New snapshot (yes, it's wrong as well, it will get fixed later):
---
source: milli/src/update/word_prefix_pair_proximity_docids.rs
---
5 a 1 [101, ]
5 a 2 [101, ]
5 am 1 [101, ]
5 b 4 [101, ]
5 be 4 [101, ]
am a 3 [101, ]
amazing a 1 [100, ]
amazing a 2 [100, ]
amazing a 3 [100, ]
amazing an 1 [100, ]
amazing an 2 [100, ]
amazing b 2 [100, ]
amazing be 2 [100, ]
an a 1 [100, ]
an a 2 [100, 202, ]
an am 1 [100, ]
an b 3 [100, ]
an be 3 [100, ]
and a 2 [100, ]
and a 3 [100, ]
and a 4 [100, ]
and b 1 [100, ]
and be 1 [100, ]
d\0 0 [100, 202, ]
an an 2 [100, ]
and am 2 [100, ]
and an 3 [100, ]
at a 2 [100, 101, ]
at a 3 [100, ]
at am 2 [100, 101, ]
at an 1 [100, 202, ]
at an 3 [100, ]
at b 3 [101, ]
at b 4 [100, ]
at be 3 [101, ]
at be 4 [100, ]
beautiful a 2 [100, ]
beautiful a 3 [100, ]
beautiful a 4 [100, ]
beautiful am 3 [100, ]
beautiful an 2 [100, ]
beautiful an 4 [100, ]
bell a 2 [101, ]
bell a 4 [101, ]
bell am 4 [101, ]
extraordinary a 2 [202, ]
extraordinary a 3 [202, ]
extraordinary an 2 [202, ]
house a 4 [100, 202, ]
house a 4 [100, ]
house am 4 [100, ]
house an 3 [100, 202, ]
house b 2 [100, ]
house be 2 [100, ]
rings a 1 [101, ]
rings a 3 [101, ]
rings am 3 [101, ]
rings b 2 [101, ]
rings be 2 [101, ]
the a 3 [101, ]
the b 1 [101, ]
the be 1 [101, ]
556: Add EXISTS filter r=loiclec a=loiclec
## What does this PR do?
Fixes issue [#2484](https://github.com/meilisearch/meilisearch/issues/2484) in the meilisearch repo.
It creates a `field EXISTS` filter which selects all documents containing the `field` key.
For example, with the following documents:
```json
[{
"id": 0,
"colour": []
},
{
"id": 1,
"colour": ["blue", "green"]
},
{
"id": 2,
"colour": 145238
},
{
"id": 3,
"colour": null
},
{
"id": 4,
"colour": {
"green": []
}
},
{
"id": 5,
"colour": {}
},
{
"id": 6
}]
```
Then the filter `colour EXISTS` selects the ids `[0, 1, 2, 3, 4, 5]`. The filter `colour NOT EXISTS` selects `[6]`.
## Details
There is a new database named `facet-id-exists-docids`. Its keys are field ids and its values are bitmaps of all the document ids where the corresponding field exists.
To create this database, the indexing part of milli had to be adapted. The implementation there is basically copy/pasted from the code handling the `facet-id-f64-docids` database, with appropriate modifications in place.
There was an issue involving the flattening of documents during (re)indexing. Previously, the following JSON:
```json
{
"id": 0,
"colour": [],
"size": {}
}
```
would be flattened to:
```json
{
"id": 0
}
```
prior to being given to the extraction pipeline.
This transformation would lose the information that is needed to populate the `facet-id-exists-docids` database. Therefore, I have also changed the implementation of the `flatten-serde-json` crate. Now, as it traverses the Json, it keeps track of which key was encountered. Then, at the end, if a previously encountered key is not present in the flattened object, it adds that key to the object with an empty array as value. For example:
```json
{
"id": 0,
"colour": {
"green": [],
"blue": 1
},
"size": {}
}
```
becomes
```json
{
"id": 0,
"colour": [],
"colour.green": [],
"colour.blue": 1,
"size": []
}
```
Co-authored-by: Kerollmops <clement@meilisearch.com>
The idea is to directly create a sorted and merged list of bitmaps
in the form of a BTreeMap<FieldId, RoaringBitmap> instead of creating
a grenad::Reader where the keys are field_id and the values are docids.
Then we send that BTreeMap to the thing that handles TypedChunks, which
inserts its content into the database.
When a document deletion occurs, instead of deleting the document we mark it as deleted
in the new “soft deleted” bitmap. It is then removed from the search, and all the other
endpoints.
523: Improve geosearch error messages r=irevoire a=irevoire
Improve the geosearch error messages (#488).
And try to parse the string as specified in https://github.com/meilisearch/meilisearch/issues/2354
Co-authored-by: Tamo <tamo@meilisearch.com>
520: fix mistake in Settings initialization r=irevoire a=MarinPostma
fix settings not being correctly initialized and add a test to make sure that they are in the future.
fix https://github.com/meilisearch/meilisearch/issues/2358
Co-authored-by: ad hoc <postma.marin@protonmail.com>
505: normalize exact words r=curquiza a=MarinPostma
Normalize the exact words, as specified in the specification.
Co-authored-by: ad hoc <postma.marin@protonmail.com>
We need to store all the external id (primary key) in a hashmap
associated to their internal id during.
The smartstring remove heap allocation / memory usage and should
improve the cache locality.
467: optimize prefix database r=Kerollmops a=MarinPostma
This pr introduces two optimizations that greatly improve the speed of computing prefix databases.
- The time that it takes to create the prefix FST has been divided by 5 by inverting the way we iterated over the words FST.
- We unconditionally and needlessly checked for documents to remove in `word_prefix_pair`, which caused an iteration over the whole database.
Co-authored-by: ad hoc <postma.marin@protonmail.com>
436: Speed up the word prefix databases computation time r=Kerollmops a=Kerollmops
This PR depends on the fixes done in #431 and must be merged after it.
In this PR we will bring the `WordPrefixPairProximityDocids`, `WordPrefixDocids` and, `WordPrefixPositionDocids` update structures to a new era, a better era, where computing the word prefix pair proximities costs much fewer CPU cycles, an era where this update structure can use the, previously computed, set of new word docids from the newly indexed batch of documents.
---
The `WordPrefixPairProximityDocids` is an update structure, which means that it is an object that we feed with some parameters and which modifies the LMDB database of an index when asked for. This structure specifically computes the list of word prefix pair proximities, which correspond to a list of pairs of words associated with a proximity (the distance between both words) where the second word is not a word but a prefix e.g. `s`, `se`, `a`. This word prefix pair proximity is associated with the list of documents ids which contains the pair of words and prefix at the given proximity.
The origin of the performances issue that this struct brings is related to the fact that it starts its job from the beginning, it clears the LMDB database before rewriting everything from scratch, using the other LMDB databases to achieve that. I hope you understand that this is absolutely not an optimized way of doing things.
Co-authored-by: Clément Renault <clement@meilisearch.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
