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.
When a document deletion occurs, instead of deleting the document we mark it as deleted
in the new “soft deleted” bitmap. It is then removed from the search, and all the other
endpoints.
564: Rename the limitedTo parameter into maxTotalHits r=curquiza a=Kerollmops
This PR is related to https://github.com/meilisearch/meilisearch/issues/2542, it renames the `limitedTo` parameter into `maxTotalHits`.
Co-authored-by: Kerollmops <clement@meilisearch.com>
552: Fix escaped quotes in filter r=Kerollmops a=irevoire
Will fix https://github.com/meilisearch/meilisearch/issues/2380
The issue was that in the evaluation of the filter, I was using the deref implementation instead of calling the `value` method of my token.
To avoid the problem happening again, I removed the deref implementation; now, you need to either call the `lexeme` or the `value` methods but can't rely on a « default » implementation to get a string out of a token.
Co-authored-by: Tamo <tamo@meilisearch.com>
535: Reintroduce the max values by facet limit r=ManyTheFish a=Kerollmops
This PR reintroduces the max values by facet limit this is related to https://github.com/meilisearch/meilisearch/issues/2349.
~I would like some help in deciding on whether I keep the default 100 max values in milli and set up the `FacetDistribution` settings in Meilisearch to use 1000 as the new value, I expose the `max_values_by_facet` for this purpose.~
I changed the default value to 1000 and the max to 10000, thank you `@ManyTheFish` for the help!
Co-authored-by: Kerollmops <clement@meilisearch.com>
538: speedup exact words r=Kerollmops a=MarinPostma
This PR make `exact_words` return an `Option` instead of an empty set, since set creation is costly, as noticed by `@kerollmops.`
I was not convinces that this was the cause for all of the performance drop we measured, and then realized that methods that initialized it were called recursively which caused initialization times to add up. While the first fix solves the issue when not using exact words, using exact word remained way more expensive that it should be. To address this issue, the exact words are cached into the `Context`, so they are only initialized once.
Co-authored-by: ad hoc <postma.marin@protonmail.com>
525: Simplify the error creation with thiserror r=irevoire a=irevoire
I introduced [`thiserror`](https://docs.rs/thiserror/latest/thiserror/) to implements all the `Display` trait and most of the `impl From<xxx> for yyy` in way less lines.
And then I introduced a cute macro to implements the `impl<X, Y, Z> From<X> for Z where Y: From<X>, Z: From<X>` more easily.
Co-authored-by: Tamo <tamo@meilisearch.com>
523: Improve geosearch error messages r=irevoire a=irevoire
Improve the geosearch error messages (#488).
And try to parse the string as specified in https://github.com/meilisearch/meilisearch/issues/2354
Co-authored-by: Tamo <tamo@meilisearch.com>
520: fix mistake in Settings initialization r=irevoire a=MarinPostma
fix settings not being correctly initialized and add a test to make sure that they are in the future.
fix https://github.com/meilisearch/meilisearch/issues/2358
Co-authored-by: ad hoc <postma.marin@protonmail.com>
518: Return facets even when there is no value associated to it r=Kerollmops a=Kerollmops
This PR is related to https://github.com/meilisearch/meilisearch/issues/2352 and should fix the issue when Meilisearch is up-to-date with this PR.
Co-authored-by: Kerollmops <clement@meilisearch.com>
505: normalize exact words r=curquiza a=MarinPostma
Normalize the exact words, as specified in the specification.
Co-authored-by: ad hoc <postma.marin@protonmail.com>
483: Enhance matching words r=Kerollmops a=ManyTheFish
# Summary
Enhance milli word-matcher making it handle match computing and cropping.
# Implementation
## Computing best matches for cropping
Before we were considering that the first match of the attribute was the best one, this was accurate when only one word was searched but was missing the target when more than one word was searched.
Now we are searching for the best matches interval to crop around, the chosen interval is the one:
1) that have the highest count of unique matches
> for example, if we have a query `split the world`, then the interval `the split the split the` has 5 matches but only 2 unique matches (1 for `split` and 1 for `the`) where the interval `split of the world` has 3 matches and 3 unique matches. So the interval `split of the world` is considered better.
2) that have the minimum distance between matches
> for example, if we have a query `split the world`, then the interval `split of the world` has a distance of 3 (2 between `split` and `the`, and 1 between `the` and `world`) where the interval `split the world` has a distance of 2. So the interval `split the world` is considered better.
3) that have the highest count of ordered matches
> for example, if we have a query `split the world`, then the interval `the world split` has 2 ordered words where the interval `split the world` has 3. So the interval `split the world` is considered better.
## Cropping around the best matches interval
Before we were cropping around the interval without checking the context.
Now we are cropping around words in the same context as matching words.
This means that we will keep words that are farther from the matching words but are in the same phrase, than words that are nearer but separated by a dot.
> For instance, for the matching word `Split` the text:
`Natalie risk her future. Split The World is a book written by Emily Henry. I never read it.`
will be cropped like:
`…. Split The World is a book written by Emily Henry. …`
and not like:
`Natalie risk her future. Split The World is a book …`
Co-authored-by: ManyTheFish <many@meilisearch.com>
We need to store all the external id (primary key) in a hashmap
associated to their internal id during.
The smartstring remove heap allocation / memory usage and should
improve the cache locality.
472: Remove useless variables in proximity r=Kerollmops a=ManyTheFish
Was passing by plane sweep algorithm to find some inspiration, and I discover that we have useless variables that were not detected because of the recursive function.
Co-authored-by: ManyTheFish <many@meilisearch.com>
467: optimize prefix database r=Kerollmops a=MarinPostma
This pr introduces two optimizations that greatly improve the speed of computing prefix databases.
- The time that it takes to create the prefix FST has been divided by 5 by inverting the way we iterated over the words FST.
- We unconditionally and needlessly checked for documents to remove in `word_prefix_pair`, which caused an iteration over the whole database.
Co-authored-by: ad hoc <postma.marin@protonmail.com>
> "Attribute `{}` is not sortable. This index doesn't have configured sortable attributes."
> "Attribute `{}` is not sortable. Available sortable attributes are: `{}`."
coexist in the error handling
436: Speed up the word prefix databases computation time r=Kerollmops a=Kerollmops
This PR depends on the fixes done in #431 and must be merged after it.
In this PR we will bring the `WordPrefixPairProximityDocids`, `WordPrefixDocids` and, `WordPrefixPositionDocids` update structures to a new era, a better era, where computing the word prefix pair proximities costs much fewer CPU cycles, an era where this update structure can use the, previously computed, set of new word docids from the newly indexed batch of documents.
---
The `WordPrefixPairProximityDocids` is an update structure, which means that it is an object that we feed with some parameters and which modifies the LMDB database of an index when asked for. This structure specifically computes the list of word prefix pair proximities, which correspond to a list of pairs of words associated with a proximity (the distance between both words) where the second word is not a word but a prefix e.g. `s`, `se`, `a`. This word prefix pair proximity is associated with the list of documents ids which contains the pair of words and prefix at the given proximity.
The origin of the performances issue that this struct brings is related to the fact that it starts its job from the beginning, it clears the LMDB database before rewriting everything from scratch, using the other LMDB databases to achieve that. I hope you understand that this is absolutely not an optimized way of doing things.
Co-authored-by: Clément Renault <clement@meilisearch.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
442: fix phrase search r=curquiza a=MarinPostma
Run the exact match search on 7 words windows instead of only two. This makes false positive very very unlikely, and impossible on phrase query that are less than seven words.
Co-authored-by: ad hoc <postma.marin@protonmail.com>
433: fix(filter): Fix two bugs. r=Kerollmops a=irevoire
- Stop lowercasing the field when looking in the field id map
- When a field id does not exist it means there is currently zero
documents containing this field thus we return an empty RoaringBitmap
instead of throwing an internal error
Will fix https://github.com/meilisearch/MeiliSearch/issues/2082 once meilisearch is released
Co-authored-by: Tamo <tamo@meilisearch.com>
- Stop lowercasing the field when looking in the field id map
- When a field id does not exist it means there is currently zero
documents containing this field thus we returns an empty RoaringBitmap
instead of throwing an internal error
The `matching_bytes` function takes a `&Token` now and:
- gets the number of bytes to highlight (unchanged).
- uses `Token.num_graphemes_from_bytes` to get the number of grapheme
clusters to highlight.
In essence, the `matching_bytes` function returns the number of matching
grapheme clusters instead of bytes. Should this function be renamed
then?
Added proper highlighting in the HTTP UI:
- requires dependency on `unicode-segmentation` to extract grapheme
clusters from tokens
- `<mark>` tag is put around only the matched part
- before this change, the entire word was highlighted even if only a
part of it matched
returned metaimprove document addition returned metaimprove document
addition returned metaimprove document addition returned metaimprove
document addition returned metaimprove document addition returned
metaimprove document addition returned meta
402: Optimize document transform r=MarinPostma a=MarinPostma
This pr optimizes the transform of documents additions in the obkv format. Instead on accepting any serializable objects, we instead treat json and CSV specifically:
- For json, we build a serde `Visitor`, that transform the json straight into obkv without intermediate representation.
- For csv, we directly write the lines in the obkv, applying other optimization as well.
Co-authored-by: marin postma <postma.marin@protonmail.com>
390: Add helper methods on the settings r=Kerollmops a=irevoire
This would be a good addition to look at the content of a setting without consuming it.
It’s useful for analytics.
Co-authored-by: Irevoire <tamo@meilisearch.com>
384: Replace memmap with memmap2 r=Kerollmops a=palfrey
[memmap is unmaintained](https://rustsec.org/advisories/RUSTSEC-2020-0077.html) and needs replacing. memmap2 is a drop-in replacement fork that's well maintained. Note that the version numbers got reset on fork, hence the lower values.
Co-authored-by: Tom Parker-Shemilt <palfrey@tevp.net>
388: fix primary key inference r=MarinPostma a=MarinPostma
The primary key is was infered from a hashtable index of the field. For this reason the order in which the fields were interated upon was not deterministic, and the primary key was chosed ffrom the first field containing "id".
This fix sorts the the index by field_id when infering the primary key.
Co-authored-by: mpostma <postma.marin@protonmail.com>
Instead of using an arbitrary limit we encode the absolute position in a u32
using one strong u16 for the field id and a weak u16 for the relative position in the attribute.
Latitude are not supposed to go beyound 90 degrees or below -90.
The same goes for longitude with 180 or -180.
This was badly implemented in the filters, and was not implemented for the AscDesc rules.
379: Revert "Change chunk size to 4MiB to fit more the end user usage" r=curquiza a=ManyTheFish
Reverts meilisearch/milli#370
Co-authored-by: Many <legendre.maxime.isn@gmail.com>
373: Improve error message for bad sort syntax with geosearch r=Kerollmops a=irevoire
`@Kerollmops` This should be the last PR for the geosearch and error handling, sorry for doing it in so many steps 😬
Co-authored-by: Tamo <tamo@meilisearch.com>
372: Fix Meilisearch 1714 r=Kerollmops a=ManyTheFish
The bug comes from the typo tolerance, to know how many typos are accepted we were counting bytes instead of characters in a word.
On Chinese Script characters, we were allowing 2 typos on 3 characters words.
We are now counting the number of char instead of counting bytes to assign the typo tolerance.
Related to [Meilisearch#1714](https://github.com/meilisearch/MeiliSearch/issues/1714)
Co-authored-by: many <maxime@meilisearch.com>
322: Geosearch r=ManyTheFish a=irevoire
This PR introduces [basic geo-search functionalities](https://github.com/meilisearch/specifications/pull/59), it makes the engine able to index, filter and, sort by geo-point. We decided to use [the rstar library](https://docs.rs/rstar) and to save the points in [an RTree](https://docs.rs/rstar/0.9.1/rstar/struct.RTree.html) that we de/serialize in the index database [by using serde](https://serde.rs/) with [bincode](https://docs.rs/bincode). This is not an efficient way to query this tree as it will consume a lot of CPU and memory when a search is made, but at least it is an easy first way to do so.
### What we will have to do on the indexing part:
- [x] Index the `_geo` fields from the documents.
- [x] Create a new module with an extractor in the `extract` module that takes the `obkv_documents` and retrieves the latitude and longitude coordinates, outputting them in a `grenad::Reader` for further process.
- [x] Call the extractor in the `extract::extract_documents_data` function and send the result to the `TypedChunk` module.
- [x] Get the `grenad::Reader` in the `typed_chunk::write_typed_chunk_into_index` function and store all the points in the `rtree`
- [x] Delete the documents from the `RTree` when deleting documents from the database. All this can be done in the `delete_documents.rs` file by getting the data structure and removing the points from it, inserting it back after the modification.
- [x] Clearing the `RTree` entirely when we clear the documents from the database, everything happens in the `clear_documents.rs` file.
- [x] save a Roaring bitmap of all documents containing the `_geo` field
### What we will have to do on the query part:
- [x] Filter the documents at a certain distance around a point, this is done by [collecting the documents from the searched point](https://docs.rs/rstar/0.9.1/rstar/struct.RTree.html#method.nearest_neighbor_iter) while they are in range.
- [x] We must introduce new `geoLowerThan` and `geoGreaterThan` variants to the `Operator` filter enum.
- [x] Implement the `negative` method on both variants where the `geoGreaterThan` variant is implemented by executing the `geoLowerThan` and removing the results found from the whole list of geo faceted documents.
- [x] Add the `_geoRadius` function in the pest parser.
- [x] Introduce a `_geo` ascending ranking function that takes a point in parameter, ~~this function must keep the iterator on the `RTree` and make it peekable~~ This was not possible for now, we had to collect the whole iterator. Only the documents that are part of the candidates must be sent too!
- [x] This ascending ranking rule will only be active if the search is set up with the `_geoPoint` parameter that indicates the center point of the ascending ranking rule.
-----------
- On Meilisearch part: We must introduce a new concept, returning the documents with a new `_geoDistance` field when it passed by the `_geo` ranking rule, this has never been done before. We could maybe just do it afterward when the documents have been retrieved from the database, computing the distance from the `_geoPoint` and all of the documents to be returned.
Co-authored-by: Irevoire <tamo@meilisearch.com>
Co-authored-by: cvermand <33010418+bidoubiwa@users.noreply.github.com>
Co-authored-by: Tamo <tamo@meilisearch.com>
342: Let the caller decide what kind of error they want to returns when parsing `AscDesc` r=Kerollmops a=irevoire
This is one possible fix for #339
We would then need to patch these lines https://github.com/meilisearch/MeiliSearch/blob/main/meilisearch-http/src/index/search.rs#L110-L114 to return the error we want.
Another solution would be to add a parameter to the `from_str` to specify which context we are in.
Co-authored-by: Tamo <tamo@meilisearch.com>
344: Move the sort ranking rule before the exactness ranking rule r=ManyTheFish a=Kerollmops
This PR moves the sort ranking rule at the 5th position by default, right before the exactness one.
Co-authored-by: Kerollmops <clement@meilisearch.com>
308: Implement a better parallel indexer r=Kerollmops a=ManyTheFish
Rewrite the indexer:
- enhance memory consumption control
- optimize parallelism using rayon and crossbeam channel
- factorize the different parts and make new DB implementation easier
- optimize and fix prefix databases
Co-authored-by: many <maxime@meilisearch.com>
300: Fix prefix level position docids database r=curquiza a=ManyTheFish
The prefix search was inverted when we generated the DB.
Instead of searching if word had a prefix in prefix fst,
we were searching if the word was a prefix of a prefix contained in the prefix fst.
The indexer, now, iterate over prefix contained in the fst
and search them by prefix in the word-level-position-docids database,
aggregating matches in a sorter.
Fix#299
Co-authored-by: many <maxime@meilisearch.com>
The prefix search was inverted when we generated the DB.
Instead of searching if word had a prefix in prefix fst,
we were searching if the word was a prefix of a prefix contained in the prefix fst.
The indexer, now, iterate over prefix contained in the fst
and search them by prefix in the word-level-position-docids database,
aggregating matches in a sorter.
Fix#299
291: Fix a bug about zero bytes in the inputs r=irevoire a=Kerollmops
Ok, good news, after a little session of debugging with `@irevoire` we found out that the bug seems to be related to zeroes in the input update. The engine wasn't designed to accept those. The chosen solution is to update the tokenizer to remove those zeroes. We are waiting on https://github.com/meilisearch/tokenizer/pull/52 to be merged and a new version to be released.
It is not an undefined behavior, I repeat: it is a "normal" bug 🎉👏
----
This PR tries to fix a bug where we use LMDB in the wrong way, leading to panic due to an undefined behavior on the Rust side. I thought [we fixed it in a previous PR](https://github.com/meilisearch/milli/pull/264) but we found out that _a similar_ bug was still present. `@bb` found a way to trigger this bug and helped us find the origin of it.
As I don't have a minimal reproducible example of this bug I bet on the unsafe `put_current` calls when we index new documents as the bug was trigger after a big indexation on a clean database, thus not triggering a deletion update. I only replaced the unsafe `put_current` with two safe calls to `get`/`put`.
I hope it helps and fixes the bug, only `@bb` can help us check that. I am not even sure how I can create a custom Docker image and expose it for testing purposes.
<details>
<summary>The backtrace leading us to a panic in grenad.</summary>
```
meilisearch_1 | thread 'tokio-runtime-worker' panicked at 'assertion failed: key > &last_key', /root/.cargo/git/checkouts/grenad-e2cb77f65d31bb02/3adcb26/src/block_builder.rs:38:17
meilisearch_1 | stack backtrace:
meilisearch_1 | 0: rust_begin_unwind
meilisearch_1 | at ./rustc/53cb7b09b00cbea8754ffb78e7e3cb521cb8af4b/library/std/src/panicking.rs:493:5
meilisearch_1 | 1: core::panicking::panic_fmt
meilisearch_1 | at ./rustc/53cb7b09b00cbea8754ffb78e7e3cb521cb8af4b/library/core/src/panicking.rs:92:14
meilisearch_1 | 2: core::panicking::panic
meilisearch_1 | at ./rustc/53cb7b09b00cbea8754ffb78e7e3cb521cb8af4b/library/core/src/panicking.rs:50:5
meilisearch_1 | 3: grenad::block_builder::BlockBuilder::insert
meilisearch_1 | at ./root/.cargo/git/checkouts/grenad-e2cb77f65d31bb02/3adcb26/src/block_builder.rs:38:17
meilisearch_1 | 4: grenad::writer::Writer<W>::insert
meilisearch_1 | at ./root/.cargo/git/checkouts/grenad-e2cb77f65d31bb02/3adcb26/src/writer.rs:92:12
meilisearch_1 | 5: milli::update::words_level_positions::write_level_entry
meilisearch_1 | at ./root/.cargo/git/checkouts/milli-00376cd5db949a15/007fec2/milli/src/update/words_level_positions.rs:262:5
meilisearch_1 | 6: milli::update::words_level_positions::compute_positions_levels
meilisearch_1 | at ./root/.cargo/git/checkouts/milli-00376cd5db949a15/007fec2/milli/src/update/words_level_positions.rs:211:13
meilisearch_1 | 7: milli::update::words_level_positions::WordsLevelPositions::execute
meilisearch_1 | at ./root/.cargo/git/checkouts/milli-00376cd5db949a15/007fec2/milli/src/update/words_level_positions.rs:65:23
meilisearch_1 | 8: milli::update::index_documents::IndexDocuments::execute_raw
meilisearch_1 | at ./root/.cargo/git/checkouts/milli-00376cd5db949a15/007fec2/milli/src/update/index_documents/mod.rs:831:9
meilisearch_1 | 9: milli::update::index_documents::IndexDocuments::execute
meilisearch_1 | at ./root/.cargo/git/checkouts/milli-00376cd5db949a15/007fec2/milli/src/update/index_documents/mod.rs:372:9
meilisearch_1 | 10: meilisearch_http::index::updates::<impl meilisearch_http::index::Index>::update_documents_txn
meilisearch_1 | at ./meilisearch/meilisearch-http/src/index/updates.rs:225:30
meilisearch_1 | 11: meilisearch_http::index::updates::<impl meilisearch_http::index::Index>::update_documents
meilisearch_1 | at ./meilisearch/meilisearch-http/src/index/updates.rs:183:22
meilisearch_1 | 12: meilisearch_http::index::update_handler::UpdateHandler::handle_update
meilisearch_1 | at ./meilisearch/meilisearch-http/src/index/update_handler.rs:75:18
meilisearch_1 | 13: meilisearch_http::index_controller::index_actor::actor::IndexActor<S>::handle_update::{{closure}}::{{closure}}
meilisearch_1 | at ./meilisearch/meilisearch-http/src/index_controller/index_actor/actor.rs:174:35
meilisearch_1 | 14: <tokio::runtime::blocking::task::BlockingTask<T> as core::future::future::Future>::poll
meilisearch_1 | at ./root/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.7.1/src/runtime/blocking/task.rs:42:21
meilisearch_1 | 15: tokio::runtime::task::core::CoreStage<T>::poll::{{closure}}
meilisearch_1 | at ./root/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.7.1/src/runtime/task/core.rs:243:17
meilisearch_1 | 16: tokio::loom::std::unsafe_cell::UnsafeCell<T>::with_mut
meilisearch_1 | at ./root/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.7.1/src/loom/std/unsafe_cell.rs:14:9
meilisearch_1 | 17: tokio::runtime::task::core::CoreStage<T>::poll
meilisearch_1 | at ./root/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.7.1/src/runtime/task/core.rs:233:13
meilisearch_1 | 18: tokio::runtime::task::harness::poll_future::{{closure}}
meilisearch_1 | at ./root/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.7.1/src/runtime/task/harness.rs:427:23
meilisearch_1 | 19: <std::panic::AssertUnwindSafe<F> as core::ops::function::FnOnce<()>>::call_once
meilisearch_1 | at ./rustc/53cb7b09b00cbea8754ffb78e7e3cb521cb8af4b/library/std/src/panic.rs:344:9
meilisearch_1 | 20: std::panicking::try::do_call
meilisearch_1 | at ./rustc/53cb7b09b00cbea8754ffb78e7e3cb521cb8af4b/library/std/src/panicking.rs:379:40
meilisearch_1 | 21: std::panicking::try
meilisearch_1 | at ./rustc/53cb7b09b00cbea8754ffb78e7e3cb521cb8af4b/library/std/src/panicking.rs:343:19
meilisearch_1 | 22: std::panic::catch_unwind
meilisearch_1 | at ./rustc/53cb7b09b00cbea8754ffb78e7e3cb521cb8af4b/library/std/src/panic.rs:431:14
meilisearch_1 | 23: tokio::runtime::task::harness::poll_future
meilisearch_1 | at ./root/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.7.1/src/runtime/task/harness.rs:414:19
meilisearch_1 | 24: tokio::runtime::task::harness::Harness<T,S>::poll_inner
meilisearch_1 | at ./root/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.7.1/src/runtime/task/harness.rs:89:9
meilisearch_1 | 25: tokio::runtime::task::harness::Harness<T,S>::poll
meilisearch_1 | at ./root/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.7.1/src/runtime/task/harness.rs:59:15
meilisearch_1 | 26: tokio::runtime::task::raw::RawTask::poll
meilisearch_1 | at ./root/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.7.1/src/runtime/task/raw.rs:66:18
meilisearch_1 | 27: tokio::runtime::task::Notified<S>::run
meilisearch_1 | at ./root/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.7.1/src/runtime/task/mod.rs:171:9
meilisearch_1 | 28: tokio::runtime::blocking::pool::Inner::run
meilisearch_1 | at ./root/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.7.1/src/runtime/blocking/pool.rs:265:17
meilisearch_1 | 29: tokio::runtime::blocking::pool::Spawner::spawn_thread::{{closure}}
meilisearch_1 | at ./root/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.7.1/src/runtime/blocking/pool.rs:245:17
meilisearch_1 | note: Some details are omitted, run with `RUST_BACKTRACE=full` for a verbose backtrace.
```
</details>
Co-authored-by: Kerollmops <clement@meilisearch.com>
269: Fix bug when inserting previously deleted documents r=Kerollmops a=Kerollmops
This PR fixes#268.
The issue was in the `ExternalDocumentsIds` implementation in the specific case that an external document id was in the soft map marked as deleted.
The bug was due to a wrong assumption on my side about how the FST unions were returning the `IndexedValue`s, I thought the values returned in an array were in the same order as the FSTs given to the `OpBuilder` but in fact, [the `IndexedValue`'s `index` field was here to indicate from which FST the values were coming from](https://docs.rs/fst/0.4.7/fst/map/struct.IndexedValue.html).
271: Remove the roaring operation functions warnings r=Kerollmops a=Kerollmops
In this PR we are just replacing the usages of the roaring operations function by the new operators. This removes a lot of warnings.
Co-authored-by: Kerollmops <clement@meilisearch.com>
It is undefined behavior to keep a reference to the database while
modifying it, we were keeping references in the database and also
feeding the heed put_current methods with keys referenced inside
the database itself.
https://github.com/Kerollmops/heed/pull/108
245: Warn for when a key is too large for LMDB r=Kerollmops a=Kerollmops
Closes#191, and resolves#140.
Co-authored-by: Kerollmops <clement@meilisearch.com>
212: Introduce integration test on criteria r=Kerollmops a=ManyTheFish
- add pre-ranked dataset
- test each criterion 1 by 1
- test all criteria in several order
222: Move the `UpdateStore` into the http-ui crate r=Kerollmops a=Kerollmops
We no more need to have the `UpdateStore` inside of the mill crate as this is the job of the caller to stack the updates and sequentially give them to milli.
223: Update dataset links r=Kerollmops a=curquiza
Co-authored-by: many <maxime@meilisearch.com>
Co-authored-by: Many <legendre.maxime.isn@gmail.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
Co-authored-by: Clémentine Urquizar <clementine@meilisearch.com>
220: Make hard separators split phrase query r=Kerollmops a=ManyTheFish
hard separators will now split a phrase query as two sequential phrases (double-quoted strings):
the query `"Radioactive (Imagine Dragons)"` would be considered equivalent to `"Radioactive" "Imagine Dragons"` which as the little disadvantage of not keeping the order of the two (or more) separate phrases.
Fix#208
Co-authored-by: many <maxime@meilisearch.com>
Co-authored-by: Many <legendre.maxime.isn@gmail.com>
193: Fix primary key behavior r=Kerollmops a=MarinPostma
this pr:
- Adds early returns on empty document additions, avoiding error messages to be returned when adding no documents and no primary key was set.
- Changes the primary key inference logic to match that of legacy meilisearch.
close#194
Co-authored-by: Marin Postma <postma.marin@protonmail.com>
Co-authored-by: marin postma <postma.marin@protonmail.com>
202: Add field id word count docids database r=Kerollmops a=LegendreM
This PR introduces a new database, `field_id_word_count_docids`, that maps the number of words in an attribute with a list of document ids. This relation is limited to attributes that contain less than 11 words.
This database is used by the exactness criterion to know if a document has an attribute that contains exactly the query without any additional word.
Fix#165Fix#196
Related to [specifications:#36](https://github.com/meilisearch/specifications/pull/36)
Co-authored-by: many <maxime@meilisearch.com>
Co-authored-by: Many <legendre.maxime.isn@gmail.com>
203: Make the MatchingWords return the number of matching bytes r=Kerollmops a=LegendreM
Make the MatchingWords return the number of matching bytes using a custom Levenshtein algorithm.
Fix#138
Co-authored-by: many <maxime@meilisearch.com>
184: Transfer numbers and strings facets into the appropriate facet databases r=Kerollmops a=Kerollmops
This pull request is related to https://github.com/meilisearch/milli/issues/152 and changes the layout of the facets values, numbers and strings are now in dedicated databases and the user no more needs to define the type of the fields. No more conversion between the two types is done, numbers (floats and integers converted to f64) go to the facet float database and strings go to the strings facet database.
There is one related issue that I found regarding CSVs, the values in a CSV are always considered to be strings, [meilisearch/specifications#28](d916b57d74/text/0028-indexing-csv.md) fixes this issue by allowing the user to define the fields types using `:` in the "CSV Formatting Rules" section.
All previous tests on facets have been modified to pass again and I have also done hand-driven tests with the 115m songs dataset. Everything seems to be good!
Fixes#192.
Co-authored-by: Clément Renault <clement@meilisearch.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
- pass excluded document to criteria to remove them in higher levels of the bucket-sort
- merge already returned document with excluded documents to avoid duplicas
Related to #125 and #112Fix#170
This reverts commit 12fb509d84.
We revert this commit because it's causing the bug #150.
The initial algorithm we implemented for the stop_words was:
1. remove the stop_words from the dataset
2. keep the stop_words in the query to see if we can generate new words by
integrating typos or if the word was a prefix
=> This was causing the bug since, in the case of “The hobbit”, we were
**always** looking for something starting with “t he” or “th e”
instead of ignoring the word completely.
For now we are going to fix the bug by completely ignoring the
stop_words in the query.
This could cause another problem were someone mistyped a normal word and
ended up typing a stop_word.
For example imagine someone searching for the music “Won't he do it”.
If that person misplace one space and write “Won' the do it” then we
will loose a part of the request.
One fix would be to update our query tree to something like that:
---------------------
OR
OR
TOLERANT hobbit # the first option is to ignore the stop_word
AND
CONSECUTIVE # the second option is to do as we are doing
EXACT t # currently
EXACT he
TOLERANT hobbit
---------------------
This would increase drastically the size of our query tree on request
with a lot of stop_words. For example think of “The Lord Of The Rings”.
For now whatsoever we decided we were going to ignore this problem and consider
that it doesn't reduce too much the relevancy of the search to do that
while it improves the performances.
fixes after review
bump the version of the tokenizer
implement a first version of the stop_words
The front must provide a BTreeSet containing the stop words
The stop_words are set at None if an empty Set is provided
add the stop-words in the http-ui interface
Use maplit in the test
and remove all the useless drop(rtxn) at the end of all tests
Integrate the stop_words in the querytree
remove the stop_words from the querytree except if it was a prefix or a typo
more fixes after review
The front must provide a BTreeSet containing the stop words
The stop_words are set at None if an empty Set is provided
add the stop-words in the http-ui interface
Use maplit in the test
and remove all the useless drop(rtxn) at the end of all tests
