It is undefined behavior to keep a reference to the database while
modifying it, we were keeping references in the database and also
feeding the heed put_current methods with keys referenced inside
the database itself.
https://github.com/Kerollmops/heed/pull/108
245: Warn for when a key is too large for LMDB r=Kerollmops a=Kerollmops
Closes#191, and resolves#140.
Co-authored-by: Kerollmops <clement@meilisearch.com>
212: Introduce integration test on criteria r=Kerollmops a=ManyTheFish
- add pre-ranked dataset
- test each criterion 1 by 1
- test all criteria in several order
222: Move the `UpdateStore` into the http-ui crate r=Kerollmops a=Kerollmops
We no more need to have the `UpdateStore` inside of the mill crate as this is the job of the caller to stack the updates and sequentially give them to milli.
223: Update dataset links r=Kerollmops a=curquiza
Co-authored-by: many <maxime@meilisearch.com>
Co-authored-by: Many <legendre.maxime.isn@gmail.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
Co-authored-by: Clémentine Urquizar <clementine@meilisearch.com>
220: Make hard separators split phrase query r=Kerollmops a=ManyTheFish
hard separators will now split a phrase query as two sequential phrases (double-quoted strings):
the query `"Radioactive (Imagine Dragons)"` would be considered equivalent to `"Radioactive" "Imagine Dragons"` which as the little disadvantage of not keeping the order of the two (or more) separate phrases.
Fix#208
Co-authored-by: many <maxime@meilisearch.com>
Co-authored-by: Many <legendre.maxime.isn@gmail.com>
193: Fix primary key behavior r=Kerollmops a=MarinPostma
this pr:
- Adds early returns on empty document additions, avoiding error messages to be returned when adding no documents and no primary key was set.
- Changes the primary key inference logic to match that of legacy meilisearch.
close#194
Co-authored-by: Marin Postma <postma.marin@protonmail.com>
Co-authored-by: marin postma <postma.marin@protonmail.com>
204: Decorrelate Distinct, Asc/Desc, Filterable fields from the faceted fields r=Kerollmops a=Kerollmops
This PR decorrelates the fields that need to be stored in facet databases (big inverted indexes for fast access) from the filterable fields, the previously named faceted fields are now named filterable fields and are the union of the distinct attribute, all the Asc/Desc criteria and, the filterable fields.
I added two tests to make sure that the engine was correctly generating the faceted databases when a distinct attribute or an Asc/Desc criteria were added, and one to make sure that it was impossible to filter on a non-filterable field even if it was a faceted one.
Note that the `AttributesForFacetting` has also been renamed into `FilterableAttributes`. But it will be the Transplant's job to do that on the API, this change is only visible to the milli's library users.
- Related to https://github.com/meilisearch/transplant/issues/187.
- Fixes#161 by returning the documents that don't have the Asc/Desc field at the end of the bucket.
- Fixes#168.
- Fixes#152.
Co-authored-by: Kerollmops <clement@meilisearch.com>
Co-authored-by: Marin Postma <postma.marin@protonmail.com>
Co-authored-by: many <maxime@meilisearch.com>
202: Add field id word count docids database r=Kerollmops a=LegendreM
This PR introduces a new database, `field_id_word_count_docids`, that maps the number of words in an attribute with a list of document ids. This relation is limited to attributes that contain less than 11 words.
This database is used by the exactness criterion to know if a document has an attribute that contains exactly the query without any additional word.
Fix#165Fix#196
Related to [specifications:#36](https://github.com/meilisearch/specifications/pull/36)
Co-authored-by: many <maxime@meilisearch.com>
Co-authored-by: Many <legendre.maxime.isn@gmail.com>
203: Make the MatchingWords return the number of matching bytes r=Kerollmops a=LegendreM
Make the MatchingWords return the number of matching bytes using a custom Levenshtein algorithm.
Fix#138
Co-authored-by: many <maxime@meilisearch.com>
184: Transfer numbers and strings facets into the appropriate facet databases r=Kerollmops a=Kerollmops
This pull request is related to https://github.com/meilisearch/milli/issues/152 and changes the layout of the facets values, numbers and strings are now in dedicated databases and the user no more needs to define the type of the fields. No more conversion between the two types is done, numbers (floats and integers converted to f64) go to the facet float database and strings go to the strings facet database.
There is one related issue that I found regarding CSVs, the values in a CSV are always considered to be strings, [meilisearch/specifications#28](d916b57d74/text/0028-indexing-csv.md) fixes this issue by allowing the user to define the fields types using `:` in the "CSV Formatting Rules" section.
All previous tests on facets have been modified to pass again and I have also done hand-driven tests with the 115m songs dataset. Everything seems to be good!
Fixes#192.
Co-authored-by: Clément Renault <clement@meilisearch.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
- pass excluded document to criteria to remove them in higher levels of the bucket-sort
- merge already returned document with excluded documents to avoid duplicas
Related to #125 and #112Fix#170
