3738: Add analytics on the get documents resource r=dureuill a=irevoire
# Pull Request
## Related issue
Fixes https://github.com/meilisearch/meilisearch/issues/3737
Related spec https://github.com/meilisearch/specifications/pull/234
## What does this PR do?
Add the analytics for the following routes:
- `GET` - `/indexes/:uid/documents`
- `GET` - `/indexes/:uid/documents/:doc_id`
- `POST` - `/indexes/:uid/documents/fetch`
These analytics are aggregated between two events:
- `Documents Fetched GET`
- `Documents Fetched POST`
That shares the same payload:
Property name | Description | Example |
|---------------|-------------|---------|
| `requests.total_received` | Total number of request received in this batch | 325 |
| `per_document_id` | `false` | false |
| `per_filter` | `true` if `POST /indexes/:indexUid/documents/fetch` endpoint was used with a filter in this batch, otherwise `false` | false |
| `pagination.max_limit` | Highest value given for the `limit` parameter in this batch | 60 |
| `pagination.max_offset` | Highest value given for the `offset` parameter in this batch | 1000 |
Co-authored-by: Tamo <tamo@meilisearch.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>
3688: Following release v1.1.1: bring back changes into `main` r=curquiza a=curquiza
`@meilisearch/engine-team` ensure the changes we bring to `main` are the ones you want
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
Co-authored-by: bors[bot] <26634292+bors[bot]@users.noreply.github.com>
Co-authored-by: Tamo <tamo@meilisearch.com>
Co-authored-by: dureuill <dureuill@users.noreply.github.com>
3568: CI: Fix `publish-aarch64` job that still uses ubuntu-18.04 r=Kerollmops a=curquiza
Fixes#3563
Main change
- add the usage of the `ubuntu-18.04` container instead of the native `ubuntu-18.04` of GitHub actions: I had to install docker in the container.
Small additional changes
- remove useless `fail-fast` and unused/irrelevant matrix inputs (`build`, `linker`, `os`, `use-cross`...)
- Remove useless step in job
Proof of work with this CI triggered on this current branch: https://github.com/meilisearch/meilisearch/actions/runs/4366233882
3569: Enhance Japanese language detection r=dureuill a=ManyTheFish
# Pull Request
This PR is a prototype and can be tested by downloading [the dedicated docker image](https://hub.docker.com/layers/getmeili/meilisearch/prototype-better-language-detection-0/images/sha256-a12847de00e21a71ab797879fd09777dadcb0881f65b5f810e7d1ed434d116ef?context=explore):
```bash
$ docker pull getmeili/meilisearch:prototype-better-language-detection-0
```
## Context
Some Languages are harder to detect than others, this miss-detection leads to bad tokenization making some words or even documents completely unsearchable. Japanese is the main Language affected and can be detected as Chinese which has a completely different way of tokenization.
A [first iteration has been implemented for v1.1.0](https://github.com/meilisearch/meilisearch/pull/3347) but is an insufficient enhancement to make Japanese work. This first implementation was detecting the Language during the indexing to avoid bad detections during the search.
Unfortunately, some documents (shorter ones) can be wrongly detected as Chinese running bad tokenization for these documents and making possible the detection of Chinese during the search because it has been detected during the indexing.
For instance, a Japanese document `{"id": 1, "name": "東京スカパラダイスオーケストラ"}` is detected as Japanese during indexing, during the search the query `東京` will be detected as Japanese because only Japanese documents have been detected during indexing despite the fact that v1.0.2 would detect it as Chinese.
However if in the dataset there is at least one document containing a field with only Kanjis like:
_A document with only 1 field containing only Kanjis:_
```json
{
"id":4,
"name": "東京特許許可局"
}
```
_A document with 1 field containing only Kanjis and 1 field containing several Japanese characters:_
```json
{
"id":105,
"name": "東京特許許可局",
"desc": "日経平均株価は26日 に約8カ月ぶりに2万4000円の心理的な節目を上回った。株高を支える材料のひとつは、自民党総裁選で3選を決めた安倍晋三首相の経済政策への期待だ。恩恵が見込まれるとされる人材サービスや建設株の一角が買われている。ただ思惑が先行して資金が集まっている面 は否めない。実際に政策効果を取り込む企業はどこか、なお未知数だ。"
}
```
Then, in both cases, the field `name` will be detected as Chinese during indexing allowing the search to detect Chinese in queries. Therefore, the query `東京` will be detected as Chinese and only the two last documents will be retrieved by Meilisearch.
## Technical Approach
The current PR partially fixes these issues by:
1) Adding a check over potential miss-detections and rerunning the extraction of the document forcing the tokenization over the main Languages detected in it.
> 1) run a first extraction allowing the tokenizer to detect any Language in any Script
> 2) generate a distribution of tokens by Script and Languages (`script_language`)
> 3) if for a Script we have a token distribution of one of the Language that is under the threshold, then we rerun the extraction forbidding the tokenizer to detect the marginal Languages
> 4) the tokenizer will fall back on the other available Languages to tokenize the text. For example, if the Chinese were marginally detected compared to the Japanese on the CJ script, then the second extraction will force Japanese tokenization for CJ text in the document. however, the text on another script like Latin will not be impacted by this restriction.
2) Adding a filtering threshold during the search over Languages that have been marginally detected in documents
## Limits
This PR introduces 2 arbitrary thresholds:
1) during the indexing, a Language is considered miss-detected if the number of detected tokens of this Language is under 10% of the tokens detected in the same Script (Japanese and Chinese are 2 different Languages sharing the "same" script "CJK").
2) during the search, a Language is considered marginal if less than 5% of documents are detected as this Language.
This PR only partially fixes these issues:
- ✅ the query `東京` now find Japanese documents if less than 5% of documents are detected as Chinese.
- ✅ the document with the id `105` containing the Japanese field `desc` but the miss-detected field `name` is now completely detected and tokenized as Japanese and is found with the query `東京`.
- ❌ the document with the id `4` no longer breaks the search Language detection but continues to be detected as a Chinese document and can't be found during the search.
## Related issue
Fixes#3565
## Possible future enhancements
- Change or contribute to the Library used to detect the Language
- the related issue on Whatlang: https://github.com/greyblake/whatlang-rs/issues/122
Co-authored-by: curquiza <clementine@meilisearch.com>
Co-authored-by: ManyTheFish <many@meilisearch.com>
Co-authored-by: Many the fish <many@meilisearch.com>
3529: Add an analytics on the geo bounding box feature r=ManyTheFish a=irevoire
Fixes#3527
[The specification of the geoBoundingBox](https://github.com/meilisearch/specifications/pull/223) feature has been updated and now introduces a new analytics to follow the usage of the geoBoundingBox feature in the search requests.
Co-authored-by: Tamo <tamo@meilisearch.com>
3524: Update the metrics route r=irevoire a=irevoire
Fixes#3523
Make the metrics available by default without a feature flag.
+ Rename the cli-flag to `experimental-enable-metrics`.
Co-authored-by: Tamo <tamo@meilisearch.com>
3331: Limit the number of concurrently opened indexes r=dureuill a=dureuill
# Pull Request
## Related issue
Relevant to #1841, fixes#3382
## What does this PR do?
### User standpoint
- Limit the number of concurrently opened indexes (currently, the number of indexes that can be concurrently opened is computed at startup)
- When too many an index is opened, the least recently used one is closed and its virtual memory released.
- This allows a user to have an arbitrary number of indexes of an arbitrary size
### Implementation standpoint
- Added a LRU cache map in `index-scheduler::lru`. A more complete implementation (eg with helper functions not used here) is available but would better fit a dedicated crate.
- Use the LRU cache map in the `IndexScheduler`. To simplify the lifecycle of indexes, they are never removed from the cache when they are in the middle of a resize or delete operation. To achieve this, an intermediate `Vec` stores the UUIDs of the indexes that are in the middle of such an operation.
- Upon creating the index scheduler object, compute the total virtual memory that is adressable by using a dichotomic search on the max size of an index. Use this as a base to compute the number of indexes that can be open with 2TiB per index. If the virtual memory address space is lower than 2TiB, then only allow for 1 index of a fraction of that size.
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
