668: Fix many Clippy errors part 2 r=ManyTheFish a=ehiggs
This brings us a step closer to enforcing clippy on each build.
# Pull Request
## Related issue
This does not fix any issue outright, but it is a second round of fixes for clippy after https://github.com/meilisearch/milli/pull/665. This should contribute to fixing https://github.com/meilisearch/milli/pull/659.
## What does this PR do?
Satisfies many issues for clippy. The complaints are mostly:
* Passing reference where a variable is already a reference.
* Using clone where a struct already implements `Copy`
* Using `ok_or_else` when it is a closure that returns a value instead of using the closure to call function (hence we use `ok_or`)
* Unambiguous lifetimes don't need names, so we can just use `'_`
* Using `return` when it is not needed as we are on the last expression of a function.
## PR checklist
Please check if your PR fulfills the following requirements:
- [x] Does this PR fix an existing issue, or have you listed the changes applied in the PR description (and why they are needed)?
- [x] Have you read the contributing guidelines?
- [x] Have you made sure that the title is accurate and descriptive of the changes?
Thank you so much for contributing to Meilisearch!
Co-authored-by: Ewan Higgs <ewan.higgs@gmail.com>
e.g. add one facet value incrementally with a group_size = X and then
add another one with group_size = Y
It is not actually possible to do so with the public API of milli,
but I wanted to make sure the algorithm worked well in those cases
anyway.
The bugs were found by fuzzing the code with fuzzcheck, which I've added
to milli as a conditional dev-dependency. But it can be removed later.
616: Introduce an indexation abortion function when indexing documents r=Kerollmops a=Kerollmops
Co-authored-by: Kerollmops <clement@meilisearch.com>
Co-authored-by: Clément Renault <clement@meilisearch.com>
Most of these are calling clone when the struct supports Copy.
Many are using & and &mut on `self` when the function they are called
from already has an immutable or mutable borrow so this isn't needed.
I tried to stay away from actual changes or places where I'd have to
name fresh variables.
635: Use an unstable algorithm for `grenad::Sorter` when possible r=Kerollmops a=loiclec
# Pull Request
## What does this PR do?
Use an unstable algorithm to sort the internal vector used by `grenad::Sorter` whenever possible to speed up indexing.
In practice, every time the merge function creates a `RoaringBitmap`, we use an unstable sort. For every other merge function, such as `keep_first`, `keep_last`, etc., a stable sort is used.
Co-authored-by: Loïc Lecrenier <loic@meilisearch.com>
587: Word prefix pair proximity docids indexation refactor r=Kerollmops a=loiclec
# Pull Request
## What does this PR do?
Refactor the code of `WordPrefixPairProximityDocIds` to make it much faster, fix a bug, and add a unit test.
## Why is it faster?
Because we avoid using a sorter to insert the (`word1`, `prefix`, `proximity`) keys and their associated bitmaps, and thus we don't have to sort a potentially very big set of data. I have also added a couple of other optimisations:
1. reusing allocations
2. using a prefix trie instead of an array of prefixes to get all the prefixes of a word
3. inserting directly into the database instead of putting the data in an intermediary grenad when possible. Also avoid checking for pre-existing values in the database when we know for certain that they do not exist.
## What bug was fixed?
When reindexing, the `new_prefix_fst_words` prefixes may look like:
```
["ant", "axo", "bor"]
```
which we group by first letter:
```
[["ant", "axo"], ["bor"]]
```
Later in the code, if we have the word2 "axolotl", we try to find which subarray of prefixes contains its prefixes. This check is done with `word2.starts_with(subarray_prefixes[0])`, but `"axolotl".starts_with("ant")` is false, and thus we wrongly think that there are no prefixes in `new_prefix_fst_words` that are prefixes of `axolotl`.
## StrStrU8Codec
I had to change the encoding of `StrStrU8Codec` to make the second string null-terminated as well. I don't think this should be a problem, but I may have missed some nuances about the impacts of this change.
## Requests when reviewing this PR
I have explained what the code does in the module documentation of `word_pair_proximity_prefix_docids`. It would be nice if someone could read it and give their opinion on whether it is a clear explanation or not.
I also have a couple questions regarding the code itself:
- Should we clean up and factor out the `PrefixTrieNode` code to try and make broader use of it outside this module? For now, the prefixes undergo a few transformations: from FST, to array, to prefix trie. It seems like it could be simplified.
- I wrote a function called `write_into_lmdb_database_without_merging`. (1) Are we okay with such a function existing? (2) Should it be in `grenad_helpers` instead?
## Benchmark Results
We reduce the time it takes to index about 8% in most cases, but it varies between -3% and -20%.
```
group indexing_main_ce90fc62 indexing_word-prefix-pair-proximity-docids-refactor_cbad2023
----- ---------------------- ------------------------------------------------------------
indexing/-geo-delete-facetedNumber-facetedGeo-searchable- 1.00 1893.0±233.03µs ? ?/sec 1.01 1921.2±260.79µs ? ?/sec
indexing/-movies-delete-facetedString-facetedNumber-searchable- 1.05 9.4±3.51ms ? ?/sec 1.00 9.0±2.14ms ? ?/sec
indexing/-movies-delete-facetedString-facetedNumber-searchable-nested- 1.22 18.3±11.42ms ? ?/sec 1.00 15.0±5.79ms ? ?/sec
indexing/-songs-delete-facetedString-facetedNumber-searchable- 1.00 41.4±4.20ms ? ?/sec 1.28 53.0±13.97ms ? ?/sec
indexing/-wiki-delete-searchable- 1.00 285.6±18.12ms ? ?/sec 1.03 293.1±16.09ms ? ?/sec
indexing/Indexing geo_point 1.03 60.8±0.45s ? ?/sec 1.00 58.8±0.68s ? ?/sec
indexing/Indexing movies in three batches 1.14 16.5±0.30s ? ?/sec 1.00 14.5±0.24s ? ?/sec
indexing/Indexing movies with default settings 1.11 13.7±0.07s ? ?/sec 1.00 12.3±0.28s ? ?/sec
indexing/Indexing nested movies with default settings 1.10 10.6±0.11s ? ?/sec 1.00 9.6±0.15s ? ?/sec
indexing/Indexing nested movies without any facets 1.11 9.4±0.15s ? ?/sec 1.00 8.5±0.10s ? ?/sec
indexing/Indexing songs in three batches with default settings 1.18 66.2±0.39s ? ?/sec 1.00 56.0±0.67s ? ?/sec
indexing/Indexing songs with default settings 1.07 58.7±1.26s ? ?/sec 1.00 54.7±1.71s ? ?/sec
indexing/Indexing songs without any facets 1.08 53.1±0.88s ? ?/sec 1.00 49.3±1.43s ? ?/sec
indexing/Indexing songs without faceted numbers 1.08 57.7±1.33s ? ?/sec 1.00 53.3±0.98s ? ?/sec
indexing/Indexing wiki 1.06 1051.1±21.46s ? ?/sec 1.00 989.6±24.55s ? ?/sec
indexing/Indexing wiki in three batches 1.20 1184.8±8.93s ? ?/sec 1.00 989.7±7.06s ? ?/sec
indexing/Reindexing geo_point 1.04 67.5±0.75s ? ?/sec 1.00 64.9±0.32s ? ?/sec
indexing/Reindexing movies with default settings 1.12 13.9±0.17s ? ?/sec 1.00 12.4±0.13s ? ?/sec
indexing/Reindexing songs with default settings 1.05 60.6±0.84s ? ?/sec 1.00 57.5±0.99s ? ?/sec
indexing/Reindexing wiki 1.07 1725.0±17.92s ? ?/sec 1.00 1611.4±9.90s ? ?/sec
```
Co-authored-by: Loïc Lecrenier <loic@meilisearch.com>
608: Fix soft deleted documents r=ManyTheFish a=ManyTheFish
When we replaced or updated some documents, the indexing was skipping the replaced documents.
Related to https://github.com/meilisearch/meilisearch/issues/2672
Co-authored-by: ManyTheFish <many@meilisearch.com>
New full snapshot:
---
source: milli/src/update/word_prefix_pair_proximity_docids.rs
---
5 a 1 [101, ]
5 a 2 [101, ]
5 am 1 [101, ]
5 b 4 [101, ]
5 be 4 [101, ]
am a 3 [101, ]
amazing a 1 [100, ]
amazing a 2 [100, ]
amazing a 3 [100, ]
amazing an 1 [100, ]
amazing an 2 [100, ]
amazing b 2 [100, ]
amazing be 2 [100, ]
an a 1 [100, ]
an a 2 [100, 202, ]
an am 1 [100, ]
an an 2 [100, ]
an b 3 [100, ]
an be 3 [100, ]
and a 2 [100, ]
and a 3 [100, ]
and a 4 [100, ]
and am 2 [100, ]
and an 3 [100, ]
and b 1 [100, ]
and be 1 [100, ]
at a 1 [100, 202, ]
at a 2 [100, 101, ]
at a 3 [100, ]
at am 2 [100, 101, ]
at an 1 [100, 202, ]
at an 3 [100, ]
at b 3 [101, ]
at b 4 [100, ]
at be 3 [101, ]
at be 4 [100, ]
beautiful a 2 [100, ]
beautiful a 3 [100, ]
beautiful a 4 [100, ]
beautiful am 3 [100, ]
beautiful an 2 [100, ]
beautiful an 4 [100, ]
bell a 2 [101, ]
bell a 4 [101, ]
bell am 4 [101, ]
extraordinary a 2 [202, ]
extraordinary a 3 [202, ]
extraordinary an 2 [202, ]
house a 3 [100, 202, ]
house a 4 [100, 202, ]
house am 4 [100, ]
house an 3 [100, 202, ]
house b 2 [100, ]
house be 2 [100, ]
rings a 1 [101, ]
rings a 3 [101, ]
rings am 3 [101, ]
rings b 2 [101, ]
rings be 2 [101, ]
the a 3 [101, ]
the b 1 [101, ]
the be 1 [101, ]
