Previously, if the primary key was set and a Settings update contained
a primary key, an error would be returned.
However, this error is not needed if the new PK == the current PK.
This commit just checks to see if the PK actually changes
before raising an error.
3319: Transparently resize indexes on MaxDatabaseSizeReached errors r=Kerollmops a=dureuill
# Pull Request
## Related issue
Related to https://github.com/meilisearch/meilisearch/discussions/3280, depends on https://github.com/meilisearch/milli/pull/760
## What does this PR do?
### User standpoint
- Meilisearch no longer fails tasks that encounter the `milli::UserError(MaxDatabaseSizeReached)` error.
- Instead, these tasks are retried after increasing the maximum size allocated to the index where the failure occurred.
### Implementation standpoint
- Add `Batch::index_uid` to get the `index_uid` of a batch of task if there is one
- `IndexMapper::create_or_open_index` now takes an additional `size` argument that allows to (re)open indexes with a size different from the base `IndexScheduler::index_size` field
- `IndexScheduler::tick` now returns a `Result<TickOutcome>` instead of a `Result<usize>`. This offers more explicit control over what the behavior should be wrt the next tick.
- Add `IndexStatus::BeingResized` that contains a handle that a thread can use to await for the resize operation to complete and the index to be available again.
- Add `IndexMapper::resize_index` to increase the size of an index.
- In `IndexScheduler::tick`, intercept task batches that failed due to `MaxDatabaseSizeReached` and resize the index that caused the error, then request a new tick that will eventually handle the still enqueued task.
## Testing the PR
The following diff can be applied to this branch to make testing the PR easier:
<details>
```diff
diff --git a/index-scheduler/src/index_mapper.rs b/index-scheduler/src/index_mapper.rs
index 553ab45a..022b2f00 100644
--- a/index-scheduler/src/index_mapper.rs
+++ b/index-scheduler/src/index_mapper.rs
`@@` -228,13 +228,15 `@@` impl IndexMapper {
drop(lock);
+ std:🧵:sleep_ms(2000);
+
let current_size = index.map_size()?;
let closing_event = index.prepare_for_closing();
- log::info!("Resizing index {} from {} to {} bytes", name, current_size, current_size * 2);
+ log::error!("Resizing index {} from {} to {} bytes", name, current_size, current_size * 2);
closing_event.wait();
- log::info!("Resized index {} from {} to {} bytes", name, current_size, current_size * 2);
+ log::error!("Resized index {} from {} to {} bytes", name, current_size, current_size * 2);
let index_path = self.base_path.join(uuid.to_string());
let index = self.create_or_open_index(&index_path, None, 2 * current_size)?;
`@@` -268,8 +270,10 `@@` impl IndexMapper {
match index {
Some(Available(index)) => break index,
Some(BeingResized(ref resize_operation)) => {
+ log::error!("waiting for resize end");
// Deadlock: no lock taken while doing this operation.
resize_operation.wait();
+ log::error!("trying our luck again!");
continue;
}
Some(BeingDeleted) => return Err(Error::IndexNotFound(name.to_string())),
diff --git a/index-scheduler/src/lib.rs b/index-scheduler/src/lib.rs
index 11b17d05..242dc095 100644
--- a/index-scheduler/src/lib.rs
+++ b/index-scheduler/src/lib.rs
`@@` -908,6 +908,7 `@@` impl IndexScheduler {
///
/// Returns the number of processed tasks.
fn tick(&self) -> Result<TickOutcome> {
+ log::error!("ticking!");
#[cfg(test)]
{
*self.run_loop_iteration.write().unwrap() += 1;
diff --git a/meilisearch/src/main.rs b/meilisearch/src/main.rs
index 050c825a..63f312f6 100644
--- a/meilisearch/src/main.rs
+++ b/meilisearch/src/main.rs
`@@` -25,7 +25,7 `@@` fn setup(opt: &Opt) -> anyhow::Result<()> {
#[actix_web::main]
async fn main() -> anyhow::Result<()> {
- let (opt, config_read_from) = Opt::try_build()?;
+ let (mut opt, config_read_from) = Opt::try_build()?;
setup(&opt)?;
`@@` -56,6 +56,8 `@@` We generated a secure master key for you (you can safely copy this token):
_ => (),
}
+ opt.max_index_size = byte_unit::Byte::from_str("1MB").unwrap();
+
let (index_scheduler, auth_controller) = setup_meilisearch(&opt)?;
#[cfg(all(not(debug_assertions), feature = "analytics"))]
```
</details>
Mainly, these debug changes do the following:
- Set the default index size to 1MiB so that index resizes are initially frequent
- Turn some logs from info to error so that they can be displayed with `--log-level ERROR` (hiding the other infos)
- Add a long sleep between the beginning and the end of the resize so that we can observe the `BeingResized` index status (otherwise it would never come up in my tests)
## Open questions
- Is the growth factor of x2 the correct solution? For a `Vec` in memory it makes sense, but here we're manipulating quantities that are potentially in the order of 500GiBs. For bigger indexes it may make more sense to add at most e.g. 100GiB on each resize operation, avoiding big steps like 500GiB -> 1TiB.
## PR checklist
Please check if your PR fulfills the following requirements:
- [ ] Does this PR fix an existing issue, or have you listed the changes applied in the PR description (and why they are needed)?
- [ ] Have you read the contributing guidelines?
- [ ] Have you made sure that the title is accurate and descriptive of the changes?
Thank you so much for contributing to Meilisearch!
3470: Autobatch addition and deletion r=irevoire a=irevoire
This PR adds the capability to meilisearch to batch document addition and deletion together.
Fix https://github.com/meilisearch/meilisearch/issues/3440
--------------
Things to check before merging;
- [x] What happens if we delete multiple time the same documents -> add a test
- [x] If a documentDeletion gets batched with a documentAddition but the index doesn't exist yet? It should not work
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
Co-authored-by: Tamo <tamo@meilisearch.com>
This database can easily contain millions of entries. Thus, iterating
over it can be very expensive.
For regular `documentAdditionOrUpdate` tasks, `del_prefix_fst_words`
will always be empty. Thus, we can save a significant amount of time
by adding this `if !del_prefix_fst_words.is_empty()` condition.
The code's behaviour remains completely unchanged.
733: Avoid a prefix-related worst-case scenario in the proximity criterion r=loiclec a=loiclec
# Pull Request
## Related issue
Somewhat fixes (until merged into meilisearch) https://github.com/meilisearch/meilisearch/issues/3118
## What does this PR do?
When a query ends with a word and a prefix, such as:
```
word pr
```
Then we first determine whether `pre` *could possibly* be in the proximity prefix database before querying it. There are then three possibilities:
1. `pr` is not in any prefix cache because it is not the prefix of many words. We don't query the proximity prefix database. Instead, we list all the word derivations of `pre` through the FST and query the regular proximity databases.
2. `pr` is in the prefix cache but cannot be found in the proximity prefix databases. **In this case, we partially disable the proximity ranking rule for the pair `word pre`.** This is done as follows:
1. Only find the documents where `word` is in proximity to `pre` **exactly** (no derivations)
2. Otherwise, assume that their proximity in all the documents in which they coexist is >= 8
3. `pr` is in the prefix cache and can be found in the proximity prefix databases. In this case we simply query the proximity prefix databases.
Note that if a prefix is longer than 2 bytes, then it cannot be in the proximity prefix databases. Also, proximities larger than 4 are not present in these databases either. Therefore, the impact on relevancy is:
1. For common prefixes of one or two letters: we no longer distinguish between proximities from 4 to 8
2. For common prefixes of more than two letters: we no longer distinguish between any proximities
3. For uncommon prefixes: nothing changes
Regarding (1), it means that these two documents would be considered equally relevant according to the proximity rule for the query `heard pr` (IF `pr` is the prefix of more than 200 words in the dataset):
```json
[
{ "text": "I heard there is a faster proximity criterion" },
{ "text": "I heard there is a faster but less relevant proximity criterion" }
]
```
Regarding (2), it means that two documents would be considered equally relevant according to the proximity rule for the query "faster pro":
```json
[
{ "text": "I heard there is a faster but less relevant proximity criterion" }
{ "text": "I heard there is a faster proximity criterion" },
]
```
But the following document would be considered more relevant than the two documents above:
```json
{ "text": "I heard there is a faster swimmer who is competing in the pro section of the competition " }
```
Note, however, that this change of behaviour only occurs when using the set-based version of the proximity criterion. In cases where there are fewer than 1000 candidate documents when the proximity criterion is called, this PR does not change anything.
---
## Performance
I couldn't use the existing search benchmarks to measure the impact of the PR, but I did some manual tests with the `songs` benchmark dataset.
```
1. 10x 'a':
- 640ms ⟹ 630ms = no significant difference
2. 10x 'b':
- set-based: 4.47s ⟹ 7.42 = bad, ~2x regression
- dynamic: 1s ⟹ 870 ms = no significant difference
3. 'Someone I l':
- set-based: 250ms ⟹ 12 ms = very good, x20 speedup
- dynamic: 21ms ⟹ 11 ms = good, x2 speedup
4. 'billie e':
- set-based: 623ms ⟹ 2ms = very good, x300 speedup
- dynamic: ~4ms ⟹ 4ms = no difference
5. 'billie ei':
- set-based: 57ms ⟹ 20ms = good, ~2x speedup
- dynamic: ~4ms ⟹ ~2ms. = no significant difference
6. 'i am getting o'
- set-based: 300ms ⟹ 60ms = very good, 5x speedup
- dynamic: 30ms ⟹ 6ms = very good, 5x speedup
7. 'prologue 1 a 1:
- set-based: 3.36s ⟹ 120ms = very good, 30x speedup
- dynamic: 200ms ⟹ 30ms = very good, 6x speedup
8. 'prologue 1 a 10':
- set-based: 590ms ⟹ 18ms = very good, 30x speedup
- dynamic: 82ms ⟹ 35ms = good, ~2x speedup
```
Performance is often significantly better, but there is also one regression in the set-based implementation with the query `b b b b b b b b b b`.
Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.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>
Displays log message in the form:
```
[2022-12-21T09:19:42Z INFO milli::update::index_documents::enrich] Primary key was not specified in index. Inferred to 'id'
```
By creating snapshots and updating the format of the existing
snapshots. The next commit will apply the fix, which will show
its effects cleanly on the old and new snapshot tests
706: Limit the reindexing caused by updating settings when not needed r=curquiza a=GregoryConrad
## What does this PR do?
When updating index settings using `update::Settings`, sometimes a `reindex` of `update::Settings` is triggered when it doesn't need to be. This PR aims to prevent those unnecessary `reindex` calls.
For reference, here is a snippet from the current `execute` method in `update::Settings`:
```rust
// ...
if stop_words_updated
|| faceted_updated
|| synonyms_updated
|| searchable_updated
|| exact_attributes_updated
{
self.reindex(&progress_callback, &should_abort, old_fields_ids_map)?;
}
```
- [x] `faceted_updated` - looks good as-is ✅
- [x] `stop_words_updated` - looks good as-is ✅
- [x] `synonyms_updated` - looks good as-is ✅
- [x] `searchable_updated` - fixed in this PR
- [x] `exact_attributes_updated` - fixed in this PR
## 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: Gregory Conrad <gregorysconrad@gmail.com>
708: Reduce memory usage of the MatchingWords structure r=ManyTheFish a=loiclec
# Pull Request
## Related issue
Fixes (partially) https://github.com/meilisearch/meilisearch/issues/3115
## What does this PR do?
1. Reduces the memory usage caused by the creation of a 10-word query tree by 20x.
This is done by deduplicating the `MatchingWord` values, which are heavy because of their inner DFA. The deduplication works by wrapping each `MatchingWord` in a reference-counted box and using a hash map to determine whether a `MatchingWord` DFA already exists for a certain signature, or whether a new one needs to be built.
2. Avoid the worst-case scenario of creating a `MatchingWord` for extremely long words that cannot be indexed by milli.
Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com>
