- DistributionShift in Search object (to be set from model in embed?)
- Fix issue where embedder index wasn't computed at search time
- Accept as default embedder either the "default" one, or the only embedder when there is only one
4223: Update to heed 0.20 r=dureuill a=Kerollmops
This PR brings the v0.20-alpha.9 version of heed into Meilisearch 🎉 The main goal is to test it in a real environment to make the necessary changes if needed. We also want to merge it as soon as possible during the pre-release phase to ensure we catch bugs before the release.
Most of the calls to heed are the same as before, except:
- The `PolyDatabase` has been replaced with a `Database<Unspecified, Unspecified>`. We replaced the `get<T, U>()` by a `remap<T, U>().get()` calls.
- The `Database` `append(...)` method has been replaced with a `put_with_flags(PutFlags::APPEND, ...)`.
- The `RwTxn<'e, 'p>` has been simplified into a `RwTxn<'e>`.
- The `BytesEncode/Decode` traits return a `Result<_, BoxedError>` instead of an `Option<_>`.
- We no longer need to wrap and unwrap the `BEU32` integer when storing/getting them from heed.
### TODO
- [x] Create actual, simple error types instead of using strings in the codecs.
### Follow-up work
- Move the codecs into another member crate (we depend on the uuid one in the meilitool crate).
- Display the internal decoding error in the `SerializationError` internal error variant.
Co-authored-by: Clément Renault <clement@meilisearch.com>
4090: Diff indexing r=ManyTheFish a=ManyTheFish
This pull request aims to reduce the indexing time by computing a difference between the data added to the index and the data removed from the index before writing in LMDB.
## Why focus on reducing the writings in LMDB?
The indexing in Meilisearch is split into 3 main phases:
1) The computing or the extraction of the data (Multi-threaded)
2) The writing of the data in LMDB (Mono-threaded)
3) The processing of the prefix databases (Mono-threaded)
see below:
Because the writing is mono-threaded, it represents a bottleneck in the indexing, reducing the number of writes in LMDB will reduce the pressure on the main thread and should reduce the global time spent on the indexing.
## Give Feedback
We created [a dedicated discussion](https://github.com/meilisearch/meilisearch/discussions/4196) for users to try this new feature and to give feedback on bugs or performance issues.
## Technical approach
### Part 1: merge the addition and the deletion process
This part:
a) Aims to reduce the time spent on indexing only the filterable/sortable fields of documents, for example:
- Updating the number of "likes" or "stars" of a song or a movie
- Updating the "stock count" or the "price" of a product
b) Aims to reduce the time spent on writing in LMDB which should reduce the global indexing time for the highly multi-threaded machines by reducing the writing bottleneck.
c) Aims to reduce the average time spent to delete documents without having to keep the soft-deleted documents implementation
- [x] Create a preprocessing function that creates the diff-based documents chuck (`OBKV<fid, OBKV<AddDel, value>>`)
- [x] and clearly separate the faceted fields and the searchable fields in two different chunks
- Change the parameters of the input extractor by taking an `OBKV<fid, OBKV<AddDel, value>>` instead of `OBKV<fid, value>`.
- [x] extract_docid_word_positions
- [x] extract_geo_points
- [x] extract_vector_points
- [x] extract_fid_docid_facet_values
- Adapt the searchable extractors to the new diff-chucks
- [x] extract_fid_word_count_docids
- [x] extract_word_pair_proximity_docids
- [x] extract_word_position_docids
- [x] extract_word_docids
- Adapt the facet extractors to the new diff-chucks
- [x] extract_facet_number_docids
- [x] extract_facet_string_docids
- [x] extract_fid_docid_facet_values
- [x] FacetsUpdate
- [x] Adapt the prefix database extractors ⚠️⚠️
- [x] Make the LMDB writer remove the document_ids to delete at the same time the new document_ids are added
- [x] Remove document deletion pipeline
- [x] remove `new_documents_ids` entirely and `replaced_documents_ids`
- [x] reuse extracted external id from transform instead of re-extracting in `TypedChunks::Documents`
- [x] Remove deletion pipeline after autobatcher
- [x] remove autobatcher deletion pipeline
- [x] everything uses `IndexOperation::DocumentOperation`
- [x] repair deletion by internal id for filter by delete
- [x] Improve the deletion via internal ids by avoiding iterating over the whole set of external document ids.
- [x] Remove soft-deleted documents
#### FIXME
- [x] field distribution is not correctly updated after deletion
- [x] missing documents in the tests of tokenizer_customization
### Part 2: Only compute the documents field by field
This part aims to reduce the global indexing time for any kind of partial document modification on any size of machine from the mono-threaded one to the highly multi-threaded one.
- [ ] Make the preprocessing function only send the fields that changed to the extractors
- [ ] remove the `word_docids` and `exact_word_docids` database and adapt the search (⚠️ could impact the search performances)
- [ ] replace the `word_pair_proximity_docids` database with a `word_pair_proximity_fid_docids` database and adapt the search (⚠️ could impact the search performances)
- [ ] Adapt the prefix database extractors ⚠️⚠️
## Technical Concerns
- The part 1 implementation could increase the indexing time for the smallest machines (with few threads) by increasing the extracting time (multi-threaded) more than the writing time (mono-threaded)
- The part 2 implementation needs to change the databases which could have a significant impact on the search performances
- The prefix databases are a bit special to process and may be a pain to adapt to the difference-based indexing
Co-authored-by: ManyTheFish <many@meilisearch.com>
Co-authored-by: Clément Renault <clement@meilisearch.com>
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
The issue was that the operation « DocumentDeletionByFilter » was not
declared as an index operation. That means the indexes stats were not
reprocessed after the application of the operation.
3851: Expose lastUpdate and isIndexing in /stats endpoint r=dureuill a=gentcys
# Pull Request
## Related issue
Fixes#3843
## What does this PR do?
- expose lastUpdate in `/stats` endpoint
- expose isIndex in `stats` endpoint
- add a method `is_task_processing` in index-scheduler/src/lib.rs.
## 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?
Thank you so much for contributing to Meilisearch!
Co-authored-by: Cong Chen <cong.chen@ocrlabs.com>
Co-authored-by: ManyTheFish <many@meilisearch.com>
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
3842: fix some typos r=dureuill a=cuishuang
# Pull Request
## Related issue
Fixes #<issue_number>
## What does this PR do?
- fix some typos
## 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?
Thank you so much for contributing to Meilisearch!
Co-authored-by: cui fliter <imcusg@gmail.com>
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>
3550: Delete documents by filter r=irevoire a=dureuill
# Prototype `prototype-delete-by-filter-0`
Usage:
A new route is available under `POST /indexes/{index_uid}/documents/delete` that allows you to delete your documents by filter.
The expected payload looks like that:
```json
{
"filter": "doggo = bernese",
}
```
It'll then enqueue a task in your task queue that'll delete all the documents matching this filter once it's processed.
Here is an example of the associated details;
```json
"details": {
"deletedDocuments": 53,
"originalFilter": "\"doggo = bernese\""
}
```
----------
# Pull Request
## Related issue
Related to https://github.com/meilisearch/meilisearch/issues/3477
## What does this PR do?
### User standpoint
- Modifies the `/indexes/{:indexUid}/documents/delete-batch` route to accept either the existing array of documents ids, or a JSON object with a `filter` field representing a filter to apply. If that latter variant is used, any document matching the filter will be deleted.
### Implementation standpoint
- (processing time version) Adds a new BatchKind that is not autobatchable and that performs the delete by filter
- Reuse the `documentDeletion` task with a new `originalFilter` detail that replaces the `providedIds` detail.
## Example
<details>
<summary>Sample request, response and task result</summary>
Request:
```
curl \
-X POST 'http://localhost:7700/indexes/index-10/documents/delete-batch' \
-H 'Content-Type: application/json' \
--data-binary '{ "filter" : "mass = 600"}'
```
Response:
```
{
"taskUid": 3902,
"indexUid": "index-10",
"status": "enqueued",
"type": "documentDeletion",
"enqueuedAt": "2023-02-28T20:50:31.667502Z"
}
```
Task log:
```json
{
"uid": 3906,
"indexUid": "index-12",
"status": "succeeded",
"type": "documentDeletion",
"canceledBy": null,
"details": {
"deletedDocuments": 3,
"originalFilter": "\"mass = 600\""
},
"error": null,
"duration": "PT0.001819S",
"enqueuedAt": "2023-03-07T08:57:20.11387Z",
"startedAt": "2023-03-07T08:57:20.115895Z",
"finishedAt": "2023-03-07T08:57:20.117714Z"
}
```
</details>
## Draft status
- [ ] Error handling
- [ ] Analytics
- [ ] Do we want to reuse the `delete-batch` route in this way, or create a new route instead?
- [ ] Should the filter be applied at request time or when the deletion task is processed?
- The first commit in this PR applies the filter at request time, meaning that even if a document is modified in a way that no longer matches the filter in a later update, it will be deleted as long as the deletion task is processed after that update.
- The other commits in this PR apply the filter only when the asynchronous deletion task is processed, meaning that documents that match the filter at processing time are deleted even if they didn't match the filter at request time.
- [ ] If keeping the filter at request time, find a more elegant way to recover the user document ids from the internal document ids. The current way implemented in the first commit of this PR involves getting all the documents matching the filter, looking for the value of their primary key, and turning it into a string by copy-pasting routines found in milli...
- [ ] Security consideration, if any
- [ ] Fix the tests (but waiting until product questions are resolved)
- [ ] Add delete by filter specific tests
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
Co-authored-by: Tamo <tamo@meilisearch.com>
3639: Add a dedicated error variant for planned failures in index scheduler tests r=Kerollmops a=Sufflope
# Pull Request
## Related issue
Fixes#3086
## What does this PR do?
- Add a dedicated test variant in test cfg to avoid reusing a misleading existing error
## 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?
Thank you so much for contributing to Meilisearch!
Co-authored-by: Jean-Sébastien Bour <jean-sebastien@bour.name>
