699: Force vendoring of LMDB even if a system version is available r=Kerollmops a=dureuill
# Pull Request
## Related issue
Related to https://github.com/meilisearch/meilisearch/issues/3017: will fix once ported to milli and meilisearch.
## What does this PR do?
- Force using vendored version of LMDB
- **don't use lmdb master3 branch anymore**: this is a bit of a side effect of using a tag instead of branch for heed as a dependency, but it is wanted anyway for now as lmdb master3 was more of an experiment
- **modifies CI to run `cargo check` on the release rather than the debug artifacts**. This is an attempt to reduce the necessary disk space and avoid "out of space" failures.
## PR checklist
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Thank you so much for contributing to Meilisearch!
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
694: Update version for the next release (v0.36.0) in Cargo.toml files r=curquiza a=meili-bot
⚠️ This PR is automatically generated. Check the new version is the expected one before merging.
Co-authored-by: Kerollmops <Kerollmops@users.noreply.github.com>
693: use the lmdb-master.3 branch r=Kerollmops a=irevoire
After investigating https://github.com/meilisearch/meilisearch/issues/3017, we found out that it was due to lmdb and that, without any code change on our side, bumping using the lmdb-master-3 branch fix our issues.
But, we’re not really confident about what changed between the `mdb.master` and `mdb.master3` branches; thus this is a temporary change, and we hope we’ll be able to move to the new version of heed asap (either before the end of the pre-release or for the next release).
--------
The bug is hard to reproduce; I can reproduce it 100% of the time on my archlinux personal computer. But on a scaleway archlinux bare-metal machine, it doesn’t reproduce. It’s flaky on our test suite, but `@loiclec` was able to write a minimal test that reproduces it every time on macOS.
Basically, what happens is when there are multiple threads opening databases in a different directory at the same time.
If there are 10 or more threads running at the same time, lmdb starts throwing the `Invalid argument (os error 22)` error for no reason, we believe.
I would like to submit an issue to lmdb, but I don’t really have the time to write a test in C without heed currently.
`@hyc,` if you want to take a look at it, here is the repo that reproduces the issue on macOS: https://github.com/irevoire/heed-bug
Co-authored-by: Irevoire <tamo@meilisearch.com>
691: Update version for the next release (v0.35.1) in Cargo.toml files r=curquiza a=meili-bot
⚠️ This PR is automatically generated. Check the new version is the expected one before merging.
Co-authored-by: Kerollmops <Kerollmops@users.noreply.github.com>
689: Handle non-finite floats consistently in filters r=irevoire a=dureuill
# Pull Request
## Related issue
Related meilisearch/meilisearch#3000
## What does this PR do?
### User
- Filters using `field = inf`, (or `infinite`, `NaN`) now match the value as a string rather than returning an internal error.
- Filters using `field < inf` (or other comparison operators) now return an invalid_filter error rather than returning an internal error, much like when using `field < aaa`.
### Implementation
- Add new `NonFiniteFloat` error variants to the filter-parser errors
- Add `Token::parse_as_finite_float` that can fail both when the string is not a float and when the float is not finite
- Refactor `Filter::inner_evaluate` to always use `parse_as_finite_float` instead of just `parse`
- Add corresponding tests
## PR checklist
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- [x] Does this PR fix an existing issue, or have you listed the changes applied in the PR description (and why they are needed)?
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Thank you so much for contributing to Meilisearch!
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
673: Add clippy job r=ManyTheFish a=unvalley
# Pull Request
## Related issue
Fixes#231
## What does this PR do?
- fix some clippy errors remain
- add clippy job to CI (I set `nightly` as toolchain)
## PR checklist
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Co-authored-by: unvalley <kirohi.code@gmail.com>
659: Fix clippy error to add clippy job on Ci r=Kerollmops a=unvalley
## Related PR
This PR is for #673
## What does this PR do?
- ~~add `Run Clippy` job to CI (rust.yml)~~
- apply `cargo clippy --fix` command
- fix some `cargo clippy` error manually (but warnings still remain on tests)
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Co-authored-by: unvalley <kirohi.code@gmail.com>
Co-authored-by: unvalley <38400669+unvalley@users.noreply.github.com>
679: Bump Swatinem/rust-cache from 2.0.0 to 2.0.1 r=curquiza a=dependabot[bot]
Bumps [Swatinem/rust-cache](https://github.com/Swatinem/rust-cache) from 2.0.0 to 2.0.1.
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<blockquote>
<h2>v2.0.1</h2>
<ul>
<li>Primarily just updating dependencies to fix GitHub deprecation notices.</li>
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<ul>
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<li><a href="c4652c677c"><code>c4652c6</code></a> Update <code>`@actions/core</code>` (<a href="https://github-redirect.dependabot.com/Swatinem/rust-cache/issues/83">#83</a>)</li>
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<li><a href="86531941c2"><code>8653194</code></a> Merge branch 'master' of <a href="https://github.com/Swatinem/rust-cache">https://github.com/Swatinem/rust-cache</a></li>
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677: run the tests in all workspaces r=curquiza a=irevoire
With #676 I noticed the tests were not running in any of our sub crates.
Most of our sub crates didn't includes any tests though.
But the filter-parser did and we're lucky we never broke these one without noticing 😁
Co-authored-by: Irevoire <tamo@meilisearch.com>
676: chore: added `IN`,`NOT IN` to `invalid_filter` msg r=irevoire a=Pranav-yadav
# Pull Request
## Related issue
`Fixes` https://github.com/meilisearch/meilisearch/issues/3004
## What does this PR do?
- Improves correct error msg in response
## 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: Pranav Yadav <Pranavyadav3912@gmail.com>
675: Deleted empty files r=Kerollmops a=SKVKPandey
# Pull Request
## Related issue
Fixes#674
## What does this PR do?
Delete empty files:
- `milli/src/heed_codec/facet/facet_string_level_zero_value_codec.rs`
- `milli/src/heed_codec/facet/facet_string_zero_bounds_value_codec.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: Shashank Kashyap <50551759+SKVKPandey@users.noreply.github.com>
664: Fix phrase search containing stop words r=ManyTheFish a=Samyak2
# Pull Request
This a WIP draft PR I wanted to create to let other potential contributors know that I'm working on this issue. I'll be completing this in a few hours from opening this.
## Related issue
Fixes#661 and towards fixing meilisearch/meilisearch#2905
## What does this PR do?
- [x] Change Phrase Operation to use a `Vec<Option<String>>` instead of `Vec<String>` where `None` corresponds to a stop word
- [x] Update all other uses of phrase operation
- [x] Update `resolve_phrase`
- [x] Update `create_primitive_query`?
- [x] Add test
## 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?
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Co-authored-by: Samyak S Sarnayak <samyak201@gmail.com>
Co-authored-by: Samyak Sarnayak <samyak201@gmail.com>
619: Refactor the Facets databases to enable incremental indexing r=curquiza a=loiclec
# Pull Request
## What does this PR do?
Party fixes https://github.com/meilisearch/milli/issues/605 by making the indexing of the facet databases (i.e. `facet_id_f64_docids` and `facet_id_string_docids`) incremental. It also closes#327 and https://github.com/meilisearch/meilisearch/issues/2820 . Two more untracked bugs were also fixed:
1. The facet distribution algorithm did not respect the `maxFacetValues` parameter when there were only a few candidate document ids.
2. The structure of the levels > 0 of the facet databases were not updated following the deletion of documents
## How to review this PR
First, read this comment to get an overview of the changes.
Then, based on this comment, raise any concerns you might have about:
1. the new structure of the databases
2. the algorithms for sort, facet distribution, and range search
3. the new/removed heed codecs
Then, weigh in on the following concerns:
1. adding `fuzzcheck` as a fuzz-only dependency may add too much complexity for the benefits it provides
2. the `ByteSliceRef` and `StrRefCodec` are misnamed or should not exist
3. the new behaviour of facet distributions can be considered incorrect
4. incremental deletion is useless given that documents are always deleted in bulk
## What's left for me to do
1. Re-read everything once to make sure I haven't forgotten anything
2. Wait for the results of the benchmarks and see if (1) they provide enough information (2) there was any change in performance, especially for search queries. Then, maybe, spend some time optimising the code.
3. Test whether the `info`/`http-ui` crates survived the refactor
## Old structure of the `facet_id_f64_docids` and `facet_id_string_docids` databases
Previously, these two databases had different but conceptually similar structures. For each field id, the facet number database had the following format:
```
┌───────────────────────────────┬───────────────────────────────┬───────────────┐
┌───────┐ │ 1.2 – 2 │ 3.4 – 100 │ 102 – 104 │
│Level 2│ │ │ │ │
└───────┘ │ a, b, d, f, z │ c, d, e, f, g │ u, y │
├───────────────┬───────────────┼───────────────┬───────────────┼───────────────┤
┌───────┐ │ 1.2 – 1.3 │ 1.6 – 2 │ 3.4 – 12 │ 12.3 – 100 │ 102 – 104 │
│Level 1│ │ │ │ │ │ │
└───────┘ │ a, b, d, z │ a, b, f │ c, d, g │ e, f │ u, y │
├───────┬───────┼───────┬───────┼───────┬───────┼───────┬───────┼───────┬───────┤
┌───────┐ │ 1.2 │ 1.3 │ 1.6 │ 2 │ 3.4 │ 12 │ 12.3 │ 100 │ 102 │ 104 │
│Level 0│ │ │ │ │ │ │ │ │ │ │ │
└───────┘ │ a, b │ d, z │ b, f │ a, f │ c, d │ g │ e │ e, f │ y │ u │
└───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┘
```
where the first line is the key of the database, consisting of :
- the field id
- the level height
- the left and right bound of the group
and the second line is the value of the database, consisting of:
- a bitmap of all the docids that have a facet value within the bounds
The `facet_id_string_docids` had a similar structure:
```
┌───────────────────────────────┬───────────────────────────────┬───────────────┐
┌───────┐ │ 0 – 3 │ 4 – 7 │ 8 – 9 │
│Level 2│ │ │ │ │
└───────┘ │ a, b, d, f, z │ c, d, e, f, g │ u, y │
├───────────────┬───────────────┼───────────────┬───────────────┼───────────────┤
┌───────┐ │ 0 – 1 │ 2 – 3 │ 4 – 5 │ 6 – 7 │ 8 – 9 │
│Level 1│ │ "ab" – "ac" │ "ba" – "bac" │ "gaf" – "gal" │"form" – "wow" │ "woz" – "zz" │
└───────┘ │ a, b, d, z │ a, b, f │ c, d, g │ e, f │ u, y │
├───────┬───────┼───────┬───────┼───────┬───────┼───────┬───────┼───────┬───────┤
┌───────┐ │ "ab" │ "ac" │ "ba" │ "bac" │ "gaf" │ "gal" │ "form"│ "wow" │ "woz" │ "zz" │
│Level 0│ │ "AB" │ " Ac" │ "ba " │ "Bac" │ " GAF"│ "gal" │ "Form"│ " wow"│ "woz" │ "ZZ" │
└───────┘ │ a, b │ d, z │ b, f │ a, f │ c, d │ g │ e │ e, f │ y │ u │
└───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┘
```
where, **at level 0**, the key is:
* the normalised facet value (string)
and the value is:
* the original facet value (string)
* a bitmap of all the docids that have this normalised string facet value
**At level 1**, the key is:
* the left bound of the range as an index in level 0
* the right bound of the range as an index in level 0
and the value is:
* the left bound of the range as a normalised string
* the right bound of the range as a normalised string
* a bitmap of all the docids that have a string facet value within the bounds
**At level > 1**, the key is:
* the left bound of the range as an index in level 0
* the right bound of the range as an index in level 0
and the value is:
* a bitmap of all the docids that have a string facet value within the bounds
## New structure of the `facet_id_f64_docids` and `facet_id_string_docids` databases
Now both the `facet_id_f64_docids` and `facet_id_string_docids` databases have the exact same structure:
```
┌───────────────────────────────┬───────────────────────────────┬───────────────┐
┌───────┐ │ "ab" (2) │ "gaf" (2) │ "woz" (1) │
│Level 2│ │ │ │ │
└───────┘ │ [a, b, d, f, z] │ [c, d, e, f, g] │ [u, y] │
├───────────────┬───────────────┼───────────────┬───────────────┼───────────────┤
┌───────┐ │ "ab" (2) │ "ba" (2) │ "gaf" (2) │ "form" (2) │ "woz" (2) │
│Level 1│ │ │ │ │ │ │
└───────┘ │ [a, b, d, z] │ [a, b, f] │ [c, d, g] │ [e, f] │ [u, y] │
├───────┬───────┼───────┬───────┼───────┬───────┼───────┬───────┼───────┬───────┤
┌───────┐ │ "ab" │ "ac" │ "ba" │ "bac" │ "gaf" │ "gal" │ "form"│ "wow" │ "woz" │ "zz" │
│Level 0│ │ │ │ │ │ │ │ │ │ │ │
└───────┘ │ [a, b]│ [d, z]│ [b, f]│ [a, f]│ [c, d]│ [g] │ [e] │ [e, f]│ [y] │ [u] │
└───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┘
```
where for all levels, the key is a `FacetGroupKey<T>` containing:
* the field id (`u16`)
* the level height (`u8`)
* the left bound of the range (`T`)
and the value is a `FacetGroupValue` containing:
* the number of elements from the level below that are part of the range (`u8`, =0 for level 0)
* a bitmap of all the docids that have a facet value within the bounds (`RoaringBitmap`)
The right bound of the range is now implicit, it is equal to `Excluded(next_left_bound)`.
In the code, the key is always encoded using `FacetGroupKeyCodec<C>` where `C` is the codec used to encode the facet value (either `OrderedF64Codec` or `StrRefCodec`) and the value is encoded with `FacetGroupValueCodec`.
Since both databases share the same structure, we can implement almost all operations only once by treating the facet value as a byte slice (i.e. `FacetGroupKey<&[u8]>` encoded as `FacetGroupKeyCodec<ByteSliceRef>`). This is, in my opinion, a big simplification.
The reason for changing the structure of the databases is to make it possible to incrementally add a facet value to an existing database. Since the `facet_id_string_docids` used to store indices to `level 0` in all levels > 0, adding an element to level 0 would potentially invalidate all the indices.
Note that the original string value of a facet is no longer stored in this database.
## Incrementally adding a facet value
Here I describe how we can add a facet value to the new database incrementally. If we want to add the document with id `z` and facet value `gap`., then we want to add/modify the elements highlighted below in pink:
<img width="946" alt="Screenshot 2022-09-12 at 10 14 54" src="https://user-images.githubusercontent.com/6040237/189605532-fe4b0f52-e13d-4b3c-92d9-10c705953e3d.png">
which results in:
<img width="662" alt="Screenshot 2022-09-12 at 10 23 29" src="https://user-images.githubusercontent.com/6040237/189607015-c3a37588-b825-43c2-878a-f8f85c000b94.png">
* one element was added in level 0
* one key/value was modified in level 1
* one value was modified in level 2
Adding this element was easy since we could simply add it to level 0 and then increase the `group_size` part of the value for the level above. However, in order to keep the structure balanced, we can't always do this. If the group size reaches a threshold (`max_group_size`), then we split the node into two. For example, let's imagine that `max_group_size` is `4` and we add the docid `y` with facet value `gas`. First, we add it in level 0:
<img width="904" alt="Screenshot 2022-09-12 at 10 30 40" src="https://user-images.githubusercontent.com/6040237/189608391-531f9df1-3424-4f1f-8344-73eb194570e5.png">
Then, we realise that the group size of its parent is going to reach the maximum group size (=4) and thus we split the parent into two nodes:
<img width="919" alt="Screenshot 2022-09-12 at 10 33 16" src="https://user-images.githubusercontent.com/6040237/189608884-66f87635-1fc6-41d2-a459-87c995491ac4.png">
and since we inserted an element in level 1, we also update level 2 accordingly, by increasing the group size of the parent:
<img width="915" alt="Screenshot 2022-09-12 at 10 34 42" src="https://user-images.githubusercontent.com/6040237/189609233-d4a893ff-254a-48a7-a5ad-c0dc337f23ca.png">
We also have two other parameters:
* `group_size` is the default group size when building the database from scratch
* `min_level_size` is the minimum number of elements that a level should contain
When the highest level size is greater than `group_size * min_level_size`, then we create an additional level above it.
There is one more edge case for the insertion algorithm. While we normally don't modify the existing left bounds of a key, we have to do it if the facet value being inserted is smaller than the first left bound. For example, inserting `"aa"` with the docid `w` would change the database to:
<img width="756" alt="Screenshot 2022-09-12 at 10 41 56" src="https://user-images.githubusercontent.com/6040237/189610637-a043ef71-7159-4bf1-b4fd-9903134fc095.png">
The root of the code for incremental indexing is the `FacetUpdateIncremental` builder.
## Incrementally removing a facet value
TODO: the algorithm was implemented and works, but its current API is: `fn delete(self, facet_value, single_docid)`. It removes the given document id from all keys containing the given facet value. I don't think it is the right way to implement it anymore. Perhaps a bitmap of docids should be given instead. This is fairly easy to do. But since we batch document deletions together (because of soft deletion), it's not clear to me anymore that incremental deletion should be implemented at all.
## Bulk insertion
While it's faster to incrementally add a single facet value to the database, it is sometimes **slower** to repeatedly add facet values one-by-one instead of doing it in bulk. For example, during initial indexing, we'd like to build the database from a list of facet values and associated document ids in one go. The `FacetUpdateBulk` builder provides a way to do so. It works by:
1. clearing all levels > 0 from the DB
2. adding all new elements in level 0
3. rebuilding the higher levels from scratch
The algorithm for bulk insertion is the same as the previous one.
## Choosing between incremental and bulk insertion
On my computer, I measured that is about 50x slower to add N facet values incrementally than it is to re-build a database with N facet values in level 0. Therefore, we dynamically choose to use either incremental insertion or bulk insertion based on (1) the number of existing elements in level 0 of the database and (2) the number of facet values from the new documents.
This is imprecise but is mainly aimed at avoiding the worst-case scenario where the incremental insertion method is used repeatedly millions of times.
## Fuzz-testing
**Potentially controversial:**
I fuzz-tested incremental addition and deletion using fuzzcheck, which found many bugs. The fuzz-test consists of inserting/deleting facet values and docids in succession, each operation is processed with different parameters for `group_size`, `max_group_size`, and `min_level_size`. After all the operations are processed, the content of level 0 is compared to the content of an equivalent structure with a simple and easily-checked implementation. Furthermore, we check that the database has a correct structure (all groups from levels > 0 correctly combine the content of their children). I also visualised the code coverage found by the fuzz-test. It covered 100% of the relevant code except for `unreachable/panic` statements and errors returned by `heed`.
The fuzz-test and the fuzzcheck dependency are only compiled when `cargo fuzzcheck` is used. For now, the dependency is from a local path on my computer, but it can be changed to a crate version if we decide to keep it.
## Algorithms operating on the facet databases
There are four important algorithms making use of the facet databases:
1. Sort, ascending
2. Sort, descending
3. Facet distribution
4. Range search
Previously, the implementation of all four algorithms was based on a number of iterators specific to each database kind (number or string): `FacetNumberRange`, `FacetNumberRevRange`, `FacetNumberIter` (with a reversed and reducing/non-reducing option), `FacetStringGroupRange`, `FacetStringGroupRevRange`, `FacetStringLevel0Range`, `FacetStringLevel0RevRange`, and `FacetStringIter` (reversed + reducing/non-reducing).
Now, all four algorithms have a unique implementation shared by both the string and number databases. There are four functions:
1. `ascending_facet_sort` in `search/facet/facet_sort_ascending.rs`
2. `descending_facet_sort` in `search/facet/facet_sort_descending.rs`
3. `iterate_over_facet_distribution` in `search/facet/facet_distribution_iter.rs`
4. `find_docids_of_facet_within_bounds` in `search/facet/facet_range_search.rs`
I have tried to test them with some snapshot tests but more testing could still be done. I don't *think* that the performance of these algorithms regressed, but that will need to be confirmed by benchmarks.
## Change of behaviour for facet distributions
Previously, the original string value of a facet was stored in the level 0 of `facet_id_string_docids `. This is no longer the case. The original string value was used in the implementation of the facet distribution algorithm. Now, to recover it, we pick a random document id which contains the normalised string value and look up the original one in `field_id_docid_facet_strings`. As a consequence, it may be that the string value returned in the field distribution does not appear in any of the candidates. For example,
```json
{ "id": 0, "colour": "RED" }
{ "id": 1, "colour": "red" }
```
Facet distribution for the `colour` field among the candidates `[1]`:
```
{ "RED": 1 }
```
Here, "RED" was given as the original facet value even though it does not appear in the document id `1`.
## Heed codecs
A number of heed codecs related to the facet databases were removed:
* `FacetLevelValueF64Codec`
* `FacetLevelValueU32Codec`
* `FacetStringLevelZeroCodec`
* `StringValueCodec`
* `FacetStringZeroBoundsValueCodec`
* `FacetValueStringCodec`
* `FieldDocIdFacetStringCodec`
* `FieldDocIdFacetF64Codec`
They were replaced by:
* `FacetGroupKeyCodec<C>` (replaces all key codecs for the facet databases)
* `FacetGroupValueCodec` (replaces all value codecs for the facet databases)
* `FieldDocIdFacetCodec<C>` (replaces `FieldDocIdFacetStringCodec` and `FieldDocIdFacetF64Codec`)
Since the associated encoded item of `FacetGroupKeyCodec<C>` is `FacetKey<T>` and we often work with `FacetKey<&[u8]>` and `FacetKey<&str>`, then we need to have codecs that encode values of type `&str` and `&[u8]`. The existing `ByteSlice` and `Str` codecs do not work for that purpose (their `EItem` are `[u8]` and `str`), I have also created two new codecs:
* `ByteSliceRef` is a codec with a `EItem = DItem = &[u8]`
* `StrRefCodec` is a codec with a `EItem = DItem = &str`
I have also factored out the code used to encode an ordered f64 into its own `OrderedF64Codec`.
Co-authored-by: Loïc Lecrenier <loic@meilisearch.com>
