub(crate) fn add_value(&mut self, value: u64) {
if value < self.min_value {
return;
}
let delta = value - self.min_value;
let delta_u64 = delta.to_u64();
let bucket_id: usize = self.divider.divide(delta_u64) as usize;
if bucket_id < self.counts.len() {
self.counts[bucket_id] += 1;
}
}
fn harvest(self) -> Vec<u64> {
self.counts
}
}
pub struct SegmentHistogramCollector {
histogram_computer: HistogramComputer,
ff_reader: DynamicFastFieldReader<u64>,
}
impl SegmentCollector for SegmentHistogramCollector {
type Fruit = Vec<u64>;
fn collect(&mut self, doc: DocId, _score: Score) {
let value = self.ff_reader.get(doc);
self.histogram_computer.add_value(value);
}
fn harvest(self) -> Self::Fruit {
self.histogram_computer.harvest()
}
}
impl Collector for HistogramCollector {
type Fruit = Vec<u64>;
type Child = SegmentHistogramCollector;
fn for_segment(
&self,
_segment_local_id: crate::SegmentOrdinal,
segment: &crate::SegmentReader,
) -> crate::Result<Self::Child> {
let ff_reader = segment.fast_fields().u64_lenient(self.field)?;
Ok(SegmentHistogramCollector {
histogram_computer: HistogramComputer {
counts: vec![0; self.num_buckets],
min_value: self.min_value,
divider: self.divider,
},
ff_reader,
})
}
fn requires_scoring(&self) -> bool {
false
}
fn merge_fruits(&self, child_histograms: Vec<Vec<u64>>) -> crate::Result<Vec<u64>> {
Ok(add_vecs(child_histograms, self.num_buckets))
}
}
pub fn add_arrays_into(acc: &mut [u64], add: &[u64]) {
assert_eq!(acc.len(), add.len());
for (dest_bucket, bucket_count) in acc.iter_mut().zip(add) {
*dest_bucket += bucket_count;
}
}
fn add_vecs(mut vals_list: Vec<Vec<u64>>, len: usize) -> Vec<u64> {
let mut acc = vals_list.pop().unwrap_or_else(|| vec![0u64; len]);
assert_eq!(acc.len(), len);
for vals in vals_list {
add_arrays_into(&mut acc, &vals);
}
acc
}
#[cfg(test)]
mod tests {
use fastdivide::DividerU64;
use query::AllQuery;
use super::{add_vecs, HistogramCollector, HistogramComputer};
use crate::chrono::{TimeZone, Utc};
use crate::schema::{Schema, FAST};
use crate::{doc, query, Index};
#[test]
fn test_add_histograms_simple() {
assert_eq!(
add_vecs(vec![vec![1, 0, 3], vec![11, 2, 3], vec![0, 0, 1]], 3),
vec![12, 2, 7]
)
}
#[test]
fn test_add_histograms_empty() {
assert_eq!(add_vecs(vec![], 3), vec![0, 0, 0])
}
#[test]
fn test_histogram_builder_simple() {
// [1..3)
// [3..5)
// ..
// [9..11)
let mut histogram_computer = HistogramComputer {
counts: vec![0; 5],
min_value: 1,
divider: DividerU64::divide_by(2),
};
histogram_computer.add_value(1);
histogram_computer.add_value(7);
assert_eq!(histogram_computer.harvest(), vec![1, 0, 0, 1, 0]);
}
#[test]
fn test_histogram_too_low_is_ignored() {
let mut histogram_computer = HistogramComputer {
counts: vec![0; 5],
min_value: 2,
divider: DividerU64::divide_by(2),
};
histogram_computer.add_value(0);
assert_eq!(histogram_computer.harvest(), vec![0, 0, 0, 0, 0]);
}
#[test]
fn test_histogram_too_high_is_ignored() {
let mut histogram_computer = HistogramComputer {
counts: vec![0u64; 5],
min_value: 0,
divider: DividerU64::divide_by(2),
};
histogram_computer.add_value(10);
assert_eq!(histogram_computer.harvest(), vec![0, 0, 0, 0, 0]);
}
#[test]
fn test_no_segments() -> crate::Result<()> {
let mut schema_builder = Schema::builder();
let val_field = schema_builder.add_u64_field("val_field", FAST);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let reader = index.reader()?;
let searcher = reader.searcher();
let all_query = AllQuery;
let histogram_collector = HistogramCollector::new(val_field, 0u64, 2, 5);
let histogram = searcher.search(&all_query, &histogram_collector)?;
assert_eq!(histogram, vec![0; 5]);
Ok(())
}
#[test]
fn test_histogram_i64() -> crate::Result<()> {
let mut schema_builder = Schema::builder();
let val_field = schema_builder.add_i64_field("val_field", FAST);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut writer = index.writer_with_num_threads(1, 4_000_000)?;
writer.add_document(doc!(val_field=>12i64))?;
writer.add_document(doc!(val_field=>-30i64))?;
writer.add_document(doc!(val_field=>-12i64))?;
writer.add_document(doc!(val_field=>-10i64))?;
writer.commit()?;
let reader = index.reader()?;
let searcher = reader.searcher();
let all_query = AllQuery;
let histogram_collector = HistogramCollector::new(val_field, -20i64, 10u64, 4);
let histogram = searcher.search(&all_query, &histogram_collector)?;
assert_eq!(histogram, vec![1, 1, 0, 1]);
Ok(())
}
#[test]
fn test_histogram_merge() -> crate::Result<()> {
let mut schema_builder = Schema::builder();
let val_field = schema_builder.add_i64_field("val_field", FAST);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut writer = index.writer_with_num_threads(1, 4_000_000)?;
writer.add_document(doc!(val_field=>12i64))?;
writer.commit()?;
writer.add_document(doc!(val_field=>-30i64))?;
writer.commit()?;
writer.add_document(doc!(val_field=>-12i64))?;
writer.commit()?;
writer.add_document(doc!(val_field=>-10i64))?;
writer.commit()?;
let reader = index.reader()?;
let searcher = reader.searcher();
let all_query = AllQuery;
let histogram_collector = HistogramCollector::new(val_field, -20i64, 10u64, 4);
let histogram = searcher.search(&all_query, &histogram_collector)?;
assert_eq!(histogram, vec![1, 1, 0, 1]);
Ok(())
}
#[test]
fn test_histogram_dates() -> crate::Result<()> {
let mut schema_builder = Schema::builder();
let date_field = schema_builder.add_date_field("date_field", FAST);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
let mut writer = index.writer_with_num_threads(1, 4_000_000)?;
writer.add_document(doc!(date_field=>Utc.ymd(1982, 9, 17).and_hms(0, 0,0)))?;
writer.add_document(doc!(date_field=>Utc.ymd(1986, 3, 9).and_hms(0, 0, 0)))?;
writer.add_document(doc!(date_field=>Utc.ymd(1983, 9, 27).and_hms(0, 0, 0)))?;
writer.commit()?;
let reader = index.reader()?;
let searcher = reader.searcher();
let all_query = AllQuery;
let week_histogram_collector = HistogramCollector::new(
date_field,
Utc.ymd(1980, 1, 1).and_hms(0, 0, 0),
3600 * 24 * 365, // it is just for a unit test... sorry leap years.
10,
);
let week_histogram = searcher.search(&all_query, &week_histogram_collector)?;
assert_eq!(week_histogram, vec![0, 0, 1, 1, 0, 0, 1, 0, 0, 0]);
Ok(())
}
}

View File

@@ -0,0 +1,127 @@
use std::cmp::Eq;
use std::collections::HashMap;
use std::hash::Hash;
use collector::Collector;
use fastfield::FastFieldReader;
use schema::Field;
use DocId;
use Result;
use Score;
use SegmentReader;
use SegmentLocalId;
/// Facet collector for i64/u64 fast field
pub struct IntFacetCollector<T>
where
T: FastFieldReader,
T::ValueType: Eq + Hash,
{
counters: HashMap<T::ValueType, u64>,
field: Field,
ff_reader: Option<T>,
}
impl<T> IntFacetCollector<T>
where
T: FastFieldReader,
T::ValueType: Eq + Hash,
{
/// Creates a new facet collector for aggregating a given field.
pub fn new(field: Field) -> IntFacetCollector<T> {
IntFacetCollector {
counters: HashMap::new(),
field: field,
ff_reader: None,
}
}
}
impl<T> Collector for IntFacetCollector<T>
where
T: FastFieldReader,
T::ValueType: Eq + Hash,
{
fn set_segment(&mut self, _: SegmentLocalId, reader: &SegmentReader) -> Result<()> {
self.ff_reader = Some(reader.get_fast_field_reader(self.field)?);
Ok(())
}
fn collect(&mut self, doc: DocId, _: Score) {
let val = self.ff_reader
.as_ref()
.expect(
"collect() was called before set_segment. \
This should never happen.",
)
.get(doc);
*(self.counters.entry(val).or_insert(0)) += 1;
}
}
#[cfg(test)]
mod tests {
use collector::{chain, IntFacetCollector};
use query::QueryParser;
use fastfield::{I64FastFieldReader, U64FastFieldReader};
use schema::{self, FAST, STRING};
use Index;
#[test]
// create 10 documents, set num field value to 0 or 1 for even/odd ones
// make sure we have facet counters correctly filled
fn test_facet_collector_results() {
let mut schema_builder = schema::Schema::builder();
let num_field_i64 = schema_builder.add_i64_field("num_i64", FAST);
let num_field_u64 = schema_builder.add_u64_field("num_u64", FAST);
let num_field_f64 = schema_builder.add_f64_field("num_f64", FAST);
let text_field = schema_builder.add_text_field("text", STRING);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema.clone());
{
let mut index_writer = index.writer_with_num_threads(1, 3_000_000).unwrap();
{
for i in 0u64..10u64 {
index_writer.add_document(doc!(
num_field_i64 => ((i as i64) % 3i64) as i64,
num_field_u64 => (i % 2u64) as u64,
num_field_f64 => (i % 4u64) as f64,
text_field => "text"
));
}
}
assert_eq!(index_writer.commit().unwrap(), 10u64);
}
let searcher = index.reader().searcher();
let mut ffvf_i64: IntFacetCollector<I64FastFieldReader> = IntFacetCollector::new(num_field_i64);
let mut ffvf_u64: IntFacetCollector<U64FastFieldReader> = IntFacetCollector::new(num_field_u64);
let mut ffvf_f64: IntFacetCollector<F64FastFieldReader> = IntFacetCollector::new(num_field_f64);
{
// perform the query
let mut facet_collectors = chain().push(&mut ffvf_i64).push(&mut ffvf_u64).push(&mut ffvf_f64);
let mut query_parser = QueryParser::for_index(index, vec![text_field]);
let query = query_parser.parse_query("text:text").unwrap();
query.search(&searcher, &mut facet_collectors).unwrap();
}
assert_eq!(ffvf_u64.counters[&0], 5);
assert_eq!(ffvf_u64.counters[&1], 5);
assert_eq!(ffvf_i64.counters[&0], 4);
assert_eq!(ffvf_i64.counters[&1], 3);
assert_eq!(ffvf_f64.counters[&0.0], 3);
assert_eq!(ffvf_f64.counters[&2.0], 2);
}
}

View File

@@ -1,96 +1,99 @@
//! # Collectors
//!
//! Collectors define the information you want to extract from the documents matching the queries.
//! In tantivy jargon, we call this information your search "fruit".
//!
//! Your fruit could for instance be :
//! - [the count of matching documents](./struct.Count.html)
//! - [the top 10 documents, by relevancy or by a fast field](./struct.TopDocs.html)
//! - [facet counts](./struct.FacetCollector.html)
//!
//! At one point in your code, you will trigger the actual search operation by calling
//! [the `search(...)` method of your `Searcher` object](../struct.Searcher.html#method.search).
//! This call will look like this.
//!
//! ```verbatim
//! let fruit = searcher.search(&query, &collector)?;
//! ```
//!
//! Here the type of fruit is actually determined as an associated type of the collector
//! (`Collector::Fruit`).
//!
//!
//! # Combining several collectors
//!
//! A rich search experience often requires to run several collectors on your search query.
//! For instance,
//! - selecting the top-K products matching your query
//! - counting the matching documents
//! - computing several facets
//! - computing statistics about the matching product prices
//!
//! A simple and efficient way to do that is to pass your collectors as one tuple.
//! The resulting `Fruit` will then be a typed tuple with each collector's original fruits
//! in their respective position.
//!
//! ```rust
//! # use tantivy::schema::*;
//! # use tantivy::*;
//! # use tantivy::query::*;
//! use tantivy::collector::{Count, TopDocs};
//! #
//! # fn main() -> tantivy::Result<()> {
//! # let mut schema_builder = Schema::builder();
//! # let title = schema_builder.add_text_field("title", TEXT);
//! # let schema = schema_builder.build();
//! # let index = Index::create_in_ram(schema);
//! # let mut index_writer = index.writer(3_000_000)?;
//! # index_writer.add_document(doc!(
//! # title => "The Name of the Wind",
//! # ))?;
//! # index_writer.add_document(doc!(
//! # title => "The Diary of Muadib",
//! # ))?;
//! # index_writer.commit()?;
//! # let reader = index.reader()?;
//! # let searcher = reader.searcher();
//! # let query_parser = QueryParser::for_index(&index, vec![title]);
//! # let query = query_parser.parse_query("diary")?;
//! let (doc_count, top_docs): (usize, Vec<(Score, DocAddress)>) =
//! searcher.search(&query, &(Count, TopDocs::with_limit(2)))?;
//! # Ok(())
//! # }
//! ```
//!
//! The `Collector` trait is implemented for up to 4 collectors.
//! If you have more than 4 collectors, you can either group them into
//! tuples of tuples `(a,(b,(c,d)))`, or rely on [`MultiCollector`](./struct.MultiCollector.html).
//!
//! # Combining several collectors dynamically
//!
//! Combining collectors into a tuple is a zero-cost abstraction: everything
//! happens as if you had manually implemented a single collector
//! combining all of our features.
//!
//! Unfortunately it requires you to know at compile time your collector types.
//! If on the other hand, the collectors depend on some query parameter,
//! you can rely on `MultiCollector`'s.
//!
//!
//! # Implementing your own collectors.
//!
//! See the `custom_collector` example.
/*!
# Collectors
Collectors define the information you want to extract from the documents matching the queries.
In tantivy jargon, we call this information your search "fruit".
Your fruit could for instance be :
- [the count of matching documents](./struct.Count.html)
- [the top 10 documents, by relevancy or by a fast field](./struct.TopDocs.html)
- [facet counts](./struct.FacetCollector.html)
At one point in your code, you will trigger the actual search operation by calling
[the `search(...)` method of your `Searcher` object](../struct.Searcher.html#method.search).
This call will look like this.
```verbatim
let fruit = searcher.search(&query, &collector)?;
```
Here the type of fruit is actually determined as an associated type of the collector (`Collector::Fruit`).
# Combining several collectors
A rich search experience often requires to run several collectors on your search query.
For instance,
- selecting the top-K products matching your query
- counting the matching documents
- computing several facets
- computing statistics about the matching product prices
A simple and efficient way to do that is to pass your collectors as one tuple.
The resulting `Fruit` will then be a typed tuple with each collector's original fruits
in their respective position.
```rust
# use tantivy::schema::*;
# use tantivy::*;
# use tantivy::query::*;
use tantivy::collector::{Count, TopDocs};
#
# fn main() -> tantivy::Result<()> {
# let mut schema_builder = Schema::builder();
# let title = schema_builder.add_text_field("title", TEXT);
# let schema = schema_builder.build();
# let index = Index::create_in_ram(schema);
# let mut index_writer = index.writer(3_000_000)?;
# index_writer.add_document(doc!(
# title => "The Name of the Wind",
# ));
# index_writer.add_document(doc!(
# title => "The Diary of Muadib",
# ));
# index_writer.commit()?;
# let reader = index.reader()?;
# let searcher = reader.searcher();
# let query_parser = QueryParser::for_index(&index, vec![title]);
# let query = query_parser.parse_query("diary")?;
let (doc_count, top_docs): (usize, Vec<(Score, DocAddress)>) =
searcher.search(&query, &(Count, TopDocs::with_limit(2)))?;
# Ok(())
# }
```
The `Collector` trait is implemented for up to 4 collectors.
If you have more than 4 collectors, you can either group them into
tuples of tuples `(a,(b,(c,d)))`, or rely on [`MultiCollector`](./struct.MultiCollector.html).
# Combining several collectors dynamically
Combining collectors into a tuple is a zero-cost abstraction: everything
happens as if you had manually implemented a single collector
combining all of our features.
Unfortunately it requires you to know at compile time your collector types.
If on the other hand, the collectors depend on some query parameter,
you can rely on `MultiCollector`'s.
# Implementing your own collectors.
See the `custom_collector` example.
*/
use crate::DocId;
use crate::Result;
use crate::Score;
use crate::SegmentLocalId;
use crate::SegmentReader;
use downcast_rs::impl_downcast;
use crate::{DocId, Score, SegmentOrdinal, SegmentReader};
mod count_collector;
pub use self::count_collector::Count;
mod histogram_collector;
pub use histogram_collector::HistogramCollector;
mod multi_collector;
pub use self::multi_collector::MultiCollector;
@@ -106,14 +109,7 @@ mod tweak_score_top_collector;
pub use self::tweak_score_top_collector::{ScoreSegmentTweaker, ScoreTweaker};
mod facet_collector;
pub use self::facet_collector::{FacetCollector, FacetCounts};
use crate::query::Weight;
mod docset_collector;
pub use self::docset_collector::DocSetCollector;
mod filter_collector_wrapper;
pub use self::filter_collector_wrapper::FilterCollector;
pub use self::facet_collector::FacetCollector;
/// `Fruit` is the type for the result of our collection.
/// e.g. `usize` for the `Count` collector.
@@ -137,109 +133,28 @@ impl<T> Fruit for T where T: Send + downcast_rs::Downcast {}
/// The collection logic itself is in the `SegmentCollector`.
///
/// Segments are not guaranteed to be visited in any specific order.
pub trait Collector: Sync + Send {
pub trait Collector: Sync {
/// `Fruit` is the type for the result of our collection.
/// e.g. `usize` for the `Count` collector.
type Fruit: Fruit;
/// Type of the `SegmentCollector` associated to this collector.
type Child: SegmentCollector;
type Child: SegmentCollector<Fruit = Self::Fruit>;
/// `set_segment` is called before beginning to enumerate
/// on this segment.
fn for_segment(
&self,
segment_local_id: SegmentOrdinal,
segment_local_id: SegmentLocalId,
segment: &SegmentReader,
) -> crate::Result<Self::Child>;
) -> Result<Self::Child>;
/// Returns true iff the collector requires to compute scores for documents.
fn requires_scoring(&self) -> bool;
/// Combines the fruit associated to the collection of each segments
/// into one fruit.
fn merge_fruits(
&self,
segment_fruits: Vec<<Self::Child as SegmentCollector>::Fruit>,
) -> crate::Result<Self::Fruit>;
/// Created a segment collector and
fn collect_segment(
&self,
weight: &dyn Weight,
segment_ord: u32,
reader: &SegmentReader,
) -> crate::Result<<Self::Child as SegmentCollector>::Fruit> {
let mut segment_collector = self.for_segment(segment_ord as u32, reader)?;
if let Some(alive_bitset) = reader.alive_bitset() {
weight.for_each(reader, &mut |doc, score| {
if alive_bitset.is_alive(doc) {
segment_collector.collect(doc, score);
}
})?;
} else {
weight.for_each(reader, &mut |doc, score| {
segment_collector.collect(doc, score);
})?;
}
Ok(segment_collector.harvest())
}
}
impl<TSegmentCollector: SegmentCollector> SegmentCollector for Option<TSegmentCollector> {
type Fruit = Option<TSegmentCollector::Fruit>;
fn collect(&mut self, doc: DocId, score: Score) {
if let Some(segment_collector) = self {
segment_collector.collect(doc, score);
}
}
fn harvest(self) -> Self::Fruit {
self.map(|segment_collector| segment_collector.harvest())
}
}
impl<TCollector: Collector> Collector for Option<TCollector> {
type Fruit = Option<TCollector::Fruit>;
type Child = Option<<TCollector as Collector>::Child>;
fn for_segment(
&self,
segment_local_id: SegmentOrdinal,
segment: &SegmentReader,
) -> crate::Result<Self::Child> {
Ok(if let Some(inner) = self {
let inner_segment_collector = inner.for_segment(segment_local_id, segment)?;
Some(inner_segment_collector)
} else {
None
})
}
fn requires_scoring(&self) -> bool {
self.as_ref()
.map(|inner| inner.requires_scoring())
.unwrap_or(false)
}
fn merge_fruits(
&self,
segment_fruits: Vec<<Self::Child as SegmentCollector>::Fruit>,
) -> crate::Result<Self::Fruit> {
if let Some(inner) = self.as_ref() {
let inner_segment_fruits: Vec<_> = segment_fruits
.into_iter()
.flat_map(|fruit_opt| fruit_opt.into_iter())
.collect();
let fruit = inner.merge_fruits(inner_segment_fruits)?;
Ok(Some(fruit))
} else {
Ok(None)
}
}
fn merge_fruits(&self, segment_fruits: Vec<Self::Fruit>) -> Result<Self::Fruit>;
}
/// The `SegmentCollector` is the trait in charge of defining the
@@ -270,11 +185,7 @@ where
type Fruit = (Left::Fruit, Right::Fruit);
type Child = (Left::Child, Right::Child);
fn for_segment(
&self,
segment_local_id: u32,
segment: &SegmentReader,
) -> crate::Result<Self::Child> {
fn for_segment(&self, segment_local_id: u32, segment: &SegmentReader) -> Result<Self::Child> {
let left = self.0.for_segment(segment_local_id, segment)?;
let right = self.1.for_segment(segment_local_id, segment)?;
Ok((left, right))
@@ -286,11 +197,11 @@ where
fn merge_fruits(
&self,
segment_fruits: Vec<<Self::Child as SegmentCollector>::Fruit>,
) -> crate::Result<(Left::Fruit, Right::Fruit)> {
children: Vec<(Left::Fruit, Right::Fruit)>,
) -> Result<(Left::Fruit, Right::Fruit)> {
let mut left_fruits = vec![];
let mut right_fruits = vec![];
for (left_fruit, right_fruit) in segment_fruits {
for (left_fruit, right_fruit) in children {
left_fruits.push(left_fruit);
right_fruits.push(right_fruit);
}
@@ -329,11 +240,7 @@ where
type Fruit = (One::Fruit, Two::Fruit, Three::Fruit);
type Child = (One::Child, Two::Child, Three::Child);
fn for_segment(
&self,
segment_local_id: u32,
segment: &SegmentReader,
) -> crate::Result<Self::Child> {
fn for_segment(&self, segment_local_id: u32, segment: &SegmentReader) -> Result<Self::Child> {
let one = self.0.for_segment(segment_local_id, segment)?;
let two = self.1.for_segment(segment_local_id, segment)?;
let three = self.2.for_segment(segment_local_id, segment)?;
@@ -344,10 +251,7 @@ where
self.0.requires_scoring() || self.1.requires_scoring() || self.2.requires_scoring()
}
fn merge_fruits(
&self,
children: Vec<<Self::Child as SegmentCollector>::Fruit>,
) -> crate::Result<Self::Fruit> {
fn merge_fruits(&self, children: Vec<Self::Fruit>) -> Result<Self::Fruit> {
let mut one_fruits = vec![];
let mut two_fruits = vec![];
let mut three_fruits = vec![];
@@ -395,11 +299,7 @@ where
type Fruit = (One::Fruit, Two::Fruit, Three::Fruit, Four::Fruit);
type Child = (One::Child, Two::Child, Three::Child, Four::Child);
fn for_segment(
&self,
segment_local_id: u32,
segment: &SegmentReader,
) -> crate::Result<Self::Child> {
fn for_segment(&self, segment_local_id: u32, segment: &SegmentReader) -> Result<Self::Child> {
let one = self.0.for_segment(segment_local_id, segment)?;
let two = self.1.for_segment(segment_local_id, segment)?;
let three = self.2.for_segment(segment_local_id, segment)?;
@@ -414,10 +314,7 @@ where
|| self.3.requires_scoring()
}
fn merge_fruits(
&self,
children: Vec<<Self::Child as SegmentCollector>::Fruit>,
) -> crate::Result<Self::Fruit> {
fn merge_fruits(&self, children: Vec<Self::Fruit>) -> Result<Self::Fruit> {
let mut one_fruits = vec![];
let mut two_fruits = vec![];
let mut three_fruits = vec![];

View File

@@ -1,10 +1,15 @@
use super::Collector;
use super::SegmentCollector;
use crate::collector::Fruit;
use crate::DocId;
use crate::Result;
use crate::Score;
use crate::SegmentLocalId;
use crate::SegmentReader;
use crate::TantivyError;
use std::marker::PhantomData;
use std::ops::Deref;
use super::{Collector, SegmentCollector};
use crate::collector::Fruit;
use crate::{DocId, Score, SegmentOrdinal, SegmentReader, TantivyError};
pub struct MultiFruit {
sub_fruits: Vec<Option<Box<dyn Fruit>>>,
}
@@ -19,7 +24,7 @@ impl<TCollector: Collector> Collector for CollectorWrapper<TCollector> {
&self,
segment_local_id: u32,
reader: &SegmentReader,
) -> crate::Result<Box<dyn BoxableSegmentCollector>> {
) -> Result<Box<dyn BoxableSegmentCollector>> {
let child = self.0.for_segment(segment_local_id, reader)?;
Ok(Box::new(SegmentCollectorWrapper(child)))
}
@@ -28,21 +33,18 @@ impl<TCollector: Collector> Collector for CollectorWrapper<TCollector> {
self.0.requires_scoring()
}
fn merge_fruits(
&self,
children: Vec<<Self::Child as SegmentCollector>::Fruit>,
) -> crate::Result<Box<dyn Fruit>> {
let typed_fruit: Vec<<TCollector::Child as SegmentCollector>::Fruit> = children
fn merge_fruits(&self, children: Vec<<Self as Collector>::Fruit>) -> Result<Box<dyn Fruit>> {
let typed_fruit: Vec<TCollector::Fruit> = children
.into_iter()
.map(|untyped_fruit| {
untyped_fruit
.downcast::<<TCollector::Child as SegmentCollector>::Fruit>()
.downcast::<TCollector::Fruit>()
.map(|boxed_but_typed| *boxed_but_typed)
.map_err(|_| {
TantivyError::InvalidArgument("Failed to cast child fruit.".to_string())
})
})
.collect::<crate::Result<_>>()?;
.collect::<Result<_>>()?;
let merged_fruit = self.0.merge_fruits(typed_fruit)?;
Ok(Box::new(merged_fruit))
}
@@ -51,7 +53,7 @@ impl<TCollector: Collector> Collector for CollectorWrapper<TCollector> {
impl SegmentCollector for Box<dyn BoxableSegmentCollector> {
type Fruit = Box<dyn Fruit>;
fn collect(&mut self, doc: u32, score: Score) {
fn collect(&mut self, doc: u32, score: f32) {
self.as_mut().collect(doc, score);
}
@@ -61,7 +63,7 @@ impl SegmentCollector for Box<dyn BoxableSegmentCollector> {
}
pub trait BoxableSegmentCollector {
fn collect(&mut self, doc: u32, score: Score);
fn collect(&mut self, doc: u32, score: f32);
fn harvest_from_box(self: Box<Self>) -> Box<dyn Fruit>;
}
@@ -70,7 +72,7 @@ pub struct SegmentCollectorWrapper<TSegmentCollector: SegmentCollector>(TSegment
impl<TSegmentCollector: SegmentCollector> BoxableSegmentCollector
for SegmentCollectorWrapper<TSegmentCollector>
{
fn collect(&mut self, doc: u32, score: Score) {
fn collect(&mut self, doc: u32, score: f32) {
self.0.collect(doc, score);
}
@@ -100,8 +102,7 @@ impl<TFruit: Fruit> FruitHandle<TFruit> {
///
/// If the type of the collectors is known, you can just group yours collectors
/// in a tuple. See the
/// [Combining several collectors section of the collector
/// documentation](./index.html#combining-several-collectors).
/// [Combining several collectors section of the collector documentation](./index.html#combining-several-collectors).
///
/// ```rust
/// use tantivy::collector::{Count, TopDocs, MultiCollector};
@@ -109,19 +110,19 @@ impl<TFruit: Fruit> FruitHandle<TFruit> {
/// use tantivy::schema::{Schema, TEXT};
/// use tantivy::{doc, Index};
///
/// # fn main() -> tantivy::Result<()> {
/// let mut schema_builder = Schema::builder();
/// let title = schema_builder.add_text_field("title", TEXT);
/// let schema = schema_builder.build();
/// let index = Index::create_in_ram(schema);
/// let mut index_writer = index.writer(3_000_000)?;
/// index_writer.add_document(doc!(title => "The Name of the Wind"))?;
/// index_writer.add_document(doc!(title => "The Diary of Muadib"))?;
/// index_writer.add_document(doc!(title => "A Dairy Cow"))?;
/// index_writer.add_document(doc!(title => "The Diary of a Young Girl"))?;
/// index_writer.commit()?;
///
/// let reader = index.reader()?;
/// let mut index_writer = index.writer(3_000_000).unwrap();
/// index_writer.add_document(doc!(title => "The Name of the Wind"));
/// index_writer.add_document(doc!(title => "The Diary of Muadib"));
/// index_writer.add_document(doc!(title => "A Dairy Cow"));
/// index_writer.add_document(doc!(title => "The Diary of a Young Girl"));
/// assert!(index_writer.commit().is_ok());
///
/// let reader = index.reader().unwrap();
/// let searcher = reader.searcher();
///
/// let mut collectors = MultiCollector::new();
@@ -136,8 +137,6 @@ impl<TFruit: Fruit> FruitHandle<TFruit> {
///
/// assert_eq!(count, 2);
/// assert_eq!(top_docs.len(), 2);
/// # Ok(())
/// # }
/// ```
#[allow(clippy::type_complexity)]
#[derive(Default)]
@@ -174,14 +173,14 @@ impl<'a> Collector for MultiCollector<'a> {
fn for_segment(
&self,
segment_local_id: SegmentOrdinal,
segment_local_id: SegmentLocalId,
segment: &SegmentReader,
) -> crate::Result<MultiCollectorChild> {
) -> Result<MultiCollectorChild> {
let children = self
.collector_wrappers
.iter()
.map(|collector_wrapper| collector_wrapper.for_segment(segment_local_id, segment))
.collect::<crate::Result<Vec<_>>>()?;
.collect::<Result<Vec<_>>>()?;
Ok(MultiCollectorChild { children })
}
@@ -192,7 +191,7 @@ impl<'a> Collector for MultiCollector<'a> {
.any(Collector::requires_scoring)
}
fn merge_fruits(&self, segments_multifruits: Vec<MultiFruit>) -> crate::Result<MultiFruit> {
fn merge_fruits(&self, segments_multifruits: Vec<MultiFruit>) -> Result<MultiFruit> {
let mut segment_fruits_list: Vec<Vec<Box<dyn Fruit>>> = (0..self.collector_wrappers.len())
.map(|_| Vec::with_capacity(segments_multifruits.len()))
.collect::<Vec<_>>();
@@ -210,7 +209,7 @@ impl<'a> Collector for MultiCollector<'a> {
.map(|(child_collector, segment_fruits)| {
Ok(Some(child_collector.merge_fruits(segment_fruits)?))
})
.collect::<crate::Result<_>>()?;
.collect::<Result<_>>()?;
Ok(MultiFruit { sub_fruits })
}
}
@@ -245,38 +244,39 @@ mod tests {
use super::*;
use crate::collector::{Count, TopDocs};
use crate::query::TermQuery;
use crate::schema::{IndexRecordOption, Schema, TEXT};
use crate::{Index, Term};
use crate::schema::IndexRecordOption;
use crate::schema::{Schema, TEXT};
use crate::Index;
use crate::Term;
#[test]
fn test_multi_collector() -> crate::Result<()> {
fn test_multi_collector() {
let mut schema_builder = Schema::builder();
let text = schema_builder.add_text_field("text", TEXT);
let schema = schema_builder.build();
let index = Index::create_in_ram(schema);
{
let mut index_writer = index.writer_for_tests()?;
index_writer.add_document(doc!(text=>"abc"))?;
index_writer.add_document(doc!(text=>"abc abc abc"))?;
index_writer.add_document(doc!(text=>"abc abc"))?;
index_writer.commit()?;
index_writer.add_document(doc!(text=>""))?;
index_writer.add_document(doc!(text=>"abc abc abc abc"))?;
index_writer.add_document(doc!(text=>"abc"))?;
index_writer.commit()?;
let mut index_writer = index.writer_with_num_threads(1, 3_000_000).unwrap();
index_writer.add_document(doc!(text=>"abc"));
index_writer.add_document(doc!(text=>"abc abc abc"));
index_writer.add_document(doc!(text=>"abc abc"));
index_writer.commit().unwrap();
index_writer.add_document(doc!(text=>""));
index_writer.add_document(doc!(text=>"abc abc abc abc"));
index_writer.add_document(doc!(text=>"abc"));
index_writer.commit().unwrap();
}
let searcher = index.reader()?.searcher();
let searcher = index.reader().unwrap().searcher();
let term = Term::from_field_text(text, "abc");
let query = TermQuery::new(term, IndexRecordOption::Basic);
let mut collectors = MultiCollector::new();
let topdocs_handler = collectors.add_collector(TopDocs::with_limit(2));
let count_handler = collectors.add_collector(Count);
let mut multifruits = searcher.search(&query, &collectors).unwrap();
let mut multifruits = searcher.search(&query, &mut collectors).unwrap();
assert_eq!(count_handler.extract(&mut multifruits), 5);
assert_eq!(topdocs_handler.extract(&mut multifruits).len(), 2);
Ok(())
}
}

View File

@@ -1,12 +1,12 @@
use std::str::FromStr;
use super::*;
use crate::collector::{Count, FilterCollector, TopDocs};
use crate::core::SegmentReader;
use crate::fastfield::{BytesFastFieldReader, DynamicFastFieldReader, FastFieldReader};
use crate::query::{AllQuery, QueryParser};
use crate::schema::{Field, Schema, FAST, TEXT};
use crate::{doc, DateTime, DocAddress, DocId, Document, Index, Score, Searcher, SegmentOrdinal};
use crate::fastfield::BytesFastFieldReader;
use crate::fastfield::FastFieldReader;
use crate::schema::Field;
use crate::DocAddress;
use crate::DocId;
use crate::Score;
use crate::SegmentLocalId;
pub const TEST_COLLECTOR_WITH_SCORE: TestCollector = TestCollector {
compute_score: true,
@@ -16,55 +16,6 @@ pub const TEST_COLLECTOR_WITHOUT_SCORE: TestCollector = TestCollector {
compute_score: true,
};
#[test]
pub fn test_filter_collector() -> crate::Result<()> {
let mut schema_builder = Schema::builder();
let title = schema_builder.add_text_field("title", TEXT);
let price = schema_builder.add_u64_field("price", FAST);
let date = 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_with_num_threads(1, 10_000_000)?;
index_writer.add_document(doc!(title => "The Name of the Wind", price => 30_200u64, date => DateTime::from_str("1898-04-09T00:00:00+00:00").unwrap()))?;
index_writer.add_document(doc!(title => "The Diary of Muadib", price => 29_240u64, date => DateTime::from_str("2020-04-09T00:00:00+00:00").unwrap()))?;
index_writer.add_document(doc!(title => "The Diary of Anne Frank", price => 18_240u64, date => DateTime::from_str("2019-04-20T00:00:00+00:00").unwrap()))?;
index_writer.add_document(doc!(title => "A Dairy Cow", price => 21_240u64, date => DateTime::from_str("2019-04-09T00:00:00+00:00").unwrap()))?;
index_writer.add_document(doc!(title => "The Diary of a Young Girl", price => 20_120u64, date => DateTime::from_str("2018-04-09T00:00:00+00:00").unwrap()))?;
index_writer.commit()?;
let reader = index.reader()?;
let searcher = reader.searcher();
let query_parser = QueryParser::for_index(&index, vec![title]);
let query = query_parser.parse_query("diary")?;
let filter_some_collector = FilterCollector::new(
price,
&|value: u64| value > 20_120u64,
TopDocs::with_limit(2),
);
let top_docs = searcher.search(&query, &filter_some_collector)?;
assert_eq!(top_docs.len(), 1);
assert_eq!(top_docs[0].1, DocAddress::new(0, 1));
let filter_all_collector: FilterCollector<_, _, u64> =
FilterCollector::new(price, &|value| value < 5u64, TopDocs::with_limit(2));
let filtered_top_docs = searcher.search(&query, &filter_all_collector).unwrap();
assert_eq!(filtered_top_docs.len(), 0);
fn date_filter(value: DateTime) -> bool {
(value - DateTime::from_str("2019-04-09T00:00:00+00:00").unwrap()).num_weeks() > 0
}
let filter_dates_collector = FilterCollector::new(date, &date_filter, TopDocs::with_limit(5));
let filtered_date_docs = searcher.search(&query, &filter_dates_collector)?;
assert_eq!(filtered_date_docs.len(), 2);
Ok(())
}
/// Stores all of the doc ids.
/// This collector is only used for tests.
/// It is unusable in pr
@@ -76,7 +27,7 @@ pub struct TestCollector {
}
pub struct TestSegmentCollector {
segment_id: SegmentOrdinal,
segment_id: SegmentLocalId,
fruit: TestFruit,
}
@@ -102,9 +53,9 @@ impl Collector for TestCollector {
fn for_segment(
&self,
segment_id: SegmentOrdinal,
segment_id: SegmentLocalId,
_reader: &SegmentReader,
) -> crate::Result<TestSegmentCollector> {
) -> Result<TestSegmentCollector> {
Ok(TestSegmentCollector {
segment_id,
fruit: TestFruit::default(),
@@ -115,12 +66,12 @@ impl Collector for TestCollector {
self.compute_score
}
fn merge_fruits(&self, mut children: Vec<TestFruit>) -> crate::Result<TestFruit> {
fn merge_fruits(&self, mut children: Vec<TestFruit>) -> Result<TestFruit> {
children.sort_by_key(|fruit| {
if fruit.docs().is_empty() {
0
} else {
fruit.docs()[0].segment_ord
fruit.docs()[0].segment_ord()
}
});
let mut docs = vec![];
@@ -137,7 +88,7 @@ impl SegmentCollector for TestSegmentCollector {
type Fruit = TestFruit;
fn collect(&mut self, doc: DocId, score: Score) {
self.fruit.docs.push(DocAddress::new(self.segment_id, doc));
self.fruit.docs.push(DocAddress(self.segment_id, doc));
self.fruit.scores.push(score);
}
@@ -156,7 +107,7 @@ pub struct FastFieldTestCollector {
pub struct FastFieldSegmentCollector {
vals: Vec<u64>,
reader: DynamicFastFieldReader<u64>,
reader: FastFieldReader<u64>,
}
impl FastFieldTestCollector {
@@ -171,9 +122,9 @@ impl Collector for FastFieldTestCollector {
fn for_segment(
&self,
_: SegmentOrdinal,
_: SegmentLocalId,
segment_reader: &SegmentReader,
) -> crate::Result<FastFieldSegmentCollector> {
) -> Result<FastFieldSegmentCollector> {
let reader = segment_reader
.fast_fields()
.u64(self.field)
@@ -188,7 +139,7 @@ impl Collector for FastFieldTestCollector {
false
}
fn merge_fruits(&self, children: Vec<Vec<u64>>) -> crate::Result<Vec<u64>> {
fn merge_fruits(&self, children: Vec<Vec<u64>>) -> Result<Vec<u64>> {
Ok(children.into_iter().flat_map(|v| v.into_iter()).collect())
}
}
@@ -233,11 +184,13 @@ impl Collector for BytesFastFieldTestCollector {
&self,
_segment_local_id: u32,
segment_reader: &SegmentReader,
) -> crate::Result<BytesFastFieldSegmentCollector> {
let reader = segment_reader.fast_fields().bytes(self.field)?;
) -> Result<BytesFastFieldSegmentCollector> {
Ok(BytesFastFieldSegmentCollector {
vals: Vec::new(),
reader,
reader: segment_reader
.fast_fields()
.bytes(self.field)
.expect("Field is not a bytes fast field."),
})
}
@@ -245,7 +198,7 @@ impl Collector for BytesFastFieldTestCollector {
false
}
fn merge_fruits(&self, children: Vec<Vec<u8>>) -> crate::Result<Vec<u8>> {
fn merge_fruits(&self, children: Vec<Vec<u8>>) -> Result<Vec<u8>> {
Ok(children.into_iter().flat_map(|c| c.into_iter()).collect())
}
}
@@ -253,7 +206,7 @@ impl Collector for BytesFastFieldTestCollector {
impl SegmentCollector for BytesFastFieldSegmentCollector {
type Fruit = Vec<u8>;
fn collect(&mut self, doc: u32, _score: Score) {
fn collect(&mut self, doc: u32, _score: f32) {
let data = self.reader.get_bytes(doc);
self.vals.extend(data);
}
@@ -262,30 +215,3 @@ impl SegmentCollector for BytesFastFieldSegmentCollector {
self.vals
}
}
fn make_test_searcher() -> crate::Result<crate::LeasedItem<Searcher>> {
let schema = Schema::builder().build();
let index = Index::create_in_ram(schema);
let mut index_writer = index.writer_for_tests()?;
index_writer.add_document(Document::default())?;
index_writer.add_document(Document::default())?;
index_writer.commit()?;
Ok(index.reader()?.searcher())
}
#[test]
fn test_option_collector_some() -> crate::Result<()> {
let searcher = make_test_searcher()?;
let counts = searcher.search(&AllQuery, &Some(Count))?;
assert_eq!(counts, Some(2));
Ok(())
}
#[test]
fn test_option_collector_none() -> crate::Result<()> {
let searcher = make_test_searcher()?;
let none_collector: Option<Count> = None;
let counts = searcher.search(&AllQuery, &none_collector)?;
assert_eq!(counts, None);
Ok(())
}

View File

@@ -1,8 +1,11 @@
use crate::DocAddress;
use crate::DocId;
use crate::Result;
use crate::SegmentLocalId;
use crate::SegmentReader;
use serde::export::PhantomData;
use std::cmp::Ordering;
use std::collections::BinaryHeap;
use std::marker::PhantomData;
use crate::{DocAddress, DocId, SegmentOrdinal, SegmentReader};
/// Contains a feature (field, score, etc.) of a document along with the document address.
///
@@ -16,9 +19,9 @@ use crate::{DocAddress, DocId, SegmentOrdinal, SegmentReader};
/// Two elements are equal if their feature is equal, and regardless of whether `doc`
/// is equal. This should be perfectly fine for this usage, but let's make sure this
/// struct is never public.
pub(crate) struct ComparableDoc<T, D> {
pub feature: T,
pub doc: D,
struct ComparableDoc<T, D> {
feature: T,
doc: D,
}
impl<T: PartialOrd, D: PartialOrd> PartialOrd for ComparableDoc<T, D> {
@@ -54,47 +57,43 @@ impl<T: PartialOrd, D: PartialOrd> PartialEq for ComparableDoc<T, D> {
impl<T: PartialOrd, D: PartialOrd> Eq for ComparableDoc<T, D> {}
pub(crate) struct TopCollector<T> {
pub limit: usize,
pub offset: usize,
limit: usize,
_marker: PhantomData<T>,
}
impl<T> TopCollector<T>
where T: PartialOrd + Clone
where
T: PartialOrd + Clone,
{
/// Creates a top collector, with a number of documents equal to "limit".
///
/// # Panics
/// The method panics if limit is 0
pub fn with_limit(limit: usize) -> TopCollector<T> {
assert!(limit >= 1, "Limit must be strictly greater than 0.");
Self {
if limit < 1 {
panic!("Limit must be strictly greater than 0.");
}
TopCollector {
limit,
offset: 0,
_marker: PhantomData,
}
}
/// Skip the first "offset" documents when collecting.
///
/// This is equivalent to `OFFSET` in MySQL or PostgreSQL and `start` in
/// Lucene's TopDocsCollector.
pub fn and_offset(mut self, offset: usize) -> TopCollector<T> {
self.offset = offset;
self
pub fn limit(&self) -> usize {
self.limit
}
pub fn merge_fruits(
&self,
children: Vec<Vec<(T, DocAddress)>>,
) -> crate::Result<Vec<(T, DocAddress)>> {
) -> Result<Vec<(T, DocAddress)>> {
if self.limit == 0 {
return Ok(Vec::new());
}
let mut top_collector = BinaryHeap::new();
for child_fruit in children {
for (feature, doc) in child_fruit {
if top_collector.len() < (self.limit + self.offset) {
if top_collector.len() < self.limit {
top_collector.push(ComparableDoc { feature, doc });
} else if let Some(mut head) = top_collector.peek_mut() {
if head.feature < feature {
@@ -106,30 +105,16 @@ where T: PartialOrd + Clone
Ok(top_collector
.into_sorted_vec()
.into_iter()
.skip(self.offset)
.map(|cdoc| (cdoc.feature, cdoc.doc))
.collect())
}
pub(crate) fn for_segment<F: PartialOrd>(
&self,
segment_id: SegmentOrdinal,
segment_id: SegmentLocalId,
_: &SegmentReader,
) -> TopSegmentCollector<F> {
TopSegmentCollector::new(segment_id, self.limit + self.offset)
}
/// Create a new TopCollector with the same limit and offset.
///
/// Ideally we would use Into but the blanket implementation seems to cause the Scorer traits
/// to fail.
#[doc(hidden)]
pub(crate) fn into_tscore<TScore: PartialOrd + Clone>(self) -> TopCollector<TScore> {
TopCollector {
limit: self.limit,
offset: self.offset,
_marker: PhantomData,
}
) -> Result<TopSegmentCollector<F>> {
Ok(TopSegmentCollector::new(segment_id, self.limit))
}
}
@@ -142,32 +127,29 @@ where T: PartialOrd + Clone
pub(crate) struct TopSegmentCollector<T> {
limit: usize,
heap: BinaryHeap<ComparableDoc<T, DocId>>,
segment_ord: u32,
segment_id: u32,
}
impl<T: PartialOrd> TopSegmentCollector<T> {
fn new(segment_ord: SegmentOrdinal, limit: usize) -> TopSegmentCollector<T> {
fn new(segment_id: SegmentLocalId, limit: usize) -> TopSegmentCollector<T> {
TopSegmentCollector {
limit,
heap: BinaryHeap::with_capacity(limit),
segment_ord,
segment_id,
}
}
}
impl<T: PartialOrd + Clone> TopSegmentCollector<T> {
pub fn harvest(self) -> Vec<(T, DocAddress)> {
let segment_ord = self.segment_ord;
let segment_id = self.segment_id;
self.heap
.into_sorted_vec()
.into_iter()
.map(|comparable_doc| {
(
comparable_doc.feature,
DocAddress {
segment_ord,
doc_id: comparable_doc.doc,
},
DocAddress(segment_id, comparable_doc.doc),
)
})
.collect()
@@ -175,7 +157,7 @@ impl<T: PartialOrd + Clone> TopSegmentCollector<T> {
/// Return true iff at least K documents have gone through
/// the collector.
#[inline]
#[inline(always)]
pub(crate) fn at_capacity(&self) -> bool {
self.heap.len() >= self.limit
}
@@ -184,7 +166,7 @@ impl<T: PartialOrd + Clone> TopSegmentCollector<T> {
///
/// It collects documents until it has reached the max capacity. Once it reaches capacity, it
/// will compare the lowest scoring item with the given one and keep whichever is greater.
#[inline]
#[inline(always)]
pub fn collect(&mut self, doc: DocId, feature: T) {
if self.at_capacity() {
// It's ok to unwrap as long as a limit of 0 is forbidden.
@@ -206,7 +188,7 @@ impl<T: PartialOrd + Clone> TopSegmentCollector<T> {
#[cfg(test)]
mod tests {
use super::{TopCollector, TopSegmentCollector};
use super::TopSegmentCollector;
use crate::DocAddress;
#[test]
@@ -218,9 +200,9 @@ mod tests {
assert_eq!(
top_collector.harvest(),
vec![
(0.8, DocAddress::new(0, 1)),
(0.3, DocAddress::new(0, 5)),
(0.2, DocAddress::new(0, 3))
(0.8, DocAddress(0, 1)),
(0.3, DocAddress(0, 5)),
(0.2, DocAddress(0, 3))
]
);
}
@@ -236,10 +218,10 @@ mod tests {
assert_eq!(
top_collector.harvest(),
vec![
(0.9, DocAddress::new(0, 7)),
(0.8, DocAddress::new(0, 1)),
(0.3, DocAddress::new(0, 5)),
(0.2, DocAddress::new(0, 3))
(0.9, DocAddress(0, 7)),
(0.8, DocAddress(0, 1)),
(0.3, DocAddress(0, 5)),
(0.2, DocAddress(0, 3))
]
);
}
@@ -250,7 +232,7 @@ mod tests {
// when harvesting we have to guarantee stable sorting in case of a tie
// on the score
let doc_ids_collection = [4, 5, 6];
let score = 3.3f32;
let score = 3.14;
let mut top_collector_limit_2 = TopSegmentCollector::new(0, 2);
for id in &doc_ids_collection {
@@ -267,61 +249,12 @@ mod tests {
top_collector_limit_3.harvest()[..2].to_vec(),
);
}
#[test]
fn test_top_collector_with_limit_and_offset() {
let collector = TopCollector::with_limit(2).and_offset(1);
let results = collector
.merge_fruits(vec![vec![
(0.9, DocAddress::new(0, 1)),
(0.8, DocAddress::new(0, 2)),
(0.7, DocAddress::new(0, 3)),
(0.6, DocAddress::new(0, 4)),
(0.5, DocAddress::new(0, 5)),
]])
.unwrap();
assert_eq!(
results,
vec![(0.8, DocAddress::new(0, 2)), (0.7, DocAddress::new(0, 3)),]
);
}
#[test]
fn test_top_collector_with_limit_larger_than_set_and_offset() {
let collector = TopCollector::with_limit(2).and_offset(1);
let results = collector
.merge_fruits(vec![vec![
(0.9, DocAddress::new(0, 1)),
(0.8, DocAddress::new(0, 2)),
]])
.unwrap();
assert_eq!(results, vec![(0.8, DocAddress::new(0, 2)),]);
}
#[test]
fn test_top_collector_with_limit_and_offset_larger_than_set() {
let collector = TopCollector::with_limit(2).and_offset(20);
let results = collector
.merge_fruits(vec![vec![
(0.9, DocAddress::new(0, 1)),
(0.8, DocAddress::new(0, 2)),
]])
.unwrap();
assert_eq!(results, vec![]);
}
}
#[cfg(all(test, feature = "unstable"))]
mod bench {
use test::Bencher;
use super::TopSegmentCollector;
use test::Bencher;
#[bench]
fn bench_top_segment_collector_collect_not_at_capacity(b: &mut Bencher) {

File diff suppressed because it is too large Load Diff

View File

@@ -1,6 +1,7 @@
use crate::collector::top_collector::{TopCollector, TopSegmentCollector};
use crate::collector::{Collector, SegmentCollector};
use crate::{DocAddress, DocId, Result, Score, SegmentReader};
use crate::DocAddress;
use crate::{DocId, Result, Score, SegmentReader};
pub(crate) struct TweakedScoreTopCollector<TScoreTweaker, TScore = Score> {
score_tweaker: TScoreTweaker,
@@ -8,15 +9,16 @@ pub(crate) struct TweakedScoreTopCollector<TScoreTweaker, TScore = Score> {
}
impl<TScoreTweaker, TScore> TweakedScoreTopCollector<TScoreTweaker, TScore>
where TScore: Clone + PartialOrd
where
TScore: Clone + PartialOrd,
{
pub fn new(
score_tweaker: TScoreTweaker,
collector: TopCollector<TScore>,
limit: usize,
) -> TweakedScoreTopCollector<TScoreTweaker, TScore> {
TweakedScoreTopCollector {
score_tweaker,
collector,
collector: TopCollector::with_limit(limit),
}
}
}
@@ -27,7 +29,7 @@ where TScore: Clone + PartialOrd
/// It is the segment local version of the [`ScoreTweaker`](./trait.ScoreTweaker.html).
pub trait ScoreSegmentTweaker<TScore>: 'static {
/// Tweak the given `score` for the document `doc`.
fn score(&mut self, doc: DocId, score: Score) -> TScore;
fn score(&self, doc: DocId, score: Score) -> TScore;
}
/// `ScoreTweaker` makes it possible to tweak the score
@@ -47,7 +49,7 @@ pub trait ScoreTweaker<TScore>: Sync {
impl<TScoreTweaker, TScore> Collector for TweakedScoreTopCollector<TScoreTweaker, TScore>
where
TScoreTweaker: ScoreTweaker<TScore> + Send + Sync,
TScoreTweaker: ScoreTweaker<TScore>,
TScore: 'static + PartialOrd + Clone + Send + Sync,
{
type Fruit = Vec<(TScore, DocAddress)>;
@@ -60,7 +62,9 @@ where
segment_reader: &SegmentReader,
) -> Result<Self::Child> {
let segment_scorer = self.score_tweaker.segment_tweaker(segment_reader)?;
let segment_collector = self.collector.for_segment(segment_local_id, segment_reader);
let segment_collector = self
.collector
.for_segment(segment_local_id, segment_reader)?;
Ok(TopTweakedScoreSegmentCollector {
segment_collector,
segment_scorer,
@@ -116,9 +120,10 @@ where
}
impl<F, TScore> ScoreSegmentTweaker<TScore> for F
where F: 'static + FnMut(DocId, Score) -> TScore
where
F: 'static + Sync + Send + Fn(DocId, Score) -> TScore,
{
fn score(&mut self, doc: DocId, score: Score) -> TScore {
fn score(&self, doc: DocId, score: Score) -> TScore {
(self)(doc, score)
}
}

View File

@@ -1,15 +1,12 @@
use std::convert::TryInto;
use byteorder::{ByteOrder, LittleEndian, WriteBytesExt};
use std::io;
use std::ops::Deref;
pub struct BitPacker {
pub(crate) struct BitPacker {
mini_buffer: u64,
mini_buffer_written: usize,
}
impl Default for BitPacker {
fn default() -> Self {
BitPacker::new()
}
}
impl BitPacker {
pub fn new() -> BitPacker {
BitPacker {
@@ -18,7 +15,6 @@ impl BitPacker {
}
}
#[inline]
pub fn write<TWrite: io::Write>(
&mut self,
val: u64,
@@ -29,14 +25,14 @@ impl BitPacker {
let num_bits = num_bits as usize;
if self.mini_buffer_written + num_bits > 64 {
self.mini_buffer |= val_u64.wrapping_shl(self.mini_buffer_written as u32);
output.write_all(self.mini_buffer.to_le_bytes().as_ref())?;
output.write_u64::<LittleEndian>(self.mini_buffer)?;
self.mini_buffer = val_u64.wrapping_shr((64 - self.mini_buffer_written) as u32);
self.mini_buffer_written = self.mini_buffer_written + num_bits - 64;
} else {
self.mini_buffer |= val_u64 << self.mini_buffer_written;
self.mini_buffer_written += num_bits;
if self.mini_buffer_written == 64 {
output.write_all(self.mini_buffer.to_le_bytes().as_ref())?;
output.write_u64::<LittleEndian>(self.mini_buffer)?;
self.mini_buffer_written = 0;
self.mini_buffer = 0u64;
}
@@ -47,10 +43,10 @@ impl BitPacker {
pub fn flush<TWrite: io::Write>(&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();
output.write_all(&bytes[..num_bytes])?;
let mut arr: [u8; 8] = [0u8; 8];
LittleEndian::write_u64(&mut arr, self.mini_buffer);
output.write_all(&arr[..num_bytes])?;
self.mini_buffer_written = 0;
self.mini_buffer = 0;
}
Ok(())
}
@@ -63,14 +59,21 @@ impl BitPacker {
}
}
#[derive(Clone, Debug, Default)]
pub struct BitUnpacker {
#[derive(Clone)]
pub struct BitUnpacker<Data>
where
Data: Deref<Target = [u8]>,
{
num_bits: u64,
mask: u64,
data: Data,
}
impl BitUnpacker {
pub fn new(num_bits: u8) -> BitUnpacker {
impl<Data> BitUnpacker<Data>
where
Data: Deref<Target = [u8]>,
{
pub fn new(data: Data, num_bits: u8) -> BitUnpacker<Data> {
let mask: u64 = if num_bits == 64 {
!0u64
} else {
@@ -79,14 +82,15 @@ impl BitUnpacker {
BitUnpacker {
num_bits: u64::from(num_bits),
mask,
data,
}
}
#[inline]
pub fn get(&self, idx: u64, data: &[u8]) -> u64 {
pub fn get(&self, idx: u64) -> u64 {
if self.num_bits == 0 {
return 0u64;
}
let data: &[u8] = &*self.data;
let num_bits = self.num_bits;
let mask = self.mask;
let addr_in_bits = idx * num_bits;
@@ -96,10 +100,7 @@ impl BitUnpacker {
addr + 8 <= data.len() as u64,
"The fast field field should have been padded with 7 bytes."
);
let bytes: [u8; 8] = (&data[(addr as usize)..(addr as usize) + 8])
.try_into()
.unwrap();
let val_unshifted_unmasked: u64 = u64::from_le_bytes(bytes);
let val_unshifted_unmasked: u64 = LittleEndian::read_u64(&data[(addr as usize)..]);
let val_shifted = (val_unshifted_unmasked >> bit_shift) as u64;
val_shifted & mask
}
@@ -109,7 +110,7 @@ impl BitUnpacker {
mod test {
use super::{BitPacker, BitUnpacker};
fn create_fastfield_bitpacker(len: usize, num_bits: u8) -> (BitUnpacker, Vec<u64>, Vec<u8>) {
fn create_fastfield_bitpacker(len: usize, num_bits: u8) -> (BitUnpacker<Vec<u8>>, Vec<u64>) {
let mut data = Vec::new();
let mut bitpacker = BitPacker::new();
let max_val: u64 = (1u64 << num_bits as u64) - 1u64;
@@ -121,14 +122,14 @@ mod test {
}
bitpacker.close(&mut data).unwrap();
assert_eq!(data.len(), ((num_bits as usize) * len + 7) / 8 + 7);
let bitunpacker = BitUnpacker::new(num_bits);
(bitunpacker, vals, data)
let bitunpacker = BitUnpacker::new(data, num_bits);
(bitunpacker, vals)
}
fn test_bitpacker_util(len: usize, num_bits: u8) {
let (bitunpacker, vals, data) = create_fastfield_bitpacker(len, num_bits);
let (bitunpacker, vals) = create_fastfield_bitpacker(len, num_bits);
for (i, val) in vals.iter().enumerate() {
assert_eq!(bitunpacker.get(i as u64, &data), *val);
assert_eq!(bitunpacker.get(i as u64), *val);
}
}

395
src/common/bitset.rs Normal file
View File

@@ -0,0 +1,395 @@
use std::fmt;
use std::u64;
#[derive(Clone, Copy, Eq, PartialEq)]
pub(crate) struct TinySet(u64);
impl fmt::Debug for TinySet {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
self.into_iter().collect::<Vec<u32>>().fmt(f)
}
}
pub struct TinySetIterator(TinySet);
impl Iterator for TinySetIterator {
type Item = u32;
fn next(&mut self) -> Option<Self::Item> {
self.0.pop_lowest()
}
}
impl IntoIterator for TinySet {
type Item = u32;
type IntoIter = TinySetIterator;
fn into_iter(self) -> Self::IntoIter {
TinySetIterator(self)
}
}
impl TinySet {
/// Returns an empty `TinySet`.
pub fn empty() -> TinySet {
TinySet(0u64)
}
/// Returns the complement of the set in `[0, 64[`.
fn complement(self) -> TinySet {
TinySet(!self.0)
}
/// Returns true iff the `TinySet` contains the element `el`.
pub fn contains(self, el: u32) -> bool {
!self.intersect(TinySet::singleton(el)).is_empty()
}
/// Returns the intersection of `self` and `other`
pub fn intersect(self, other: TinySet) -> TinySet {
TinySet(self.0 & other.0)
}
/// Creates a new `TinySet` containing only one element
/// within `[0; 64[`
#[inline(always)]
pub fn singleton(el: u32) -> TinySet {
TinySet(1u64 << u64::from(el))
}
/// Insert a new element within [0..64[
#[inline(always)]
pub fn insert(self, el: u32) -> TinySet {
self.union(TinySet::singleton(el))
}
/// Insert a new element within [0..64[
#[inline(always)]
pub fn insert_mut(&mut self, el: u32) -> bool {
let old = *self;
*self = old.insert(el);
old != *self
}
/// Returns the union of two tinysets
#[inline(always)]
pub fn union(self, other: TinySet) -> TinySet {
TinySet(self.0 | other.0)
}
/// Returns true iff the `TinySet` is empty.
#[inline(always)]
pub fn is_empty(self) -> bool {
self.0 == 0u64
}
/// Returns the lowest element in the `TinySet`
/// and removes it.
#[inline(always)]
pub fn pop_lowest(&mut self) -> Option<u32> {
if self.is_empty() {
None
} else {
let lowest = self.0.trailing_zeros() as u32;
self.0 ^= TinySet::singleton(lowest).0;
Some(lowest)
}
}
/// Returns a `TinySet` than contains all values up
/// to limit excluded.
///
/// The limit is assumed to be strictly lower than 64.
pub fn range_lower(upper_bound: u32) -> TinySet {
TinySet((1u64 << u64::from(upper_bound % 64u32)) - 1u64)
}
/// Returns a `TinySet` that contains all values greater
/// or equal to the given limit, included. (and up to 63)
///
/// The limit is assumed to be strictly lower than 64.
pub fn range_greater_or_equal(from_included: u32) -> TinySet {
TinySet::range_lower(from_included).complement()
}
pub fn clear(&mut self) {
self.0 = 0u64;
}
pub fn len(self) -> u32 {
self.0.count_ones()
}
}
#[derive(Clone)]
pub struct BitSet {
tinysets: Box<[TinySet]>,
len: usize, //< Technically it should be u32, but we
// count multiple inserts.
// `usize` guards us from overflow.
max_value: u32,
}
fn num_buckets(max_val: u32) -> u32 {
(max_val + 63u32) / 64u32
}
impl BitSet {
/// Create a new `BitSet` that may contain elements
/// within `[0, max_val[`.
pub fn with_max_value(max_value: u32) -> BitSet {
let num_buckets = num_buckets(max_value);
let tinybisets = vec![TinySet::empty(); num_buckets as usize].into_boxed_slice();
BitSet {
tinysets: tinybisets,
len: 0,
max_value,
}
}
/// Removes all elements from the `BitSet`.
pub fn clear(&mut self) {
for tinyset in self.tinysets.iter_mut() {
*tinyset = TinySet::empty();
}
}
/// Returns the number of elements in the `BitSet`.
pub fn len(&self) -> usize {
self.len
}
/// Inserts an element in the `BitSet`
pub fn insert(&mut self, el: u32) {
// we do not check saturated els.
let higher = el / 64u32;
let lower = el % 64u32;
self.len += if self.tinysets[higher as usize].insert_mut(lower) {
1
} else {
0
};
}
/// Returns true iff the elements is in the `BitSet`.
pub fn contains(&self, el: u32) -> bool {
self.tinyset(el / 64u32).contains(el % 64)
}
/// Returns the first non-empty `TinySet` associated to a bucket lower
/// or greater than bucket.
///
/// Reminder: the tiny set with the bucket `bucket`, represents the
/// elements from `bucket * 64` to `(bucket+1) * 64`.
pub(crate) fn first_non_empty_bucket(&self, bucket: u32) -> Option<u32> {
self.tinysets[bucket as usize..]
.iter()
.cloned()
.position(|tinyset| !tinyset.is_empty())
.map(|delta_bucket| bucket + delta_bucket as u32)
}
pub fn max_value(&self) -> u32 {
self.max_value
}
/// Returns the tiny bitset representing the
/// the set restricted to the number range from
/// `bucket * 64` to `(bucket + 1) * 64`.
pub(crate) fn tinyset(&self, bucket: u32) -> TinySet {
self.tinysets[bucket as usize]
}
}
#[cfg(test)]
mod tests {
use super::BitSet;
use super::TinySet;
use crate::docset::DocSet;
use crate::query::BitSetDocSet;
use crate::tests;
use crate::tests::generate_nonunique_unsorted;
use std::collections::BTreeSet;
use std::collections::HashSet;
#[test]
fn test_tiny_set() {
assert!(TinySet::empty().is_empty());
{
let mut u = TinySet::empty().insert(1u32);
assert_eq!(u.pop_lowest(), Some(1u32));
assert!(u.pop_lowest().is_none())
}
{
let mut u = TinySet::empty().insert(1u32).insert(1u32);
assert_eq!(u.pop_lowest(), Some(1u32));
assert!(u.pop_lowest().is_none())
}
{
let mut u = TinySet::empty().insert(2u32);
assert_eq!(u.pop_lowest(), Some(2u32));
u.insert_mut(1u32);
assert_eq!(u.pop_lowest(), Some(1u32));
assert!(u.pop_lowest().is_none());
}
{
let mut u = TinySet::empty().insert(63u32);
assert_eq!(u.pop_lowest(), Some(63u32));
assert!(u.pop_lowest().is_none());
}
}
#[test]
fn test_bitset() {
let test_against_hashset = |els: &[u32], max_value: u32| {
let mut hashset: HashSet<u32> = HashSet::new();
let mut bitset = BitSet::with_max_value(max_value);
for &el in els {
assert!(el < max_value);
hashset.insert(el);
bitset.insert(el);
}
for el in 0..max_value {
assert_eq!(hashset.contains(&el), bitset.contains(el));
}
assert_eq!(bitset.max_value(), max_value);
};
test_against_hashset(&[], 0);
test_against_hashset(&[], 1);
test_against_hashset(&[0u32], 1);
test_against_hashset(&[0u32], 100);
test_against_hashset(&[1u32, 2u32], 4);
test_against_hashset(&[99u32], 100);
test_against_hashset(&[63u32], 64);
test_against_hashset(&[62u32, 63u32], 64);
}
#[test]
fn test_bitset_large() {
let arr = generate_nonunique_unsorted(100_000, 5_000);
let mut btreeset: BTreeSet<u32> = BTreeSet::new();
let mut bitset = BitSet::with_max_value(100_000);
for el in arr {
btreeset.insert(el);
bitset.insert(el);
}
for i in 0..100_000 {
assert_eq!(btreeset.contains(&i), bitset.contains(i));
}
assert_eq!(btreeset.len(), bitset.len());
let mut bitset_docset = BitSetDocSet::from(bitset);
for el in btreeset.into_iter() {
bitset_docset.advance();
assert_eq!(bitset_docset.doc(), el);
}
assert!(!bitset_docset.advance());
}
#[test]
fn test_bitset_num_buckets() {
use super::num_buckets;
assert_eq!(num_buckets(0u32), 0);
assert_eq!(num_buckets(1u32), 1);
assert_eq!(num_buckets(64u32), 1);
assert_eq!(num_buckets(65u32), 2);
assert_eq!(num_buckets(128u32), 2);
assert_eq!(num_buckets(129u32), 3);
}
#[test]
fn test_tinyset_range() {
assert_eq!(
TinySet::range_lower(3).into_iter().collect::<Vec<u32>>(),
[0, 1, 2]
);
assert!(TinySet::range_lower(0).is_empty());
assert_eq!(
TinySet::range_lower(63).into_iter().collect::<Vec<u32>>(),
(0u32..63u32).collect::<Vec<_>>()
);
assert_eq!(
TinySet::range_lower(1).into_iter().collect::<Vec<u32>>(),
[0]
);
assert_eq!(
TinySet::range_lower(2).into_iter().collect::<Vec<u32>>(),
[0, 1]
);
assert_eq!(
TinySet::range_greater_or_equal(3)
.into_iter()
.collect::<Vec<u32>>(),
(3u32..64u32).collect::<Vec<_>>()
);
}
#[test]
fn test_bitset_len() {
let mut bitset = BitSet::with_max_value(1_000);
assert_eq!(bitset.len(), 0);
bitset.insert(3u32);
assert_eq!(bitset.len(), 1);
bitset.insert(103u32);
assert_eq!(bitset.len(), 2);
bitset.insert(3u32);
assert_eq!(bitset.len(), 2);
bitset.insert(103u32);
assert_eq!(bitset.len(), 2);
bitset.insert(104u32);
assert_eq!(bitset.len(), 3);
}
#[test]
fn test_bitset_clear() {
let mut bitset = BitSet::with_max_value(1_000);
let els = tests::sample(1_000, 0.01f64);
for &el in &els {
bitset.insert(el);
}
assert!(els.iter().all(|el| bitset.contains(*el)));
bitset.clear();
for el in 0u32..1000u32 {
assert!(!bitset.contains(el));
}
}
}
#[cfg(all(test, feature = "unstable"))]
mod bench {
use super::BitSet;
use super::TinySet;
use test;
#[bench]
fn bench_tinyset_pop(b: &mut test::Bencher) {
b.iter(|| {
let mut tinyset = TinySet::singleton(test::black_box(31u32));
tinyset.pop_lowest();
tinyset.pop_lowest();
tinyset.pop_lowest();
tinyset.pop_lowest();
tinyset.pop_lowest();
tinyset.pop_lowest();
});
}
#[bench]
fn bench_tinyset_sum(b: &mut test::Bencher) {
let tiny_set = TinySet::empty().insert(10u32).insert(14u32).insert(21u32);
b.iter(|| {
assert_eq!(test::black_box(tiny_set).into_iter().sum::<u32>(), 45u32);
});
}
#[bench]
fn bench_tinyarr_sum(b: &mut test::Bencher) {
let v = [10u32, 14u32, 21u32];
b.iter(|| test::black_box(v).iter().cloned().sum::<u32>());
}
#[bench]
fn bench_bitset_initialize(b: &mut test::Bencher) {
b.iter(|| BitSet::with_max_value(1_000_000));
}
}

View File

@@ -1,13 +1,14 @@
use std::collections::HashMap;
use std::io::{self, Read, Write};
use std::iter::ExactSizeIterator;
use std::ops::Range;
use common::{BinarySerializable, CountingWriter, HasLen, VInt};
use crate::directory::{FileSlice, TerminatingWrite, WritePtr};
use crate::common::BinarySerializable;
use crate::common::CountingWriter;
use crate::common::VInt;
use crate::directory::ReadOnlySource;
use crate::directory::{TerminatingWrite, WritePtr};
use crate::schema::Field;
use crate::space_usage::{FieldUsage, PerFieldSpaceUsage};
use crate::space_usage::FieldUsage;
use crate::space_usage::PerFieldSpaceUsage;
use std::collections::HashMap;
use std::io::Write;
use std::io::{self, Read};
#[derive(Eq, PartialEq, Hash, Copy, Ord, PartialOrd, Clone, Debug)]
pub struct FileAddr {
@@ -102,26 +103,25 @@ impl<W: TerminatingWrite + Write> CompositeWrite<W> {
/// for each field.
#[derive(Clone)]
pub struct CompositeFile {
data: FileSlice,
offsets_index: HashMap<FileAddr, Range<usize>>,
data: ReadOnlySource,
offsets_index: HashMap<FileAddr, (usize, usize)>,
}
impl CompositeFile {
/// Opens a composite file stored in a given
/// `FileSlice`.
pub fn open(data: &FileSlice) -> io::Result<CompositeFile> {
/// `ReadOnlySource`.
pub fn open(data: &ReadOnlySource) -> io::Result<CompositeFile> {
let end = data.len();
let footer_len_data = data.slice_from(end - 4).read_bytes()?;
let footer_len_data = data.slice_from(end - 4);
let footer_len = u32::deserialize(&mut footer_len_data.as_slice())? as usize;
let footer_start = end - 4 - footer_len;
let footer_data = data
.slice(footer_start..footer_start + footer_len)
.read_bytes()?;
let footer_data = data.slice(footer_start, footer_start + footer_len);
let mut footer_buffer = footer_data.as_slice();
let num_fields = VInt::deserialize(&mut footer_buffer)?.0 as usize;
let mut file_addrs = vec![];
let mut offsets = vec![];
let mut field_index = HashMap::new();
let mut offset = 0;
@@ -136,7 +136,7 @@ impl CompositeFile {
let file_addr = file_addrs[i];
let start_offset = offsets[i];
let end_offset = offsets[i + 1];
field_index.insert(file_addr, start_offset..end_offset);
field_index.insert(file_addr, (start_offset, end_offset));
}
Ok(CompositeFile {
@@ -150,31 +150,31 @@ impl CompositeFile {
pub fn empty() -> CompositeFile {
CompositeFile {
offsets_index: HashMap::new(),
data: FileSlice::empty(),
data: ReadOnlySource::empty(),
}
}
/// Returns the `FileSlice` associated
/// Returns the `ReadOnlySource` associated
/// to a given `Field` and stored in a `CompositeFile`.
pub fn open_read(&self, field: Field) -> Option<FileSlice> {
pub fn open_read(&self, field: Field) -> Option<ReadOnlySource> {
self.open_read_with_idx(field, 0)
}
/// Returns the `FileSlice` associated
/// Returns the `ReadOnlySource` associated
/// to a given `Field` and stored in a `CompositeFile`.
pub fn open_read_with_idx(&self, field: Field, idx: usize) -> Option<FileSlice> {
pub fn open_read_with_idx(&self, field: Field, idx: usize) -> Option<ReadOnlySource> {
self.offsets_index
.get(&FileAddr { field, idx })
.map(|byte_range| self.data.slice(byte_range.clone()))
.map(|&(from, to)| self.data.slice(from, to))
}
pub fn space_usage(&self) -> PerFieldSpaceUsage {
let mut fields = HashMap::new();
for (&field_addr, byte_range) in &self.offsets_index {
for (&field_addr, &(start, end)) in self.offsets_index.iter() {
fields
.entry(field_addr.field)
.or_insert_with(|| FieldUsage::empty(field_addr.field))
.add_field_idx(field_addr.idx, byte_range.len());
.add_field_idx(field_addr.idx, end - start);
}
PerFieldSpaceUsage::new(fields)
}
@@ -183,54 +183,55 @@ impl CompositeFile {
#[cfg(test)]
mod test {
use super::{CompositeFile, CompositeWrite};
use crate::common::BinarySerializable;
use crate::common::VInt;
use crate::directory::{Directory, RAMDirectory, ReadOnlyDirectory};
use crate::schema::Field;
use std::io::Write;
use std::path::Path;
use common::{BinarySerializable, VInt};
use super::{CompositeFile, CompositeWrite};
use crate::directory::{Directory, RamDirectory};
use crate::schema::Field;
#[test]
fn test_composite_file() -> crate::Result<()> {
fn test_composite_file() {
let path = Path::new("test_path");
let directory = RamDirectory::create();
let mut directory = RAMDirectory::create();
{
let w = directory.open_write(path).unwrap();
let mut composite_write = CompositeWrite::wrap(w);
let mut write_0 = composite_write.for_field(Field::from_field_id(0u32));
VInt(32431123u64).serialize(&mut write_0)?;
write_0.flush()?;
let mut write_4 = composite_write.for_field(Field::from_field_id(4u32));
VInt(2).serialize(&mut write_4)?;
write_4.flush()?;
composite_write.close()?;
{
let mut write_0 = composite_write.for_field(Field::from_field_id(0u32));
VInt(32431123u64).serialize(&mut write_0).unwrap();
write_0.flush().unwrap();
}
{
let mut write_4 = composite_write.for_field(Field::from_field_id(4u32));
VInt(2).serialize(&mut write_4).unwrap();
write_4.flush().unwrap();
}
composite_write.close().unwrap();
}
{
let r = directory.open_read(path)?;
let composite_file = CompositeFile::open(&r)?;
let r = directory.open_read(path).unwrap();
let composite_file = CompositeFile::open(&r).unwrap();
{
let file0 = composite_file
.open_read(Field::from_field_id(0u32))
.unwrap()
.read_bytes()?;
.unwrap();
let mut file0_buf = file0.as_slice();
let payload_0 = VInt::deserialize(&mut file0_buf)?.0;
let payload_0 = VInt::deserialize(&mut file0_buf).unwrap().0;
assert_eq!(file0_buf.len(), 0);
assert_eq!(payload_0, 32431123u64);
}
{
let file4 = composite_file
.open_read(Field::from_field_id(4u32))
.unwrap()
.read_bytes()?;
.unwrap();
let mut file4_buf = file4.as_slice();
let payload_4 = VInt::deserialize(&mut file4_buf)?.0;
let payload_4 = VInt::deserialize(&mut file4_buf).unwrap().0;
assert_eq!(file4_buf.len(), 0);
assert_eq!(payload_4, 2u64);
}
}
Ok(())
}
}

View File

@@ -0,0 +1,70 @@
use crate::directory::AntiCallToken;
use crate::directory::TerminatingWrite;
use std::io;
use std::io::Write;
pub struct CountingWriter<W> {
underlying: W,
written_bytes: u64,
}
impl<W: Write> CountingWriter<W> {
pub fn wrap(underlying: W) -> CountingWriter<W> {
CountingWriter {
underlying,
written_bytes: 0,
}
}
pub fn written_bytes(&self) -> u64 {
self.written_bytes
}
pub fn finish(mut self) -> io::Result<(W, u64)> {
self.flush()?;
Ok((self.underlying, self.written_bytes))
}
}
impl<W: Write> Write for CountingWriter<W> {
fn write(&mut self, buf: &[u8]) -> io::Result<usize> {
let written_size = self.underlying.write(buf)?;
self.written_bytes += written_size as u64;
Ok(written_size)
}
fn write_all(&mut self, buf: &[u8]) -> io::Result<()> {
self.underlying.write_all(buf)?;
self.written_bytes += buf.len() as u64;
Ok(())
}
fn flush(&mut self) -> io::Result<()> {
self.underlying.flush()
}
}
impl<W: TerminatingWrite> TerminatingWrite for CountingWriter<W> {
fn terminate_ref(&mut self, token: AntiCallToken) -> io::Result<()> {
self.flush()?;
self.underlying.terminate_ref(token)
}
}
#[cfg(test)]
mod test {
use super::CountingWriter;
use std::io::Write;
#[test]
fn test_counting_writer() {
let buffer: Vec<u8> = vec![];
let mut counting_writer = CountingWriter::wrap(buffer);
let bytes = (0u8..10u8).collect::<Vec<u8>>();
counting_writer.write_all(&bytes).unwrap();
let (w, len): (Vec<u8>, u64) = counting_writer.finish().unwrap();
assert_eq!(len, 10u64);
assert_eq!(w.len(), 10);
}
}

View File

@@ -1,18 +1,59 @@
#![allow(clippy::len_without_is_empty)]
use std::ops::Deref;
pub use byteorder::LittleEndian as Endianness;
pub mod bitpacker;
mod bitset;
mod composite_file;
mod counting_writer;
mod serialize;
mod vint;
mod writer;
pub use bitset::*;
pub use serialize::{BinarySerializable, DeserializeFrom, FixedSize};
pub use vint::{read_u32_vint, read_u32_vint_no_advance, serialize_vint_u32, write_u32_vint, VInt};
pub use writer::{AntiCallToken, CountingWriter, TerminatingWrite};
pub use self::bitset::BitSet;
pub(crate) use self::bitset::TinySet;
pub(crate) use self::composite_file::{CompositeFile, CompositeWrite};
pub use self::counting_writer::CountingWriter;
pub use self::serialize::{BinarySerializable, FixedSize};
pub use self::vint::{read_u32_vint, serialize_vint_u32, write_u32_vint, VInt};
pub use byteorder::LittleEndian as Endianness;
/// Segment's max doc must be `< MAX_DOC_LIMIT`.
///
/// We do not allow segments with more than
pub const MAX_DOC_LIMIT: u32 = 1 << 31;
/// Computes the number of bits that will be used for bitpacking.
///
/// In general the target is the minimum number of bits
/// required to express the amplitude given in argument.
///
/// e.g. If the amplitude is 10, we can store all ints on simply 4bits.
///
/// The logic is slightly more convoluted here as for optimization
/// reasons, we want to ensure that a value spawns over at most 8 bytes
/// of aligns bytes.
///
/// Spanning over 9 bytes is possible for instance, if we do
/// bitpacking with an amplitude of 63 bits.
/// In this case, the second int will start on bit
/// 63 (which belongs to byte 7) and ends at byte 15;
/// Hence 9 bytes (from byte 7 to byte 15 included).
///
/// To avoid this, we force the number of bits to 64bits
/// when the result is greater than `64-8 = 56 bits`.
///
/// Note that this only affects rare use cases spawning over
/// a very large range of values. Even in this case, it results
/// in an extra cost of at most 12% compared to the optimal
/// number of bits.
pub(crate) fn compute_num_bits(n: u64) -> u8 {
let amplitude = (64u32 - n.leading_zeros()) as u8;
if amplitude <= 64 - 8 {
amplitude
} else {
64
}
}
pub(crate) fn is_power_of_2(n: usize) -> bool {
(n > 0) && (n & (n - 1) == 0)
}
/// Has length trait
pub trait HasLen {
@@ -25,12 +66,6 @@ pub trait HasLen {
}
}
impl<T: Deref<Target = [u8]>> HasLen for T {
fn len(&self) -> usize {
self.deref().len()
}
}
const HIGHEST_BIT: u64 = 1 << 63;
/// Maps a `i64` to `u64`
@@ -53,13 +88,13 @@ const HIGHEST_BIT: u64 = 1 << 63;
///
/// # See also
/// The [reverse mapping is `u64_to_i64`](./fn.u64_to_i64.html).
#[inline]
#[inline(always)]
pub fn i64_to_u64(val: i64) -> u64 {
(val as u64) ^ HIGHEST_BIT
}
/// Reverse the mapping given by [`i64_to_u64`](./fn.i64_to_u64.html).
#[inline]
#[inline(always)]
pub fn u64_to_i64(val: u64) -> i64 {
(val ^ HIGHEST_BIT) as i64
}
@@ -69,19 +104,14 @@ pub fn u64_to_i64(val: u64) -> i64 {
/// For simplicity, tantivy internally handles `f64` as `u64`.
/// The mapping is defined by this function.
///
/// Maps `f64` to `u64` in a monotonic manner, so that bytes lexical order is preserved.
/// Maps `f64` to `u64` so that lexical order is preserved.
///
/// This is more suited than simply casting (`val as u64`)
/// which would truncate the result
///
/// # Reference
///
/// Daniel Lemire's [blog post](https://lemire.me/blog/2020/12/14/converting-floating-point-numbers-to-integers-while-preserving-order/)
/// explains the mapping in a clear manner.
///
/// # See also
/// The [reverse mapping is `u64_to_f64`](./fn.u64_to_f64.html).
#[inline]
#[inline(always)]
pub fn f64_to_u64(val: f64) -> u64 {
let bits = val.to_bits();
if val.is_sign_positive() {
@@ -92,7 +122,7 @@ pub fn f64_to_u64(val: f64) -> u64 {
}
/// Reverse the mapping given by [`i64_to_u64`](./fn.i64_to_u64.html).
#[inline]
#[inline(always)]
pub fn u64_to_f64(val: u64) -> f64 {
f64::from_bits(if val & HIGHEST_BIT != 0 {
val ^ HIGHEST_BIT
@@ -102,14 +132,12 @@ pub fn u64_to_f64(val: u64) -> f64 {
}
#[cfg(test)]
pub mod test {
pub(crate) mod test {
pub use super::serialize::test::fixed_size_test;
use super::{compute_num_bits, f64_to_u64, i64_to_u64, u64_to_f64, u64_to_i64};
use std::f64;
use proptest::prelude::*;
use super::{f64_to_u64, i64_to_u64, u64_to_f64, u64_to_i64, BinarySerializable, FixedSize};
fn test_i64_converter_helper(val: i64) {
assert_eq!(u64_to_i64(i64_to_u64(val)), val);
}
@@ -118,21 +146,6 @@ pub mod test {
assert_eq!(u64_to_f64(f64_to_u64(val)), val);
}
pub fn fixed_size_test<O: BinarySerializable + FixedSize + Default>() {
let mut buffer = Vec::new();
O::default().serialize(&mut buffer).unwrap();
assert_eq!(buffer.len(), O::SIZE_IN_BYTES);
}
proptest! {
#[test]
fn test_f64_converter_monotonicity_proptest((left, right) in (proptest::num::f64::NORMAL, proptest::num::f64::NORMAL)) {
let left_u64 = f64_to_u64(left);
let right_u64 = f64_to_u64(right);
assert_eq!(left_u64 < right_u64, left < right);
}
}
#[test]
fn test_i64_converter() {
assert_eq!(i64_to_u64(i64::min_value()), u64::min_value());
@@ -158,13 +171,32 @@ pub mod test {
#[test]
fn test_f64_order() {
assert!(!(f64_to_u64(f64::NEG_INFINITY)..f64_to_u64(f64::INFINITY))
.contains(&f64_to_u64(f64::NAN))); // nan is not a number
assert!(f64_to_u64(1.5) > f64_to_u64(1.0)); // same exponent, different mantissa
assert!(f64_to_u64(2.0) > f64_to_u64(1.0)); // same mantissa, different exponent
assert!(f64_to_u64(2.0) > f64_to_u64(1.5)); // different exponent and mantissa
.contains(&f64_to_u64(f64::NAN))); //nan is not a number
assert!(f64_to_u64(1.5) > f64_to_u64(1.0)); //same exponent, different mantissa
assert!(f64_to_u64(2.0) > f64_to_u64(1.0)); //same mantissa, different exponent
assert!(f64_to_u64(2.0) > f64_to_u64(1.5)); //different exponent and mantissa
assert!(f64_to_u64(1.0) > f64_to_u64(-1.0)); // pos > neg
assert!(f64_to_u64(-1.5) < f64_to_u64(-1.0));
assert!(f64_to_u64(-2.0) < f64_to_u64(1.0));
assert!(f64_to_u64(-2.0) < f64_to_u64(-1.5));
}
#[test]
fn test_compute_num_bits() {
assert_eq!(compute_num_bits(1), 1u8);
assert_eq!(compute_num_bits(0), 0u8);
assert_eq!(compute_num_bits(2), 2u8);
assert_eq!(compute_num_bits(3), 2u8);
assert_eq!(compute_num_bits(4), 3u8);
assert_eq!(compute_num_bits(255), 8u8);
assert_eq!(compute_num_bits(256), 9u8);
assert_eq!(compute_num_bits(5_000_000_000), 33u8);
}
#[test]
fn test_max_doc() {
// this is the first time I write a unit test for a constant.
assert!(((super::MAX_DOC_LIMIT - 1) as i32) >= 0);
assert!((super::MAX_DOC_LIMIT as i32) < 0);
}
}

View File

@@ -1,9 +1,10 @@
use std::io::{Read, Write};
use std::{fmt, io};
use crate::common::Endianness;
use crate::common::VInt;
use byteorder::{ReadBytesExt, WriteBytesExt};
use crate::{Endianness, VInt};
use std::fmt;
use std::io;
use std::io::Read;
use std::io::Write;
/// Trait for a simple binary serialization.
pub trait BinarySerializable: fmt::Debug + Sized {
@@ -13,20 +14,6 @@ pub trait BinarySerializable: fmt::Debug + Sized {
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self>;
}
pub trait DeserializeFrom<T: BinarySerializable> {
fn deserialize(&mut self) -> io::Result<T>;
}
/// Implement deserialize from &[u8] for all types which implement BinarySerializable.
///
/// TryFrom would actually be preferrable, but not possible because of the orphan
/// rules (not completely sure if this could be resolved)
impl<T: BinarySerializable> DeserializeFrom<T> for &[u8] {
fn deserialize(&mut self) -> io::Result<T> {
T::deserialize(self)
}
}
/// `FixedSize` marks a `BinarySerializable` as
/// always serializing to the same size.
pub trait FixedSize: BinarySerializable {
@@ -74,11 +61,6 @@ impl<Left: BinarySerializable, Right: BinarySerializable> BinarySerializable for
Ok((Left::deserialize(reader)?, Right::deserialize(reader)?))
}
}
impl<Left: BinarySerializable + FixedSize, Right: BinarySerializable + FixedSize> FixedSize
for (Left, Right)
{
const SIZE_IN_BYTES: usize = Left::SIZE_IN_BYTES + Right::SIZE_IN_BYTES;
}
impl BinarySerializable for u32 {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
@@ -107,19 +89,6 @@ impl FixedSize for u64 {
const SIZE_IN_BYTES: usize = 8;
}
impl BinarySerializable for f32 {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
writer.write_f32::<Endianness>(*self)
}
fn deserialize<R: Read>(reader: &mut R) -> io::Result<Self> {
reader.read_f32::<Endianness>()
}
}
impl FixedSize for f32 {
const SIZE_IN_BYTES: usize = 4;
}
impl BinarySerializable for i64 {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
writer.write_i64::<Endianness>(*self)
@@ -159,28 +128,6 @@ impl FixedSize for u8 {
const SIZE_IN_BYTES: usize = 1;
}
impl BinarySerializable for bool {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
let val = if *self { 1 } else { 0 };
writer.write_u8(val)
}
fn deserialize<R: Read>(reader: &mut R) -> io::Result<bool> {
let val = reader.read_u8()?;
match val {
0 => Ok(false),
1 => Ok(true),
_ => Err(io::Error::new(
io::ErrorKind::InvalidData,
"invalid bool value on deserialization, data corrupted",
)),
}
}
}
impl FixedSize for bool {
const SIZE_IN_BYTES: usize = 1;
}
impl BinarySerializable for String {
fn serialize<W: Write>(&self, writer: &mut W) -> io::Result<()> {
let data: &[u8] = self.as_bytes();
@@ -201,8 +148,9 @@ impl BinarySerializable for String {
#[cfg(test)]
pub mod test {
use super::{VInt, *};
use crate::serialize::BinarySerializable;
use super::*;
use crate::common::VInt;
pub fn fixed_size_test<O: BinarySerializable + FixedSize + Default>() {
let mut buffer = Vec::new();
O::default().serialize(&mut buffer).unwrap();

View File

@@ -1,17 +1,16 @@
use std::io;
use std::io::{Read, Write};
use byteorder::{ByteOrder, LittleEndian};
use super::BinarySerializable;
use byteorder::{ByteOrder, LittleEndian};
use std::io;
use std::io::Read;
use std::io::Write;
/// Wrapper over a `u64` that serializes as a variable int.
#[derive(Clone, Copy, Debug, Eq, PartialEq)]
#[derive(Debug, Eq, PartialEq)]
pub struct VInt(pub u64);
const STOP_BIT: u8 = 128;
pub fn serialize_vint_u32(val: u32, buf: &mut [u8; 8]) -> &[u8] {
pub fn serialize_vint_u32(val: u32) -> (u64, usize) {
const START_2: u64 = 1 << 7;
const START_3: u64 = 1 << 14;
const START_4: u64 = 1 << 21;
@@ -30,7 +29,7 @@ pub fn serialize_vint_u32(val: u32, buf: &mut [u8; 8]) -> &[u8] {
let val = u64::from(val);
const STOP_BIT: u64 = 128u64;
let (res, num_bytes) = match val {
match val {
0..=STOP_1 => (val | STOP_BIT, 1),
START_2..=STOP_2 => (
(val & MASK_1) | ((val & MASK_2) << 1) | (STOP_BIT << (8)),
@@ -57,9 +56,7 @@ pub fn serialize_vint_u32(val: u32, buf: &mut [u8; 8]) -> &[u8] {
| (STOP_BIT << (8 * 4)),
5,
),
};
LittleEndian::write_u64(&mut buf[..], res);
&buf[0..num_bytes]
}
}
/// Returns the number of bytes covered by a
@@ -88,26 +85,23 @@ fn vint_len(data: &[u8]) -> usize {
/// If the buffer does not start by a valid
/// vint payload
pub fn read_u32_vint(data: &mut &[u8]) -> u32 {
let (result, vlen) = read_u32_vint_no_advance(*data);
*data = &data[vlen..];
result
}
pub fn read_u32_vint_no_advance(data: &[u8]) -> (u32, usize) {
let vlen = vint_len(data);
let vlen = vint_len(*data);
let mut result = 0u32;
let mut shift = 0u64;
for &b in &data[..vlen] {
result |= u32::from(b & 127u8) << shift;
shift += 7;
}
(result, vlen)
*data = &data[vlen..];
result
}
/// Write a `u32` as a vint payload.
pub fn write_u32_vint<W: io::Write>(val: u32, writer: &mut W) -> io::Result<()> {
let mut buf = [0u8; 8];
let data = serialize_vint_u32(val, &mut buf);
writer.write_all(data)
let (val, num_bytes) = serialize_vint_u32(val);
let mut buffer = [0u8; 8];
LittleEndian::write_u64(&mut buffer, val);
writer.write_all(&buffer[..num_bytes])
}
impl VInt {
@@ -175,13 +169,16 @@ impl BinarySerializable for VInt {
#[cfg(test)]
mod tests {
use super::{serialize_vint_u32, BinarySerializable, VInt};
use super::serialize_vint_u32;
use super::VInt;
use crate::common::BinarySerializable;
use byteorder::{ByteOrder, LittleEndian};
fn aux_test_vint(val: u64) {
let mut v = [14u8; 10];
let num_bytes = VInt(val).serialize_into(&mut v);
for el in &v[num_bytes..10] {
assert_eq!(el, &14u8);
for i in num_bytes..10 {
assert_eq!(v[i], 14u8);
}
assert!(num_bytes > 0);
if num_bytes < 10 {
@@ -211,10 +208,12 @@ mod tests {
fn aux_test_serialize_vint_u32(val: u32) {
let mut buffer = [0u8; 10];
let mut buffer2 = [0u8; 8];
let mut buffer2 = [0u8; 10];
let len_vint = VInt(val as u64).serialize_into(&mut buffer);
let res2 = serialize_vint_u32(val, &mut buffer2);
assert_eq!(&buffer[..len_vint], res2, "array wrong for {}", val);
let (vint, len) = serialize_vint_u32(val);
assert_eq!(len, len_vint, "len wrong for val {}", val);
LittleEndian::write_u64(&mut buffer2, vint);
assert_eq!(&buffer[..len], &buffer2[..len], "array wrong for {}", val);
}
#[test]

View File

@@ -1,3 +1,4 @@
use crate::Result;
use crossbeam::channel;
use rayon::{ThreadPool, ThreadPoolBuilder};
@@ -9,9 +10,7 @@ use rayon::{ThreadPool, ThreadPoolBuilder};
/// API of a dependency, knowing it might conflict with a different version
/// used by the client. Second, we may stop using rayon in the future.
pub enum Executor {
/// Single thread variant of an Executor
SingleThread,
/// Thread pool variant of an Executor
ThreadPool(ThreadPool),
}
@@ -21,8 +20,8 @@ impl Executor {
Executor::SingleThread
}
/// Creates an Executor that dispatches the tasks in a thread pool.
pub fn multi_thread(num_threads: usize, prefix: &'static str) -> crate::Result<Executor> {
// Creates an Executor that dispatches the tasks in a thread pool.
pub fn multi_thread(num_threads: usize, prefix: &'static str) -> Result<Executor> {
let pool = ThreadPoolBuilder::new()
.num_threads(num_threads)
.thread_name(move |num| format!("{}{}", prefix, num))
@@ -30,22 +29,22 @@ impl Executor {
Ok(Executor::ThreadPool(pool))
}
/// Perform a map in the thread pool.
///
/// Regardless of the executor (`SingleThread` or `ThreadPool`), panics in the task
/// will propagate to the caller.
// Perform a map in the thread pool.
//
// Regardless of the executor (`SingleThread` or `ThreadPool`), panics in the task
// will propagate to the caller.
pub fn map<
A: Send,
R: Send,
AIterator: Iterator<Item = A>,
F: Sized + Sync + Fn(A) -> crate::Result<R>,
F: Sized + Sync + Fn(A) -> Result<R>,
>(
&self,
f: F,
args: AIterator,
) -> crate::Result<Vec<R>> {
) -> Result<Vec<R>> {
match self {
Executor::SingleThread => args.map(f).collect::<crate::Result<_>>(),
Executor::SingleThread => args.map(f).collect::<Result<_>>(),
Executor::ThreadPool(pool) => {
let args_with_indices: Vec<(usize, A)> = args.enumerate().collect();
let num_fruits = args_with_indices.len();
@@ -57,11 +56,7 @@ impl Executor {
let (idx, arg) = arg_with_idx;
let fruit = f(arg);
if let Err(err) = fruit_sender.send((idx, fruit)) {
error!(
"Failed to send search task. It probably means all search \
threads have panicked. {:?}",
err
);
error!("Failed to send search task. It probably means all search threads have panicked. {:?}", err);
}
});
}

View File

@@ -1,206 +1,63 @@
use super::segment::create_segment;
use super::segment::Segment;
use crate::core::Executor;
use crate::core::IndexMeta;
use crate::core::SegmentId;
use crate::core::SegmentMeta;
use crate::core::SegmentMetaInventory;
use crate::core::META_FILEPATH;
use crate::directory::ManagedDirectory;
#[cfg(feature = "mmap")]
use crate::directory::MmapDirectory;
use crate::directory::INDEX_WRITER_LOCK;
use crate::directory::{Directory, RAMDirectory};
use crate::error::DataCorruption;
use crate::error::TantivyError;
use crate::indexer::index_writer::HEAP_SIZE_MIN;
use crate::indexer::segment_updater::save_new_metas;
use crate::reader::IndexReader;
use crate::reader::IndexReaderBuilder;
use crate::schema::Field;
use crate::schema::FieldType;
use crate::schema::Schema;
use crate::tokenizer::BoxedTokenizer;
use crate::tokenizer::TokenizerManager;
use crate::IndexWriter;
use crate::Result;
use num_cpus;
use std::borrow::BorrowMut;
use std::collections::HashSet;
use std::fmt;
#[cfg(feature = "mmap")]
use std::path::Path;
use std::path::PathBuf;
use std::path::{Path, PathBuf};
use std::sync::Arc;
use super::segment::Segment;
use super::IndexSettings;
use crate::core::{
Executor, IndexMeta, SegmentId, SegmentMeta, SegmentMetaInventory, META_FILEPATH,
};
use crate::directory::error::OpenReadError;
#[cfg(feature = "mmap")]
use crate::directory::MmapDirectory;
use crate::directory::{Directory, ManagedDirectory, RamDirectory, INDEX_WRITER_LOCK};
use crate::error::{DataCorruption, TantivyError};
use crate::indexer::index_writer::{MAX_NUM_THREAD, MEMORY_ARENA_NUM_BYTES_MIN};
use crate::indexer::segment_updater::save_new_metas;
use crate::reader::{IndexReader, IndexReaderBuilder};
use crate::schema::{Field, FieldType, Schema};
use crate::tokenizer::{TextAnalyzer, TokenizerManager};
use crate::IndexWriter;
fn load_metas(
directory: &dyn Directory,
inventory: &SegmentMetaInventory,
) -> crate::Result<IndexMeta> {
fn load_metas(directory: &dyn Directory, inventory: &SegmentMetaInventory) -> Result<IndexMeta> {
let meta_data = directory.atomic_read(&META_FILEPATH)?;
let meta_string = String::from_utf8(meta_data).map_err(|_utf8_err| {
error!("Meta data is not valid utf8.");
DataCorruption::new(
META_FILEPATH.to_path_buf(),
"Meta file does not contain valid utf8 file.".to_string(),
)
})?;
IndexMeta::deserialize(&meta_string, inventory)
let meta_string = String::from_utf8_lossy(&meta_data);
IndexMeta::deserialize(&meta_string, &inventory)
.map_err(|e| {
DataCorruption::new(
META_FILEPATH.to_path_buf(),
format!(
"Meta file cannot be deserialized. {:?}. Content: {:?}",
e, meta_string
),
format!("Meta file cannot be deserialized. {:?}.", e),
)
})
.map_err(From::from)
}
/// IndexBuilder can be used to create an index.
///
/// Use in conjunction with `SchemaBuilder`. Global index settings
/// can be configured with `IndexSettings`
///
/// # Examples
///
/// ```
/// use tantivy::schema::*;
/// use tantivy::{Index, IndexSettings, IndexSortByField, Order};
///
/// let mut schema_builder = Schema::builder();
/// let id_field = schema_builder.add_text_field("id", STRING);
/// let title_field = schema_builder.add_text_field("title", TEXT);
/// let body_field = schema_builder.add_text_field("body", TEXT);
/// let number_field = schema_builder.add_u64_field(
/// "number",
/// IntOptions::default().set_fast(Cardinality::SingleValue),
/// );
///
/// let schema = schema_builder.build();
/// let settings = IndexSettings{sort_by_field: Some(IndexSortByField{field:"number".to_string(), order:Order::Asc}), ..Default::default()};
/// let index = Index::builder().schema(schema).settings(settings).create_in_ram();
/// ```
pub struct IndexBuilder {
schema: Option<Schema>,
index_settings: IndexSettings,
}
impl Default for IndexBuilder {
fn default() -> Self {
IndexBuilder::new()
}
}
impl IndexBuilder {
/// Creates a new `IndexBuilder`
pub fn new() -> Self {
Self {
schema: None,
index_settings: IndexSettings::default(),
}
}
/// Set the settings
#[must_use]
pub fn settings(mut self, settings: IndexSettings) -> Self {
self.index_settings = settings;
self
}
/// Set the schema
#[must_use]
pub fn schema(mut self, schema: Schema) -> Self {
self.schema = Some(schema);
self
}
/// Creates a new index using the `RAMDirectory`.
///
/// The index will be allocated in anonymous memory.
/// This should only be used for unit tests.
pub fn create_in_ram(self) -> Result<Index, TantivyError> {
let ram_directory = RamDirectory::create();
Ok(self
.create(ram_directory)
.expect("Creating a RAMDirectory should never fail"))
}
/// Creates a new index in a given filepath.
/// The index will use the `MMapDirectory`.
///
/// If a previous index was in this directory, it returns an `IndexAlreadyExists` error.
#[cfg(feature = "mmap")]
pub fn create_in_dir<P: AsRef<Path>>(self, directory_path: P) -> crate::Result<Index> {
let mmap_directory: Box<dyn Directory> = Box::new(MmapDirectory::open(directory_path)?);
if Index::exists(&*mmap_directory)? {
return Err(TantivyError::IndexAlreadyExists);
}
self.create(mmap_directory)
}
/// Creates a new index in a temp directory.
///
/// The index will use the `MMapDirectory` in a newly created directory.
/// The temp directory will be destroyed automatically when the `Index` object
/// is destroyed.
///
/// The temp directory is only used for testing the `MmapDirectory`.
/// For other unit tests, prefer the `RAMDirectory`, see: `create_in_ram`.
#[cfg(feature = "mmap")]
pub fn create_from_tempdir(self) -> crate::Result<Index> {
let mmap_directory: Box<dyn Directory> = Box::new(MmapDirectory::create_from_tempdir()?);
self.create(mmap_directory)
}
fn get_expect_schema(&self) -> crate::Result<Schema> {
self.schema
.as_ref()
.cloned()
.ok_or(TantivyError::IndexBuilderMissingArgument("schema"))
}
/// Opens or creates a new index in the provided directory
pub fn open_or_create<T: Into<Box<dyn Directory>>>(self, dir: T) -> crate::Result<Index> {
let dir = dir.into();
if !Index::exists(&*dir)? {
return self.create(dir);
}
let index = Index::open(dir)?;
if index.schema() == self.get_expect_schema()? {
Ok(index)
} else {
Err(TantivyError::SchemaError(
"An index exists but the schema does not match.".to_string(),
))
}
}
/// Creates a new index given an implementation of the trait `Directory`.
///
/// If a directory previously existed, it will be erased.
fn create<T: Into<Box<dyn Directory>>>(self, dir: T) -> crate::Result<Index> {
let dir = dir.into();
let directory = ManagedDirectory::wrap(dir)?;
save_new_metas(
self.get_expect_schema()?,
self.index_settings.clone(),
&directory,
)?;
let mut metas = IndexMeta::with_schema(self.get_expect_schema()?);
metas.index_settings = self.index_settings;
let index = Index::open_from_metas(directory, &metas, SegmentMetaInventory::default());
Ok(index)
}
}
/// Search Index
#[derive(Clone)]
pub struct Index {
directory: ManagedDirectory,
schema: Schema,
settings: IndexSettings,
executor: Arc<Executor>,
tokenizers: TokenizerManager,
inventory: SegmentMetaInventory,
}
impl Index {
/// Creates a new builder.
pub fn builder() -> IndexBuilder {
IndexBuilder::new()
}
/// Examines the directory to see if it contains an index.
///
/// Effectively, it only checks for the presence of the `meta.json` file.
pub fn exists(dir: &dyn Directory) -> Result<bool, OpenReadError> {
/// Examines the director to see if it contains an index
pub fn exists<Dir: Directory>(dir: &Dir) -> bool {
dir.exists(&META_FILEPATH)
}
@@ -216,48 +73,53 @@ impl Index {
/// Replace the default single thread search executor pool
/// by a thread pool with a given number of threads.
pub fn set_multithread_executor(&mut self, num_threads: usize) -> crate::Result<()> {
self.executor = Arc::new(Executor::multi_thread(num_threads, "tantivy-search-")?);
pub fn set_multithread_executor(&mut self, num_threads: usize) -> Result<()> {
self.executor = Arc::new(Executor::multi_thread(num_threads, "thrd-tantivy-search-")?);
Ok(())
}
/// Replace the default single thread search executor pool
/// by a thread pool with a given number of threads.
pub fn set_default_multithread_executor(&mut self) -> crate::Result<()> {
pub fn set_default_multithread_executor(&mut self) -> Result<()> {
let default_num_threads = num_cpus::get();
self.set_multithread_executor(default_num_threads)
}
/// Creates a new index using the `RamDirectory`.
/// Creates a new index using the `RAMDirectory`.
///
/// The index will be allocated in anonymous memory.
/// This is useful for indexing small set of documents
/// for instances like unit test or temporary in memory index.
/// This should only be used for unit tests.
pub fn create_in_ram(schema: Schema) -> Index {
IndexBuilder::new().schema(schema).create_in_ram().unwrap()
let ram_directory = RAMDirectory::create();
Index::create(ram_directory, schema).expect("Creating a RAMDirectory should never fail")
}
/// Creates a new index in a given filepath.
/// The index will use the `MMapDirectory`.
///
/// If a previous index was in this directory, then it returns an `IndexAlreadyExists` error.
/// If a previous index was in this directory, then its meta file will be destroyed.
#[cfg(feature = "mmap")]
pub fn create_in_dir<P: AsRef<Path>>(
directory_path: P,
schema: Schema,
) -> crate::Result<Index> {
IndexBuilder::new()
.schema(schema)
.create_in_dir(directory_path)
pub fn create_in_dir<P: AsRef<Path>>(directory_path: P, schema: Schema) -> Result<Index> {
let mmap_directory = MmapDirectory::open(directory_path)?;
if Index::exists(&mmap_directory) {
return Err(TantivyError::IndexAlreadyExists);
}
Index::create(mmap_directory, schema)
}
/// Opens or creates a new index in the provided directory
pub fn open_or_create<T: Into<Box<dyn Directory>>>(
dir: T,
schema: Schema,
) -> crate::Result<Index> {
let dir = dir.into();
IndexBuilder::new().schema(schema).open_or_create(dir)
pub fn open_or_create<Dir: Directory>(dir: Dir, schema: Schema) -> Result<Index> {
if !Index::exists(&dir) {
return Index::create(dir, schema);
}
let index = Index::open(dir)?;
if index.schema() == schema {
Ok(index)
} else {
Err(TantivyError::SchemaError(
"An index exists but the schema does not match.".to_string(),
))
}
}
/// Creates a new index in a temp directory.
@@ -267,41 +129,42 @@ impl Index {
/// is destroyed.
///
/// The temp directory is only used for testing the `MmapDirectory`.
/// For other unit tests, prefer the `RamDirectory`, see: `create_in_ram`.
/// For other unit tests, prefer the `RAMDirectory`, see: `create_in_ram`.
#[cfg(feature = "mmap")]
pub fn create_from_tempdir(schema: Schema) -> crate::Result<Index> {
IndexBuilder::new().schema(schema).create_from_tempdir()
pub fn create_from_tempdir(schema: Schema) -> Result<Index> {
let mmap_directory = MmapDirectory::create_from_tempdir()?;
Index::create(mmap_directory, schema)
}
/// Creates a new index given an implementation of the trait `Directory`.
/// Creates a new index given an implementation of the trait `Directory`
pub fn create<Dir: Directory>(dir: Dir, schema: Schema) -> Result<Index> {
let directory = ManagedDirectory::wrap(dir)?;
Index::from_directory(directory, schema)
}
/// Create a new index from a directory.
///
/// If a directory previously existed, it will be erased.
pub fn create<T: Into<Box<dyn Directory>>>(
dir: T,
schema: Schema,
settings: IndexSettings,
) -> crate::Result<Index> {
let dir: Box<dyn Directory> = dir.into();
let mut builder = IndexBuilder::new().schema(schema);
builder = builder.settings(settings);
builder.create(dir)
/// This will overwrite existing meta.json
fn from_directory(mut directory: ManagedDirectory, schema: Schema) -> Result<Index> {
save_new_metas(schema.clone(), directory.borrow_mut())?;
let metas = IndexMeta::with_schema(schema);
Index::create_from_metas(directory, &metas, SegmentMetaInventory::default())
}
/// Creates a new index given a directory and an `IndexMeta`.
fn open_from_metas(
fn create_from_metas(
directory: ManagedDirectory,
metas: &IndexMeta,
inventory: SegmentMetaInventory,
) -> Index {
) -> Result<Index> {
let schema = metas.schema.clone();
Index {
settings: metas.index_settings.clone(),
Ok(Index {
directory,
schema,
tokenizers: TokenizerManager::default(),
executor: Arc::new(Executor::single_thread()),
inventory,
}
})
}
/// Accessor for the tokenizer manager.
@@ -310,11 +173,11 @@ impl Index {
}
/// Helper to access the tokenizer associated to a specific field.
pub fn tokenizer_for_field(&self, field: Field) -> crate::Result<TextAnalyzer> {
pub fn tokenizer_for_field(&self, field: Field) -> Result<BoxedTokenizer> {
let field_entry = self.schema.get_field_entry(field);
let field_type = field_entry.field_type();
let tokenizer_manager: &TokenizerManager = self.tokenizers();
let tokenizer_name_opt: Option<TextAnalyzer> = match field_type {
let tokenizer_name_opt: Option<BoxedTokenizer> = match field_type {
FieldType::Str(text_options) => text_options
.get_indexing_options()
.map(|text_indexing_options| text_indexing_options.tokenizer().to_string())
@@ -333,7 +196,7 @@ impl Index {
/// Create a default `IndexReader` for the given index.
///
/// See [`Index.reader_builder()`](#method.reader_builder).
pub fn reader(&self) -> crate::Result<IndexReader> {
pub fn reader(&self) -> Result<IndexReader> {
self.reader_builder().try_into()
}
@@ -348,7 +211,7 @@ impl Index {
/// Opens a new directory from an index path.
#[cfg(feature = "mmap")]
pub fn open_in_dir<P: AsRef<Path>>(directory_path: P) -> crate::Result<Index> {
pub fn open_in_dir<P: AsRef<Path>>(directory_path: P) -> Result<Index> {
let mmap_directory = MmapDirectory::open(directory_path)?;
Index::open(mmap_directory)
}
@@ -358,7 +221,7 @@ impl Index {
/// Such segments can of course be part of the index,
/// but also they could be segments being currently built or in the middle of a merge
/// operation.
pub(crate) fn list_all_segment_metas(&self) -> Vec<SegmentMeta> {
pub fn list_all_segment_metas(&self) -> Vec<SegmentMeta> {
self.inventory.all()
}
@@ -372,17 +235,15 @@ impl Index {
}
/// Open the index using the provided directory
pub fn open<T: Into<Box<dyn Directory>>>(directory: T) -> crate::Result<Index> {
let directory = directory.into();
pub fn open<D: Directory>(directory: D) -> Result<Index> {
let directory = ManagedDirectory::wrap(directory)?;
let inventory = SegmentMetaInventory::default();
let metas = load_metas(&directory, &inventory)?;
let index = Index::open_from_metas(directory, &metas, inventory);
Ok(index)
Index::create_from_metas(directory, &metas, inventory)
}
/// Reads the index meta file from the directory.
pub fn load_metas(&self) -> crate::Result<IndexMeta> {
pub fn load_metas(&self) -> Result<IndexMeta> {
load_metas(self.directory(), &self.inventory)
}
@@ -397,19 +258,20 @@ impl Index {
/// - `num_threads` defines the number of indexing workers that
/// should work at the same time.
///
/// - `overall_memory_arena_in_bytes` sets the amount of memory
/// - `overall_heap_size_in_bytes` sets the amount of memory
/// allocated for all indexing thread.
/// Each thread will receive a budget of `overall_memory_arena_in_bytes / num_threads`.
/// Each thread will receive a budget of `overall_heap_size_in_bytes / num_threads`.
///
/// # Errors
/// If the lockfile already exists, returns `Error::DirectoryLockBusy` or an `Error::IoError`.
/// If the memory arena per thread is too small or too big, returns
/// `TantivyError::InvalidArgument`
/// If the lockfile already exists, returns `Error::DirectoryLockBusy` or an `Error::IOError`.
///
/// # Panics
/// If the heap size per thread is too small, panics.
pub fn writer_with_num_threads(
&self,
num_threads: usize,
overall_memory_arena_in_bytes: usize,
) -> crate::Result<IndexWriter> {
overall_heap_size_in_bytes: usize,
) -> Result<IndexWriter> {
let directory_lock = self
.directory
.acquire_lock(&INDEX_WRITER_LOCK)
@@ -417,59 +279,40 @@ impl Index {
TantivyError::LockFailure(
err,
Some(
"Failed to acquire index lock. If you are using a regular directory, this \
means there is already an `IndexWriter` working on this `Directory`, in \
this process or in a different process."
"Failed to acquire index lock. If you are using\
a regular directory, this means there is already an \
`IndexWriter` working on this `Directory`, in this process \
or in a different process."
.to_string(),
),
)
})?;
let memory_arena_in_bytes_per_thread = overall_memory_arena_in_bytes / num_threads;
let heap_size_in_bytes_per_thread = overall_heap_size_in_bytes / num_threads;
IndexWriter::new(
self,
num_threads,
memory_arena_in_bytes_per_thread,
heap_size_in_bytes_per_thread,
directory_lock,
)
}
/// Helper to create an index writer for tests.
///
/// That index writer only simply has a single thread and a memory arena of 10 MB.
/// Using a single thread gives us a deterministic allocation of DocId.
#[cfg(test)]
pub fn writer_for_tests(&self) -> crate::Result<IndexWriter> {
self.writer_with_num_threads(1, 10_000_000)
}
/// Creates a multithreaded writer
///
/// Tantivy will automatically define the number of threads to use, but
/// no more than 8 threads.
/// `overall_memory_arena_in_bytes` is the total target memory usage that will be split
/// Tantivy will automatically define the number of threads to use.
/// `overall_heap_size_in_bytes` is the total target memory usage that will be split
/// between a given number of threads.
///
/// # Errors
/// If the lockfile already exists, returns `Error::FileAlreadyExists`.
/// If the memory arena per thread is too small or too big, returns
/// `TantivyError::InvalidArgument`
pub fn writer(&self, memory_arena_num_bytes: usize) -> crate::Result<IndexWriter> {
let mut num_threads = std::cmp::min(num_cpus::get(), MAX_NUM_THREAD);
let memory_arena_num_bytes_per_thread = memory_arena_num_bytes / num_threads;
if memory_arena_num_bytes_per_thread < MEMORY_ARENA_NUM_BYTES_MIN {
num_threads = (memory_arena_num_bytes / MEMORY_ARENA_NUM_BYTES_MIN).max(1);
/// # Panics
/// If the heap size per thread is too small, panics.
pub fn writer(&self, overall_heap_size_in_bytes: usize) -> Result<IndexWriter> {
let mut num_threads = num_cpus::get();
let heap_size_in_bytes_per_thread = overall_heap_size_in_bytes / num_threads;
if heap_size_in_bytes_per_thread < HEAP_SIZE_MIN {
num_threads = (overall_heap_size_in_bytes / HEAP_SIZE_MIN).max(1);
}
self.writer_with_num_threads(num_threads, memory_arena_num_bytes)
}
/// Accessor to the index settings
pub fn settings(&self) -> &IndexSettings {
&self.settings
}
/// Accessor to the index settings
pub fn settings_mut(&mut self) -> &mut IndexSettings {
&mut self.settings
self.writer_with_num_threads(num_threads, overall_heap_size_in_bytes)
}
/// Accessor to the index schema
@@ -480,7 +323,7 @@ impl Index {
}
/// Returns the list of segments that are searchable
pub fn searchable_segments(&self) -> crate::Result<Vec<Segment>> {
pub fn searchable_segments(&self) -> Result<Vec<Segment>> {
Ok(self
.searchable_segment_metas()?
.into_iter()
@@ -490,12 +333,12 @@ impl Index {
#[doc(hidden)]
pub fn segment(&self, segment_meta: SegmentMeta) -> Segment {
Segment::for_index(self.clone(), segment_meta)
create_segment(self.clone(), segment_meta)
}
/// Creates a new segment.
pub fn new_segment(&self) -> Segment {
let segment_meta = self
let mut segment_meta = self
.inventory
.new_segment_meta(SegmentId::generate_random(), 0);
self.segment(segment_meta)
@@ -513,12 +356,12 @@ impl Index {
/// Reads the meta.json and returns the list of
/// `SegmentMeta` from the last commit.
pub fn searchable_segment_metas(&self) -> crate::Result<Vec<SegmentMeta>> {
pub fn searchable_segment_metas(&self) -> Result<Vec<SegmentMeta>> {
Ok(self.load_metas()?.segments)
}
/// Returns the list of segment ids that are searchable.
pub fn searchable_segment_ids(&self) -> crate::Result<Vec<SegmentId>> {
pub fn searchable_segment_ids(&self) -> Result<Vec<SegmentId>> {
Ok(self
.searchable_segment_metas()?
.iter()
@@ -527,23 +370,8 @@ impl Index {
}
/// Returns the set of corrupted files
pub fn validate_checksum(&self) -> crate::Result<HashSet<PathBuf>> {
let managed_files = self.directory.list_managed_files();
let active_segments_files: HashSet<PathBuf> = self
.searchable_segment_metas()?
.iter()
.flat_map(|segment_meta| segment_meta.list_files())
.collect();
let active_existing_files: HashSet<&PathBuf> =
active_segments_files.intersection(&managed_files).collect();
let mut damaged_files = HashSet::new();
for path in active_existing_files {
if !self.directory.validate_checksum(path)? {
damaged_files.insert((*path).clone());
}
}
Ok(damaged_files)
pub fn validate_checksum(&self) -> Result<HashSet<PathBuf>> {
self.directory.list_damaged().map_err(Into::into)
}
}
@@ -555,9 +383,12 @@ impl fmt::Debug for Index {
#[cfg(test)]
mod tests {
use crate::directory::{RamDirectory, WatchCallback};
use crate::schema::{Field, Schema, INDEXED, TEXT};
use crate::{Directory, Index, IndexReader, IndexSettings, ReloadPolicy};
use crate::directory::RAMDirectory;
use crate::schema::Field;
use crate::schema::{Schema, INDEXED, TEXT};
use crate::IndexReader;
use crate::ReloadPolicy;
use crate::{Directory, Index};
#[test]
fn test_indexer_for_field() {
@@ -575,66 +406,41 @@ mod tests {
#[test]
fn test_index_exists() {
let directory: Box<dyn Directory> = Box::new(RamDirectory::create());
assert!(!Index::exists(directory.as_ref()).unwrap());
assert!(Index::create(
directory.clone(),
throw_away_schema(),
IndexSettings::default()
)
.is_ok());
assert!(Index::exists(directory.as_ref()).unwrap());
let directory = RAMDirectory::create();
assert!(!Index::exists(&directory));
assert!(Index::create(directory.clone(), throw_away_schema()).is_ok());
assert!(Index::exists(&directory));
}
#[test]
fn open_or_create_should_create() {
let directory = RamDirectory::create();
assert!(!Index::exists(&directory).unwrap());
let directory = RAMDirectory::create();
assert!(!Index::exists(&directory));
assert!(Index::open_or_create(directory.clone(), throw_away_schema()).is_ok());
assert!(Index::exists(&directory).unwrap());
assert!(Index::exists(&directory));
}
#[test]
fn open_or_create_should_open() {
let directory: Box<dyn Directory> = Box::new(RamDirectory::create());
assert!(Index::create(
directory.clone(),
throw_away_schema(),
IndexSettings::default()
)
.is_ok());
assert!(Index::exists(directory.as_ref()).unwrap());
let directory = RAMDirectory::create();
assert!(Index::create(directory.clone(), throw_away_schema()).is_ok());
assert!(Index::exists(&directory));
assert!(Index::open_or_create(directory, throw_away_schema()).is_ok());
}
#[test]
fn create_should_wipeoff_existing() {
let directory: Box<dyn Directory> = Box::new(RamDirectory::create());
assert!(Index::create(
directory.clone(),
throw_away_schema(),
IndexSettings::default()
)
.is_ok());
assert!(Index::exists(directory.as_ref()).unwrap());
assert!(Index::create(
directory,
Schema::builder().build(),
IndexSettings::default()
)
.is_ok());
let directory = RAMDirectory::create();
assert!(Index::create(directory.clone(), throw_away_schema()).is_ok());
assert!(Index::exists(&directory));
assert!(Index::create(directory.clone(), Schema::builder().build()).is_ok());
}
#[test]
fn open_or_create_exists_but_schema_does_not_match() {
let directory = RamDirectory::create();
assert!(Index::create(
directory.clone(),
throw_away_schema(),
IndexSettings::default()
)
.is_ok());
assert!(Index::exists(&directory).unwrap());
let directory = RAMDirectory::create();
assert!(Index::create(directory.clone(), throw_away_schema()).is_ok());
assert!(Index::exists(&directory));
assert!(Index::open_or_create(directory.clone(), throw_away_schema()).is_ok());
let err = Index::open_or_create(directory, Schema::builder().build());
assert_eq!(
@@ -650,7 +456,7 @@ mod tests {
}
#[test]
fn test_index_on_commit_reload_policy() -> crate::Result<()> {
fn test_index_on_commit_reload_policy() {
let schema = throw_away_schema();
let field = schema.get_field("num_likes").unwrap();
let index = Index::create_in_ram(schema);
@@ -660,21 +466,19 @@ mod tests {
.try_into()
.unwrap();
assert_eq!(reader.searcher().num_docs(), 0);
test_index_on_commit_reload_policy_aux(field, &index, &reader)
test_index_on_commit_reload_policy_aux(field, &index, &reader);
}
#[cfg(feature = "mmap")]
mod mmap_specific {
use std::path::PathBuf;
use tempfile::TempDir;
use super::*;
use crate::Directory;
use std::path::PathBuf;
use tempfile::TempDir;
#[test]
fn test_index_on_commit_reload_policy_mmap() -> crate::Result<()> {
fn test_index_on_commit_reload_policy_mmap() {
let schema = throw_away_schema();
let field = schema.get_field("num_likes").unwrap();
let tempdir = TempDir::new().unwrap();
@@ -686,36 +490,36 @@ mod tests {
.try_into()
.unwrap();
assert_eq!(reader.searcher().num_docs(), 0);
test_index_on_commit_reload_policy_aux(field, &index, &reader)
test_index_on_commit_reload_policy_aux(field, &index, &reader);
}
#[test]
fn test_index_manual_policy_mmap() -> crate::Result<()> {
fn test_index_manual_policy_mmap() {
let schema = throw_away_schema();
let field = schema.get_field("num_likes").unwrap();
let mut index = Index::create_from_tempdir(schema)?;
let mut writer = index.writer_for_tests()?;
writer.commit()?;
let mut index = Index::create_from_tempdir(schema).unwrap();
let mut writer = index.writer_with_num_threads(1, 3_000_000).unwrap();
writer.commit().unwrap();
let reader = index
.reader_builder()
.reload_policy(ReloadPolicy::Manual)
.try_into()?;
.try_into()
.unwrap();
assert_eq!(reader.searcher().num_docs(), 0);
writer.add_document(doc!(field=>1u64))?;
writer.add_document(doc!(field=>1u64));
let (sender, receiver) = crossbeam::channel::unbounded();
let _handle = index.directory_mut().watch(WatchCallback::new(move || {
let _handle = index.directory_mut().watch(Box::new(move || {
let _ = sender.send(());
}));
writer.commit()?;
writer.commit().unwrap();
assert!(receiver.recv().is_ok());
assert_eq!(reader.searcher().num_docs(), 0);
reader.reload()?;
reader.reload().unwrap();
assert_eq!(reader.searcher().num_docs(), 1);
Ok(())
}
#[test]
fn test_index_on_commit_reload_policy_different_directories() -> crate::Result<()> {
fn test_index_on_commit_reload_policy_different_directories() {
let schema = throw_away_schema();
let field = schema.get_field("num_likes").unwrap();
let tempdir = TempDir::new().unwrap();
@@ -728,73 +532,57 @@ mod tests {
.try_into()
.unwrap();
assert_eq!(reader.searcher().num_docs(), 0);
test_index_on_commit_reload_policy_aux(field, &write_index, &reader)
test_index_on_commit_reload_policy_aux(field, &write_index, &reader);
}
}
fn test_index_on_commit_reload_policy_aux(
field: Field,
index: &Index,
reader: &IndexReader,
) -> crate::Result<()> {
fn test_index_on_commit_reload_policy_aux(field: Field, index: &Index, reader: &IndexReader) {
let mut reader_index = reader.index();
let (sender, receiver) = crossbeam::channel::unbounded();
let _watch_handle = reader_index
.directory_mut()
.watch(WatchCallback::new(move || {
let _ = sender.send(());
}));
let mut writer = index.writer_for_tests()?;
let _watch_handle = reader_index.directory_mut().watch(Box::new(move || {
let _ = sender.send(());
}));
let mut writer = index.writer_with_num_threads(1, 3_000_000).unwrap();
assert_eq!(reader.searcher().num_docs(), 0);
writer.add_document(doc!(field=>1u64))?;
writer.add_document(doc!(field=>1u64));
writer.commit().unwrap();
// We need a loop here because it is possible for notify to send more than
// one modify event. It was observed on CI on MacOS.
loop {
assert!(receiver.recv().is_ok());
if reader.searcher().num_docs() == 1 {
break;
}
}
writer.add_document(doc!(field=>2u64))?;
assert!(receiver.recv().is_ok());
assert_eq!(reader.searcher().num_docs(), 1);
writer.add_document(doc!(field=>2u64));
writer.commit().unwrap();
// ... Same as above
loop {
assert!(receiver.recv().is_ok());
if reader.searcher().num_docs() == 2 {
break;
}
}
Ok(())
assert!(receiver.recv().is_ok());
assert_eq!(reader.searcher().num_docs(), 2);
}
// This test will not pass on windows, because windows
// prevent deleting files that are MMapped.
#[cfg(not(target_os = "windows"))]
#[test]
fn garbage_collect_works_as_intended() -> crate::Result<()> {
let directory = RamDirectory::create();
fn garbage_collect_works_as_intended() {
let directory = RAMDirectory::create();
let schema = throw_away_schema();
let field = schema.get_field("num_likes").unwrap();
let index = Index::create(directory.clone(), schema, IndexSettings::default())?;
let index = Index::create(directory.clone(), schema).unwrap();
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))?;
writer.add_document(doc!(field => i));
}
let (sender, receiver) = crossbeam::channel::unbounded();
let _handle = directory.watch(WatchCallback::new(move || {
let _handle = directory.watch(Box::new(move || {
let _ = sender.send(());
}));
writer.commit()?;
writer.commit().unwrap();
let mem_right_after_commit = directory.total_mem_usage();
assert!(receiver.recv().is_ok());
let reader = index
.reader_builder()
.reload_policy(ReloadPolicy::Manual)
.try_into()?;
.try_into()
.unwrap();
assert_eq!(reader.searcher().num_docs(), 8_000);
writer.wait_merging_threads()?;
writer.wait_merging_threads().unwrap();
let mem_right_after_merge_finished = directory.total_mem_usage();
reader.reload().unwrap();
@@ -806,6 +594,5 @@ mod tests {
mem_right_after_merge_finished,
mem_right_after_commit
);
Ok(())
}
}

View File

@@ -1,16 +1,13 @@
use std::collections::HashSet;
use std::fmt;
use std::path::PathBuf;
use std::sync::atomic::AtomicBool;
use std::sync::Arc;
use serde::{Deserialize, Serialize};
use super::SegmentComponent;
use crate::core::SegmentId;
use crate::schema::Schema;
use crate::store::Compressor;
use crate::{Inventory, Opstamp, TrackedObject};
use crate::Opstamp;
use census::{Inventory, TrackedObject};
use serde;
use serde_json;
use std::collections::HashSet;
use std::fmt;
use std::path::PathBuf;
#[derive(Clone, Debug, Serialize, Deserialize)]
struct DeleteMeta {
@@ -37,8 +34,8 @@ impl SegmentMetaInventory {
let inner = InnerSegmentMeta {
segment_id,
max_doc,
include_temp_doc_store: Arc::new(AtomicBool::new(true)),
deletes: None,
bundled: false,
};
SegmentMeta::from(self.inventory.track(inner))
}
@@ -85,13 +82,17 @@ impl SegmentMeta {
self.tracked.segment_id
}
/// Removes the Component::TempStore from the alive list and
/// therefore marks the temp docstore file to be deleted by
/// the garbage collection.
pub fn untrack_temp_docstore(&self) {
self.tracked
.include_temp_doc_store
.store(false, std::sync::atomic::Ordering::Relaxed);
pub fn with_bundled(self) -> SegmentMeta {
SegmentMeta::from(self.tracked.map(|inner| InnerSegmentMeta {
segment_id: inner.segment_id,
max_doc: inner.max_doc,
deletes: inner.deletes.clone(),
bundled: true,
}))
}
pub fn is_bundled(&self) -> bool {
self.tracked.bundled
}
/// Returns the number of deleted documents.
@@ -105,26 +106,14 @@ impl SegmentMeta {
/// Returns the list of files that
/// are required for the segment meta.
/// Note: Some of the returned files may not exist depending on the state of the segment.
///
/// This is useful as the way tantivy removes files
/// is by removing all files that have been created by tantivy
/// and are not used by any segment anymore.
pub fn list_files(&self) -> HashSet<PathBuf> {
if self
.tracked
.include_temp_doc_store
.load(std::sync::atomic::Ordering::Relaxed)
{
SegmentComponent::iterator()
.map(|component| self.relative_path(*component))
.collect::<HashSet<PathBuf>>()
} else {
SegmentComponent::iterator()
.filter(|comp| *comp != &SegmentComponent::TempStore)
.map(|component| self.relative_path(*component))
.collect::<HashSet<PathBuf>>()
}
SegmentComponent::iterator()
.map(|component| self.relative_path(*component))
.collect::<HashSet<PathBuf>>()
}
/// Returns the relative path of a component of our segment.
@@ -132,17 +121,31 @@ impl SegmentMeta {
/// It just joins the segment id with the extension
/// associated to a segment component.
pub fn relative_path(&self, component: SegmentComponent) -> PathBuf {
let suffix = self.suffix(component);
self.relative_path_from_suffix(&suffix)
}
fn suffix(&self, component: SegmentComponent) -> String {
match component {
SegmentComponent::POSTINGS => ".idx".to_string(),
SegmentComponent::POSITIONS => ".pos".to_string(),
SegmentComponent::POSITIONSSKIP => ".posidx".to_string(),
SegmentComponent::TERMS => ".term".to_string(),
SegmentComponent::STORE => ".store".to_string(),
SegmentComponent::FASTFIELDS => ".fast".to_string(),
SegmentComponent::FIELDNORMS => ".fieldnorm".to_string(),
SegmentComponent::DELETE => format!(".{}.del", self.delete_opstamp().unwrap_or(0)),
}
}
/// Returns the relative path of a component of our segment.
///
/// It just joins the segment id with the extension
/// associated to a segment component.
pub fn relative_path_from_suffix(&self, suffix: &str) -> PathBuf {
let mut path = self.id().uuid_string();
path.push_str(&*match component {
SegmentComponent::Postings => ".idx".to_string(),
SegmentComponent::Positions => ".pos".to_string(),
SegmentComponent::Terms => ".term".to_string(),
SegmentComponent::Store => ".store".to_string(),
SegmentComponent::TempStore => ".store.temp".to_string(),
SegmentComponent::FastFields => ".fast".to_string(),
SegmentComponent::FieldNorms => ".fieldnorm".to_string(),
SegmentComponent::Delete => format!(".{}.del", self.delete_opstamp().unwrap_or(0)),
});
path.push_str(".");
path.push_str(&suffix);
PathBuf::from(path)
}
@@ -186,18 +189,13 @@ impl SegmentMeta {
segment_id: inner_meta.segment_id,
max_doc,
deletes: None,
include_temp_doc_store: Arc::new(AtomicBool::new(true)),
bundled: inner_meta.bundled,
});
SegmentMeta { tracked }
}
#[doc(hidden)]
#[must_use]
pub fn with_delete_meta(self, num_deleted_docs: u32, opstamp: Opstamp) -> SegmentMeta {
assert!(
num_deleted_docs <= self.max_doc(),
"There cannot be more deleted docs than there are docs."
);
let delete_meta = DeleteMeta {
num_deleted_docs,
opstamp,
@@ -205,8 +203,8 @@ impl SegmentMeta {
let tracked = self.tracked.map(move |inner_meta| InnerSegmentMeta {
segment_id: inner_meta.segment_id,
max_doc: inner_meta.max_doc,
include_temp_doc_store: Arc::new(AtomicBool::new(true)),
deletes: Some(delete_meta),
bundled: inner_meta.bundled,
});
SegmentMeta { tracked }
}
@@ -217,14 +215,7 @@ struct InnerSegmentMeta {
segment_id: SegmentId,
max_doc: u32,
deletes: Option<DeleteMeta>,
/// If you want to avoid the SegmentComponent::TempStore file to be covered by
/// garbage collection and deleted, set this to true. This is used during merge.
#[serde(skip)]
#[serde(default = "default_temp_store")]
pub(crate) include_temp_doc_store: Arc<AtomicBool>,
}
fn default_temp_store() -> Arc<AtomicBool> {
Arc::new(AtomicBool::new(false))
bundled: bool,
}
impl InnerSegmentMeta {
@@ -235,51 +226,6 @@ impl InnerSegmentMeta {
}
}
/// Search Index Settings.
///
/// Contains settings which are applied on the whole
/// index, like presort documents.
#[derive(Clone, Default, Serialize, Deserialize, Eq, PartialEq)]
pub struct IndexSettings {
/// Sorts the documents by information
/// provided in `IndexSortByField`
#[serde(skip_serializing_if = "Option::is_none")]
pub sort_by_field: Option<IndexSortByField>,
/// The `Compressor` used to compress the doc store.
#[serde(default)]
pub docstore_compression: Compressor,
}
/// Settings to presort the documents in an index
///
/// Presorting documents can greatly performance
/// in some scenarios, by applying top n
/// optimizations.
#[derive(Clone, Serialize, Deserialize, Eq, PartialEq)]
pub struct IndexSortByField {
/// The field to sort the documents by
pub field: String,
/// The order to sort the documents by
pub order: Order,
}
/// The order to sort by
#[derive(Clone, Serialize, Deserialize, Eq, PartialEq)]
pub enum Order {
/// Ascending Order
Asc,
/// Descending Order
Desc,
}
impl Order {
/// return if the Order is ascending
pub fn is_asc(&self) -> bool {
self == &Order::Asc
}
/// return if the Order is descending
pub fn is_desc(&self) -> bool {
self == &Order::Desc
}
}
/// Meta information about the `Index`.
///
/// This object is serialized on disk in the `meta.json` file.
@@ -287,11 +233,9 @@ impl Order {
/// * the searchable segments,
/// * the index `docstamp`
/// * the schema
///
#[derive(Clone, Serialize)]
pub struct IndexMeta {
/// `IndexSettings` to configure index options.
#[serde(default)]
pub index_settings: IndexSettings,
/// List of `SegmentMeta` informations associated to each finalized segment of the index.
pub segments: Vec<SegmentMeta>,
/// Index `Schema`
@@ -301,7 +245,7 @@ pub struct IndexMeta {
#[serde(skip_serializing_if = "Option::is_none")]
/// Payload associated to the last commit.
///
/// Upon commit, clients can optionally add a small `String` payload to their commit
/// Upon commit, clients can optionally add a small `Striing` payload to their commit
/// to help identify this commit.
/// This payload is entirely unused by tantivy.
pub payload: Option<String>,
@@ -310,8 +254,6 @@ pub struct IndexMeta {
#[derive(Deserialize)]
struct UntrackedIndexMeta {
pub segments: Vec<InnerSegmentMeta>,
#[serde(default)]
pub index_settings: IndexSettings,
pub schema: Schema,
pub opstamp: Opstamp,
#[serde(skip_serializing_if = "Option::is_none")]
@@ -321,7 +263,6 @@ struct UntrackedIndexMeta {
impl UntrackedIndexMeta {
pub fn track(self, inventory: &SegmentMetaInventory) -> IndexMeta {
IndexMeta {
index_settings: self.index_settings,
segments: self
.segments
.into_iter()
@@ -342,7 +283,6 @@ impl IndexMeta {
/// Opstamp will the value `0u64`.
pub fn with_schema(schema: Schema) -> IndexMeta {
IndexMeta {
index_settings: IndexSettings::default(),
segments: vec![],
schema,
opstamp: 0u64,
@@ -375,7 +315,7 @@ mod tests {
use super::IndexMeta;
use crate::schema::{Schema, TEXT};
use crate::{IndexSettings, IndexSortByField, Order};
use serde_json;
#[test]
fn test_serialize_metas() {
@@ -385,13 +325,6 @@ mod tests {
schema_builder.build()
};
let index_metas = IndexMeta {
index_settings: IndexSettings {
sort_by_field: Some(IndexSortByField {
field: "text".to_string(),
order: Order::Asc,
}),
..Default::default()
},
segments: Vec::new(),
schema,
opstamp: 0u64,
@@ -400,7 +333,7 @@ mod tests {
let json = serde_json::ser::to_string(&index_metas).expect("serialization failed");
assert_eq!(
json,
r#"{"index_settings":{"sort_by_field":{"field":"text","order":"Asc"},"docstore_compression":"lz4"},"segments":[],"schema":[{"name":"text","type":"text","options":{"indexing":{"record":"position","fieldnorms":true,"tokenizer":"default"},"stored":false}}],"opstamp":0}"#
r#"{"segments":[],"schema":[{"name":"text","type":"text","options":{"indexing":{"record":"position","tokenizer":"default"},"stored":false}}],"opstamp":0}"#
);
}
}

View File

@@ -1,12 +1,13 @@
use std::io;
use common::BinarySerializable;
use crate::directory::FileSlice;
use crate::common::BinarySerializable;
use crate::directory::ReadOnlySource;
use crate::positions::PositionReader;
use crate::postings::{BlockSegmentPostings, SegmentPostings, TermInfo};
use crate::schema::{IndexRecordOption, Term};
use crate::postings::TermInfo;
use crate::postings::{BlockSegmentPostings, SegmentPostings};
use crate::schema::FieldType;
use crate::schema::IndexRecordOption;
use crate::schema::Term;
use crate::termdict::TermDictionary;
use owned_read::OwnedRead;
/// The inverted index reader is in charge of accessing
/// the inverted index associated to a specific field.
@@ -15,7 +16,7 @@ use crate::termdict::TermDictionary;
///
/// It is safe to delete the segment associated to
/// an `InvertedIndexReader`. As long as it is open,
/// the `FileSlice` it is relying on should
/// the `ReadOnlySource` it is relying on should
/// stay available.
///
///
@@ -23,8 +24,9 @@ use crate::termdict::TermDictionary;
/// the `SegmentReader`'s [`.inverted_index(...)`] method
pub struct InvertedIndexReader {
termdict: TermDictionary,
postings_file_slice: FileSlice,
positions_file_slice: FileSlice,
postings_source: ReadOnlySource,
positions_source: ReadOnlySource,
positions_idx_source: ReadOnlySource,
record_option: IndexRecordOption,
total_num_tokens: u64,
}
@@ -33,35 +35,42 @@ impl InvertedIndexReader {
#[cfg_attr(feature = "cargo-clippy", allow(clippy::needless_pass_by_value))] // for symmetry
pub(crate) fn new(
termdict: TermDictionary,
postings_file_slice: FileSlice,
positions_file_slice: FileSlice,
postings_source: ReadOnlySource,
positions_source: ReadOnlySource,
positions_idx_source: ReadOnlySource,
record_option: IndexRecordOption,
) -> io::Result<InvertedIndexReader> {
let (total_num_tokens_slice, postings_body) = postings_file_slice.split(8);
let total_num_tokens = u64::deserialize(&mut total_num_tokens_slice.read_bytes()?)?;
Ok(InvertedIndexReader {
) -> InvertedIndexReader {
let total_num_tokens_data = postings_source.slice(0, 8);
let mut total_num_tokens_cursor = total_num_tokens_data.as_slice();
let total_num_tokens = u64::deserialize(&mut total_num_tokens_cursor).unwrap_or(0u64);
InvertedIndexReader {
termdict,
postings_file_slice: postings_body,
positions_file_slice,
postings_source: postings_source.slice_from(8),
positions_source,
positions_idx_source,
record_option,
total_num_tokens,
})
}
}
/// Creates an empty `InvertedIndexReader` object, which
/// contains no terms at all.
pub fn empty(record_option: IndexRecordOption) -> InvertedIndexReader {
pub fn empty(field_type: &FieldType) -> InvertedIndexReader {
let record_option = field_type
.get_index_record_option()
.unwrap_or(IndexRecordOption::Basic);
InvertedIndexReader {
termdict: TermDictionary::empty(),
postings_file_slice: FileSlice::empty(),
positions_file_slice: FileSlice::empty(),
termdict: TermDictionary::empty(&field_type),
postings_source: ReadOnlySource::empty(),
positions_source: ReadOnlySource::empty(),
positions_idx_source: ReadOnlySource::empty(),
record_option,
total_num_tokens: 0u64,
}
}
/// Returns the term info associated with the term.
pub fn get_term_info(&self, term: &Term) -> io::Result<Option<TermInfo>> {
pub fn get_term_info(&self, term: &Term) -> Option<TermInfo> {
self.termdict.get(term.value_bytes())
}
@@ -84,12 +93,12 @@ impl InvertedIndexReader {
&self,
term_info: &TermInfo,
block_postings: &mut BlockSegmentPostings,
) -> io::Result<()> {
let postings_slice = self
.postings_file_slice
.slice(term_info.postings_range.clone());
block_postings.reset(term_info.doc_freq, postings_slice.read_bytes()?);
Ok(())
) {
let offset = term_info.postings_offset as usize;
let end_source = self.postings_source.len();
let postings_slice = self.postings_source.slice(offset, end_source);
let postings_reader = OwnedRead::new(postings_slice);
block_postings.reset(term_info.doc_freq, postings_reader);
}
/// Returns a block postings given a `Term`.
@@ -100,10 +109,9 @@ impl InvertedIndexReader {
&self,
term: &Term,
option: IndexRecordOption,
) -> io::Result<Option<BlockSegmentPostings>> {
self.get_term_info(term)?
) -> Option<BlockSegmentPostings> {
self.get_term_info(term)
.map(move |term_info| self.read_block_postings_from_terminfo(&term_info, option))
.transpose()
}
/// Returns a block postings given a `term_info`.
@@ -114,13 +122,12 @@ impl InvertedIndexReader {
&self,
term_info: &TermInfo,
requested_option: IndexRecordOption,
) -> io::Result<BlockSegmentPostings> {
let postings_data = self
.postings_file_slice
.slice(term_info.postings_range.clone());
BlockSegmentPostings::open(
) -> BlockSegmentPostings {
let offset = term_info.postings_offset as usize;
let postings_data = self.postings_source.slice_from(offset);
BlockSegmentPostings::from_data(
term_info.doc_freq,
postings_data,
OwnedRead::new(postings_data),
self.record_option,
requested_option,
)
@@ -134,23 +141,20 @@ impl InvertedIndexReader {
&self,
term_info: &TermInfo,
option: IndexRecordOption,
) -> io::Result<SegmentPostings> {
let block_postings = self.read_block_postings_from_terminfo(term_info, option)?;
let position_reader = {
) -> SegmentPostings {
let block_postings = self.read_block_postings_from_terminfo(term_info, option);
let position_stream = {
if option.has_positions() {
let positions_data = self
.positions_file_slice
.read_bytes_slice(term_info.positions_range.clone())?;
let position_reader = PositionReader::open(positions_data)?;
let position_reader = self.positions_source.clone();
let skip_reader = self.positions_idx_source.clone();
let position_reader =
PositionReader::new(position_reader, skip_reader, term_info.positions_idx);
Some(position_reader)
} else {
None
}
};
Ok(SegmentPostings::from_block_postings(
block_postings,
position_reader,
))
SegmentPostings::from_block_postings(block_postings, position_stream)
}
/// Returns the total number of tokens recorded for all documents
@@ -169,31 +173,24 @@ impl InvertedIndexReader {
/// For instance, requesting `IndexRecordOption::Freq` for a
/// `TextIndexingOptions` that does not index position will return a `SegmentPostings`
/// with `DocId`s and frequencies.
pub fn read_postings(
&self,
term: &Term,
option: IndexRecordOption,
) -> io::Result<Option<SegmentPostings>> {
self.get_term_info(term)?
pub fn read_postings(&self, term: &Term, option: IndexRecordOption) -> Option<SegmentPostings> {
self.get_term_info(term)
.map(move |term_info| self.read_postings_from_terminfo(&term_info, option))
.transpose()
}
pub(crate) fn read_postings_no_deletes(
&self,
term: &Term,
option: IndexRecordOption,
) -> io::Result<Option<SegmentPostings>> {
self.get_term_info(term)?
) -> Option<SegmentPostings> {
self.get_term_info(term)
.map(|term_info| self.read_postings_from_terminfo(&term_info, option))
.transpose()
}
/// Returns the number of documents containing the term.
pub fn doc_freq(&self, term: &Term) -> io::Result<u32> {
Ok(self
.get_term_info(term)?
pub fn doc_freq(&self, term: &Term) -> u32 {
self.get_term_info(term)
.map(|term_info| term_info.doc_freq)
.unwrap_or(0u32))
.unwrap_or(0u32)
}
}

View File

@@ -8,22 +8,20 @@ mod segment_component;
mod segment_id;
mod segment_reader;
use std::path::Path;
use once_cell::sync::Lazy;
pub use self::executor::Executor;
pub use self::index::{Index, IndexBuilder};
pub use self::index_meta::{
IndexMeta, IndexSettings, IndexSortByField, Order, SegmentMeta, SegmentMetaInventory,
};
pub use self::index::Index;
pub use self::index_meta::{IndexMeta, SegmentMeta, SegmentMetaInventory};
pub use self::inverted_index_reader::InvertedIndexReader;
pub use self::searcher::{Searcher, SearcherGeneration};
pub use self::searcher::Searcher;
pub use self::segment::Segment;
pub use self::segment::SerializableSegment;
pub use self::segment_component::SegmentComponent;
pub use self::segment_id::SegmentId;
pub use self::segment_reader::SegmentReader;
use once_cell::sync::Lazy;
use std::path::Path;
/// The meta file contains all the information about the list of segments and the schema
/// of the index.
pub static META_FILEPATH: Lazy<&'static Path> = Lazy::new(|| Path::new("meta.json"));

View File

@@ -1,73 +1,53 @@
use std::collections::BTreeMap;
use std::{fmt, io};
use crate::collector::Collector;
use crate::core::{Executor, SegmentReader};
use crate::collector::SegmentCollector;
use crate::core::Executor;
use crate::core::InvertedIndexReader;
use crate::core::SegmentReader;
use crate::query::Query;
use crate::schema::{Document, Schema, Term};
use crate::query::Scorer;
use crate::query::Weight;
use crate::schema::Document;
use crate::schema::Schema;
use crate::schema::{Field, Term};
use crate::space_usage::SearcherSpaceUsage;
use crate::store::StoreReader;
use crate::{DocAddress, Index, Opstamp, SegmentId, TrackedObject};
use crate::termdict::TermMerger;
use crate::DocAddress;
use crate::Index;
use crate::Result;
use std::fmt;
use std::sync::Arc;
/// Identifies the searcher generation accessed by a [Searcher].
///
/// While this might seem redundant, a [SearcherGeneration] contains
/// both a `generation_id` AND a list of `(SegmentId, DeleteOpstamp)`.
///
/// This is on purpose. This object is used by the `Warmer` API.
/// Having both information makes it possible to identify which
/// artifact should be refreshed or garbage collected.
///
/// Depending on the use case, `Warmer`'s implementers can decide to
/// produce artifacts per:
/// - `generation_id` (e.g. some searcher level aggregates)
/// - `(segment_id, delete_opstamp)` (e.g. segment level aggregates)
/// - `segment_id` (e.g. for immutable document level information)
/// - `(generation_id, segment_id)` (e.g. for consistent dynamic column)
/// - ...
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord, Hash)]
pub struct SearcherGeneration {
segments: BTreeMap<SegmentId, Option<Opstamp>>,
generation_id: u64,
}
impl SearcherGeneration {
pub(crate) fn from_segment_readers(
segment_readers: &[SegmentReader],
generation_id: u64,
) -> Self {
let mut segment_id_to_del_opstamp = BTreeMap::new();
for segment_reader in segment_readers {
segment_id_to_del_opstamp
.insert(segment_reader.segment_id(), segment_reader.delete_opstamp());
}
Self {
segments: segment_id_to_del_opstamp,
generation_id,
}
}
/// Returns the searcher generation id.
pub fn generation_id(&self) -> u64 {
self.generation_id
}
/// Return a `(SegmentId -> DeleteOpstamp)` mapping.
pub fn segments(&self) -> &BTreeMap<SegmentId, Option<Opstamp>> {
&self.segments
fn collect_segment<C: Collector>(
collector: &C,
weight: &dyn Weight,
segment_ord: u32,
segment_reader: &SegmentReader,
) -> Result<C::Fruit> {
let mut scorer = weight.scorer(segment_reader)?;
let mut segment_collector = collector.for_segment(segment_ord as u32, segment_reader)?;
if let Some(delete_bitset) = segment_reader.delete_bitset() {
scorer.for_each(&mut |doc, score| {
if delete_bitset.is_alive(doc) {
segment_collector.collect(doc, score);
}
});
} else {
scorer.for_each(&mut |doc, score| segment_collector.collect(doc, score));
}
Ok(segment_collector.harvest())
}
/// Holds a list of `SegmentReader`s ready for search.
///
/// It guarantees that the `Segment` will not be removed before
/// the destruction of the `Searcher`.
///
pub struct Searcher {
schema: Schema,
index: Index,
segment_readers: Vec<SegmentReader>,
store_readers: Vec<StoreReader>,
generation: TrackedObject<SearcherGeneration>,
}
impl Searcher {
@@ -76,19 +56,17 @@ impl Searcher {
schema: Schema,
index: Index,
segment_readers: Vec<SegmentReader>,
generation: TrackedObject<SearcherGeneration>,
) -> io::Result<Searcher> {
let store_readers: Vec<StoreReader> = segment_readers
) -> Searcher {
let store_readers = segment_readers
.iter()
.map(SegmentReader::get_store_reader)
.collect::<io::Result<Vec<_>>>()?;
Ok(Searcher {
.collect();
Searcher {
schema,
index,
segment_readers,
store_readers,
generation,
})
}
}
/// Returns the `Index` associated to the `Searcher`
@@ -96,18 +74,14 @@ impl Searcher {
&self.index
}
/// [SearcherGeneration] which identifies the version of the snapshot held by this `Searcher`.
pub fn generation(&self) -> &SearcherGeneration {
self.generation.as_ref()
}
/// Fetches a document from tantivy's store given a `DocAddress`.
///
/// The searcher uses the segment ordinal to route the
/// the request to the right `Segment`.
pub fn doc(&self, doc_address: DocAddress) -> crate::Result<Document> {
let store_reader = &self.store_readers[doc_address.segment_ord as usize];
store_reader.get(doc_address.doc_id)
pub fn doc(&self, doc_address: DocAddress) -> Result<Document> {
let DocAddress(segment_local_id, doc_id) = doc_address;
let store_reader = &self.store_readers[segment_local_id as usize];
store_reader.get(doc_id)
}
/// Access the schema associated to the index of this searcher.
@@ -125,14 +99,13 @@ impl Searcher {
/// Return the overall number of documents containing
/// the given term.
pub fn doc_freq(&self, term: &Term) -> crate::Result<u64> {
let mut total_doc_freq = 0;
for segment_reader in &self.segment_readers {
let inverted_index = segment_reader.inverted_index(term.field())?;
let doc_freq = inverted_index.doc_freq(term)?;
total_doc_freq += u64::from(doc_freq);
}
Ok(total_doc_freq)
pub fn doc_freq(&self, term: &Term) -> u64 {
self.segment_readers
.iter()
.map(|segment_reader| {
u64::from(segment_reader.inverted_index(term.field()).doc_freq(term))
})
.sum::<u64>()
}
/// Return the list of segment readers
@@ -140,7 +113,7 @@ impl Searcher {
&self.segment_readers
}
/// Returns the segment_reader associated with the given segment_ord
/// Returns the segment_reader associated with the given segment_ordinal
pub fn segment_reader(&self, segment_ord: u32) -> &SegmentReader {
&self.segment_readers[segment_ord as usize]
}
@@ -159,11 +132,7 @@ impl Searcher {
///
/// Finally, the Collector merges each of the child collectors into itself for result usability
/// by the caller.
pub fn search<C: Collector>(
&self,
query: &dyn Query,
collector: &C,
) -> crate::Result<C::Fruit> {
pub fn search<C: Collector>(&self, query: &dyn Query, collector: &C) -> Result<C::Fruit> {
let executor = self.index.search_executor();
self.search_with_executor(query, collector, executor)
}
@@ -185,26 +154,62 @@ impl Searcher {
query: &dyn Query,
collector: &C,
executor: &Executor,
) -> crate::Result<C::Fruit> {
) -> Result<C::Fruit> {
let scoring_enabled = collector.requires_scoring();
let weight = query.weight(self, scoring_enabled)?;
let segment_readers = self.segment_readers();
let fruits = executor.map(
|(segment_ord, segment_reader)| {
collector.collect_segment(weight.as_ref(), segment_ord as u32, segment_reader)
collect_segment(
collector,
weight.as_ref(),
segment_ord as u32,
segment_reader,
)
},
segment_readers.iter().enumerate(),
)?;
collector.merge_fruits(fruits)
}
/// Return the field searcher associated to a `Field`.
pub fn field(&self, field: Field) -> FieldSearcher {
let inv_index_readers = self
.segment_readers
.iter()
.map(|segment_reader| segment_reader.inverted_index(field))
.collect::<Vec<_>>();
FieldSearcher::new(inv_index_readers)
}
/// Summarize total space usage of this searcher.
pub fn space_usage(&self) -> io::Result<SearcherSpaceUsage> {
pub fn space_usage(&self) -> SearcherSpaceUsage {
let mut space_usage = SearcherSpaceUsage::new();
for segment_reader in &self.segment_readers {
space_usage.add_segment(segment_reader.space_usage()?);
for segment_reader in self.segment_readers.iter() {
space_usage.add_segment(segment_reader.space_usage());
}
Ok(space_usage)
space_usage
}
}
pub struct FieldSearcher {
inv_index_readers: Vec<Arc<InvertedIndexReader>>,
}
impl FieldSearcher {
fn new(inv_index_readers: Vec<Arc<InvertedIndexReader>>) -> FieldSearcher {
FieldSearcher { inv_index_readers }
}
/// Returns a Stream over all of the sorted unique terms of
/// for the given field.
pub fn terms(&self) -> TermMerger<'_> {
let term_streamers: Vec<_> = self
.inv_index_readers
.iter()
.map(|inverted_index| inverted_index.terms().stream())
.collect();
TermMerger::new(term_streamers)
}
}

View File

@@ -1,12 +1,15 @@
use std::fmt;
use std::path::PathBuf;
use super::SegmentComponent;
use crate::core::{Index, SegmentId, SegmentMeta};
use crate::core::Index;
use crate::core::SegmentId;
use crate::core::SegmentMeta;
use crate::directory::error::{OpenReadError, OpenWriteError};
use crate::directory::{Directory, FileSlice, WritePtr};
use crate::directory::Directory;
use crate::directory::{ReadOnlyDirectory, ReadOnlySource, WritePtr};
use crate::indexer::segment_serializer::SegmentSerializer;
use crate::schema::Schema;
use crate::Opstamp;
use std::fmt;
use std::path::PathBuf;
/// A segment is a piece of the index.
#[derive(Clone)]
@@ -21,12 +24,15 @@ impl fmt::Debug for Segment {
}
}
impl Segment {
/// Creates a new segment given an `Index` and a `SegmentId`
pub(crate) fn for_index(index: Index, meta: SegmentMeta) -> Segment {
Segment { index, meta }
}
/// Creates a new segment given an `Index` and a `SegmentId`
///
/// The function is here to make it private outside `tantivy`.
/// #[doc(hidden)]
pub fn create_segment(index: Index, meta: SegmentMeta) -> Segment {
Segment { index, meta }
}
impl Segment {
/// Returns the index the segment belongs to.
pub fn index(&self) -> &Index {
&self.index
@@ -54,7 +60,6 @@ impl Segment {
}
#[doc(hidden)]
#[must_use]
pub fn with_delete_meta(self, num_deleted_docs: u32, opstamp: Opstamp) -> Segment {
Segment {
index: self.index,
@@ -76,15 +81,38 @@ impl Segment {
}
/// Open one of the component file for a *regular* read.
pub fn open_read(&self, component: SegmentComponent) -> Result<FileSlice, OpenReadError> {
pub fn open_read(&self, component: SegmentComponent) -> Result<ReadOnlySource, OpenReadError> {
let path = self.relative_path(component);
self.index.directory().open_read(&path)
let source = self.index.directory().open_read(&path)?;
Ok(source)
}
/// Open one of the component file for *regular* write.
pub fn open_write(&mut self, component: SegmentComponent) -> Result<WritePtr, OpenWriteError> {
let path = self.relative_path(component);
let write = self.index.directory_mut().open_write(&path)?;
Ok(write)
self.index.directory_mut().open_write(&path)
}
pub fn open_bundle_writer(&mut self) -> Result<WritePtr, OpenWriteError> {
let path = self.meta.relative_path_from_suffix("bundle");
self.index.directory_mut().open_write(&path)
}
pub(crate) fn open_write_in_directory(
&mut self,
component: SegmentComponent,
directory: &mut dyn Directory,
) -> Result<WritePtr, OpenWriteError> {
let path = self.relative_path(component);
directory.open_write(&path)
}
}
pub trait SerializableSegment {
/// Writes a view of a segment by pushing information
/// to the `SegmentSerializer`.
///
/// # Returns
/// The number of documents in the segment.
fn write(&self, serializer: SegmentSerializer) -> crate::Result<u32>;
}

View File

@@ -4,42 +4,42 @@ use std::slice;
/// Each component is stored in its own file,
/// using the pattern `segment_uuid`.`component_extension`,
/// except the delete component that takes an `segment_uuid`.`delete_opstamp`.`component_extension`
#[derive(Copy, Clone, Eq, PartialEq)]
#[derive(Copy, Clone)]
pub enum SegmentComponent {
/// Postings (or inverted list). Sorted lists of document ids, associated to terms
Postings,
POSTINGS,
/// Positions of terms in each document.
Positions,
POSITIONS,
/// Index to seek within the position file
POSITIONSSKIP,
/// Column-oriented random-access storage of fields.
FastFields,
FASTFIELDS,
/// Stores the sum of the length (in terms) of each field for each document.
/// Field norms are stored as a special u64 fast field.
FieldNorms,
FIELDNORMS,
/// Dictionary associating `Term`s to `TermInfo`s which is
/// simply an address into the `postings` file and the `positions` file.
Terms,
/// Row-oriented, compressed storage of the documents.
TERMS,
/// Row-oriented, LZ4-compressed storage of the documents.
/// Accessing a document from the store is relatively slow, as it
/// requires to decompress the entire block it belongs to.
Store,
/// Temporary storage of the documents, before streamed to `Store`.
TempStore,
STORE,
/// Bitset describing which document of the segment is deleted.
Delete,
DELETE,
}
impl SegmentComponent {
/// Iterates through the components.
pub fn iterator() -> slice::Iter<'static, SegmentComponent> {
static SEGMENT_COMPONENTS: [SegmentComponent; 8] = [
SegmentComponent::Postings,
SegmentComponent::Positions,
SegmentComponent::FastFields,
SegmentComponent::FieldNorms,
SegmentComponent::Terms,
SegmentComponent::Store,
SegmentComponent::TempStore,
SegmentComponent::Delete,
SegmentComponent::POSTINGS,
SegmentComponent::POSITIONS,
SegmentComponent::POSITIONSSKIP,
SegmentComponent::FASTFIELDS,
SegmentComponent::FIELDNORMS,
SegmentComponent::TERMS,
SegmentComponent::STORE,
SegmentComponent::DELETE,
];
SEGMENT_COMPONENTS.iter()
}

View File

@@ -1,14 +1,13 @@
use std::cmp::{Ord, Ordering};
use std::error::Error;
use std::fmt;
use std::str::FromStr;
#[cfg(test)]
use std::sync::atomic;
use uuid::Uuid;
#[cfg(test)]
use once_cell::sync::Lazy;
use serde::{Deserialize, Serialize};
use uuid::Uuid;
use std::error::Error;
use std::str::FromStr;
#[cfg(test)]
use std::sync::atomic;
/// Uuid identifying a segment.
///
@@ -22,7 +21,7 @@ use uuid::Uuid;
pub struct SegmentId(Uuid);
#[cfg(test)]
static AUTO_INC_COUNTER: Lazy<atomic::AtomicUsize> = Lazy::new(atomic::AtomicUsize::default);
static AUTO_INC_COUNTER: Lazy<atomic::AtomicUsize> = Lazy::new(|| atomic::AtomicUsize::default());
#[cfg(test)]
const ZERO_ARRAY: [u8; 8] = [0u8; 8];
@@ -108,12 +107,6 @@ impl fmt::Debug for SegmentId {
}
}
impl fmt::Display for SegmentId {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
write!(f, "Seg({:?})", self.short_uuid_string())
}
}
impl PartialOrd for SegmentId {
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
Some(self.cmp(other))

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