493: Use smartstring to store the external id in our hashmap r=Kerollmops a=irevoire
We need to store all the external id (primary key) in a hashmap
associated to their internal id.
The smartstring remove heap allocation / memory usage and should
improve the cache locality.
I ran the benchmarks to measure the impact of this PR on the indexing time.
I think we should merge it whatever happens thought because it'll decrease the memory consumption.
---------
This improve really sliiiiiightly the performances but improve the memory usage thus it should be merged.
```
group indexing_main_6b073738 indexing_use-smartsring_3f343511
----- ---------------------- --------------------------------
indexing/Indexing geo_point 1.02 25.2±0.20s ? ?/sec 1.00 24.8±0.13s ? ?/sec
indexing/Indexing movies in three batches 1.00 18.2±0.10s ? ?/sec 1.00 18.2±0.23s ? ?/sec
indexing/Indexing movies with default settings 1.00 17.5±0.09s ? ?/sec 1.01 17.7±0.11s ? ?/sec
indexing/Indexing songs in three batches with default settings 1.00 68.3±1.01s ? ?/sec 1.00 68.0±0.95s ? ?/sec
indexing/Indexing songs with default settings 1.00 63.2±0.78s ? ?/sec 1.00 63.0±0.58s ? ?/sec
indexing/Indexing songs without any facets 1.02 59.6±1.00s ? ?/sec 1.00 58.5±1.03s ? ?/sec
indexing/Indexing songs without faceted numbers 1.00 62.8±0.38s ? ?/sec 1.00 62.6±1.02s ? ?/sec
indexing/Indexing wiki 1.01 1009.2±25.25s ? ?/sec 1.00 998.1±11.27s ? ?/sec
indexing/Indexing wiki in three batches 1.01 1142.0±9.97s ? ?/sec 1.00 1134.4±11.21s ? ?/sec
```
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.
2316: Add version flag r=Kerollmops a=sanders41
# Pull Request
## What does this PR do?
Fixes#2315
## PR checklist
Please check if your PR fulfills the following requirements:
- [x] Does this PR fix an existing issue?
- [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: Paul Sanders <psanders1@gmail.com>
496: Improve the performances of the flattening subcrate r=irevoire a=Kerollmops
This PR adds some benchmarks to the _flatten-serde-json_ crate, this crate is responsible for transforming the original documents into flat versions that the engine can understand. It can probably be speed-up and this is why I added benchmarks to it.
I make some interesting performance improvements when I replaced the `json!` macro calls.
```
flatten/simple time: [452.44 ns 453.31 ns 454.18 ns]
change: [-15.036% -14.751% -14.473%] (p = 0.00 < 0.05)
Performance has improved.
Found 2 outliers among 100 measurements (2.00%)
2 (2.00%) high mild
Benchmarking flatten/complex: Collecting 100 samples in estimated 5.0007 s (4.9M i flatten/complex time: [1.0101 us 1.0131 us 1.0160 us]
change: [-18.001% -17.775% -17.536%] (p = 0.00 < 0.05)
Performance has improved.
Found 6 outliers among 100 measurements (6.00%)
5 (5.00%) high mild
1 (1.00%) high severe
```
---
_I removed this particular commit from this PR._ The reason is that the two other commits were enough for this PR to give enough impact and be merged. We will continue to explore where we can get performances later.
But when I changed the flattening function to accept an owned version of the objects, we lost a lot of performances. Yes, I rewrote the benchmarks (locally) to clone the input object (and measured both, previous and new versions, with the cloning benchmarks). Maybe cloning the benchmark inputs is not the right thing to do...
```
Benchmarking flatten/simple: Collecting 100 samples in estimated 5.0005 s (6.7M it flatten/simple time: [746.46 ns 749.59 ns 752.70 ns]
change: [+40.082% +40.714% +41.347%] (p = 0.00 < 0.05)
Performance has regressed.
Benchmarking flatten/complex: Collecting 100 samples in estimated 5.0047 s (2.9M i flatten/complex time: [1.7311 us 1.7342 us 1.7368 us]
change: [+40.976% +41.398% +41.807%] (p = 0.00 < 0.05)
Performance has regressed.
Found 1 outliers among 100 measurements (1.00%)
1 (1.00%) low mild
```
Co-authored-by: Kerollmops <clement@meilisearch.com>
489: fix distinct count bug r=curquiza a=MarinPostma
fix https://github.com/meilisearch/meilisearch/issues/2152
I think the issue was that we didn't take off the excluded candidates from the initial candidates when returning the candidates with the search result.
Co-authored-by: ad hoc <postma.marin@protonmail.com>
2313: fix(search): remove the back and forth between the IndexMap and the serde_json::Map r=irevoire a=irevoire
This is ok because we're using the preserve_order feature in serde_json which is already internally using an IndexMap.
See https://github.com/meilisearch/meilisearch/pull/2298#discussion_r845228412_
Co-authored-by: Tamo <tamo@meilisearch.com>
491: remove the unused key warning r=curquiza a=irevoire
When I copy-pasted my flatten crate I forgot to remove the key used to publish the package and that throw a warning.
Co-authored-by: Tamo <tamo@meilisearch.com>
2178: Refacto docker r=irevoire a=irevoire
closes#2166 and #2085
-----------
I noticed many people had issues with the default configuration of our Dockerfile.
Some examples:
- #2166: If you use ubuntu and mount your `data.ms` in a volume (as shown in the [doc](https://docs.meilisearch.com/learn/getting_started/installation.html#download-and-launch)), you can't run meilisearch
- #2085: Here, meilisearch was not able to erase the `data.ms` when loading a dump because it's the mount point
Currently, we don't show how to use the snapshot and dumps with docker in the documentation. And it's quite hard to do:
- You either send a big command to meilisearch to change the dump-path, snapshot-path and db-path a single directory and then mount that one
- Or you mount three volumes
- And there were other issues on the slack community
I think this PR solve the problem.
Now the image contains the `meilisearch` binary in the `/bin` directory, so it's easy to find and always in the `PATH`.
It creates a `data` directory and moves the working-dir in it.
So now you can find the `dumps`, `snapshots` and `data.ms` directory in `/data`.
Here is the new command to run meilisearch with a volume:
```
docker run -it --rm -v $PWD/meili_data:/data -p 7700:7700 getmeili/meilisearch:latest
```
And if you need to import a dump or a snapshot, you don't need to restart your container and mount another volume. You can directly hit the `POST /dumps` route and then run:
```
docker run -it --rm -v $PWD/meili_data:/data -p 7700:7700 getmeili/meilisearch:latest meilisearch --import-dump dumps/20220217-152115159.dump
```
-------
You can already try this PR with the following docker image:
```
getmeili/meilisearch:test-docker-v0.26.0
```
If you want to use the v0.25.2 I created another image;
```
getmeili/meilisearch:test-docker-v0.25.2
```
------
If you're using helm I created a branch [here](https://github.com/meilisearch/meilisearch-kubernetes/tree/test-docker-v0.26.0) that use the v0.26.0 image with the good volume 👍
If you use this conf with the v0.25.2, it should also work.
Co-authored-by: Tamo <tamo@meilisearch.com>
2298: Nested fields r=irevoire a=irevoire
There are a few things that I want to fix _AFTER_ merging this PR.
For the following RCs.
## Stop the useless conversion
In the `search.rs` I convert a `Document` to a `Value`, and then the `Value` to a `Document` and then back to a `Value` etc. I should stop doing all these conversion and stick to one format.
Probably by merging my `permissive-json-pointer` crate into meilisearch.
That would also give me the opportunity to work directly with obkvs and stops deserializing fields I don't need.
## Add more test specific to the nested
Everything seems to works but I should write tests to double check that the nested works well with the `formatted` field.
## See how I could stop iterating on hashmap and instead fill them correctly
This is related to milli. I really often needs to iterate over hashmap to see if a field is a subset of another field. I could probably generate a structure containing all the possible key values.
ie. the user say `doggo` is an attribute to retrieve. Instead of iterating on all the attributes to retrieve to check if `doggo.name` is a subset of `doggo`. I should insert `doggo.name` in the attributes to retrieve map.
Co-authored-by: Tamo <tamo@meilisearch.com>
2304: chore(bors): comments clippy out r=curquiza a=irevoire
There is currently an issue with clippy that stops us from merging PRs.
https://github.com/rust-lang/rust-clippy/issues/8662#issuecomment-1093899755
We can't use clippy in the CI while that's not merged
Co-authored-by: Tamo <tamo@meilisearch.com>
490: Enforce labelling for the PRs r=curquiza a=curquiza
- Enforce one of the following labels to make the CI pass: `no breaking`, `DB breaking`, `API breaking` (milli API, not the Meilisearch API of course), or `skip changelog`. This new CI is now `Required` in the GitHub settings for merging a PR.
- Adapt the release drafter to these new labels
- rename `skip-changelog` into `skip changelog` according to the new label name
Co-authored-by: Clémentine Urquizar <clementine@meilisearch.com>
458: Nested fields r=Kerollmops a=irevoire
For the following document:
```json
{
"id": 1,
"person": {
"name": "tamo",
"age": 25,
}
}
```
Suppose the user sets `person` as a filterable attribute. We need to store `person` in the filterable _obviously_. But we also need to keep track of `person.name` and `person.age` somewhere.
That’s where I changed a little bit the logic of the engine.
Currently, we have a function called `faceted_field` that returns the union of the filterable and sortable.
I renamed this function in `user_defined_faceted_field`. And now, when we finish indexing documents, we look at all the fields and see if they « match » a `user_defined_faceted_field`.
So in our case:
- does `id` match `person`: 🔴
- does `person.name` match `person`: 🟢
- does `person.age` match `person`: 🟢
And thus, we insert in the database the following faceted fields: `person, person.name, person.age`.
The good thing about that solution is that we generate everything during the indexing phase, and then during the search, we can access our field without recomputing too much globbing.
-----
Now the bad thing is that I had to create a new db.
And if that was only one db, that would be ok, but actually, I need to do the same for the:
- Displayed attributes
- Attributes to retrieve
- Attributes to highlight
- Attribute to crop
`@Kerollmops`
Do you think there is a better way to do it?
Apart from all the code, can we have a problem because we have too many dbs?
Co-authored-by: Irevoire <tamo@meilisearch.com>
Co-authored-by: Tamo <tamo@meilisearch.com>
