A previous change (#53029) is causing analysis jobs to wait for certain indices to be made available. While this it is good for jobs to wait, they could fail early on _start.
This change will cause the persistent task to continually retry node assignment when the failure is due to shards not being available.
If the shards are not available by the time `timeout` is reached by the predicate, it is treated as a _start failure and the task is canceled.
For tasks seeking a new assignment after a node failure, that behavior is unchanged.
closes#53188
Tests have been periodically failing due to a race condition on checking a recently `STOPPED` task's state. The `.ml-state` index is not created until the task has already been transitioned to `STARTED`. This allows the `_start` API call to return. But, if a user (or test) immediately attempts to `_stop` that job, the job could stop and the task removed BEFORE the `.ml-state|stats` indices are created/updated.
This change moves towards the task cleaning up itself in its main execution thread. `stop` flips the flag of the task to `isStopping` and now we check `isStopping` at every necessary method. Allowing the task to gracefully stop.
closes#53007
Currently _rollup_search requires manage privilege to access. It should really be
a read only operation. This PR changes the requirement to be read indices privilege.
Resolves: #50245
* [ML][Inference] Add support for multi-value leaves to the tree model (#52531)
This adds support for multi-value leaves. This is a prerequisite for multi-class boosted tree classification.
This adds a new configurable field called `indices_options`. This allows users to create or update the indices_options used when a datafeed reads from an index.
This is necessary for the following use cases:
- Reading from frozen indices
- Allowing certain indices in multiple index patterns to not exist yet
These index options are available on datafeed creation and update. Users may specify them as URL parameters or within the configuration object.
closes https://github.com/elastic/elasticsearch/issues/48056
This adds machine learning model feature importance calculations to the inference processor.
The new flag in the configuration matches the analytics parameter name: `num_top_feature_importance_values`
Example:
```
"inference": {
"field_mappings": {},
"model_id": "my_model",
"inference_config": {
"regression": {
"num_top_feature_importance_values": 3
}
}
}
```
This will write to the document as follows:
```
"inference" : {
"feature_importance" : {
"FlightTimeMin" : -76.90955548511226,
"FlightDelayType" : 114.13514762158526,
"DistanceMiles" : 13.731580450792187
},
"predicted_value" : 108.33165831875137,
"model_id" : "my_model"
}
```
This is done through calculating the [SHAP values](https://arxiv.org/abs/1802.03888).
It requires that models have populated `number_samples` for each tree node. This is not available to models that were created before 7.7.
Additionally, if the inference config is requesting feature_importance, and not all nodes have been upgraded yet, it will not allow the pipeline to be created. This is to safe-guard in a mixed-version environment where only some ingest nodes have been upgraded.
NOTE: the algorithm is a Java port of the one laid out in ml-cpp: https://github.com/elastic/ml-cpp/blob/master/lib/maths/CTreeShapFeatureImportance.cc
usability blocked by: https://github.com/elastic/ml-cpp/pull/991
When `PUT` is called to store a trained model, it is useful to return the newly create model config. But, it is NOT useful to return the inflated definition.
These definitions can be large and returning the inflated definition causes undo work on the server and client side.
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
This adds `_all` to Calendar searches. This enables users to supply the `_all` string in the `job_ids` array when creating a Calendar. That calendar will now be applied to all jobs (existing and newly created).
Closes#45013
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
ML mappings and index templates have so far been created
programmatically. While this had its merits due to static typing,
there is consensus it would be clear to maintain those in json files.
In addition, we are going to adding ILM policies to these indices
and the component for a plugin to register ILM policies is
`IndexTemplateRegistry`. It expects the templates to be in resource
json files.
For the above reasons this commit refactors ML mappings and index
templates into json resource files that are registered via
`MlIndexTemplateRegistry`.
Backport of #51765
Changes the misleading error message when attempting to open
a job while the "cluster.persistent_tasks.allocation.enable"
setting is set to "none" to a clearer message that names the
setting.
Closes#51956
If the configs are removed (by some horrific means), we should still allow tasks to be cleaned up easily.
Datafeeds and jobs with missing configs are now visible in their respective _stats calls and can be stopped/closed.
* [ML][Inference] Fix weighted mode definition (#51648)
Weighted mode inaccurately assumed that the "max value" of the input values would be the maximum class value. This does not make sense.
Weighted Mode should know how many classes there are. Hence the new parameter `num_classes`. This indicates what the maximum class value to be expected.
Data frame analytics classification currently only supports 2 classes for the
dependent variable. We were checking that the field's cardinality is not higher
than 2 but we should also check it is not less than that as otherwise the process
fails.
Backport of #51232
* [ML][Inference] Adding classification_weights to ensemble models
classification_weights are a way to allow models to
prefer specific classification results over others
this might be advantageous if classification value
probabilities are a known quantity and can improve
model error rates.
Adds a new parameter to regression and classification that enables computation
of importance for the top most important features. The computation of the importance
is based on SHAP (SHapley Additive exPlanations) method.
Backport of #50914
* [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 adds the necessary named XContent classes to the HLRC for the lang ident model. This is so the HLRC can call `GET _ml/inference/lang_ident_model_1?include_definition=true` without XContent parsing errors.
The constructors are package private as since this classes are used exclusively within the pre-packaged model (and require the specific weights, etc. to be of any use).
Eclipse 4.13 shows a type mismatch error in the affected line because it cannot
correctly infer the boolean return type for the method call. Assigning return
value to a local variable resolves this problem.
This commit adds removal of unused data frame analytics state
from the _delete_expired_data API (and in extend th ML daily
maintenance task). At the moment the potential state docs
include the progress document and state for regression and
classification analyses.
Backport of #50243
This commit fixes a bug that caused the data frame analytics
_explain API to time out in a multi-node setup when the source
index was missing. When we try to create the extracted fields detector,
we check the index settings. If the index is missing that responds
with a failure that could be wrapped as a remote exception.
While we unwrapped correctly to check if the cause was an
`IndexNotFoundException`, we then proceeded to cast the original
exception instead of the cause.
Backport of #50176
* [ML] Add graceful retry for anomaly detector result indexing failures (#49508)
All results indexing now retry the amount of times configured in `xpack.ml.persist_results_max_retries`. The retries are done in a semi-random, exponential backoff.
* fixing test
The `ClassificationIT.testTwoJobsWithSameRandomizeSeedUseSameTrainingSet`
test was previously set up to just have 10 rows. With `training_percent`
of 50%, only 5 rows will be used for training. There is a good chance that
all 5 rows will be of one class which results to failure.
This commit increases the rows to 100. Now 50 rows should be used for training
and the chance of failure should be very small.
Backport of #50072
This adds a new `randomize_seed` for regression and classification.
When not explicitly set, the seed is randomly generated. One can
reuse the seed in a similar job in order to ensure the same docs
are picked for training.
Backport of #49990
When checking the cardinality of a field, the query should be take into account. The user might know about some bad data in their index and want to filter down to the target_field values they care about.
Work in progress in the c++ side is increasing memory estimates
a bit and this test fails. At the time of this commit the mem
estimate when there is no source query is a about 2Mb. So I
am relaxing the test to assert memory estimate is less than 1Mb
instead of 500Kb.
Backport of #49924
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
* [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.
Aggregatable mutli-fields are at the moment wrongly mapped
as normal doc_value fields and thus they support fetching from
source. However, they do not exist in the source. This results
to failure to extract such fields.
This commit fixes this bug. While a fix could be worked out
on top of the existing code, it is evident the extraction logic
has become difficult to understand and maintain. As we also
want to deduplicate multi-fields for data frame analytics,
it seemed appropriate to refactor the code to simplify and
better handle the extraction of multi-fields.
Relates #48756
Backport of #48770
If a job stops right after reindexing is finished but before
we refresh the destination index, we don't refresh at all.
If the job is started again right after, it jumps into the analyzing state.
However, the data is still not searchable.
This is why we were seeing test failures that we start the process
expecting X rows (where X is lower than the expected number of docs)
and we end up getting X+.
We fix this by moving the refresh of the dest index right before
we start the process so it always ensures the data is searchable.
Closes#47612
Backport of #48090
Audit messages are stored with millisecond timestamps. If two
messages have the same millisecond timestamp then asserting on
their order is impossible given the information available.
This PR changes the assertion on audit messages in the native
data frame analytics tests to assert that the expected audit
messages exist in any order.
Fixes#48035
This change adds:
- A new option, allow_lazy_open, to anomaly detection jobs
- A new option, allow_lazy_start, to data frame analytics jobs
Both work in the same way: they allow a job to be
opened/started even if no ML node exists that can
accommodate the job immediately. In this situation
the job waits in the opening/starting state until ML
node capacity is available. (The starting state for data
frame analytics jobs is new in this change.)
Additionally, the ML nightly maintenance tasks now
creates audit warnings for ML jobs that are unassigned.
This means that jobs that cannot be assigned to an ML
node for a very long time will show a yellow warning
triangle in the UI.
A final change is that it is now possible to close a job
that is not assigned to a node without using force.
This is because previously jobs that were open but
not assigned to a node were an aberration, whereas
after this change they'll be relatively common.
Adds a new datafeed config option, max_empty_searches,
that tells a datafeed that has never found any data to stop
itself and close its associated job after a certain number
of real-time searches have returned no data.
Backport of #47922
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
This commit restores the model state if available in data
frame analytics jobs.
In addition, this changes the start API so that a stopped job
can be restarted. As we now store the progress in the state index
when the task is stopped, we can use it to determine what state
the job was in when it got stopped.
Note that in order to be able to distinguish between a job
that runs for the first time and another that is restarting,
we ensure reindexing progress is reported to be at least 1
for a running task.
Backport of #45794 to 7.x. Convert most `awaitBusy` calls to
`assertBusy`, and use asserts where possible. Follows on from #28548 by
@liketic.
There were a small number of places where it didn't make sense to me to
call `assertBusy`, so I kept the existing calls but renamed the method to
`waitUntil`. This was partly to better reflect its usage, and partly so
that anyone trying to add a new call to awaitBusy wouldn't be able to find
it.
I also didn't change the usage in `TransportStopRollupAction` as the
comments state that the local awaitBusy method is a temporary
copy-and-paste.
Other changes:
* Rework `waitForDocs` to scale its timeout. Instead of calling
`assertBusy` in a loop, work out a reasonable overall timeout and await
just once.
* Some tests failed after switching to `assertBusy` and had to be fixed.
* Correct the expect templates in AbstractUpgradeTestCase. The ES
Security team confirmed that they don't use templates any more, so
remove this from the expected templates. Also rewrite how the setup
code checks for templates, in order to give more information.
* Remove an expected ML template from XPackRestTestConstants The ML team
advised that the ML tests shouldn't be waiting for any
`.ml-notifications*` templates, since such checks should happen in the
production code instead.
* Also rework the template checking code in `XPackRestTestHelper` to give
more helpful failure messages.
* Fix issue in `DataFrameSurvivesUpgradeIT` when upgrading from < 7.4
When the ML native multi-node tests use _cat/indices/_all
and the request goes to a non-master node, _all is
translated to a list of concrete indices by the authz layer
on the coordinating node before the request is forwarded
to the master node. Then it is possible for the master
node to return an index_not_found_exception if one of
the concrete indices that was expanded on the
coordinating node has been deleted in the meantime.
(#47159 has been opened to track the underlying problem.)
It has been observed that the index that gets deleted when
the problem affects the ML native multi-node tests is
always the ML notifications index. The tests that fail are
only interested in the presence or absense of ML results
indices. Therefore the workaround is to only _cat indices
that match the ML results index pattern.
Fixes#45652
This commit reuses the same state processor that is used for autodetect
to parse state output from data frame analytics jobs. We then index the
state document into the state index.
Backport of #46804
It is possible for a running analytics job that its config is removed
from the '.ml-config' index (perhaps the user deleted the entire index,
etc.). In that case the task remains without a matching config. I have
raised #46781 to discuss how to deal with this issue.
This commit focuses on `MlMemoryTracker` and changes it so that when
we get the configs for the running tasks we leniently ignore missing ones.
This at least means memory tracking will keep working for other jobs
if one or more are missing.
In addition, this commit makes the cleanup code for native analytics
tests more robust by explicitly stopping all jobs and force-stopping
if an error occurs. This helps so that a single failing test does
not cause other tests fail due to pending tasks.
Backport of #46789
After starting the analytics job and checking its state
the state can be any of "started", "reindexing" or
"analyzing" depending on how quickly the work is done.
This refactors `DataFrameAnalyticsTask` into its own class.
The task has quite a lot of functionality now and I believe it would
make code more readable to have it live as its own class rather than
an inner class of the start action class.
Backport of #46402
The test seems to have been failing due to a race condition between
stopping the task and refreshing the destination index. In particular,
we were going forward with refreshing the destination index even
though the task stopped in the meantime. This was fixed in
request.
Closes#43960
Backport of #46271
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.
Previously, the stats API reports a progress percentage
for DF analytics tasks that are running and are in the
`reindexing` or `analyzing` state.
This means that when the task is `stopped` there is no progress
reported. Thus, one cannot distinguish between a task that never
run to one that completed.
In addition, there are blind spots in the progress reporting.
In particular, we do not account for when data is loaded into the
process. We also do not account for when results are written.
This commit addresses the above issues. It changes progress
to being a list of objects, each one describing the phase
and its progress as a percentage. We currently have 4 phases:
reindexing, loading_data, analyzing, writing_results.
When the task stops, progress is persisted as a document in the
state index. The stats API now reports progress from in-memory
if the task is running, or returns the persisted document
(if there is one).
Regression analysis support missing fields. Even more, it is expected
that the dependent variable has missing fields to the part of the
data frame that is not for training.
This commit allows to declare that an analysis supports missing values.
For such analysis, rows with missing values are not skipped. Instead,
they are written as normal with empty strings used for the missing values.
This also contains a fix to the integration test.
Closes#45425
* Reenable Integ Tests in native-multi-node-tests
* The tests broken here were likely fixed by #45463 => let's reenable them and see if things run fine again
* Relates #45405, #45455
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.
As data frame rows with missing values for analyzed fields are skipped,
we can be more efficient by including a query that only picks documents
that have values for all analyzed fields. Besides improving the number
of documents we go through, we also provide a more accurate measurement
of how many rows we need which reduces the memory requirements.
This also adds an integration test that runs outlier detection on data
with missing fields.
* Mute failing test
tracked in #44552
* mute EvilSecurityTests
tracking in #44558
* Fix line endings in ESJsonLayoutTests
* Mute failing ForecastIT test on windows
Tracking in #44609
* mute BasicRenormalizationIT.testDefaultRenormalization
tracked in #44613
* fix mute testDefaultRenormalization
* Increase busyWait timeout windows is slow
* Mute failure unconfigured node name
* mute x-pack internal cluster test windows
tracking #44610
* Mute JvmErgonomicsTests on windows
Tracking #44669
* mute SharedClusterSnapshotRestoreIT testParallelRestoreOperationsFromSingleSnapshot
Tracking #44671
* Mute NodeTests on Windows
Tracking #44256
* Add Snapshot Lifecycle Management (#43934)
* Add SnapshotLifecycleService and related CRUD APIs
This commit adds `SnapshotLifecycleService` as a new service under the ilm
plugin. This service handles snapshot lifecycle policies by scheduling based on
the policies defined schedule.
This also includes the get, put, and delete APIs for these policies
Relates to #38461
* Make scheduledJobIds return an immutable set
* Use Object.equals for SnapshotLifecyclePolicy
* Remove unneeded TODO
* Implement ToXContentFragment on SnapshotLifecyclePolicyItem
* Copy contents of the scheduledJobIds
* Handle snapshot lifecycle policy updates and deletions (#40062)
(Note this is a PR against the `snapshot-lifecycle-management` feature branch)
This adds logic to `SnapshotLifecycleService` to handle updates and deletes for
snapshot policies. Policies with incremented versions have the old policy
cancelled and the new one scheduled. Deleted policies have their schedules
cancelled when they are no longer present in the cluster state metadata.
Relates to #38461
* Take a snapshot for the policy when the SLM policy is triggered (#40383)
(This is a PR for the `snapshot-lifecycle-management` branch)
This commit fills in `SnapshotLifecycleTask` to actually perform the
snapshotting when the policy is triggered. Currently there is no handling of the
results (other than logging) as that will be added in subsequent work.
This also adds unit tests and an integration test that schedules a policy and
ensures that a snapshot is correctly taken.
Relates to #38461
* Record most recent snapshot policy success/failure (#40619)
Keeping a record of the results of the successes and failures will aid
troubleshooting of policies and make users more confident that their
snapshots are being taken as expected.
This is the first step toward writing history in a more permanent
fashion.
* Validate snapshot lifecycle policies (#40654)
(This is a PR against the `snapshot-lifecycle-management` branch)
With the commit, we now validate the content of snapshot lifecycle policies when
the policy is being created or updated. This checks for the validity of the id,
name, schedule, and repository. Additionally, cluster state is checked to ensure
that the repository exists prior to the lifecycle being added to the cluster
state.
Part of #38461
* Hook SLM into ILM's start and stop APIs (#40871)
(This pull request is for the `snapshot-lifecycle-management` branch)
This change allows the existing `/_ilm/stop` and `/_ilm/start` APIs to also
manage snapshot lifecycle scheduling. When ILM is stopped all scheduled jobs are
cancelled.
Relates to #38461
* Add tests for SnapshotLifecyclePolicyItem (#40912)
Adds serialization tests for SnapshotLifecyclePolicyItem.
* Fix improper import in build.gradle after master merge
* Add human readable version of modified date for snapshot lifecycle policy (#41035)
* Add human readable version of modified date for snapshot lifecycle policy
This small change changes it from:
```
...
"modified_date": 1554843903242,
...
```
To
```
...
"modified_date" : "2019-04-09T21:05:03.242Z",
"modified_date_millis" : 1554843903242,
...
```
Including the `"modified_date"` field when the `?human` field is used.
Relates to #38461
* Fix test
* Add API to execute SLM policy on demand (#41038)
This commit adds the ability to perform a snapshot on demand for a policy. This
can be useful to take a snapshot immediately prior to performing some sort of
maintenance.
```json
PUT /_ilm/snapshot/<policy>/_execute
```
And it returns the response with the generated snapshot name:
```json
{
"snapshot_name" : "production-snap-2019.04.09-rfyv3j9qreixkdbnfuw0ug"
}
```
Note that this does not allow waiting for the snapshot, and the snapshot could
still fail. It *does* record this information into the cluster state similar to
a regularly trigged SLM job.
Relates to #38461
* Add next_execution to SLM policy metadata (#41221)
* Add next_execution to SLM policy metadata
This adds the next time a snapshot lifecycle policy will be executed when
retriving a policy's metadata, for example:
```json
GET /_ilm/snapshot?human
{
"production" : {
"version" : 1,
"modified_date" : "2019-04-15T21:16:21.865Z",
"modified_date_millis" : 1555362981865,
"policy" : {
"name" : "<production-snap-{now/d}>",
"schedule" : "*/30 * * * * ?",
"repository" : "repo",
"config" : {
"indices" : [
"foo-*",
"important"
],
"ignore_unavailable" : true,
"include_global_state" : false
}
},
"next_execution" : "2019-04-15T21:16:30.000Z",
"next_execution_millis" : 1555362990000
},
"other" : {
"version" : 1,
"modified_date" : "2019-04-15T21:12:19.959Z",
"modified_date_millis" : 1555362739959,
"policy" : {
"name" : "<other-snap-{now/d}>",
"schedule" : "0 30 2 * * ?",
"repository" : "repo",
"config" : {
"indices" : [
"other"
],
"ignore_unavailable" : false,
"include_global_state" : true
}
},
"next_execution" : "2019-04-16T02:30:00.000Z",
"next_execution_millis" : 1555381800000
}
}
```
Relates to #38461
* Fix and enhance tests
* Figured out how to Cron
* Change SLM endpoint from /_ilm/* to /_slm/* (#41320)
This commit changes the endpoint for snapshot lifecycle management from:
```
GET /_ilm/snapshot/<policy>
```
to:
```
GET /_slm/policy/<policy>
```
It mimics the ILM path only using `slm` instead of `ilm`.
Relates to #38461
* Add initial documentation for SLM (#41510)
* Add initial documentation for SLM
This adds the initial documentation for snapshot lifecycle management.
It also includes the REST spec API json files since they're sort of
documentation.
Relates to #38461
* Add `manage_slm` and `read_slm` roles (#41607)
* Add `manage_slm` and `read_slm` roles
This adds two more built in roles -
`manage_slm` which has permission to perform any of the SLM actions, as well as
stopping, starting, and retrieving the operation status of ILM.
`read_slm` which has permission to retrieve snapshot lifecycle policies as well
as retrieving the operation status of ILM.
Relates to #38461
* Add execute to the test
* Fix ilm -> slm typo in test
* Record SLM history into an index (#41707)
It is useful to have a record of the actions that Snapshot Lifecycle
Management takes, especially for the purposes of alerting when a
snapshot fails or has not been taken successfully for a certain amount of
time.
This adds the infrastructure to record SLM actions into an index that
can be queried at leisure, along with a lifecycle policy so that this
history does not grow without bound.
Additionally,
SLM automatically setting up an index + lifecycle policy leads to
`index_lifecycle` custom metadata in the cluster state, which some of
the ML tests don't know how to deal with due to setting up custom
`NamedXContentRegistry`s. Watcher would cause the same problem, but it
is already disabled (for the same reason).
* High Level Rest Client support for SLM (#41767)
* High Level Rest Client support for SLM
This commit add HLRC support for SLM.
Relates to #38461
* Fill out documentation tests with tags
* Add more callouts and asciidoc for HLRC
* Update javadoc links to real locations
* Add security test testing SLM cluster privileges (#42678)
* Add security test testing SLM cluster privileges
This adds a test to `PermissionsIT` that uses the `manage_slm` and `read_slm`
cluster privileges.
Relates to #38461
* Don't redefine vars
* Add Getting Started Guide for SLM (#42878)
This commit adds a basic Getting Started Guide for SLM.
* Include SLM policy name in Snapshot metadata (#43132)
Keep track of which SLM policy in the metadata field of the Snapshots
taken by SLM. This allows users to more easily understand where the
snapshot came from, and will enable future SLM features such as
retention policies.
* Fix compilation after master merge
* [TEST] Move exception wrapping for devious exception throwing
Fixes an issue where an exception was created from one line and thrown in another.
* Fix SLM for the change to AcknowledgedResponse
* Add Snapshot Lifecycle Management Package Docs (#43535)
* Fix compilation for transport actions now that task is required
* Add a note mentioning the privileges needed for SLM (#43708)
* Add a note mentioning the privileges needed for SLM
This adds a note to the top of the "getting started with SLM"
documentation mentioning that there are two built-in privileges to
assist with creating roles for SLM users and administrators.
Relates to #38461
* Mention that you can create snapshots for indices you can't read
* Fix REST tests for new number of cluster privileges
* Mute testThatNonExistingTemplatesAreAddedImmediately (#43951)
* Fix SnapshotHistoryStoreTests after merge
* Remove overridden newResponse functions that have been removed
* Fix compilation for backport
* Fix get snapshot output parsing in test
* [DOCS] Add redirects for removed autogen anchors (#44380)
* Switch <tt>...</tt> in javadocs for {@code ...}
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.
Renames `_id_copy` to `ml__id_copy` as field names starting with
underscore are deprecated. The new field name `ml__id_copy` was
chosen as an obscure enough field that users won't have in their data.
Otherwise, this field is only intented to be used by df-analytics.
If a job is opened and then closed and does nothing in
between then it should not persist any results or state
documents. This change adapts the no-op job test to
assert no results in addition to no state, and to log
any documents that cause this assertion to fail.
Relates elastic/ml-cpp#512
Relates #43680
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
This trace logging looks like it was copy/pasted from another test,
where the logging in that test was only added to investigate a test
failure. This commit removes the trace logging.
* [ML] Adding support for geo_shape, geo_centroid, geo_point in datafeeds
* only supporting doc_values for geo_point fields
* moving validation into GeoPointField ctor
Re-enable muted tests and accommodate recent backend changes
that result in higher memory usage being reported for a job
at the start of its life-cycle
The date_histogram accepts an interval which can be either a calendar
interval (DST-aware, leap seconds, arbitrary length of months, etc) or
fixed interval (strict multiples of SI units). Unfortunately this is inferred
by first trying to parse as a calendar interval, then falling back to fixed
if that fails.
This leads to confusing arrangement where `1d` == calendar, but
`2d` == fixed. And if you want a day of fixed time, you have to
specify `24h` (e.g. the next smallest unit). This arrangement is very
error-prone for users.
This PR adds `calendar_interval` and `fixed_interval` parameters to any
code that uses intervals (date_histogram, rollup, composite, datafeed, etc).
Calendar only accepts calendar intervals, fixed accepts any combination of
units (meaning `1d` can be used to specify `24h` in fixed time), and both
are mutually exclusive.
The old interval behavior is deprecated and will throw a deprecation warning.
It is also mutually exclusive with the two new parameters. In the future the
old dual-purpose interval will be removed.
The change applies to both REST and java clients.
Muting a number of AutoDetectMemoryLimitIT tests to give CI a chance to
settle before easing in required backend changes.
relates elastic/ml-cpp#486
relates #42086
Ensure that there is at least a 1s delay between the time that state
is persisted by each of the two jobs in the test.
Model snapshot IDs use the current time in epoch seconds to
distinguish themselves, hence snapshots will be overwritten
by another if it occurs in the same 1s window.
Closes#40347
* [ML] Refactor common utils out of ML plugin to XPack.Core
* implementing GET filters with abstract transport
* removing added rest param
* adjusting how defaults can be supplied
The problem here was that `DatafeedJob` was updating the last end time searched
based on the `now` even though when there are aggregations, the extactor will
only search up to the floor of `now` against the histogram interval.
This commit fixes the issue by using the end time as calculated by the extractor.
It also adds an integration test that uses aggregations. This test would fail
before this fix. Unfortunately the test is slow as we need to wait for the
datafeed to work in real time.
Closes#39842
* [ML] refactoring lazy query and agg parsing
* Clean up and addressing PR comments
* removing unnecessary try/catch block
* removing bad call to logger
* removing unused import
* fixing bwc test failure due to serialization and config migrator test
* fixing style issues
* Adjusting DafafeedUpdate class serialization
* Adding todo for refactor in v8
* Making query non-optional so it does not write a boolean byte
Investigating how to make DeleteExpiredDataIT faster, it was
revealed that the security audit trail threads were quite hot.
Disabling that seems to be helping quite a bit with making this
test faster. This commit also unmutes the test to see how it goes
with the audit trail disabled.
Relates #39658Closes#39575
ML has historically used doc as the single mapping type but reindex in 7.x
will change the mapping to _doc. Switching to the typeless APIs handles
case where the mapping type is either doc or _doc. This change removes
deprecated typed usages.
This commit parallelizes some parts of the test
and its remove an unnecessary refresh call.
On my local machine it shaves off about 15 seconds
for a test execution time of ~64s (down from ~80s).
This test is still slow but progress over perfection.
Relates #37339
The ScheduledEvent class has never preserved the time
zone so it makes more sense for it to store the start and
end time using Instant rather than ZonedDateTime.
Closes#38620
Dimitris Athanasiou
Przemysław Witek
Benjamin Trent
Mayya Sharipova
Mark Vieira
James Baiera
Yang Wang
David Kyle
Jack Conradson
David Roberts
Tom Veasey
Christoph Büscher
Rory Hunter
Igor Motov
Yannick Welsch
Armin Braun
Przemyslaw Gomulka
Alpar Torok
Lee Hinman
Ryan Ernst
Jason Tedor
David Turner
Ed Savage
Zachary Tong
Ed Savage
Mehran Koushkebaghi