728: Add some integration tests on the sort criterion r=ManyTheFish a=loiclec
This is simply an integration test ensuring that the sort criterion works properly.
However, only one version of the algorithm is tested here (the iterative one). To test the version that uses the facet DB, one has to manually set the `CANDIDATES_THRESHOLD` constant to `0`. I have done that and ensured that the test still succeeds. However, in the future, we will probably want to have an option to force which algorithm is used at runtime, for testing purposes.
Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com>
737: Fix typo initial candidates computation r=Kerollmops a=ManyTheFish
When `Typo` criterion was after a different criterion than `Words` and the previous criterion wasn't returning any candidates at the first iteration of the bucket sort, then the `initial_candidates` were lost.
Now, `Typo`ensure to keep the `initial_candidates` between iterations.
related to https://github.com/meilisearch/meilisearch/issues/3200#issuecomment-1345179578
related to https://github.com/meilisearch/meilisearch/issues/3228
Co-authored-by: ManyTheFish <many@meilisearch.com>
By creating snapshots and updating the format of the existing
snapshots. The next commit will apply the fix, which will show
its effects cleanly on the old and new snapshot tests
723: Fix bug in handling of soft deleted documents when updating settings r=Kerollmops a=loiclec
# Pull Request
## Related issue
Fixes (partially, until merged into meilisearch) https://github.com/meilisearch/meilisearch/issues/3021
## What does this PR do?
This PR fixes the bug where a `missing key in documents database` internal error message could appear when indexing documents.
When updating the settings, before clearing the database and before creating the transform output, we now modify the `ExternalDocumentsIds` structure to get rid of all references to soft deleted document ids in its FSTs.
It used to be that updating the settings would clear the soft-deleted document ids, but keep the original `ExternalDocumentsIds` structure. As a consequence of this, when processing a future document addition, we could wrongly believe that a document was being replaced when, in fact, it was a completely new document. See the tests `bug_3021_first`, `bug_3021_second`, and `bug_3021` for a minimal test case that would have reproduced the issue.
We need to take special care to:
- evaluate how users should update to v0.30.1 (containing this fix): dump? reimporting all documents from scratch?
- understand IF/HOW this bug could have caused duplicate documents to be returned
- and evaluate the correctness of the fix, of course :)
Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com>
719: Add more members of `filter_parser` to `milli::` & `From<&str>` implementation for `Token` r=Kerollmops a=GregoryConrad
## What does this PR do?
The current `milli::Filter` and `milli::FilterCondition` APIs require working with some members of `filter_parser` directly that `milli::` does *not* re-export to its users (at least when not parsing input using `parse`). Also, using `filter_parser` does not make sense when using milli from an embedded context where there is no query to parse.
Instead of reworking `milli::Filter` and `milli::FilterCondition`, this PR adds two non-breaking changes that ease the use of milli:
- Re-exports more members of the dependent version of `filter_parser` in `milli`
- Implements `From<&str>` for `filter_parser::Token`
- This will also allow some basic tests that need to create a `Token` from a string to avoid some boilerplate.
In conjunction, both of these will allow milli users to easily create a `Token` from a `&str` without needing to add `filter_parser` as an extra dependency.
Note: I wanted to use `FromStr` for the `From` implementation; however, it requires returning a `Result` which is not needed for the conversion. Thus, I just left it as `From<&str>`.
Co-authored-by: Gregory Conrad <gregorysconrad@gmail.com>
706: Limit the reindexing caused by updating settings when not needed r=curquiza a=GregoryConrad
## What does this PR do?
When updating index settings using `update::Settings`, sometimes a `reindex` of `update::Settings` is triggered when it doesn't need to be. This PR aims to prevent those unnecessary `reindex` calls.
For reference, here is a snippet from the current `execute` method in `update::Settings`:
```rust
// ...
if stop_words_updated
|| faceted_updated
|| synonyms_updated
|| searchable_updated
|| exact_attributes_updated
{
self.reindex(&progress_callback, &should_abort, old_fields_ids_map)?;
}
```
- [x] `faceted_updated` - looks good as-is ✅
- [x] `stop_words_updated` - looks good as-is ✅
- [x] `synonyms_updated` - looks good as-is ✅
- [x] `searchable_updated` - fixed in this PR
- [x] `exact_attributes_updated` - fixed in this PR
## 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: Gregory Conrad <gregorysconrad@gmail.com>
708: Reduce memory usage of the MatchingWords structure r=ManyTheFish a=loiclec
# Pull Request
## Related issue
Fixes (partially) https://github.com/meilisearch/meilisearch/issues/3115
## What does this PR do?
1. Reduces the memory usage caused by the creation of a 10-word query tree by 20x.
This is done by deduplicating the `MatchingWord` values, which are heavy because of their inner DFA. The deduplication works by wrapping each `MatchingWord` in a reference-counted box and using a hash map to determine whether a `MatchingWord` DFA already exists for a certain signature, or whether a new one needs to be built.
2. Avoid the worst-case scenario of creating a `MatchingWord` for extremely long words that cannot be indexed by milli.
Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com>
712: Fix bulk facet indexing bug r=Kerollmops a=loiclec
# Pull Request
## Related issue
Fixes (partially, until merged into meilisearch) https://github.com/meilisearch/meilisearch/issues/3165
## What does this PR do?
Fixes a bug where indexing certain numbers of filterable attribute values in bulk led to corrupted facet databases. This was due to a lossy integer conversion which would ultimately prevent entire levels of the facet database to be written into LMDB.
More specifically, this change was made:
```diff
- if cur_writer_len as u8 >= self.min_level_size {
+ if cur_writer_len >= self.min_level_size as usize {
```
I also checked other comparisons to `min_level_size` and other conversions such as `x as u8` in this part of the codebase.
Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com>
697: Fix bug in prefix DB indexing r=loiclec a=loiclec
Where the batch's information was not properly updated in cases where only the proximity changed between two consecutive word pair proximities.
Closes partially https://github.com/meilisearch/meilisearch/issues/3043
Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com>
1. Handle keys with variable length correctly
This fixes https://github.com/meilisearch/meilisearch/issues/3042 and
is easily reproducible with the updated fuzz tests, which now generate
keys with variable lengths.
2. Prevent adding facets to the database if their encoded value does
not satisfy `valid_lmdb_key`.
This fixes an indexing failure when a document had a filterable
attribute containing a value whose length is higher than ~500 bytes.
689: Handle non-finite floats consistently in filters r=irevoire a=dureuill
# Pull Request
## Related issue
Related meilisearch/meilisearch#3000
## What does this PR do?
### User
- Filters using `field = inf`, (or `infinite`, `NaN`) now match the value as a string rather than returning an internal error.
- Filters using `field < inf` (or other comparison operators) now return an invalid_filter error rather than returning an internal error, much like when using `field < aaa`.
### Implementation
- Add new `NonFiniteFloat` error variants to the filter-parser errors
- Add `Token::parse_as_finite_float` that can fail both when the string is not a float and when the float is not finite
- Refactor `Filter::inner_evaluate` to always use `parse_as_finite_float` instead of just `parse`
- Add corresponding tests
## 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: Louis Dureuil <louis@meilisearch.com>
659: Fix clippy error to add clippy job on Ci r=Kerollmops a=unvalley
## Related PR
This PR is for #673
## What does this PR do?
- ~~add `Run Clippy` job to CI (rust.yml)~~
- apply `cargo clippy --fix` command
- fix some `cargo clippy` error manually (but warnings still remain on tests)
## 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?
Co-authored-by: unvalley <kirohi.code@gmail.com>
Co-authored-by: unvalley <38400669+unvalley@users.noreply.github.com>
664: Fix phrase search containing stop words r=ManyTheFish a=Samyak2
# Pull Request
This a WIP draft PR I wanted to create to let other potential contributors know that I'm working on this issue. I'll be completing this in a few hours from opening this.
## Related issue
Fixes#661 and towards fixing meilisearch/meilisearch#2905
## What does this PR do?
- [x] Change Phrase Operation to use a `Vec<Option<String>>` instead of `Vec<String>` where `None` corresponds to a stop word
- [x] Update all other uses of phrase operation
- [x] Update `resolve_phrase`
- [x] Update `create_primitive_query`?
- [x] Add test
## 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?
Co-authored-by: Samyak S Sarnayak <samyak201@gmail.com>
Co-authored-by: Samyak Sarnayak <samyak201@gmail.com>
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>
665: Fixing piles of clippy errors. r=ManyTheFish a=ehiggs
## Related issue
No issue fixed. Simply cleaning up some code for clippy on the march towards a clean build when #659 is merged.
## What does this PR do?
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.
## 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?
Co-authored-by: Ewan Higgs <ewan.higgs@gmail.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.
662: Enhance word splitting strategy r=ManyTheFish a=akki1306
# Pull Request
## Related issue
Fixes#648
## What does this PR do?
- [split_best_frequency](55d889522b/milli/src/search/query_tree.rs (L282-L301)) to use frequency of word pairs near together with proximity value of 1 instead of considering the frequency of individual words. Word pairs having max frequency are considered.
## 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: Akshay Kulkarni <akshayk.gj@gmail.com>
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>
596: Filter operators: NOT + IN[..] r=irevoire a=loiclec
# Pull Request
## What does this PR do?
Implements the changes described in https://github.com/meilisearch/meilisearch/issues/2580
It is based on top of #556
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>
594: Fix(Search): Fix phrase search candidates computation r=Kerollmops a=ManyTheFish
This bug is an old bug but was hidden by the proximity criterion,
Phrase searches were always returning an empty candidates list when the proximity criterion is deactivated.
Before the fix, we were trying to find any words[n] near words[n]
instead of finding any words[n] near words[n+1], for example:
for a phrase search '"Hello world"' we were searching for "hello" near "hello" first, instead of "hello" near "world".
Co-authored-by: ManyTheFish <many@meilisearch.com>
NOTE: The token_at_depth is method is a bit useless now, as the only
cases where there would be a toke at depth 1000 are the cases where
the parser already stack-overflowed earlier.
Example: (((((... (x=1) ...)))))
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, ]
New snapshot (yes, it's wrong as well, it will get fixed later):
---
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 b 3 [100, ]
an be 3 [100, ]
and a 2 [100, ]
and a 3 [100, ]
and a 4 [100, ]
and b 1 [100, ]
and be 1 [100, ]
d\0 0 [100, 202, ]
an an 2 [100, ]
and am 2 [100, ]
and an 3 [100, ]
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 4 [100, 202, ]
house a 4 [100, ]
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, ]
556: Add EXISTS filter r=loiclec a=loiclec
## What does this PR do?
Fixes issue [#2484](https://github.com/meilisearch/meilisearch/issues/2484) in the meilisearch repo.
It creates a `field EXISTS` filter which selects all documents containing the `field` key.
For example, with the following documents:
```json
[{
"id": 0,
"colour": []
},
{
"id": 1,
"colour": ["blue", "green"]
},
{
"id": 2,
"colour": 145238
},
{
"id": 3,
"colour": null
},
{
"id": 4,
"colour": {
"green": []
}
},
{
"id": 5,
"colour": {}
},
{
"id": 6
}]
```
Then the filter `colour EXISTS` selects the ids `[0, 1, 2, 3, 4, 5]`. The filter `colour NOT EXISTS` selects `[6]`.
## Details
There is a new database named `facet-id-exists-docids`. Its keys are field ids and its values are bitmaps of all the document ids where the corresponding field exists.
To create this database, the indexing part of milli had to be adapted. The implementation there is basically copy/pasted from the code handling the `facet-id-f64-docids` database, with appropriate modifications in place.
There was an issue involving the flattening of documents during (re)indexing. Previously, the following JSON:
```json
{
"id": 0,
"colour": [],
"size": {}
}
```
would be flattened to:
```json
{
"id": 0
}
```
prior to being given to the extraction pipeline.
This transformation would lose the information that is needed to populate the `facet-id-exists-docids` database. Therefore, I have also changed the implementation of the `flatten-serde-json` crate. Now, as it traverses the Json, it keeps track of which key was encountered. Then, at the end, if a previously encountered key is not present in the flattened object, it adds that key to the object with an empty array as value. For example:
```json
{
"id": 0,
"colour": {
"green": [],
"blue": 1
},
"size": {}
}
```
becomes
```json
{
"id": 0,
"colour": [],
"colour.green": [],
"colour.blue": 1,
"size": []
}
```
Co-authored-by: Kerollmops <clement@meilisearch.com>
This bug is an old bug but was hidden by the proximity criterion,
Phrase search were always returning an empty candidates list.
Before the fix, we were trying to find any words[n] near words[n]
instead of finding any words[n] near words[n+1], for example:
for a phrase search '"Hello world"' we were searching for "hello" near "hello" first, instead of "hello" near "world".
561: Enriched documents batch reader r=curquiza a=Kerollmops
~This PR is based on #555 and must be rebased on main after it has been merged to ease the review.~
This PR contains the work in #555 and can be merged on main as soon as reviewed and approved.
- [x] Create an `EnrichedDocumentsBatchReader` that contains the external documents id.
- [x] Extract the primary key name and make it accessible in the `EnrichedDocumentsBatchReader`.
- [x] Use the external id from the `EnrichedDocumentsBatchReader` in the `Transform::read_documents`.
- [x] Remove the `update_primary_key` from the _transform.rs_ file.
- [x] Really generate the auto-generated documents ids.
- [x] Insert the (auto-generated) document ids in the document while processing it in `Transform::read_documents`.
Co-authored-by: Kerollmops <clement@meilisearch.com>
The idea is to directly create a sorted and merged list of bitmaps
in the form of a BTreeMap<FieldId, RoaringBitmap> instead of creating
a grenad::Reader where the keys are field_id and the values are docids.
Then we send that BTreeMap to the thing that handles TypedChunks, which
inserts its content into the database.
