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1012 Commits

Author SHA1 Message Date
ManyTheFish
2668f841d1 Fix update indexing 2022-08-17 15:03:37 +02:00
ManyTheFish
7384650d85 Update test to showcase the bug 2022-08-17 15:03:08 +02:00
Loïc Lecrenier
6cc975704d Add some documentation to facets.rs 2022-08-17 12:59:52 +02:00
Loïc Lecrenier
93252769af Apply review suggestions 2022-08-17 12:41:22 +02:00
Loïc Lecrenier
196f79115a Run cargo fmt 2022-08-17 12:28:33 +02:00
Loïc Lecrenier
ca97cb0eda Implement the IN filter operator 2022-08-17 12:28:33 +02:00
Loïc Lecrenier
cc7415bb31 Simplify FilterCondition code, made possible by the new NOT operator 2022-08-17 12:28:33 +02:00
Loïc Lecrenier
44744d9e67 Implement the simplified NOT operator 2022-08-17 12:28:33 +02:00
Loïc Lecrenier
01675771d5 Reimplement != filter to select all docids not selected by = 2022-08-17 12:28:33 +02:00
Loïc Lecrenier
258c3dd563 Make AND+OR filters n-ary (store a vector of subfilters instead of 2) 
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) ...)))))
 2022-08-17 12:28:33 +02:00
Loïc Lecrenier
39687908f1 Add documentation and comments to facets.rs 2022-08-17 12:26:49 +02:00
Loïc Lecrenier
8d4b21a005 Switch string facet levels indexation to new algo 
Write the algorithm once for both numbers and strings
 2022-08-17 12:26:49 +02:00
Loïc Lecrenier
cf0cd92ed4 Refactor Facets::execute to increase performance 2022-08-17 12:26:49 +02:00
Loïc Lecrenier
78d9f0622d cargo fmt 2022-08-17 12:21:24 +02:00
Loïc Lecrenier
4f9edf13d7 Remove commented-out function 2022-08-17 12:21:24 +02:00
Loïc Lecrenier
405555b401 Add some documentation to PrefixTrieNode 2022-08-17 12:21:24 +02:00
Loïc Lecrenier
1bc4788e59 Remove cached Allocations struct from wpppd indexing 2022-08-17 12:18:22 +02:00
Loïc Lecrenier
ef75a77464 Fix undefined behaviour caused by reusing key from the database 
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, ]
 2022-08-17 12:17:45 +02:00
Loïc Lecrenier
7309111433 Don't run block code in doc tests of word_pair_proximity_docids 2022-08-17 12:17:18 +02:00
Loïc Lecrenier
f6f8f543e1 Run cargo fmt 2022-08-17 12:17:18 +02:00
Loïc Lecrenier
34c991ea02 Add newlines in documentation of word_prefix_pair_proximity_docids 2022-08-17 12:17:18 +02:00
Loïc Lecrenier
06f3fd8c6d Add more comments to WordPrefixPairProximityDocids::execute 2022-08-17 12:17:18 +02:00
Loïc Lecrenier
474500362c Update wpppd snapshots 
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, ]
 2022-08-17 12:17:18 +02:00
Loïc Lecrenier
ea4a96761c Move content of readme for WordPrefixPairProximityDocids into the code 2022-08-17 12:05:37 +02:00
Loïc Lecrenier
220921628b Simplify and document WordPrefixPairProximityDocIds::execute 2022-08-17 11:59:19 +02:00
Loïc Lecrenier
044356d221 Optimise WordPrefixPairProximityDocIds merge operation 2022-08-17 11:59:18 +02:00
Loïc Lecrenier
d350114159 Add tests for WordPrefixPairProximityDocIds 2022-08-17 11:59:15 +02:00
Loïc Lecrenier
86807ca848 Refactor word prefix pair proximity indexation further 2022-08-17 11:59:13 +02:00
Loïc Lecrenier
306593144d Refactor word prefix pair proximity indexation 2022-08-17 11:59:00 +02:00
Loïc Lecrenier
dea00311b6 Add type annotations to remove compiler error 2022-08-16 09:19:30 +02:00
Loïc Lecrenier
6f49126223 Fix db_snap macro with inline parameter 2022-08-10 15:55:22 +02:00
Loïc Lecrenier
12920f2a4f Fix paths of snapshot tests 2022-08-10 15:53:46 +02:00
Loïc Lecrenier
ce560fdcb5 Add documentation for db_snap! 2022-08-10 15:53:46 +02:00
Loïc Lecrenier
748bb86b5b cargo fmt 2022-08-10 15:53:46 +02:00
Loïc Lecrenier
051f24f674 Switch to snapshot tests for search/matches/mod.rs 2022-08-10 15:53:46 +02:00
Loïc Lecrenier
d2e01528a6 Switch to snapshot tests for search/criteria/typo.rs 2022-08-10 15:53:46 +02:00
Loïc Lecrenier
a9c7d82693 Switch to snapshot tests for search/criteria/attribute.rs 2022-08-10 15:53:46 +02:00
Loïc Lecrenier
4bba2f41d7 Switch to snapshot tests for query_tree.rs 2022-08-10 15:53:46 +02:00
Loïc Lecrenier
8ac24d3114 Cargo fmt + fix compiler warnings/error 2022-08-10 15:53:46 +02:00
Loïc Lecrenier
6066256689 Add snapshot tests for indexing of word_prefix_pair_proximity_docids 2022-08-10 15:53:46 +02:00
Loïc Lecrenier
3a734af159 Add snapshot tests for Facets::execute 2022-08-10 15:53:46 +02:00
Loïc Lecrenier
b9907997e4 Remove old snapshot tests code 2022-08-10 15:53:46 +02:00
Loïc Lecrenier
ef889ade5d Refactor snapshot tests 2022-08-10 15:53:46 +02:00
Loïc Lecrenier
334098a7e0 Add index snapshot test helper function 2022-08-10 15:53:46 +02:00
ManyTheFish
b389be48a0 Factorize phrase computation 2022-08-08 10:37:31 +02:00
Loïc Lecrenier
58cb1c1bda Simplify unit tests in facet/filter.rs 2022-08-04 12:03:44 +02:00
Loïc Lecrenier
acff17fb88 Simplify indexing tests 2022-08-04 12:03:13 +02:00
bors[bot]
21284cf235
Merge #556 
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>
 2022-08-04 09:46:06 +00:00
bors[bot]
50f6524ff2
Merge #579 
579: Stop reindexing already indexed documents r=ManyTheFish a=irevoire

```
 % ./compare.sh indexing_stop-reindexing-unchanged-documents_cb5a1669.json indexing_main_eeba1960.json
group                                                                     indexing_main_eeba1960                 indexing_stop-reindexing-unchanged-documents_cb5a1669
-----                                                                     ----------------------                 -----------------------------------------------------
indexing/-geo-delete-facetedNumber-facetedGeo-searchable-                 1.03      2.0±0.22ms        ? ?/sec    1.00  1955.4±336.24µs        ? ?/sec
indexing/-movies-delete-facetedString-facetedNumber-searchable-           1.08     11.0±2.93ms        ? ?/sec    1.00     10.2±4.04ms        ? ?/sec
indexing/-movies-delete-facetedString-facetedNumber-searchable-nested-    1.00     15.1±3.89ms        ? ?/sec    1.14     17.1±5.18ms        ? ?/sec
indexing/-songs-delete-facetedString-facetedNumber-searchable-            1.26    59.2±12.01ms        ? ?/sec    1.00     47.1±8.52ms        ? ?/sec
indexing/-wiki-delete-searchable-                                         1.08   316.6±31.53ms        ? ?/sec    1.00   293.6±17.00ms        ? ?/sec
indexing/Indexing geo_point                                               1.01      60.9±0.31s        ? ?/sec    1.00      60.6±0.36s        ? ?/sec
indexing/Indexing movies in three batches                                 1.04      20.0±0.30s        ? ?/sec    1.00      19.2±0.25s        ? ?/sec
indexing/Indexing movies with default settings                            1.02      19.1±0.18s        ? ?/sec    1.00      18.7±0.24s        ? ?/sec
indexing/Indexing nested movies with default settings                     1.02      26.2±0.29s        ? ?/sec    1.00      25.9±0.22s        ? ?/sec
indexing/Indexing nested movies without any facets                        1.02      25.3±0.32s        ? ?/sec    1.00      24.7±0.26s        ? ?/sec
indexing/Indexing songs in three batches with default settings            1.00      66.7±0.41s        ? ?/sec    1.01      67.1±0.86s        ? ?/sec
indexing/Indexing songs with default settings                             1.00      58.3±0.90s        ? ?/sec    1.01      58.8±1.32s        ? ?/sec
indexing/Indexing songs without any facets                                1.00      54.5±1.43s        ? ?/sec    1.01      55.2±1.29s        ? ?/sec
indexing/Indexing songs without faceted numbers                           1.00      57.9±1.20s        ? ?/sec    1.01      58.4±0.93s        ? ?/sec
indexing/Indexing wiki                                                    1.00   1052.0±10.95s        ? ?/sec    1.02   1069.4±20.38s        ? ?/sec
indexing/Indexing wiki in three batches                                   1.00    1193.1±8.83s        ? ?/sec    1.00    1189.5±9.40s        ? ?/sec
indexing/Reindexing geo_point                                             3.22      67.5±0.73s        ? ?/sec    1.00      21.0±0.16s        ? ?/sec
indexing/Reindexing movies with default settings                          3.75      19.4±0.28s        ? ?/sec    1.00       5.2±0.05s        ? ?/sec
indexing/Reindexing songs with default settings                           8.90      61.4±0.91s        ? ?/sec    1.00       6.9±0.07s        ? ?/sec
indexing/Reindexing wiki                                                  1.00   1748.2±35.68s        ? ?/sec    1.00   1750.5±18.53s        ? ?/sec
```

tldr: We do not lose any performance on the normal indexing benchmark, but we get between 3 and 8 times faster on the reindexing benchmarks 👍 

Co-authored-by: Tamo <tamo@meilisearch.com>
 2022-08-04 08:10:37 +00:00
ManyTheFish
d6f9a60a32 fix: Remove whitespace trimming during document id validation 
fix #592
 2022-08-03 11:38:40 +02:00
Tamo
7fc35c5586
remove the useless prints 2022-08-02 10:31:22 +02:00
Tamo
f156d7dd3b
Stop reindexing already indexed documents 2022-08-02 10:31:20 +02:00
Loïc Lecrenier
07003704a8 Merge branch 'filter/field-exist' 2022-07-21 14:51:41 +02:00
ManyTheFish
cbb3b25459 Fix(Search): Fix phrase search candidates computation 
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".
 2022-07-21 10:04:30 +02:00
bors[bot]
941af58239
Merge #561 
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>
 2022-07-21 07:08:50 +00:00
Loïc Lecrenier
41a0ce07cb
Add a code comment, as suggested in PR review 
Co-authored-by: Many the fish <many@meilisearch.com>
 2022-07-20 16:20:35 +02:00
Loïc Lecrenier
1506683705 Avoid using too much memory when indexing facet-exists-docids 2022-07-19 14:42:35 +02:00
Loïc Lecrenier
d0eee5ff7a Fix compiler error 2022-07-19 13:54:30 +02:00
Loïc Lecrenier
aed8c69bcb Refactor indexation of the "facet-id-exists-docids" database 
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.
 2022-07-19 10:07:33 +02:00
Loïc Lecrenier
1eb1e73bb3 Add integration tests for the EXISTS filter 2022-07-19 10:07:33 +02:00
Loïc Lecrenier
4f0bd317df Remove custom implementation of BytesEncode/Decode for the FieldId 2022-07-19 10:07:33 +02:00
Loïc Lecrenier
80b962b4f4 Run cargo fmt 2022-07-19 10:07:33 +02:00
Loïc Lecrenier
c17d616250 Refactor index_documents_check_exists_database tests 2022-07-19 10:07:33 +02:00
Loïc Lecrenier
30bd4db0fc Simplify indexing task for facet_exists_docids database 2022-07-19 10:07:33 +02:00
Loïc Lecrenier
392472f4bb Apply suggestions from code review 
Co-authored-by: Tamo <tamo@meilisearch.com>
 2022-07-19 10:07:33 +02:00
Loïc Lecrenier
0388b2d463 Run cargo fmt 2022-07-19 10:07:33 +02:00
Loïc Lecrenier
dc64170a69 Improve syntax of EXISTS filter, allow “value NOT EXISTS” 2022-07-19 10:07:33 +02:00
Loïc Lecrenier
72452f0cb2 Implements the EXIST filter operator 2022-07-19 10:07:33 +02:00
Loïc Lecrenier
453d593ce8 Add a database containing the docids where each field exists 2022-07-19 10:07:33 +02:00
Many the fish
2d79720f5d
Update milli/src/search/matches/mod.rs 2022-07-18 17:48:04 +02:00
Many the fish
8ddb4e750b
Update milli/src/search/matches/mod.rs 2022-07-18 17:47:39 +02:00
Many the fish
a277daa1f2
Update milli/src/search/matches/mod.rs 2022-07-18 17:47:13 +02:00
Many the fish
fb794c6b5e
Update milli/src/search/matches/mod.rs 2022-07-18 17:46:00 +02:00
Many the fish
1237cfc249
Update milli/src/search/matches/mod.rs 2022-07-18 17:45:37 +02:00
Many the fish
d7fd5c58cd
Update milli/src/search/matches/mod.rs 2022-07-18 17:45:06 +02:00
Loïc Lecrenier
fc9f3f31e7 Change DocumentsBatchReader to access cursor and index at same time 
Otherwise it is not possible to iterate over all documents while
using the fields index at the same time.
 2022-07-18 16:08:14 +02:00
Loïc Lecrenier
ab1571cdec Simplify Transform::read_documents, enabled by enriched documents reader 2022-07-18 12:45:47 +02:00
Many the fish
e261ef64d7
Update milli/src/search/matches/mod.rs 
Co-authored-by: Clément Renault <clement@meilisearch.com>
 2022-07-18 10:18:51 +02:00
Many the fish
1da4ab5918
Update milli/src/search/matches/mod.rs 
Co-authored-by: Clément Renault <clement@meilisearch.com>
 2022-07-18 10:18:03 +02:00
Kerollmops
448114cc1c
Fix the benchmarks with the new indexation API 2022-07-12 15:22:09 +02:00
Kerollmops
25e768f31c
Fix another issue with the nested primary key selector 2022-07-12 15:14:07 +02:00
Kerollmops
192793ee38
Add some tests to check for the nested documents ids 2022-07-12 15:14:07 +02:00
Kerollmops
a892a4a79c
Introduce a function to extend from a JSON array of objects 2022-07-12 15:14:06 +02:00
Kerollmops
dc61105554
Fix the nested document id fetching function 2022-07-12 15:14:06 +02:00
Kerollmops
2eec290424
Check the validity of the latitute and longitude numbers 2022-07-12 15:14:06 +02:00
Kerollmops
5d149d631f
Remove tests for a function that no more exists 2022-07-12 15:14:06 +02:00
Kerollmops
0bbcc7b180
Expose the DocumentId struct to be sure to inject the generated ids 2022-07-12 15:14:06 +02:00
Kerollmops
d1a4da9812
Generate a real UUIDv4 when ids are auto-generated 2022-07-12 15:14:06 +02:00
Kerollmops
c8ebf0de47
Rename the validate function as an enriching function 2022-07-12 15:14:06 +02:00
Kerollmops
905af2a2e9
Use the primary key and external id in the transform 2022-07-12 15:14:05 +02:00
Kerollmops
742543091e
Constify the default primary key name 2022-07-12 14:55:52 +02:00
Kerollmops
5f1bfb73ee
Extract the primary key name and make it accessible 2022-07-12 14:55:52 +02:00
Kerollmops
6a0a0ae94f
Make the Transform read from an EnrichedDocumentsBatchReader 2022-07-12 14:55:52 +02:00
Kerollmops
dc3f092d07
Do not leak an internal grenad Error 2022-07-12 14:55:52 +02:00
Kerollmops
8ebf5eed0d
Make the nested primary key work 2022-07-12 14:55:52 +02:00
Kerollmops
19eb3b4708
Make sur that we do not accept floats as documents ids 2022-07-12 14:55:52 +02:00
Kerollmops
2ceeb51c37
Support the auto-generated ids when validating documents 2022-07-12 14:55:51 +02:00
Kerollmops
399eec5c01
Fix the indexation tests 2022-07-12 14:55:51 +02:00
Kerollmops
fcfc4caf8c
Move the Object type in the lib.rs file and use it everywhere 2022-07-12 14:55:51 +02:00
Kerollmops
0146175fe6
Introduce the validate_documents_batch function 2022-07-12 14:55:51 +02:00
Kerollmops
bdc4263883
Introduce the validate_documents_batch function 2022-07-12 14:55:51 +02:00
Kerollmops
e8297ad27e
Fix the tests for the new DocumentsBatchBuilder/Reader 2022-07-12 14:52:56 +02:00
Kerollmops
419ce3966c
Rework the DocumentsBatchBuilder/Reader to use grenad 2022-07-12 14:52:55 +02:00
Kerollmops
048e174efb
Do not allocate when parsing CSV headers 2022-07-12 14:52:55 +02:00
ManyTheFish
5d79617a56 Chores: Enhance smart-crop code comments 2022-07-07 16:28:09 +02:00
bors[bot]
ebddfdb9a3
Merge #578 
578: Bump uuid to 1.1.2 r=ManyTheFish a=Kerollmops

Just to [align the version with Meilisearch](https://github.com/meilisearch/meilisearch/pull/2584).

Co-authored-by: Kerollmops <clement@meilisearch.com>
 2022-07-05 14:56:08 +00:00
Kerollmops
1bfdcfc84f
Bump uuid to 1.1.2 2022-07-05 16:23:36 +02:00
Tamo
250be9fe6c
put the threshold back to 10k 2022-07-05 15:57:44 +02:00
Tamo
b61efd09fc
Makes the internal soft deleted error a UserError 2022-07-05 15:34:45 +02:00
Tamo
eaf28b0628
Apply review suggestions 
Co-authored-by: Clément Renault <clement@meilisearch.com>
 2022-07-05 15:30:33 +02:00
Tamo
3b309f654a
Fasten the document deletion 
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.
 2022-07-05 15:30:33 +02:00
Dmytro Gordon
3ff03a3f5f Fix not equal filter when field contains both number and strings 2022-06-27 15:55:17 +03:00
Kerollmops
238692a8e7
Introduce the copy_to_path method on the Index 2022-06-22 16:49:47 +02:00
bors[bot]
290a40b7a5
Merge #564 
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>
 2022-06-22 13:48:33 +00:00
Kerollmops
d7c248042b
Rename the limitedTo parameter into maxTotalHits 2022-06-22 12:00:48 +02:00
Kerollmops
d2f84a9d9e
Improve the estimatedNbHits when distinct is enabled 2022-06-22 11:39:21 +02:00
ManyTheFish
a0ab90a4d7 Avoid having an ending separator before crop marker 2022-06-16 18:23:57 +02:00
ManyTheFish
177154828c Extends deletion tests 2022-06-13 17:34:16 +02:00
ManyTheFish
0d1d354052 Ensure that Index methods are not bypassed by Meilisearch 2022-06-13 17:34:11 +02:00
bors[bot]
f1d848bb9a
Merge #552 
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>
 2022-06-09 14:56:44 +00:00
Tamo
90afde435b
fix escaped quotes in filter 2022-06-09 16:03:49 +02:00
Kerollmops
445d5474cc
Add the pagination_limited_to setting to the database 2022-06-08 18:14:27 +02:00
Kerollmops
69931e50d2
Add the max_values_by_facet setting to the database 2022-06-08 17:54:56 +02:00
Kerollmops
52a494bd3b
Add the new pagination.limited_to and faceting.max_values_per_facet settings 2022-06-08 17:15:36 +02:00
Kerollmops
2a505503b3
Change the number of facet values returned by default to 100 2022-06-08 15:58:57 +02:00
Kerollmops
bae4007447
Remove the hard limit on the number of facet values returned 2022-06-08 15:58:57 +02:00
Tamo
d0aaa7ff00
Fix wrong internal ids assignments 2022-06-07 15:49:33 +02:00
ad hoc
31776fdc3f
add failing test 2022-06-07 15:49:33 +02:00
ManyTheFish
d212dc6b8b Remove useless newline 2022-06-02 18:22:56 +02:00
ManyTheFish
7aabe42ae0 Refactor matching words 2022-06-02 17:59:04 +02:00
ManyTheFish
86ac8568e6 Use Charabia in milli 2022-06-02 16:59:11 +02:00
bors[bot]
74d1914a64
Merge #535 
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>
 2022-06-01 14:30:50 +00:00
bors[bot]
582930dbbb
Merge #538 
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>
 2022-05-30 08:20:34 +00:00
ad hoc
25fc576696
review changes 2022-05-24 14:15:33 +02:00
ad hoc
69dc4de80f
change &Option<Set> to Option<&Set> 2022-05-24 12:14:55 +02:00
ad hoc
ac975cc747
cache context's exact words 2022-05-24 09:43:17 +02:00
ad hoc
8993fec8a3
return optional exact words 2022-05-24 09:15:49 +02:00
Matthias Wright
754f48a4fb Improves ranking rules error message 2022-05-20 21:25:43 +02:00
Kerollmops
cd7c6e19ed
Reintroduce the max values by facet limit 2022-05-18 15:57:57 +02:00
ManyTheFish
137434a1c8 Add some implementation on MatchBounds 2022-05-17 15:57:09 +02:00
bors[bot]
08c6d50cd1
Merge #531 
531: fix the mixed dataset geosearch indexing bug r=Kerollmops a=irevoire

port #529 to main

Co-authored-by: Tamo <tamo@meilisearch.com>
 2022-05-16 16:06:36 +00:00
bors[bot]
cf3e574cb4
Merge #530 
530: fix the searchable fields bug when a field is nested r=Kerollmops a=irevoire

port #528 to main

Co-authored-by: Tamo <tamo@meilisearch.com>
 2022-05-16 15:52:30 +00:00
Tamo
0af399a6d7
fix the mixed dataset geosearch indexing bug 2022-05-16 17:37:45 +02:00
Tamo
f586028f9a
fix the searchable fields bug when a field is nested 
Update milli/src/index.rs

Co-authored-by: Clément Renault <clement@meilisearch.com>
 2022-05-16 17:24:36 +02:00
bors[bot]
e1e85267fd
Merge #526 
526: remove useless comment r=irevoire a=MarinPostma



Co-authored-by: ad hoc <postma.marin@protonmail.com>
 2022-05-16 10:01:43 +00:00
bors[bot]
51809eb260
Merge #525 
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>
 2022-05-04 15:47:32 +00:00
Tamo
484a9ddb27
Simplify the error creation with thiserror and a smol friendly macro 2022-05-04 17:24:00 +02:00
bors[bot]
65e6aa0de2
Merge #523 
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>
 2022-05-04 13:36:11 +00:00
Tamo
c55368ddd4
apply code suggestion 
Co-authored-by: Kerollmops <kero@meilisearch.com>
 2022-05-04 14:11:03 +02:00
ad hoc
5ad5d56f7e
remove useless comment 2022-05-04 10:43:54 +02:00
bors[bot]
0c2c8af44e
Merge #520 
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>
 2022-05-03 15:32:18 +00:00
Kerollmops
211c8763b9
Make sure that we do not generate too long keys 2022-05-03 10:03:15 +02:00
Kerollmops
7e47031bdc
Add a test for long keys in LMDB 2022-05-03 10:03:13 +02:00
Tamo
3cb1f6d0a1
improve geosearch error messages 2022-05-02 19:20:47 +02:00
ad hoc
1ee3d6ae33
fix mistake in Settings initialization 2022-04-29 16:24:25 +02:00
bors[bot]
9db86aac51
Merge #518 
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>
 2022-04-28 09:04:36 +00:00
Kerollmops
7d1c2d97bf
Return facets even when there is no values associated to it 2022-04-26 17:59:53 +02:00
bors[bot]
d388ea0f9d
Merge #506 
506: fix cargo warnings r=Kerollmops a=MarinPostma

fix cargo warnings


Co-authored-by: ad hoc <postma.marin@protonmail.com>
 2022-04-26 15:45:20 +00:00
ad hoc
5c29258e8e
fix cargo warnings 2022-04-26 17:33:11 +02:00
Tamo
f19d2dc548
Only flatten the required fields 
apply review comments

Co-authored-by: Kerollmops <kero@meilisearch.com>
 2022-04-26 12:33:46 +02:00
bors[bot]
8010eca9c7
Merge #505 
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>
 2022-04-25 09:35:32 +00:00
ad hoc
2e0089d5ff
normalize exact words 2022-04-21 15:38:40 +02:00
ad hoc
3a2451fcba
add test normalize exact words 2022-04-21 13:52:09 +02:00
Clément Renault
eb5830aa40
Add a test to make sure that long words are handled 2022-04-21 13:45:28 +02:00
ad hoc
8b14090927
fix min-word-len-for-typo not reset properly 2022-04-19 15:20:16 +02:00
bors[bot]
ea4bb9402f
Merge #483 
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>
 2022-04-19 11:42:32 +00:00
ManyTheFish
f1115e274f Use Copy impl of FormatOption instead of clonning 2022-04-19 10:35:50 +02:00
Tamo
00f78d6b5a
Apply code suggestions 
Co-authored-by: Clément Renault <clement@meilisearch.com>
 2022-04-14 11:14:08 +02:00
Tamo
399fba16bb
only flatten an object if it's nested 2022-04-14 11:14:08 +02:00
Tamo
ee64f4a936
Use smartstring to store the external id in our hashmap 
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.
 2022-04-13 21:22:07 +02:00
ad hoc
dda28d7415
exclude excluded canditates from search result candidates 2022-04-13 12:10:35 +02:00
ad hoc
bbb6728d2f
add distinct attributes to cli 2022-04-13 12:10:35 +02:00
ManyTheFish
5809d3ae0d Add first benchmarks on formatting 2022-04-12 16:31:58 +02:00
ManyTheFish
827cedcd15 Add format option structure 2022-04-12 13:42:14 +02:00
ManyTheFish
011f8210ed Make compute_matches more rust idiomatic 2022-04-12 10:19:02 +02:00
ManyTheFish
a16de5de84 Symplify format and remove intermediate function 2022-04-08 11:20:41 +02:00
ManyTheFish
a769e09dfa Make token_crop_bounds more rust idiomatic 2022-04-07 20:15:14 +02:00
ManyTheFish
c8ed1675a7 Add some documentation 2022-04-07 17:32:13 +02:00
ManyTheFish
b1905dfa24 Make split_best_frequency returns references instead of owned data 2022-04-07 17:05:44 +02:00
Irevoire
4f3ce6d9cd
nested fields 2022-04-07 16:58:46 +02:00
ad hoc
b799f3326b
rename merge_nothing to merge_ignore_values 2022-04-05 18:44:35 +02:00
ManyTheFish
fa7d3a37c0 Make some cleaning and add comments 2022-04-05 17:48:56 +02:00
ManyTheFish
3bb1e35ada Fix match count 2022-04-05 17:48:45 +02:00
ManyTheFish
56e0edd621 Put crop markers direclty around words 2022-04-05 17:41:32 +02:00
ManyTheFish
a93cd8c61c Fix prefix highlight with special chars 2022-04-05 17:41:32 +02:00
ManyTheFish
b3f0f39106 Make some cleaning 2022-04-05 17:41:32 +02:00
ManyTheFish
6dc345bc53 Test and Fix prefix highlight 2022-04-05 17:41:32 +02:00
ManyTheFish
bd30ee97b8 Keep separators at start of the croped string 2022-04-05 17:41:32 +02:00
ManyTheFish
29c5f76d7f Use new matcher in http-ui 2022-04-05 17:41:32 +02:00
ManyTheFish
734d0899d3 Publish Matcher 2022-04-05 17:41:32 +02:00
ManyTheFish
4428cb5909 Add some tests and fix some corner cases 2022-04-05 17:41:32 +02:00
ManyTheFish
844f546a8b Add matches algorithm V1 2022-04-05 17:41:32 +02:00
ManyTheFish
3be1790803 Add crop algorithm with naive match algorithm 2022-04-05 17:41:32 +02:00
ManyTheFish
d96e72e5dc Create formater with some tests 2022-04-05 17:41:32 +02:00
ad hoc
201fea0fda
limit extract_word_docids memory usage 2022-04-05 14:14:15 +02:00
ad hoc
5cfd3d8407
add exact attributes documentation 2022-04-05 14:10:22 +02:00
ad hoc
b85cd4983e
remove field_id_from_position 2022-04-05 09:50:34 +02:00
ad hoc
ab185a59b5
fix infos 2022-04-05 09:46:56 +02:00
ad hoc
1810927dbd
rephrase exact_attributes doc 2022-04-04 21:04:49 +02:00
ad hoc
b7694c34f5
remove println 2022-04-04 21:00:07 +02:00
ad hoc
6cabd47c32
fix typo in comment 2022-04-04 20:59:20 +02:00
ad hoc
6b2c2509b2
fix bug in exact search 2022-04-04 20:54:03 +02:00
ad hoc
56b4f5dce2
add exact prefix to query_docids 2022-04-04 20:54:03 +02:00
ad hoc
21ae4143b1
add exact_word_prefix to Context 2022-04-04 20:54:03 +02:00
ad hoc
e8f06f6c06
extract exact_word_prefix_docids 2022-04-04 20:54:03 +02:00
ad hoc
6dd2e4ffbd
introduce exact_word_prefix database in index 2022-04-04 20:54:03 +02:00
ad hoc
ba0bb29cd8
refactor WordPrefixDocids to take dbs instead of indexes 2022-04-04 20:54:02 +02:00
ad hoc
c4c6e35352
query exact_word_docids in resolve_query_tree 2022-04-04 20:54:02 +02:00
ad hoc
8d46a5b0b5
extract exact word docids 2022-04-04 20:54:02 +02:00
ad hoc
0a77be4ec0
introduce exact_word_docids db 2022-04-04 20:54:02 +02:00
ad hoc
5f9f82757d
refactor spawn_extraction_task 2022-04-04 20:54:02 +02:00
ad hoc
f82d4b36eb
introduce exact attribute setting 2022-04-04 20:54:02 +02:00
ad hoc
c882d8daf0
add test for exact words 2022-04-04 20:54:01 +02:00
ad hoc
7e9d56a9e7
disable typos on exact words 2022-04-04 20:54:01 +02:00
ad hoc
30a2711bac
rename serde module to serde_impl module 
needed because of issues with rustfmt
 2022-04-04 20:10:55 +02:00
ad hoc
0fd55db21c
fmt 2022-04-04 20:10:55 +02:00
ad hoc
559e46be5e
fix bad rebase bug 2022-04-04 20:10:55 +02:00
ad hoc
8b1e5d9c6d
add test for exact words 2022-04-04 20:10:55 +02:00
ad hoc
774fa8f065
disable typos on exact words 2022-04-04 20:10:55 +02:00
ad hoc
9bbffb8fee
add exact words setting 2022-04-04 20:10:54 +02:00
ad hoc
853b4a520f
fmt 2022-04-04 10:41:46 +02:00
ad hoc
1941072bb2
implement Copy on Setting 2022-04-04 10:41:46 +02:00
ad hoc
fdaf45aab2
replace hardcoded value with constant in TestContext 2022-04-04 10:41:46 +02:00
ad hoc
950a740bd4
refactor typos for readability 2022-04-04 10:41:46 +02:00
ad hoc
66020cd923
rename min_word_len* to use plain letter numbers 2022-04-04 10:41:46 +02:00
ad hoc
4c4b336ecb
rename min word len for typo error 2022-04-01 11:17:03 +02:00
ad hoc
286dd7b2e4
rename min_word_len_2_typo 2022-04-01 11:17:03 +02:00
ad hoc
55af85db3c
add tests for min_word_len_for_typo 2022-04-01 11:17:02 +02:00
ad hoc
9102de5500
fix error message 2022-04-01 11:17:02 +02:00
ad hoc
a1a3a49bc9
dynamic minimum word len for typos in query tree builder 2022-04-01 11:17:02 +02:00
ad hoc
5a24e60572
introduce word len for typo setting 2022-04-01 11:17:02 +02:00
ad hoc
9fe40df960
add word derivations tests 2022-04-01 11:05:18 +02:00
ad hoc
d5ddc6b080
fix 2 typos word derivation bug 2022-04-01 10:51:22 +02:00
ad hoc
3e34981d9b
add test for authorize_typos in update 2022-03-31 14:12:00 +02:00
ad hoc
6ef3bb9d83
fmt 2022-03-31 14:06:23 +02:00
ad hoc
f782fe2062
add authorize_typo_test 2022-03-31 10:08:39 +02:00
ad hoc
c4653347fd
add authorize typo setting 2022-03-31 10:05:44 +02:00
bors[bot]
90276d9a2d
Merge #472 
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>
 2022-03-16 15:33:11 +00:00
ManyTheFish
49d59d88c2 Remove useless variables in proximity 2022-03-16 16:12:52 +01:00
Bruno Casali
adc71742c8 Move string concat to the struct instead of in the calling 2022-03-16 10:26:12 -03:00
Bruno Casali
4822fe1beb Add a better error message when the filterable attrs are empty 
Fixes https://github.com/meilisearch/meilisearch/issues/2140
 2022-03-15 18:13:59 -03:00
bors[bot]
ad4c982c68
Merge #439 
439: Optimize typo criterion r=Kerollmops a=MarinPostma

This pr implements a couple of optimization for the typo criterion:

- clamp max typo on concatenated query words to 1: By considering that a concatenated query word is a typo, we clamp the max number of typos allowed o it to 1. This is useful because we noticed that concatenated query words often introduced words with 2 typos in queries that otherwise didn't allow for 2 typo words.

- Make typos on the first letter count for 2. This change is a big performance gain: by considering the typos on the first letter to count as 2 typos, we drastically restrict the search space for 1 typo, and if we reach 2 typos, the search space is reduced as well, as we only consider: (2 typos ∩ correct first letter) ∪ (wrong first letter ∩ 1 typo) instead of 2 typos anywhere in the word.

## benches
```
group                                                                                                    main                                   typo
-----                                                                                                    ----                                   ----
smol-songs.csv: asc + default/Notstandskomitee                                                           2.51      5.8±0.01ms        ? ?/sec    1.00      2.3±0.01ms        ? ?/sec
smol-songs.csv: asc + default/charles                                                                    2.48      3.0±0.01ms        ? ?/sec    1.00   1190.9±1.29µs        ? ?/sec
smol-songs.csv: asc + default/charles mingus                                                             5.56     10.8±0.01ms        ? ?/sec    1.00   1935.3±1.00µs        ? ?/sec
smol-songs.csv: asc + default/david                                                                      1.65      3.9±0.00ms        ? ?/sec    1.00      2.4±0.01ms        ? ?/sec
smol-songs.csv: asc + default/david bowie                                                                3.34     12.5±0.02ms        ? ?/sec    1.00      3.7±0.00ms        ? ?/sec
smol-songs.csv: asc + default/john                                                                       1.00   1849.7±3.74µs        ? ?/sec    1.01   1875.1±4.65µs        ? ?/sec
smol-songs.csv: asc + default/marcus miller                                                              4.32     15.7±0.01ms        ? ?/sec    1.00      3.6±0.01ms        ? ?/sec
smol-songs.csv: asc + default/michael jackson                                                            3.31     12.5±0.01ms        ? ?/sec    1.00      3.8±0.00ms        ? ?/sec
smol-songs.csv: asc + default/tamo                                                                       1.05    565.4±0.86µs        ? ?/sec    1.00    539.3±1.22µs        ? ?/sec
smol-songs.csv: asc + default/thelonious monk                                                            3.49     11.5±0.01ms        ? ?/sec    1.00      3.3±0.00ms        ? ?/sec
smol-songs.csv: asc/Notstandskomitee                                                                     2.59      5.6±0.02ms        ? ?/sec    1.00      2.2±0.01ms        ? ?/sec
smol-songs.csv: asc/charles                                                                              6.05      2.1±0.00ms        ? ?/sec    1.00    347.8±0.60µs        ? ?/sec
smol-songs.csv: asc/charles mingus                                                                       14.46     9.4±0.01ms        ? ?/sec    1.00    649.2±0.97µs        ? ?/sec
smol-songs.csv: asc/david                                                                                3.87      2.4±0.00ms        ? ?/sec    1.00    618.2±0.69µs        ? ?/sec
smol-songs.csv: asc/david bowie                                                                          10.14     9.8±0.01ms        ? ?/sec    1.00    970.8±1.55µs        ? ?/sec
smol-songs.csv: asc/john                                                                                 1.00    546.5±1.10µs        ? ?/sec    1.00    547.1±2.11µs        ? ?/sec
smol-songs.csv: asc/marcus miller                                                                        11.45    10.4±0.06ms        ? ?/sec    1.00    907.9±1.37µs        ? ?/sec
smol-songs.csv: asc/michael jackson                                                                      10.56     9.7±0.01ms        ? ?/sec    1.00    919.6±1.03µs        ? ?/sec
smol-songs.csv: asc/tamo                                                                                 1.03     43.3±0.18µs        ? ?/sec    1.00     42.2±0.23µs        ? ?/sec
smol-songs.csv: asc/thelonious monk                                                                      4.16     10.7±0.02ms        ? ?/sec    1.00      2.6±0.00ms        ? ?/sec
smol-songs.csv: basic filter: <=/Notstandskomitee                                                        1.00     95.7±0.20µs        ? ?/sec    1.15   109.6±10.40µs        ? ?/sec
smol-songs.csv: basic filter: <=/charles                                                                 1.00     27.8±0.15µs        ? ?/sec    1.01     27.9±0.18µs        ? ?/sec
smol-songs.csv: basic filter: <=/charles mingus                                                          1.72    119.2±0.67µs        ? ?/sec    1.00     69.1±0.13µs        ? ?/sec
smol-songs.csv: basic filter: <=/david                                                                   1.00     22.3±0.33µs        ? ?/sec    1.05     23.4±0.19µs        ? ?/sec
smol-songs.csv: basic filter: <=/david bowie                                                             1.59     86.9±0.79µs        ? ?/sec    1.00     54.5±0.31µs        ? ?/sec
smol-songs.csv: basic filter: <=/john                                                                    1.00     17.9±0.06µs        ? ?/sec    1.06     18.9±0.15µs        ? ?/sec
smol-songs.csv: basic filter: <=/marcus miller                                                           1.65    102.7±1.63µs        ? ?/sec    1.00     62.3±0.18µs        ? ?/sec
smol-songs.csv: basic filter: <=/michael jackson                                                         1.76    128.2±1.85µs        ? ?/sec    1.00     72.9±0.19µs        ? ?/sec
smol-songs.csv: basic filter: <=/tamo                                                                    1.00     17.9±0.13µs        ? ?/sec    1.05     18.7±0.20µs        ? ?/sec
smol-songs.csv: basic filter: <=/thelonious monk                                                         1.53    157.5±2.38µs        ? ?/sec    1.00    102.8±0.88µs        ? ?/sec
smol-songs.csv: basic filter: TO/Notstandskomitee                                                        1.00    100.9±4.36µs        ? ?/sec    1.04    105.0±8.25µs        ? ?/sec
smol-songs.csv: basic filter: TO/charles                                                                 1.00     28.4±0.36µs        ? ?/sec    1.03     29.4±0.33µs        ? ?/sec
smol-songs.csv: basic filter: TO/charles mingus                                                          1.71    118.1±1.08µs        ? ?/sec    1.00     68.9±0.26µs        ? ?/sec
smol-songs.csv: basic filter: TO/david                                                                   1.00     24.0±0.26µs        ? ?/sec    1.03     24.6±0.43µs        ? ?/sec
smol-songs.csv: basic filter: TO/david bowie                                                             1.72     95.2±0.30µs        ? ?/sec    1.00     55.2±0.14µs        ? ?/sec
smol-songs.csv: basic filter: TO/john                                                                    1.00     18.8±0.09µs        ? ?/sec    1.06     19.8±0.17µs        ? ?/sec
smol-songs.csv: basic filter: TO/marcus miller                                                           1.61    102.4±1.65µs        ? ?/sec    1.00     63.4±0.24µs        ? ?/sec
smol-songs.csv: basic filter: TO/michael jackson                                                         1.77    132.1±1.41µs        ? ?/sec    1.00     74.5±0.59µs        ? ?/sec
smol-songs.csv: basic filter: TO/tamo                                                                    1.00     18.2±0.14µs        ? ?/sec    1.05     19.2±0.46µs        ? ?/sec
smol-songs.csv: basic filter: TO/thelonious monk                                                         1.49    150.8±1.92µs        ? ?/sec    1.00    101.3±0.44µs        ? ?/sec
smol-songs.csv: basic placeholder/                                                                       1.00     27.3±0.07µs        ? ?/sec    1.03     28.0±0.05µs        ? ?/sec
smol-songs.csv: basic with quote/"Notstandskomitee"                                                      1.00    122.4±0.17µs        ? ?/sec    1.03    125.6±0.16µs        ? ?/sec
smol-songs.csv: basic with quote/"charles"                                                               1.00     88.8±0.30µs        ? ?/sec    1.00     88.4±0.15µs        ? ?/sec
smol-songs.csv: basic with quote/"charles" "mingus"                                                      1.00    685.2±0.74µs        ? ?/sec    1.01    689.4±6.07µs        ? ?/sec
smol-songs.csv: basic with quote/"david"                                                                 1.00    161.6±0.42µs        ? ?/sec    1.01    162.6±0.17µs        ? ?/sec
smol-songs.csv: basic with quote/"david" "bowie"                                                         1.00    731.7±0.73µs        ? ?/sec    1.02    743.1±0.77µs        ? ?/sec
smol-songs.csv: basic with quote/"john"                                                                  1.00    267.1±0.33µs        ? ?/sec    1.01    270.9±0.33µs        ? ?/sec
smol-songs.csv: basic with quote/"marcus" "miller"                                                       1.00    138.7±0.31µs        ? ?/sec    1.02    140.9±0.13µs        ? ?/sec
smol-songs.csv: basic with quote/"michael" "jackson"                                                     1.01    841.4±0.72µs        ? ?/sec    1.00    833.8±0.92µs        ? ?/sec
smol-songs.csv: basic with quote/"tamo"                                                                  1.01    189.2±0.26µs        ? ?/sec    1.00    188.2±0.71µs        ? ?/sec
smol-songs.csv: basic with quote/"thelonious" "monk"                                                     1.00   1100.5±1.36µs        ? ?/sec    1.01   1111.7±2.17µs        ? ?/sec
smol-songs.csv: basic without quote/Notstandskomitee                                                     3.40      7.9±0.02ms        ? ?/sec    1.00      2.3±0.02ms        ? ?/sec
smol-songs.csv: basic without quote/charles                                                              2.57    494.4±0.89µs        ? ?/sec    1.00    192.5±0.18µs        ? ?/sec
smol-songs.csv: basic without quote/charles mingus                                                       1.29      2.8±0.02ms        ? ?/sec    1.00      2.1±0.01ms        ? ?/sec
smol-songs.csv: basic without quote/david                                                                1.95    623.8±0.90µs        ? ?/sec    1.00    319.2±1.22µs        ? ?/sec
smol-songs.csv: basic without quote/david bowie                                                          1.12      5.9±0.00ms        ? ?/sec    1.00      5.2±0.00ms        ? ?/sec
smol-songs.csv: basic without quote/john                                                                 1.24   1340.9±2.25µs        ? ?/sec    1.00   1084.7±7.76µs        ? ?/sec
smol-songs.csv: basic without quote/marcus miller                                                        7.97     14.6±0.01ms        ? ?/sec    1.00   1826.0±6.84µs        ? ?/sec
smol-songs.csv: basic without quote/michael jackson                                                      1.19      3.9±0.00ms        ? ?/sec    1.00      3.3±0.00ms        ? ?/sec
smol-songs.csv: basic without quote/tamo                                                                 1.65    737.7±3.58µs        ? ?/sec    1.00    446.7±0.51µs        ? ?/sec
smol-songs.csv: basic without quote/thelonious monk                                                      1.16      4.5±0.02ms        ? ?/sec    1.00      3.9±0.04ms        ? ?/sec
smol-songs.csv: big filter/Notstandskomitee                                                              3.27      7.6±0.02ms        ? ?/sec    1.00      2.3±0.01ms        ? ?/sec
smol-songs.csv: big filter/charles                                                                       8.26   1957.5±1.37µs        ? ?/sec    1.00    236.8±0.34µs        ? ?/sec
smol-songs.csv: big filter/charles mingus                                                                18.49    11.2±0.06ms        ? ?/sec    1.00    607.7±3.03µs        ? ?/sec
smol-songs.csv: big filter/david                                                                         3.78      2.4±0.00ms        ? ?/sec    1.00    622.8±0.80µs        ? ?/sec
smol-songs.csv: big filter/david bowie                                                                   9.00     12.0±0.01ms        ? ?/sec    1.00   1336.0±3.17µs        ? ?/sec
smol-songs.csv: big filter/john                                                                          1.00    554.2±0.95µs        ? ?/sec    1.01    560.4±0.79µs        ? ?/sec
smol-songs.csv: big filter/marcus miller                                                                 18.09    12.0±0.01ms        ? ?/sec    1.00    664.7±0.60µs        ? ?/sec
smol-songs.csv: big filter/michael jackson                                                               8.43     12.0±0.01ms        ? ?/sec    1.00   1421.6±1.37µs        ? ?/sec
smol-songs.csv: big filter/tamo                                                                          1.00     86.3±0.14µs        ? ?/sec    1.01     87.3±0.21µs        ? ?/sec
smol-songs.csv: big filter/thelonious monk                                                               5.55     14.3±0.02ms        ? ?/sec    1.00      2.6±0.01ms        ? ?/sec
smol-songs.csv: desc + default/Notstandskomitee                                                          2.52      5.8±0.01ms        ? ?/sec    1.00      2.3±0.01ms        ? ?/sec
smol-songs.csv: desc + default/charles                                                                   3.04      2.7±0.01ms        ? ?/sec    1.00    893.4±1.08µs        ? ?/sec
smol-songs.csv: desc + default/charles mingus                                                            6.77     10.3±0.01ms        ? ?/sec    1.00   1520.8±1.90µs        ? ?/sec
smol-songs.csv: desc + default/david                                                                     1.39      5.7±0.00ms        ? ?/sec    1.00      4.1±0.00ms        ? ?/sec
smol-songs.csv: desc + default/david bowie                                                               2.34     15.8±0.02ms        ? ?/sec    1.00      6.7±0.01ms        ? ?/sec
smol-songs.csv: desc + default/john                                                                      1.00      2.5±0.00ms        ? ?/sec    1.02      2.6±0.01ms        ? ?/sec
smol-songs.csv: desc + default/marcus miller                                                             5.06     14.5±0.02ms        ? ?/sec    1.00      2.9±0.01ms        ? ?/sec
smol-songs.csv: desc + default/michael jackson                                                           2.64     14.1±0.05ms        ? ?/sec    1.00      5.4±0.00ms        ? ?/sec
smol-songs.csv: desc + default/tamo                                                                      1.00    567.0±0.65µs        ? ?/sec    1.00    565.7±0.97µs        ? ?/sec
smol-songs.csv: desc + default/thelonious monk                                                           3.55     11.6±0.02ms        ? ?/sec    1.00      3.3±0.00ms        ? ?/sec
smol-songs.csv: desc/Notstandskomitee                                                                    2.58      5.6±0.02ms        ? ?/sec    1.00      2.2±0.02ms        ? ?/sec
smol-songs.csv: desc/charles                                                                             6.04      2.1±0.00ms        ? ?/sec    1.00    348.1±0.57µs        ? ?/sec
smol-songs.csv: desc/charles mingus                                                                      14.51     9.4±0.01ms        ? ?/sec    1.00    646.7±0.99µs        ? ?/sec
smol-songs.csv: desc/david                                                                               3.86      2.4±0.00ms        ? ?/sec    1.00    620.7±2.46µs        ? ?/sec
smol-songs.csv: desc/david bowie                                                                         10.10     9.8±0.01ms        ? ?/sec    1.00    973.9±3.31µs        ? ?/sec
smol-songs.csv: desc/john                                                                                1.00    545.5±0.78µs        ? ?/sec    1.00    547.2±0.48µs        ? ?/sec
smol-songs.csv: desc/marcus miller                                                                       11.39    10.3±0.01ms        ? ?/sec    1.00    903.7±0.95µs        ? ?/sec
smol-songs.csv: desc/michael jackson                                                                     10.51     9.7±0.01ms        ? ?/sec    1.00    924.7±2.02µs        ? ?/sec
smol-songs.csv: desc/tamo                                                                                1.01     43.2±0.33µs        ? ?/sec    1.00     42.6±0.35µs        ? ?/sec
smol-songs.csv: desc/thelonious monk                                                                     4.19     10.8±0.03ms        ? ?/sec    1.00      2.6±0.00ms        ? ?/sec
smol-songs.csv: prefix search/a                                                                          1.00   1008.7±1.00µs        ? ?/sec    1.00   1005.5±0.91µs        ? ?/sec
smol-songs.csv: prefix search/b                                                                          1.00    885.0±0.70µs        ? ?/sec    1.01    890.6±1.11µs        ? ?/sec
smol-songs.csv: prefix search/i                                                                          1.00   1051.8±1.25µs        ? ?/sec    1.00   1056.6±4.12µs        ? ?/sec
smol-songs.csv: prefix search/s                                                                          1.00    724.7±1.77µs        ? ?/sec    1.00    721.6±0.59µs        ? ?/sec
smol-songs.csv: prefix search/x                                                                          1.01    212.4±0.21µs        ? ?/sec    1.00    210.9±0.38µs        ? ?/sec
smol-songs.csv: proximity/7000 Danses Un Jour Dans Notre Vie                                             18.55    48.5±0.09ms        ? ?/sec    1.00      2.6±0.03ms        ? ?/sec
smol-songs.csv: proximity/The Disneyland Sing-Along Chorus                                               8.41     56.7±0.45ms        ? ?/sec    1.00      6.7±0.05ms        ? ?/sec
smol-songs.csv: proximity/Under Great Northern Lights                                                    15.74    38.9±0.14ms        ? ?/sec    1.00      2.5±0.00ms        ? ?/sec
smol-songs.csv: proximity/black saint sinner lady                                                        11.82    40.1±0.13ms        ? ?/sec    1.00      3.4±0.02ms        ? ?/sec
smol-songs.csv: proximity/les dangeureuses 1960                                                          6.90     26.1±0.13ms        ? ?/sec    1.00      3.8±0.04ms        ? ?/sec
smol-songs.csv: typo/Arethla Franklin                                                                    14.93     5.8±0.01ms        ? ?/sec    1.00    390.1±1.89µs        ? ?/sec
smol-songs.csv: typo/Disnaylande                                                                         3.18      7.3±0.01ms        ? ?/sec    1.00      2.3±0.00ms        ? ?/sec
smol-songs.csv: typo/dire straights                                                                      5.55     15.2±0.02ms        ? ?/sec    1.00      2.7±0.00ms        ? ?/sec
smol-songs.csv: typo/fear of the duck                                                                    28.03    20.0±0.03ms        ? ?/sec    1.00    713.3±1.54µs        ? ?/sec
smol-songs.csv: typo/indochie                                                                            19.25  1851.4±2.38µs        ? ?/sec    1.00     96.2±0.13µs        ? ?/sec
smol-songs.csv: typo/indochien                                                                           14.66  1887.7±3.18µs        ? ?/sec    1.00    128.8±0.18µs        ? ?/sec
smol-songs.csv: typo/klub des loopers                                                                    37.73    18.0±0.02ms        ? ?/sec    1.00    476.7±0.73µs        ? ?/sec
smol-songs.csv: typo/michel depech                                                                       10.17     5.8±0.01ms        ? ?/sec    1.00    565.8±1.16µs        ? ?/sec
smol-songs.csv: typo/mongus                                                                              15.33  1897.4±3.44µs        ? ?/sec    1.00    123.8±0.13µs        ? ?/sec
smol-songs.csv: typo/stromal                                                                             14.63  1859.3±2.40µs        ? ?/sec    1.00    127.1±0.29µs        ? ?/sec
smol-songs.csv: typo/the white striper                                                                   10.83     9.4±0.01ms        ? ?/sec    1.00    866.0±0.98µs        ? ?/sec
smol-songs.csv: typo/thelonius monk                                                                      14.40     3.8±0.00ms        ? ?/sec    1.00    261.5±1.30µs        ? ?/sec
smol-songs.csv: words/7000 Danses / Le Baiser / je me trompe de mots                                     5.54     70.8±0.09ms        ? ?/sec    1.00     12.8±0.03ms        ? ?/sec
smol-songs.csv: words/Bring Your Daughter To The Slaughter but now this is not part of the title         3.48    119.8±0.14ms        ? ?/sec    1.00     34.4±0.04ms        ? ?/sec
smol-songs.csv: words/The Disneyland Children's Sing-Alone song                                          8.98     71.9±0.12ms        ? ?/sec    1.00      8.0±0.01ms        ? ?/sec
smol-songs.csv: words/les liaisons dangeureuses 1793                                                     11.88    37.4±0.07ms        ? ?/sec    1.00      3.1±0.01ms        ? ?/sec
smol-songs.csv: words/seven nation mummy                                                                 22.86    23.4±0.04ms        ? ?/sec    1.00   1024.8±1.57µs        ? ?/sec
smol-songs.csv: words/the black saint and the sinner lady and the good doggo                             2.76    124.4±0.15ms        ? ?/sec    1.00     45.1±0.09ms        ? ?/sec
smol-songs.csv: words/whathavenotnsuchforth and a good amount of words to pop to match the first one     2.52    107.0±0.23ms        ? ?/sec    1.00     42.4±0.66ms        ? ?/sec

group                                                                                    main-wiki                              typo-wiki
-----                                                                                    ---------                              ---------
smol-wiki-articles.csv: basic placeholder/                                               1.02     13.7±0.02µs        ? ?/sec    1.00     13.4±0.03µs        ? ?/sec
smol-wiki-articles.csv: basic with quote/"film"                                          1.02    409.8±0.67µs        ? ?/sec    1.00    402.6±0.48µs        ? ?/sec
smol-wiki-articles.csv: basic with quote/"france"                                        1.00    325.9±0.91µs        ? ?/sec    1.00    326.4±0.49µs        ? ?/sec
smol-wiki-articles.csv: basic with quote/"japan"                                         1.00    218.4±0.26µs        ? ?/sec    1.01    220.5±0.20µs        ? ?/sec
smol-wiki-articles.csv: basic with quote/"machine"                                       1.00    143.0±0.12µs        ? ?/sec    1.04    148.8±0.21µs        ? ?/sec
smol-wiki-articles.csv: basic with quote/"miles" "davis"                                 1.00     11.7±0.06ms        ? ?/sec    1.00     11.8±0.01ms        ? ?/sec
smol-wiki-articles.csv: basic with quote/"mingus"                                        1.00      4.4±0.03ms        ? ?/sec    1.00      4.4±0.00ms        ? ?/sec
smol-wiki-articles.csv: basic with quote/"rock" "and" "roll"                             1.00     43.5±0.08ms        ? ?/sec    1.01     43.8±0.06ms        ? ?/sec
smol-wiki-articles.csv: basic with quote/"spain"                                         1.00    137.3±0.35µs        ? ?/sec    1.05    144.4±0.23µs        ? ?/sec
smol-wiki-articles.csv: basic without quote/film                                         1.00    125.3±0.30µs        ? ?/sec    1.06    133.1±0.37µs        ? ?/sec
smol-wiki-articles.csv: basic without quote/france                                       1.21   1782.6±1.65µs        ? ?/sec    1.00   1477.0±1.39µs        ? ?/sec
smol-wiki-articles.csv: basic without quote/japan                                        1.28   1363.9±0.80µs        ? ?/sec    1.00   1064.3±1.79µs        ? ?/sec
smol-wiki-articles.csv: basic without quote/machine                                      1.73    760.3±0.81µs        ? ?/sec    1.00    439.6±0.75µs        ? ?/sec
smol-wiki-articles.csv: basic without quote/miles davis                                  1.03     17.0±0.03ms        ? ?/sec    1.00     16.5±0.02ms        ? ?/sec
smol-wiki-articles.csv: basic without quote/mingus                                       1.07      5.3±0.01ms        ? ?/sec    1.00      5.0±0.00ms        ? ?/sec
smol-wiki-articles.csv: basic without quote/rock and roll                                1.01     63.9±0.18ms        ? ?/sec    1.00     63.0±0.07ms        ? ?/sec
smol-wiki-articles.csv: basic without quote/spain                                        2.07    667.4±0.93µs        ? ?/sec    1.00    322.8±0.29µs        ? ?/sec
smol-wiki-articles.csv: prefix search/c                                                  1.00    343.1±0.47µs        ? ?/sec    1.00    344.0±0.34µs        ? ?/sec
smol-wiki-articles.csv: prefix search/g                                                  1.00    374.4±3.42µs        ? ?/sec    1.00    374.1±0.44µs        ? ?/sec
smol-wiki-articles.csv: prefix search/j                                                  1.00    359.9±0.31µs        ? ?/sec    1.00    361.2±0.79µs        ? ?/sec
smol-wiki-articles.csv: prefix search/q                                                  1.01    102.0±0.12µs        ? ?/sec    1.00    101.4±0.32µs        ? ?/sec
smol-wiki-articles.csv: prefix search/t                                                  1.00    536.7±1.39µs        ? ?/sec    1.00    534.3±0.84µs        ? ?/sec
smol-wiki-articles.csv: prefix search/x                                                  1.00    400.9±1.00µs        ? ?/sec    1.00    399.5±0.45µs        ? ?/sec
smol-wiki-articles.csv: proximity/april paris                                            3.86     14.4±0.01ms        ? ?/sec    1.00      3.7±0.01ms        ? ?/sec
smol-wiki-articles.csv: proximity/diesel engine                                          12.98    10.4±0.01ms        ? ?/sec    1.00    803.5±1.13µs        ? ?/sec
smol-wiki-articles.csv: proximity/herald sings                                           1.00     12.7±0.06ms        ? ?/sec    5.29     67.1±0.09ms        ? ?/sec
smol-wiki-articles.csv: proximity/tea two                                                6.48   1452.1±2.78µs        ? ?/sec    1.00    224.1±0.38µs        ? ?/sec
smol-wiki-articles.csv: typo/Disnaylande                                                 3.89      8.5±0.01ms        ? ?/sec    1.00      2.2±0.01ms        ? ?/sec
smol-wiki-articles.csv: typo/aritmetric                                                  3.78     10.3±0.01ms        ? ?/sec    1.00      2.7±0.00ms        ? ?/sec
smol-wiki-articles.csv: typo/linax                                                       8.91   1426.7±0.97µs        ? ?/sec    1.00    160.1±0.18µs        ? ?/sec
smol-wiki-articles.csv: typo/migrosoft                                                   7.48   1417.3±5.84µs        ? ?/sec    1.00    189.5±0.88µs        ? ?/sec
smol-wiki-articles.csv: typo/nympalidea                                                  3.96      7.2±0.01ms        ? ?/sec    1.00   1810.1±2.03µs        ? ?/sec
smol-wiki-articles.csv: typo/phytogropher                                                3.71      7.2±0.01ms        ? ?/sec    1.00   1934.3±6.51µs        ? ?/sec
smol-wiki-articles.csv: typo/sisan                                                       6.44   1497.2±1.38µs        ? ?/sec    1.00    232.7±0.94µs        ? ?/sec
smol-wiki-articles.csv: typo/the fronce                                                  6.92      2.9±0.00ms        ? ?/sec    1.00    418.0±1.76µs        ? ?/sec
smol-wiki-articles.csv: words/Abraham machin                                             16.63    10.8±0.01ms        ? ?/sec    1.00    649.7±1.08µs        ? ?/sec
smol-wiki-articles.csv: words/Idaho Bellevue pizza                                       27.15    25.6±0.03ms        ? ?/sec    1.00    944.2±5.07µs        ? ?/sec
smol-wiki-articles.csv: words/Kameya Tokujirō mingus monk                                26.87    40.7±0.05ms        ? ?/sec    1.00   1515.3±2.73µs        ? ?/sec
smol-wiki-articles.csv: words/Ulrich Hensel meilisearch milli                            11.99    48.8±0.10ms        ? ?/sec    1.00      4.1±0.02ms        ? ?/sec
smol-wiki-articles.csv: words/the black saint and the sinner lady and the good doggo     4.90    110.0±0.15ms        ? ?/sec    1.00     22.4±0.03ms        ? ?/sec

```

Co-authored-by: mpostma <postma.marin@protonmail.com>
Co-authored-by: ad hoc <postma.marin@protonmail.com>
 2022-03-15 16:43:36 +00:00
ad hoc
3f24555c3d
custom fst automatons 2022-03-15 17:38:35 +01:00
ad hoc
628c835a22
fix tests 2022-03-15 17:38:34 +01:00
bors[bot]
8efac33b53
Merge #467 
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>
 2022-03-15 16:14:35 +00:00
ad hoc
d127c57f2d
review edits 2022-03-15 17:12:48 +01:00
ad hoc
d633ac5b9d
optimize word prefix pair 2022-03-15 16:37:22 +01:00
ad hoc
d68fe2b3c7
optimize word prefix fst 2022-03-15 16:36:48 +01:00
Clément Renault
0c5f4ed7de
Apply suggestions 
Co-authored-by: Many <many@meilisearch.com>
 2022-03-15 14:18:29 +01:00
Kerollmops
21ec334dcc
Fix the compilation error of the dependency versions 2022-03-15 11:17:45 +01:00