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Author SHA1 Message Date
Ewan Higgs
2ce025a906 Fixes after rebase to fix new issues. 2022-10-25 20:58:31 +02:00
Ewan Higgs
17f7922bfc Remove unneeded lifetimes. 2022-10-25 20:49:04 +02:00
Ewan Higgs
6b2fe94192 Fixes for clippy bringing us down to 18 remaining issues. 
This brings us a step closer to enforcing clippy on each build.
 2022-10-25 20:49:02 +02:00
Loïc Lecrenier
be302fd250 Remove outdated workaround for duplicate words in phrase search 2022-10-24 15:27:06 +02:00
Loïc Lecrenier
d76d0cb1bf Merge branch 'main' into word-pair-proximity-docids-refactor 2022-10-24 15:23:00 +02:00
Loïc Lecrenier
a983129613 Apply suggestions from code review 2022-10-20 09:49:37 +02:00
bors[bot]
f11a4087da
Merge #665 
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>
 2022-10-20 07:19:46 +00:00
Loïc Lecrenier
176ffd23f5 Fix compile error after rebasing wppd-refactor 2022-10-18 10:40:26 +02:00
Loïc Lecrenier
e6e76fbefe Improve performance of resolve_phrase at the cost of some relevancy 2022-10-18 10:37:34 +02:00
Loïc Lecrenier
830a7c0c7a Use resolve_phrase function for exactness criteria as well 2022-10-18 10:37:34 +02:00
Loïc Lecrenier
18d578dfc4 Adjust some algorithms using DBs of word pair proximities 2022-10-18 10:37:34 +02:00
Loïc Lecrenier
1dbbd8694f Rename StrStrU8Codec to U8StrStrCodec and reorder its fields 2022-10-18 10:37:34 +02:00
Many the fish
81919a35a2
Update milli/src/search/criteria/initial.rs 
Co-authored-by: Clément Renault <clement@meilisearch.com>
 2022-10-17 18:23:20 +02:00
Many the fish
516e838eb4
Update milli/src/search/criteria/initial.rs 
Co-authored-by: Clément Renault <clement@meilisearch.com>
 2022-10-17 18:23:15 +02:00
Kerollmops
6603437cb1
Introduce an indexation abortion function when indexing documents 2022-10-17 17:28:03 +02:00
ManyTheFish
6f55e7844c Add some code comments 2022-10-17 14:41:57 +02:00
ManyTheFish
cf203b7fde Take filter in account when computing the pages candidates 2022-10-17 14:13:44 +02:00
ManyTheFish
d71bc1e69f Compute an exact count when using distinct 2022-10-17 14:13:44 +02:00
ManyTheFish
a396806343 Add settings to force milli to exhaustively compute the total number of hits 2022-10-17 14:13:44 +02:00
Ewan Higgs
beb987d3d1 Fixing piles of clippy errors. 
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.
 2022-10-13 22:02:54 +02:00
Akshay Kulkarni
85f3028317
remove underscore and introduce back word_documents_count 2022-10-13 13:21:59 +05:30
Akshay Kulkarni
8195fc6141
revert removal of word_documents_count method 2022-10-13 13:14:27 +05:30
Akshay Kulkarni
32f825d442
move default implementation of word_pair_frequency to TestContext 2022-10-13 12:57:50 +05:30
Akshay Kulkarni
ff8b2d4422
formatting 2022-10-13 12:44:08 +05:30
Akshay Kulkarni
6cb8b46900
use word_pair_frequency and remove word_documents_count 2022-10-13 12:43:11 +05:30
Akshay Kulkarni
8c9245149e
format file 2022-10-12 15:27:56 +05:30
Akshay Kulkarni
63e79a9039
update comment 2022-10-12 13:36:48 +05:30
Akshay Kulkarni
7f9680f0a0
Enhance word splitting strategy 2022-10-12 13:18:23 +05:30
ManyTheFish
bf750e45a1 Fix word removal issue 2022-09-01 12:10:47 +02:00
ManyTheFish
a38608fe59 Add test mixing phrased and no-phrased words 2022-09-01 12:02:10 +02:00
Irevoire
f6024b3269
Remove the artifacts of the past 2022-08-23 16:10:38 +02:00
ManyTheFish
5391e3842c replace optional_words by term_matching_strategy 2022-08-22 17:47:19 +02:00
ManyTheFish
993aa1321c Fix query tree building 2022-08-18 17:56:06 +02:00
ManyTheFish
bff9653050 Fix remove count 2022-08-18 17:36:30 +02:00
ManyTheFish
9640976c79 Rename TermMatchingPolicies 2022-08-18 17:36:08 +02:00
bors[bot]
afc10acd19
Merge #596 
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>
 2022-08-18 11:24:32 +00:00
Loïc Lecrenier
9b6602cba2 Avoid cloning FilterCondition in filter array parsing 2022-08-18 13:06:57 +02:00
Loïc Lecrenier
c51dcad51b Don't recompute filterable fields in evaluation of IN[] filter 2022-08-18 10:59:21 +02:00
bors[bot]
e4a52e6e45
Merge #594 
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>
 2022-08-17 13:22:52 +00:00
ManyTheFish
8c3f1a9c39 Remove useless lifetime declaration 2022-08-17 15:20:43 +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
dea00311b6 Add type annotations to remove compiler error 2022-08-16 09:19:30 +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
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
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
d0eee5ff7a Fix compiler error 2022-07-19 13:54:30 +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
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
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
399eec5c01
Fix the indexation tests 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
ManyTheFish
5d79617a56 Chores: Enhance smart-crop code comments 2022-07-07 16:28:09 +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
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
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
69931e50d2
Add the max_values_by_facet setting to the database 2022-06-08 17:54:56 +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
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
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
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]
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
ad hoc
5c29258e8e
fix cargo warnings 2022-04-26 17:33:11 +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
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
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
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
c4c6e35352
query exact_word_docids in resolve_query_tree 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
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
853b4a520f
fmt 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
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
a1a3a49bc9
dynamic minimum word len for typos in query tree builder 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
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
Kerollmops
21ec334dcc
Fix the compilation error of the dependency versions 2022-03-15 11:17:45 +01:00
ad hoc
13de251047
rewrite word pair distance gathering 2022-02-03 15:57:20 +01:00
mpostma
7541ab99cd
review changes 2022-02-02 12:59:01 +01:00
mpostma
d0aabde502
optimize 2 typos case 2022-02-02 12:56:09 +01:00
mpostma
55e6cb9c7b
typos on first letter counts as 2 2022-02-02 12:56:09 +01:00
mpostma
642c01d0dc
set max typos on ngram to 1 2022-02-02 12:56:08 +01:00
ad hoc
d852dc0d2b
fix phrase search 2022-02-01 20:21:33 +01:00
Marin Postma
0c84a40298 document batch support 
reusable transform

rework update api

add indexer config

fix tests

review changes

Co-authored-by: Clément Renault <clement@meilisearch.com>

fmt
 2022-01-19 12:40:20 +01:00
Tamo
01968d7ca7
ensure we get no documents and no error when filtering on an empty db 2022-01-18 11:40:30 +01:00
bors[bot]
8f4499090b
Merge #433 
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>
 2022-01-17 14:06:53 +00:00
Tamo
d1ac40ea14
fix(filter): Fix two bugs. 
- 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
 2022-01-17 13:51:46 +01:00
Samyak S Sarnayak
2d7607734e
Run cargo fmt on matching_words.rs 2022-01-17 13:04:33 +05:30
Samyak S Sarnayak
5ab505be33
Fix highlight by replacing num_graphemes_from_bytes 
num_graphemes_from_bytes has been renamed in the tokenizer to
num_chars_from_bytes.

Highlight now works correctly!
 2022-01-17 13:02:55 +05:30
Samyak S Sarnayak
e752bd06f7
Fix matching_words tests to compile successfully 
The tests still fail due to a bug in https://github.com/meilisearch/tokenizer/pull/59
 2022-01-17 11:37:45 +05:30
Samyak S Sarnayak
30247d70cd
Fix search highlight for non-unicode chars 
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
 2022-01-17 11:37:44 +05:30
Tamo
98a365aaae
store the geopoint in three dimensions 2021-12-14 12:21:24 +01:00
Clément Renault
25faef67d0
Remove the database setup in the filter_depth test 2021-12-09 11:57:53 +01:00
Clément Renault
65519bc04b
Test that empty filters return a None 2021-12-09 11:57:53 +01:00
Clément Renault
ef59762d8e
Prefer returning None instead of the Empty Filter state 2021-12-09 11:57:52 +01:00
Clément Renault
ee856a7a46
Limit the max filter depth to 2000 2021-12-07 17:36:45 +01:00
Clément Renault
32bd9f091f
Detect the filters that are too deep and return an error 2021-12-07 17:20:11 +01:00
Clément Renault
90f49eab6d
Check the filter max depth limit and reject the invalid ones 2021-12-07 16:32:48 +01:00
Marin Postma
6eb47ab792 remove update_id in UpdateBuilder 2021-11-16 13:07:04 +01:00
Irevoire
0ea0146e04
implement deref &str on the tokens 2021-11-09 11:34:10 +01:00
Tamo
7483c7513a
fix the filterable fields 2021-11-07 01:52:19 +01:00
Tamo
e5af3ac65c
rename the filter_condition.rs to filter.rs 2021-11-06 16:37:55 +01:00
Tamo
6831c23449
merge with main 2021-11-06 16:34:30 +01:00
Tamo
b249989bef
fix most of the tests 2021-11-06 01:32:12 +01:00
Tamo
27a6a26b4b
makes the parse function part of the filter_parser 2021-11-05 10:46:54 +01:00
Tamo
76d961cc77
implements the last errors 2021-11-04 17:42:06 +01:00
Tamo
8234f9fdf3
recreate most filter error except for the geosearch 2021-11-04 17:24:55 +01:00
Tamo
07a5ffb04c
update http-ui 2021-11-04 15:52:22 +01:00
Tamo
a58bc5bebb
update milli with the new parser_filter 2021-11-04 15:02:36 +01:00
Tamo
76a2adb7c3
re-enable the tests in the parser and start the creation of an error type 2021-11-02 17:35:17 +01:00
many
ed6db19681
Fix PR comments 2021-10-28 11:18:32 +02:00
many
2be755ce75
Lower error check, already check in meilisearch 2021-10-27 19:50:41 +02:00
many
3599df77f0
Change some error messages 2021-10-27 19:33:01 +02:00
bors[bot]
d7943fe225
Merge #402 
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>
 2021-10-26 09:55:28 +00:00
Clémentine Urquizar
208903ddde
Revert "Replacing pest with nom " 2021-10-25 11:58:00 +02:00
marin postma
2e62925a6e
fix tests 2021-10-25 10:26:42 +02:00
marin postma
8d70b01714
optimize document deserialization 2021-10-25 10:26:42 +02:00
Tamo
1327807caa
add some error messages 2021-10-22 19:00:33 +02:00
Tamo
c8d03046bf
add a check on the fid in the geosearch 2021-10-22 18:08:18 +02:00
Tamo
3942b3732f
re-implement the geosearch 2021-10-22 18:03:39 +02:00
Tamo
7cd9109e2f
lowercase value extracted from Token 2021-10-22 17:50:15 +02:00
Tamo
e25ca9776f
start updating the exposed function to makes other modules happy 2021-10-22 17:23:22 +02:00
Tamo
6c9165b6a8
provide a helper to parse the token but to not handle the errors 2021-10-22 16:52:13 +02:00
Tamo
efb2f8b325
convert the errors 2021-10-22 16:38:35 +02:00
Tamo
c27870e765
integrate a first version without any error handling 2021-10-22 14:33:18 +02:00
Tamo
01dedde1c9
update some names and move some parser out of the lib.rs 2021-10-22 01:59:38 +02:00
Tamo
c634d43ac5
add a simple test on the filters with an integer 2021-10-21 17:10:27 +02:00
Tamo
6c15f50899
rewrite the parser logic 2021-10-21 16:45:42 +02:00