3670: Fix addition deletion bug r=irevoire a=irevoire
The first commit of this PR is a revert of https://github.com/meilisearch/meilisearch/pull/3667. It re-enable the auto-batching of addition and deletion of tasks. No new changes have been introduced outside of `milli`. So all the changes you see on the autobatcher have actually already been reviewed.
It fixes https://github.com/meilisearch/meilisearch/issues/3440.
### What was happening?
The issue was that the `external_documents_ids` generated in the `transform` were used in a very strange way that wasn’t compatible with the deletion of documents.
Instead of doing a clear merge between the external document IDs of the DB and the one returned by the transform + writing it on disk, we were doing some weird tricks with the soft-deleted to avoid writing the fst on disk as much as possible.
The new algorithm may be a bit slower but is way more straightforward and doesn’t change depending on if the soft deletion was used or not. Here is a list of the changes introduced:
1. We now do a clear distinction between the `new_external_documents_ids` coming from the transform and only held on RAM and the `external_documents_ids` coming from the DB.
2. The `new_external_documents_ids` (coming out of the transform) are now represented as an `fst`. We don't need to struggle with the hard, soft distinction + the soft_deleted => That's easier to understand
3. When indexing documents, we merge the `external_documents_ids` coming from the DB and the `new_external_documents_ids` coming from the transform.
### Other things introduced in this PR
Since we constantly have to write small, very specialized fuzzers for this kind of bug, we decided to push the one used to reproduce this bug.
It's not perfect, but it's easy to improve in the future.
It'll also run for as long as possible on every merge on the main branch.
Co-authored-by: Tamo <tamo@meilisearch.com>
Co-authored-by: Loïc Lecrenier <loic.lecrenier@icloud.com>
In PR #2773, I added the `chinese`, `hebrew`, `japanese` and `thai`
feature flags to allow melisearch to be built without huge specialed
tokenizations that took up 90% of the melisearch binary size.
Unfortunately, due to some recent changes, this doesn't work anymore.
The problem lies in excessive use of the `default` feature flag, which
infects the dependency graph.
Instead of adding `default-features = false` here and there, it's easier
and more future-proof to not declare `default` in `milli` and
`meilisearch-types`. I've renamed it to `all-tokenizers`, which also
makes it a bit clearer what it's about.
Conflicts | resolution
----------|-----------
Cargo.lock | added mimalloc
Cargo.toml | took origin/main version
milli/src/search/criteria/exactness.rs | deleted after checking it was only clippy changes
milli/src/search/query_tree.rs | deleted after checking it was only clippy changes
3571: Introduce two filters to select documents with `null` and empty fields r=irevoire a=Kerollmops
# Pull Request
## Related issue
This PR implements the `X IS NULL`, `X IS NOT NULL`, `X IS EMPTY`, `X IS NOT EMPTY` filters that [this comment](https://github.com/meilisearch/product/discussions/539#discussioncomment-5115884) is describing in a very detailed manner.
## What does this PR do?
### `IS NULL` and `IS NOT NULL`
This PR will be exposed as a prototype for now. Below is the copy/pasted version of a spec that defines this filter.
- `IS NULL` matches fields that `EXISTS` AND `= IS NULL`
- `IS NOT NULL` matches fields that `NOT EXISTS` OR `!= IS NULL`
1. `{"name": "A", "price": null}`
2. `{"name": "A", "price": 10}`
3. `{"name": "A"}`
`price IS NULL` would match 1
`price IS NOT NULL` or `NOT price IS NULL` would match 2,3
`price EXISTS` would match 1, 2
`price NOT EXISTS` or `NOT price EXISTS` would match 3
common query : `(price EXISTS) AND (price IS NOT NULL)` would match 2
### `IS EMPTY` and `IS NOT EMPTY`
- `IS EMPTY` matches Array `[]`, Object `{}`, or String `""` fields that `EXISTS` and are empty
- `IS NOT EMPTY` matches fields that `NOT EXISTS` OR are not empty.
1. `{"name": "A", "tags": null}`
2. `{"name": "A", "tags": [null]}`
3. `{"name": "A", "tags": []}`
4. `{"name": "A", "tags": ["hello","world"]}`
5. `{"name": "A", "tags": [""]}`
6. `{"name": "A"}`
7. `{"name": "A", "tags": {}}`
8. `{"name": "A", "tags": {"t1":"v1"}}`
9. `{"name": "A", "tags": {"t1":""}}`
10. `{"name": "A", "tags": ""}`
`tags IS EMPTY` would match 3,7,10
`tags IS NOT EMPTY` or `NOT tags IS EMPTY` would match 1,2,4,5,6,8,9
`tags IS NULL` would match 1
`tags IS NOT NULL` or `NOT tags IS NULL` would match 2,3,4,5,6,7,8,9,10
`tags EXISTS` would match 1,2,3,4,5,7,8,9,10
`tags NOT EXISTS` or `NOT tags EXISTS` would match 6
common query : `(tags EXISTS) AND (tags IS NOT NULL) AND (tags IS NOT EMPTY)` would match 2,4,5,8,9
## What should the reviewer do?
- Check that I tested the filters
- Check that I deleted the ids of the documents when deleting documents
Co-authored-by: Clément Renault <clement@meilisearch.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
736: Update charabia r=curquiza a=ManyTheFish
Update Charabia to the last version.
> We are now Romanizing Chinese characters into Pinyin.
> Note that we keep the accent because they are in fact never typed directly by the end-user, moreover, changing an accent leads to a different Chinese character, and I don't have sufficient knowledge to forecast the impact of removing accents in this context.
Co-authored-by: ManyTheFish <many@meilisearch.com>
616: Introduce an indexation abortion function when indexing documents r=Kerollmops a=Kerollmops
Co-authored-by: Kerollmops <clement@meilisearch.com>
Co-authored-by: Clément Renault <clement@meilisearch.com>
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.
635: Use an unstable algorithm for `grenad::Sorter` when possible r=Kerollmops a=loiclec
# Pull Request
## What does this PR do?
Use an unstable algorithm to sort the internal vector used by `grenad::Sorter` whenever possible to speed up indexing.
In practice, every time the merge function creates a `RoaringBitmap`, we use an unstable sort. For every other merge function, such as `keep_first`, `keep_last`, etc., a stable sort is used.
Co-authored-by: Loïc Lecrenier <loic@meilisearch.com>
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>
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>
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.
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>
returned metaimprove document addition returned metaimprove document
addition returned metaimprove document addition returned metaimprove
document addition returned metaimprove document addition returned
metaimprove document addition returned meta
Instead of using an arbitrary limit we encode the absolute position in a u32
using one strong u16 for the field id and a weak u16 for the relative position in the attribute.
322: Geosearch r=ManyTheFish a=irevoire
This PR introduces [basic geo-search functionalities](https://github.com/meilisearch/specifications/pull/59), it makes the engine able to index, filter and, sort by geo-point. We decided to use [the rstar library](https://docs.rs/rstar) and to save the points in [an RTree](https://docs.rs/rstar/0.9.1/rstar/struct.RTree.html) that we de/serialize in the index database [by using serde](https://serde.rs/) with [bincode](https://docs.rs/bincode). This is not an efficient way to query this tree as it will consume a lot of CPU and memory when a search is made, but at least it is an easy first way to do so.
### What we will have to do on the indexing part:
- [x] Index the `_geo` fields from the documents.
- [x] Create a new module with an extractor in the `extract` module that takes the `obkv_documents` and retrieves the latitude and longitude coordinates, outputting them in a `grenad::Reader` for further process.
- [x] Call the extractor in the `extract::extract_documents_data` function and send the result to the `TypedChunk` module.
- [x] Get the `grenad::Reader` in the `typed_chunk::write_typed_chunk_into_index` function and store all the points in the `rtree`
- [x] Delete the documents from the `RTree` when deleting documents from the database. All this can be done in the `delete_documents.rs` file by getting the data structure and removing the points from it, inserting it back after the modification.
- [x] Clearing the `RTree` entirely when we clear the documents from the database, everything happens in the `clear_documents.rs` file.
- [x] save a Roaring bitmap of all documents containing the `_geo` field
### What we will have to do on the query part:
- [x] Filter the documents at a certain distance around a point, this is done by [collecting the documents from the searched point](https://docs.rs/rstar/0.9.1/rstar/struct.RTree.html#method.nearest_neighbor_iter) while they are in range.
- [x] We must introduce new `geoLowerThan` and `geoGreaterThan` variants to the `Operator` filter enum.
- [x] Implement the `negative` method on both variants where the `geoGreaterThan` variant is implemented by executing the `geoLowerThan` and removing the results found from the whole list of geo faceted documents.
- [x] Add the `_geoRadius` function in the pest parser.
- [x] Introduce a `_geo` ascending ranking function that takes a point in parameter, ~~this function must keep the iterator on the `RTree` and make it peekable~~ This was not possible for now, we had to collect the whole iterator. Only the documents that are part of the candidates must be sent too!
- [x] This ascending ranking rule will only be active if the search is set up with the `_geoPoint` parameter that indicates the center point of the ascending ranking rule.
-----------
- On Meilisearch part: We must introduce a new concept, returning the documents with a new `_geoDistance` field when it passed by the `_geo` ranking rule, this has never been done before. We could maybe just do it afterward when the documents have been retrieved from the database, computing the distance from the `_geoPoint` and all of the documents to be returned.
Co-authored-by: Irevoire <tamo@meilisearch.com>
Co-authored-by: cvermand <33010418+bidoubiwa@users.noreply.github.com>
Co-authored-by: Tamo <tamo@meilisearch.com>
