ForecastIT.testOverflowToDisk has been observed to fail a few
times in FIPS JVMs because it takes longer than the permitted
30 seconds. This PR bumps the timeout up to 60 seconds.
Fixes#63793
Adds support for the unsigned_long type to data frame analytics.
This type is handled in the same way as the long type. Values
sent to the ML native processes are converted to floats and
hence will lose accuracy when outside the range where a float
can uniquely represent long values.
Backport of #64066
This PR adds deprecation warnings when accessing System Indices via the REST layer. At this time, these warnings are only enabled for Snapshot builds by default, to allow projects external to Elasticsearch additional time to adjust their access patterns.
Deprecation warnings will be triggered by all REST requests which access registered System Indices, except for purpose-specific APIs which access System Indices as an implementation detail a few specific APIs which will continue to allow access to system indices by default:
- `GET _cluster/health`
- `GET {index}/_recovery`
- `GET _cluster/allocation/explain`
- `GET _cluster/state`
- `POST _cluster/reroute`
- `GET {index}/_stats`
- `GET {index}/_segments`
- `GET {index}/_shard_stores`
- `GET _cat/[indices,aliases,health,recovery,shards,segments]`
Deprecation warnings for accessing system indices take the form:
```
this request accesses system indices: [.some_system_index], but in a future major version, direct access to system indices will be prevented by default
```
* [ML] optimize delete expired snapshots (#63134)
When deleting expired snapshots, we do an individual delete action per snapshot per job.
We should instead gather the expired snapshots and delete them in a single call.
This commit achieves this and a side-effect is there is less audit log spam on nightly cleanup
closes https://github.com/elastic/elasticsearch/issues/62875
* [ML] renames */inference* apis to */trained_models* (#63097)
This commit renames all `inference` CRUD APIs to `trained_models`.
This aligns with internal terminology, documentation, and use-cases.
* [ML] fixing testTwoJobsWithSameRandomizeSeedUseSameTrainingSet tests (#62976)
This fixes the two test failures.
The shard failure seems to be due to the .ml-stats index being in the middle of being created.
for get trained models include_model_definition is now deprecated.
This commit writes a deprecation warning if that parameter is used and suggests the caller to utilize the replacement
* [ML] changing to not use global bulk indexing parameters in conjunction with add(object) calls (#62694)
* [ML] changing to not use global bulk indexing parameters in conjunction with add(object) calls
global parameters, outside of the global index, are ignored for internal callers in certain cases.
If the interal caller is adding requests via the following methods:
```
- BulkRequest#add(IndexRequest)
- BulkRequest#add(UpdateRequest)
- BulkRequest#add(DocWriteRequest)
- BulkRequest#add(DocWriteRequest[])
```
It is better to specifically set the desired parameters on the requests before they are added
to the bulk request object.
This commit addresses this issue for the ML plugin
* unmuting test
This commit adjusts the following APIs so now they not only support an `_all` case, but wildcard patterned Ids as well.
- `GET _ml/calendars/<calendar_id>/events`
- `GET _ml/calendars/<calendar_id>`
- `GET _ml/anomaly_detectors/<job_id>/model_snapshots/<snapshot_id>`
- `DELETE _ml/anomaly_detectors/<job_id>/_forecast/<forecast_id>`
This commit address some build failures from the perspective of Intellij.
These changes include:
* changing an order of a dependency definition that seems to can cause Intellij build to fail.
* introduction of an abstract class out of the test source set (seems to be an issue sharing
classes cross projects with non-standard source sets.
* a couple of missing dependency definitions (not sure how the command line worked prior to this)
This commit unmutes the windows check for testTooManyPartitions test.
The assertion has since changed to include a soft_limit check.
This coupled with changes over the past years means the test should be enabled again.
related to: #32033
* [ML] only persist progress if it has changed
We already search for the previously stored progress document.
For optimization purposes, and to prevent restoring the same
progress after a failed analytics job is stopped,
this commit does an equality check between the previously stored progress and current progress
If the progress has changed, persistence continues as normal.
* [ML] adds new n_gram_encoding custom processor (#61578)
This adds a new `n_gram_encoding` feature processor for analytics and inference.
The focus of this processor is simple ngram encodings that allow:
- multiple ngrams [1..5]
- Prefix, infix, suffix
For 1/2 the plugins in x-pack, the integTest
task is now a no-op and all of the tests are now executed via a test,
yamlRestTest, javaRestTest, or internalClusterTest.
This includes the following projects:
async-search, autoscaling, ccr, enrich, eql, frozen-indicies,
data-streams, graph, ilm, mapper-constant-keyword, mapper-flattened, ml
A few of the more specialized qa projects within these plugins
have not been changed with this PR due to additional complexity which should
be addressed separately.
A follow up PR will address the remaining x-pack plugins (this PR is big enough as-is).
related: #61802
related: #56841
related: #59939
related: #55896
Inference processors asynchronously usage write stats to the .ml-stats index after they used.
In tests the write can leak into the next test causing failures depending on which test follows.
This change waits for the usage stats docs to be written at the end of the test
Backports the following commits to 7.x:
[ML] write warning if configured memory limit is too low for analytics job (#61505)
Having `_start` fail when the configured memory limit is too low can be frustrating.
We should instead warn the user that their job might not run properly if their configured limit is too low.
It might be that our estimate is too high, and their configured limit works just fine.
feature_processors allow users to create custom features from
individual document fields.
These `feature_processors` are the same object as the trained model's pre_processors.
They are passed to the native process and the native process then appends them to the
pre_processor array in the inference model.
closes https://github.com/elastic/elasticsearch/issues/59327
`foreach` processors store information within the `_ingest` metadata object.
This commit adds the contents of the `_ingest` metadata (if it is not empty).
And will append new inference results if the result field already exists.
This allows a `foreach` to execute and multiple inference results being written to the same result field.
closes https://github.com/elastic/elasticsearch/issues/60867
* Merge test runner task into RestIntegTest (#60261)
* Merge test runner task into RestIntegTest
* Reorganizing Standalone runner and RestIntegTest task
* Rework general test task configuration and extension
* Fix merge issues
* use former 7.x common test configuration
- Replace immediate task creations by using task avoidance api
- One step closer to #56610
- Still many tasks are created during configuration phase. Tackled in separate steps
Prior to this change ML memory estimation processes for a
given job would always use the same named pipe names. This
would often cause one of the processes to fail.
This change avoids this risk by adding an incrementing counter
value into the named pipe names used for memory estimation
processes.
Backport of #60395
In order to unify model inference and analytics results we
need to write the same fields.
prediction_probability and prediction_score are now written
for inference calls against classification models.
This sets up all indexing to one of our write aliases to require it actually be an alias.
This allows failures scenarios to be captured quickly, loudly, and then potentially recovered.
If a feature is created via a custom pre-processor,
we should return the importance for that feature.
This means we will not return the importance for the
original document field for custom processed features.
closes https://github.com/elastic/elasticsearch/issues/59330
Previously the test was asserting the prediction on each document
was close 10.0 from the expected. It turned out that was not enough
as we occasionally saw the test failing by little.
Instead of relaxing that assertion, this commit changes it to
assert the mean prediction error is less than 10.0. This should
reduce the chances of the test failing significantly.
Fixes#60212
Backport of #60221
David Roberts
Przemysław Witek
Benjamin Trent
Luca Cavanna
Tim Vernum
Gordon Brown
Igor Motov
Daniel Mitterdorfer
Andrei Dan
Jake Landis
David Kyle
Dimitris Athanasiou
Tanguy Leroux
Nik Everett
Henning Andersen
Rene Groeschke