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)
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
* 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
Putting an ingest pipeline used to require that the user calling
it had permission to get nodes info as well as permission to
manage ingest. This was due to an internal implementaton detail
that was not visible to the end user.
This change alters the behaviour so that a user with the
manage_pipeline cluster privilege can put an ingest pipeline
regardless of whether they have the separate privilege to get
nodes info. The internal implementation detail now runs as
the internal _xpack user when security is enabled.
Backport of #60106
The `create_doc`, `create`, `write` and `index` privileges do not grant
the PutMapping action anymore. Apart from the `write` privilege, the other
three privileges also do NOT grant (auto) updating the mapping when ingesting
a document with unmapped fields, according to the templates.
In order to maintain the BWC in the 7.x releases, the above privileges will still grant
the Put and AutoPutMapping actions, but only when the "index" entity is an alias
or a concrete index, but not a data stream or a backing index of a data stream.
This commit creates a new Gradle plugin to provide a separate task name
and source set for running YAML based REST tests. The only project
converted to use the new plugin in this PR is distribution/archives/integ-test-zip.
For which the testing has been moved to :rest-api-spec since it makes the most
sense and it avoids a small but awkward change to the distribution plugin.
The remaining cases in modules, plugins, and x-pack will be handled in followups.
This plugin is distinctly different from the plugin introduced in #55896 since
the YAML REST tests are intended to be black box tests over HTTP. As such they
should not (by default) have access to the classpath for that which they are testing.
The YAML based REST tests will be moved to separate source sets (yamlRestTest).
The which source is the target for the test resources is dependent on if this
new plugin is applied. If it is not applied, it will default to the test source
set.
Further, this introduces a breaking change for plugin developers that
use the YAML testing framework. They will now need to either use the new source set
and matching task, or configure the rest resources to use the old "test" source set that
matches the old integTest task. (The former should be preferred).
As part of this change (which is also breaking for plugin developers) the
rest resources plugin has been removed from the build plugin and now requires
either explicit application or application via the new YAML REST test plugin.
Plugin developers should be able to fix the breaking changes to the YAML tests
by adding apply plugin: 'elasticsearch.yaml-rest-test' and moving the YAML tests
under a yamlRestTest folder (instead of test)
* Remove usage of deprecated testCompile configuration
* Replace testCompile usage by testImplementation
* Make testImplementation non transitive by default (as we did for testCompile)
* Update CONTRIBUTING about using testImplementation for test dependencies
* Fail on testCompile configuration usage
This PR implements the following changes to make ML model snapshot
retention more flexible in advance of adding a UI for the feature in
an upcoming release.
- The default for `model_snapshot_retention_days` for new jobs is now
10 instead of 1
- There is a new job setting, `daily_model_snapshot_retention_after_days`,
that defaults to 1 for new jobs and `model_snapshot_retention_days`
for pre-7.8 jobs
- For days that are older than `model_snapshot_retention_days`, all
model snapshots are deleted as before
- For days that are in between `daily_model_snapshot_retention_after_days`
and `model_snapshot_retention_days` all but the first model snapshot
for that day are deleted
- The `retain` setting of model snapshots is still respected to allow
selected model snapshots to be retained indefinitely
Backport of #56125
Backport of #55858 to 7.x branch.
Currently the TransportBulkAction detects whether an index is missing and
then decides whether it should be auto created. The coordination of the
index creation also happens in the TransportBulkAction on the coordinating node.
This change adds a new transport action that the TransportBulkAction delegates to
if missing indices need to be created. The reasons for this change:
* Auto creation of data streams can't occur on the coordinating node.
Based on the index template (v2) either a regular index or a data stream should be created.
However if the coordinating node is slow in processing cluster state updates then it may be
unaware of the existence of certain index templates, which then can load to the
TransportBulkAction creating an index instead of a data stream. Therefor the coordination of
creating an index or data stream should occur on the master node. See #55377
* From a security perspective it is useful to know whether index creation originates from the
create index api or from auto creating a new index via the bulk or index api. For example
a user would be allowed to auto create an index, but not to use the create index api. The
auto create action will allow security to distinguish these two different patterns of
index creation.
This change adds the following new transport actions:
AutoCreateAction, the TransportBulkAction redirects to this action and this action will actually create the index (instead of the TransportCreateIndexAction). Later via #55377, can improve the AutoCreateAction to also determine whether an index or data stream should be created.
The create_index index privilege is also modified, so that if this permission is granted then a user is also allowed to auto create indices. This change does not yet add an auto_create index privilege. A future change can introduce this new index privilege or modify an existing index / write index privilege.
Relates to #53100
We were previously checking at least one supported field existed
when the _explain API was called. However, in the case of analyses
with required fields (e.g. regression) we were not accounting that
the dependent variable is not a feature and thus if the source index
only contains the dependent variable field there are no features to
train a model on.
This commit adds a validation that at least one feature is available
for analysis. Note that we also move that validation away from
`ExtractedFieldsDetector` and the _explain API and straight into
the _start API. The reason for doing this is to allow the user to use
the _explain API in order to understand why they would be seeing an
error like this one.
For example, the user might be using an index that has fields but
they are of unsupported types. If they start the job and get
an error that there are no features, they will wonder why that is.
Calling the _explain API will show them that all their fields are
unsupported. If the _explain API was failing instead, there would
be no way for the user to understand why all those fields are
ignored.
Closes#55593
Backport of #55876
* [ML] add new inference_config field to trained model config (#54421)
A new field called `inference_config` is now added to the trained model config object. This new field allows for default inference settings from analytics or some external model builder.
The inference processor can still override whatever is set as the default in the trained model config.
* fixing for backport
This PR:
1. Fixes the bug where a cardinality estimate of zero could cause
a 500 status
2. Adds tests for that scenario and a few others
3. Adds sensible estimates for the cases that were previously TODO
Backport of #54462
When get filters is called without setting the `size`
paramter only up to 10 filters are returned. However,
100 filters should be returned. This commit fixes this
and adds an integ test to guard it.
It seems this was accidentally broken in #39976.
Closes#54206
Backport of #54207
This PR moves the majority of the Watcher REST tests under
the Watcher x-pack plugin.
Specifically, moves the Watcher tests from:
x-pack/plugin/test
x-pack/qa/smoke-test-watcher
x-pack/qa/smoke-test-watcher-with-security
x-pack/qa/smoke-test-monitoring-with-watcher
to:
x-pack/plugin/watcher/qa/rest (/test and /qa/smoke-test-watcher)
x-pack/plugin/watcher/qa/with-security
x-pack/plugin/watcher/qa/with-monitoring
Additionally, this disables Watcher from the main
x-pack test cluster and consolidates the stop/start logic
for the tests listed.
No changes to the tests (beyond moving them) are included.
3rd party tests and doc tests (which also touch Watcher)
are not included in the changes here.
* Smarter copying of the rest specs and tests (#52114)
This PR addresses the unnecessary copying of the rest specs and allows
for better semantics for which specs and tests are copied. By default
the rest specs will get copied if the project applies
`elasticsearch.standalone-rest-test` or `esplugin` and the project
has rest tests or you configure the custom extension `restResources`.
This PR also removes the need for dozens of places where the x-pack
specs were copied by supporting copying of the x-pack rest specs too.
The plugin/task introduced here can also copy the rest tests to the
local project through a similar configuration.
The new plugin/task allows a user to minimize the surface area of
which rest specs are copied. Per project can be configured to include
only a subset of the specs (or tests). Configuring a project to only
copy the specs when actually needed should help with build cache hit
rates since we can better define what is actually in use.
However, project level optimizations for build cache hit rates are
not included with this PR.
Also, with this PR you can no longer use the includePackaged flag on
integTest task.
The following items are included in this PR:
* new plugin: `elasticsearch.rest-resources`
* new tasks: CopyRestApiTask and CopyRestTestsTask - performs the copy
* new extension 'restResources'
```
restResources {
restApi {
includeCore 'foo' , 'bar' //will include the core specs that start with foo and bar
includeXpack 'baz' //will include x-pack specs that start with baz
}
restTests {
includeCore 'foo', 'bar' //will include the core tests that start with foo and bar
includeXpack 'baz' //will include the x-pack tests that start with baz
}
}
```
This changes the tree validation code to ensure no node in the tree has a
feature index that is beyond the bounds of the feature_names array.
Specifically this handles the situation where the C++ emits a tree containing
a single node and an empty feature_names list. This is valid tree used to
centre the data in the ensemble but the validation code would reject this
as feature_names is empty. This meant a broken workflow as you cannot GET
the model and PUT it back
* [ML][Inference] PUT API (#50852)
This adds the `PUT` API for creating trained models that support our format.
This includes
* HLRC change for the API
* API creation
* Validations of model format and call
* fixing backport
This commit removes validation logic of source and dest indices
for data frame analytics and replaces it with using the common
`SourceDestValidator` class which is already used by transforms.
This way the validations and their messages become consistent
while we reduce code.
This means that where these validations fail the error messages
will be slightly different for data frame analytics.
Backport of #50841
This adds a `_source` setting under the `source` setting of a data
frame analytics config. The new `_source` is reusing the structure
of a `FetchSourceContext` like `analyzed_fields` does. Specifying
includes and excludes for source allows selecting which fields
will get reindexed and will be available in the destination index.
Closes#49531
Backport of #49690
This commit replaces the _estimate_memory_usage API with
a new API, the _explain API.
The API consolidates information that is useful before
creating a data frame analytics job.
It includes:
- memory estimation
- field selection explanation
Memory estimation is moved here from what was previously
calculated in the _estimate_memory_usage API.
Field selection is a new feature that explains to the user
whether each available field was selected to be included or
not in the analysis. In the case it was not included, it also
explains the reason why.
Backport of #49455
* [ML] ML Model Inference Ingest Processor (#49052)
* [ML][Inference] adds lazy model loader and inference (#47410)
This adds a couple of things:
- A model loader service that is accessible via transport calls. This service will load in models and cache them. They will stay loaded until a processor no longer references them
- A Model class and its first sub-class LocalModel. Used to cache model information and run inference.
- Transport action and handler for requests to infer against a local model
Related Feature PRs:
* [ML][Inference] Adjust inference configuration option API (#47812)
* [ML][Inference] adds logistic_regression output aggregator (#48075)
* [ML][Inference] Adding read/del trained models (#47882)
* [ML][Inference] Adding inference ingest processor (#47859)
* [ML][Inference] fixing classification inference for ensemble (#48463)
* [ML][Inference] Adding model memory estimations (#48323)
* [ML][Inference] adding more options to inference processor (#48545)
* [ML][Inference] handle string values better in feature extraction (#48584)
* [ML][Inference] Adding _stats endpoint for inference (#48492)
* [ML][Inference] add inference processors and trained models to usage (#47869)
* [ML][Inference] add new flag for optionally including model definition (#48718)
* [ML][Inference] adding license checks (#49056)
* [ML][Inference] Adding memory and compute estimates to inference (#48955)
* fixing version of indexed docs for model inference
Backport of #48849. Update `.editorconfig` to make the Java settings the
default for all files, and then apply a 2-space indent to all `*.gradle`
files. Then reformat all the files.
Adds the following parameters to `outlier_detection`:
- `compute_feature_influence` (boolean): whether to compute or not
feature influence scores
- `outlier_fraction` (double): the proportion of the data set assumed
to be outlying prior to running outlier detection
- `standardization_enabled` (boolean): whether to apply standardization
to the feature values
Backport of #47600
Adds a parameter `training_percent` to regression. The default
value is `100`. When the parameter is set to a value less than `100`,
from the rows that can be used for training (ie. those that have a
value for the dependent variable) we randomly choose whether to actually
use for training. This enables splitting the data into a training set and
the rest, usually called testing, validation or holdout set, which allows
for validating the model on data that have not been used for training.
Technically, the analytics process considers as training the data that
have a value for the dependent variable. Thus, when we decide a training
row is not going to be used for training, we simply clear the row's
dependent variable.
The native process requires that there be a non-zero number of rows to analyze. If the flag --rows 0 is passed to the executable, it throws and does not start.
When building the configuration for the process we should not start the native process if there are no rows.
Adding some logging to indicate what is occurring.
* [ML] better handle empty results when evaluating regression
* adding new failure test to ml_security black list
* fixing equality check for regression results
This commit adds a first draft of a regression analysis
to data frame analytics. There is high probability that
the exact syntax might change.
This commit adds the new analysis type and its parameters as
well as appropriate validation. It also modifies the extractor
and the fields detector to be able to handle categorical fields
as regression analysis supports them.
If one tries to start a DF analytics job that has already run,
the result will be that the task will fail after reindexing the
dest index from the source index. The results of the prior run
will be gone and the task state is not properly set to failed
with the failure reason.
This commit improves the behavior in this scenario. First, we
set the task state to `failed` in a set of failures that were
missed. Second, a validation is added that if the destination
index exists, it must be empty.
* Switch from using docvalue_fields to extracting values from _source
where applicable. Doing this means parsing the _source and handling the
numbers parsing just like Elasticsearch is doing it when it's indexing
a document.
* This also introduces a minor limitation: aliases type of fields that
are NOT part of a tree of sub-fields will not be able to be retrieved
anymore. field_caps API doesn't shed any light into a field being an
alias or not and at _source parsing time there is no way to know if a
root field is an alias or not. Fields of the type "a.b.c.alias" can be
extracted from docvalue_fields, only if the field they point to can be
extracted from docvalue_fields. Also, not all fields in a hierarchy of
fields can be evaluated to being an alias.
(cherry picked from commit 8bf8a055e38f00df5f49c8d97f632f69d6e00c2c)
Test clusters currently has its own set of logic for dealing with
finding different versions of Elasticsearch, downloading them, and
extracting them. This commit converts testclusters to use the
DistributionDownloadPlugin.
This commit adds support for multiple source indices.
In order to deal with multiple indices having different mappings,
it attempts a best-effort approach to merge the mappings assuming
there are no conflicts. In case conflicts exists an error will be
returned.
To allow users creating custom mappings for special use cases,
the destination index is now allowed to exist before the analytics
job runs. In addition, settings are no longer copied except for
the `index.number_of_shards` and `index.number_of_replicas`.
This merges the initial work that adds a framework for performing
machine learning analytics on data frames. The feature is currently experimental
and requires a platinum license. Note that the original commits can be
found in the `feature-ml-data-frame-analytics` branch.
A new set of APIs is added which allows the creation of data frame analytics
jobs. Configuration allows specifying different types of analysis to be performed
on a data frame. At first there is support for outlier detection.
The APIs are:
- PUT _ml/data_frame/analysis/{id}
- GET _ml/data_frame/analysis/{id}
- GET _ml/data_frame/analysis/{id}/_stats
- POST _ml/data_frame/analysis/{id}/_start
- POST _ml/data_frame/analysis/{id}/_stop
- DELETE _ml/data_frame/analysis/{id}
When a data frame analytics job is started a persistent task is created and started.
The main steps of the task are:
1. reindex the source index into the dest index
2. analyze the data through the data_frame_analyzer c++ process
3. merge the results of the process back into the destination index
In addition, an evaluation API is added which packages commonly used metrics
that provide evaluation of various analysis:
- POST _ml/data_frame/_evaluate
Przemysław Witek
Jake Landis
Dimitris Athanasiou
Rene Groeschke
David Roberts
Albert Zaharovits
David Kyle
Martijn van Groningen
Benjamin Trent
Rory Hunter
Andrei Stefan
Ryan Ernst