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
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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
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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
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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
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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
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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
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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
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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
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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
