Adds a new parameter for classification that enables choosing whether to assign labels to
maximise accuracy or to maximise the minimum class recall.
Fixes#52427.
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>
The `top_metrics` agg is kind of like `top_hits` but it only works on
doc values so it *should* be faster.
At this point it is fairly limited in that it only supports a single,
numeric sort and a single, numeric metric. And it only fetches the "very
topest" document worth of metric. We plan to support returning a
configurable number of top metrics, requesting more than one metric and
more than one sort. And, eventually, non-numeric sorts and metrics. The
trick is doing those things fairly efficiently.
Co-Authored by: Zachary Tong <zach@elastic.co>
This adds a builder and parsed results for the `string_stats`
aggregation directly to the high level rest client. Without this the
HLRC can't access the `string_stats` API without the elastic licensed
`analytics` module.
While I'm in there this adds a few of our usual unit tests and
modernizes the parsing.
This commit adds examples in our documentation for
- An HLRC instance authenticating to an elasticsearch cluster using
an elasticsearch token service access token or an API key
- An HLRC instance connecting to an elasticsearch cluster that is
setup for TLS on the HTTP layer when the CA certificate of the
cluster is available either as a PEM file or a keystore
- An HLRC instance connecting to an elasticsearch cluster that
requires client authentication where the client key and certificate
are available in a keystore
Co-Authored-By: Lisa Cawley <lcawley@elastic.co>
* [ML][Inference] add tags url param to GET (#51330)
Adds a new URL parameter, `tags` to the GET _ml/inference/<model_id> endpoint.
This parameter allows the list of models to be further reduced to those who contain all the provided tags.
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
Adds a `force` parameter to the delete data frame analytics
request. When `force` is `true`, the action force-stops the
jobs and then proceeds to the deletion. This can be used in
order to delete a non-stopped job with a single request.
Closes#48124
Backport of #50553
The additional change to the original PR (#49657), is that `org.elasticsearch.client.cluster.RemoteConnectionInfo` now parses the initial_connect_timeout field as a string instead of a TimeValue instance.
The reason that this is needed is because that the initial_connect_timeout field in the remote connection api is serialized for human consumption, but not for parsing purposes.
Therefore the HLRC can't parse it correctly (which caused test failures in CI, but not in the PR CI
:( ). The way this field is serialized needs to be changed in the remote connection api, but that is a breaking change. We should wait making this change until rest api versioning is introduced.
Co-Authored-By: j-bean <anton.shuvaev91@gmail.com>
Co-authored-by: j-bean <anton.shuvaev91@gmail.com>
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
Reindex sort never gave a guarantee about the order of documents being
indexed into the destination, though it could give a sense of locality
of source data.
It prevents us from doing resilient reindex and other optimizations and
it has therefore been deprecated.
Related to #47567
Reindex sort never gave a guarantee about the order of documents being
indexed into the destination, though it could give a sense of locality
of source data.
It prevents us from doing resilient reindex and other optimizations and
it has therefore been deprecated.
Related to #47567
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
PR #25543 removed the `_uid` field in favor of the `_id` field,
including for use in slicing.
This removes an outdated reference to `_uid` in our reindex docs.
This commit adds HLRC support and documentation for the SLM Start and
Stop APIs, as well as updating existing documentation where appropriate.
This commit also ensures that the SLM APIs are properly included in the
HLRC documentation.
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 adds support to retrieve all API keys if the authenticated
user is authorized to do so.
This removes the restriction of specifying one of the
parameters (like id, name, username and/or realm name)
when the `owner` is set to `false`.
Closes#46887
* Add API to execute SLM retention on-demand (#47405)
This is a backport of #47405
This commit adds the `/_slm/_execute_retention` API endpoint. This
endpoint kicks off SLM retention and then returns immediately.
This in particular allows us to run retention without scheduling it
(for entirely manual invocation) or perform a one-off cleanup.
This commit also includes HLRC for the new API, and fixes an issue
in SLMSnapshotBlockingIntegTests where retention invoked prior to the
test completing could resurrect an index the internal test cluster
cleanup had already deleted.
Resolves#46508
Relates to #43663
Add a section to both the low level and high level client documentation on asynchronous usage and `Cancellable` added for #44802
Co-Authored-By: Lee Hinman <dakrone@users.noreply.github.com>
The low-level REST client exposes a `performRequestAsync` method that
allows to send async requests, but today it does not expose the ability
to cancel such requests. That is something that the underlying apache
async http client supports, and it makes sense for us to expose.
This commit adds a return value to the `performRequestAsync` method,
which is backwards compatible. A `Cancellable` object gets returned,
which exposes a `cancel` public method. When calling `cancel`, the
on-going request associated with the returned `Cancellable` instance
will be cancelled by calling its `abort` method. This works throughout
multiple retries, though some special care was needed for the case where
`cancel` is called between different attempts (when one attempt has
failed and the consecutive one has not been sent yet).
Note that cancelling a request on the client side does not automatically
translate to cancelling the server side execution of it. That needs to be
specifically implemented, which is on the work for the search API (see #43332).
Relates to #44802
Changed the signature of AbstractResponseTestCase#createServerTestInstance(...)
to include the randomly selected xcontent type. This is needed for the
creating a server response instance with a query which is represented as BytesReference.
Maybe this should go into a different change?
This PR also includes HLRC docs for the get policy api.
Relates to #32789
* Add retention to Snapshot Lifecycle Management (#46407)
This commit adds retention to the existing Snapshot Lifecycle Management feature (#38461) as described in #43663. This allows a user to configure SLM to automatically delete older snapshots based on a number of criteria.
An example policy would look like:
```
PUT /_slm/policy/snapshot-every-day
{
"schedule": "0 30 2 * * ?",
"name": "<production-snap-{now/d}>",
"repository": "my-s3-repository",
"config": {
"indices": ["foo-*", "important"]
},
// Newly configured retention options
"retention": {
// Snapshots should be deleted after 14 days
"expire_after": "14d",
// Keep a maximum of thirty snapshots
"max_count": 30,
// Keep a minimum of the four most recent snapshots
"min_count": 4
}
}
```
SLM Retention is run on a scheduled configurable with the `slm.retention_schedule` setting, which supports cron expressions. Deletions are run for a configurable time bounded by the `slm.retention_duration` setting, which defaults to 1 hour.
Included in this work is a new SLM stats API endpoint available through
``` json
GET /_slm/stats
```
That returns statistics about snapshot taken and deleted, as well as successful retention runs, failures, and the time spent deleting snapshots. #45362 has more information as well as an example of the output. These stats are also included when retrieving SLM policies via the API.
* Add base framework for snapshot retention (#43605)
* Add base framework for snapshot retention
This adds a basic `SnapshotRetentionService` and `SnapshotRetentionTask`
to start as the basis for SLM's retention implementation.
Relates to #38461
* Remove extraneous 'public'
* Use a local var instead of reading class var repeatedly
* Add SnapshotRetentionConfiguration for retention configuration (#43777)
* Add SnapshotRetentionConfiguration for retention configuration
This commit adds the `SnapshotRetentionConfiguration` class and its HLRC
counterpart to encapsulate the configuration for SLM retention.
Currently only a single parameter is supported as an example (we still
need to discuss the different options we want to support and their
names) to keep the size of the PR down. It also does not yet include version serialization checks
since the original SLM branch has not yet been merged.
Relates to #43663
* Fix REST tests
* Fix more documentation
* Use Objects.equals to avoid NPE
* Put `randomSnapshotLifecyclePolicy` in only one place
* Occasionally return retention with no configuration
* Implement SnapshotRetentionTask's snapshot filtering and delet… (#44764)
* Implement SnapshotRetentionTask's snapshot filtering and deletion
This commit implements the snapshot filtering and deletion for
`SnapshotRetentionTask`. Currently only the expire-after age is used for
determining whether a snapshot is eligible for deletion.
Relates to #43663
* Fix deletes running on the wrong thread
* Handle missing or null policy in snap metadata differently
* Convert Tuple<String, List<SnapshotInfo>> to Map<String, List<SnapshotInfo>>
* Use the `OriginSettingClient` to work with security, enhance logging
* Prevent NPE in test by mocking Client
* Allow empty/missing SLM retention configuration (#45018)
Semi-related to #44465, this allows the `"retention"` configuration map
to be missing.
Relates to #43663
* Add min_count and max_count as SLM retention predicates (#44926)
This adds the configuration options for `min_count` and `max_count` as
well as the logic for determining whether a snapshot meets this criteria
to SLM's retention feature.
These options are optional and one, two, or all three can be specified
in an SLM policy.
Relates to #43663
* Time-bound deletion of snapshots in retention delete function (#45065)
* Time-bound deletion of snapshots in retention delete function
With a cluster that has a large number of snapshots, it's possible that
snapshot deletion can take a very long time (especially since deletes
currently have to happen in a serial fashion). To prevent snapshot
deletion from taking forever in a cluster and blocking other operations,
this commit adds a setting to allow configuring a maximum time to spend
deletion snapshots during retention. This dynamic setting defaults to 1
hour and is best-effort, meaning that it doesn't hard stop a deletion
at an hour mark, but ensures that once the time has passed, all
subsequent deletions are deferred until the next retention cycle.
Relates to #43663
* Wow snapshots suuuure can take a long time.
* Use a LongSupplier instead of actually sleeping
* Remove TestLogging annotation
* Remove rate limiting
* Add SLM metrics gathering and endpoint (#45362)
* Add SLM metrics gathering and endpoint
This commit adds the infrastructure to gather metrics about the different SLM actions that a cluster
takes. These actions are stored in `SnapshotLifecycleStats` and perpetuated in cluster state. The
stats stored include the number of snapshots taken, failed, deleted, the number of retention runs,
as well as per-policy counts for snapshots taken, failed, and deleted. It also includes the amount
of time spent deleting snapshots from SLM retention.
This commit also adds an endpoint for retrieving all stats (further commits will expose this in the
SLM get-policy API) that looks like:
```
GET /_slm/stats
{
"retention_runs" : 13,
"retention_failed" : 0,
"retention_timed_out" : 0,
"retention_deletion_time" : "1.4s",
"retention_deletion_time_millis" : 1404,
"policy_metrics" : {
"daily-snapshots2" : {
"snapshots_taken" : 7,
"snapshots_failed" : 0,
"snapshots_deleted" : 6,
"snapshot_deletion_failures" : 0
},
"daily-snapshots" : {
"snapshots_taken" : 12,
"snapshots_failed" : 0,
"snapshots_deleted" : 12,
"snapshot_deletion_failures" : 6
}
},
"total_snapshots_taken" : 19,
"total_snapshots_failed" : 0,
"total_snapshots_deleted" : 18,
"total_snapshot_deletion_failures" : 6
}
```
This does not yet include HLRC for this, as this commit is quite large on its own. That will be
added in a subsequent commit.
Relates to #43663
* Version qualify serialization
* Initialize counters outside constructor
* Use computeIfAbsent instead of being too verbose
* Move part of XContent generation into subclass
* Fix REST action for master merge
* Unused import
* Record history of SLM retention actions (#45513)
This commit records the deletion of snapshots by the retention component
of SLM into the SLM history index for the purposes of reviewing operations
taken by SLM and alerting.
* Retry SLM retention after currently running snapshot completes (#45802)
* Retry SLM retention after currently running snapshot completes
This commit adds a ClusterStateObserver to wait until the currently
running snapshot is complete before proceeding with snapshot deletion.
SLM retention waits for the maximum allowed deletion time for the
snapshot to complete, however, the waiting time is not factored into
the limit on actual deletions.
Relates to #43663
* Increase timeout waiting for snapshot completion
* Apply patch
From 2374316f0d.patch
* Rename test variables
* [TEST] Be less strict for stats checking
* Skip SLM retention if ILM is STOPPING or STOPPED (#45869)
This adds a check to ensure we take no action during SLM retention if
ILM is currently stopped or in the process of stopping.
Relates to #43663
* Check all actions preventing snapshot delete during retention (#45992)
* Check all actions preventing snapshot delete during retention run
Previously we only checked to see if a snapshot was currently running,
but it turns out that more things can block snapshot deletion. This
changes the check to be a check for:
- a snapshot currently running
- a deletion already in progress
- a repo cleanup in progress
- a restore currently running
This was found by CI where a third party delete in a test caused SLM
retention deletion to throw an exception.
Relates to #43663
* Add unit test for okayToDeleteSnapshots
* Fix bug where SLM retention task would be scheduled on every node
* Enhance test logging
* Ignore if snapshot is already deleted
* Missing import
* Fix SnapshotRetentionServiceTests
* Expose SLM policy stats in get SLM policy API (#45989)
This also adds support for the SLM stats endpoint to the high level rest client.
Retrieving a policy now looks like:
```json
{
"daily-snapshots" : {
"version": 1,
"modified_date": "2019-04-23T01:30:00.000Z",
"modified_date_millis": 1556048137314,
"policy" : {
"schedule": "0 30 1 * * ?",
"name": "<daily-snap-{now/d}>",
"repository": "my_repository",
"config": {
"indices": ["data-*", "important"],
"ignore_unavailable": false,
"include_global_state": false
},
"retention": {}
},
"stats": {
"snapshots_taken": 0,
"snapshots_failed": 0,
"snapshots_deleted": 0,
"snapshot_deletion_failures": 0
},
"next_execution": "2019-04-24T01:30:00.000Z",
"next_execution_millis": 1556048160000
}
}
```
Relates to #43663
* Rewrite SnapshotLifecycleIT as as ESIntegTestCase (#46356)
* Rewrite SnapshotLifecycleIT as as ESIntegTestCase
This commit splits `SnapshotLifecycleIT` into two different tests.
`SnapshotLifecycleRestIT` which includes the tests that do not require
slow repositories, and `SLMSnapshotBlockingIntegTests` which is now an
integration test using `MockRepository` to simulate a snapshot being in
progress.
Relates to #43663Resolves#46205
* Add error logging when exceptions are thrown
* Update serialization versions
* Fix type inference
* Use non-Cancellable HLRC return value
* Fix Client mocking in test
* Fix SLMSnapshotBlockingIntegTests for 7.x branch
* Update SnapshotRetentionTask for non-multi-repo snapshot retrieval
* Add serialization guards for SnapshotLifecyclePolicy
The existing privilege model for API keys with privileges like
`manage_api_key`, `manage_security` etc. are too permissive and
we would want finer-grained control over the cluster privileges
for API keys. Previously APIs created would also need these
privileges to get its own information.
This commit adds support for `manage_own_api_key` cluster privilege
which only allows api key cluster actions on API keys owned by the
currently authenticated user. Also adds support for retrieval of
the API key self-information when authenticating via API key
without the need for the additional API key privileges.
To support this privilege, we are introducing additional
authentication context along with the request context such that
it can be used to authorize cluster actions based on the current
user authentication.
The API key get and invalidate APIs introduce an `owner` flag
that can be set to true if the API key request (Get or Invalidate)
is for the API keys owned by the currently authenticated user only.
In that case, `realm` and `username` cannot be set as they are
assumed to be the currently authenticated ones.
The changes cover HLRC changes, documentation for the API changes.
Closes#40031
This commit introduces PKI realm delegation. This feature
supports the PKI authentication feature in Kibana.
In essence, this creates a new API endpoint which Kibana must
call to authenticate clients that use certificates in their TLS
connection to Kibana. The API call passes to Elasticsearch the client's
certificate chain. The response contains an access token to be further
used to authenticate as the client. The client's certificates are validated
by the PKI realms that have been explicitly configured to permit
certificates from the proxy (Kibana). The user calling the delegation
API must have the delegate_pki privilege.
Closes#34396
This change adds the support for the RankFeatureQuery in the HLRC by
providing an extra dependency on mapper-extras-client. It also removes
the dependency on lang-painless in mapper-extras which is not needed
anymore since the move of the vector field into a dedicated module.
Closes#43634
This commit replaces task_state and indexer_state in the
data frame _stats output with a single top level state
that combines the two. It is defined as:
- failed if what's currently reported as task_state is failed
- stopped if there is no persistent task
- Otherwise what's currently reported as indexer_state
Backport of #45276
* [ML][Data Frame] Add update transform api endpoint (#45154)
This adds the ability to `_update` stored data frame transforms. All mutable fields are applied when the next checkpoint starts. The exception being `description`.
This PR contains all that is necessary for this addition:
* HLRC
* Docs
* Server side
Adds an API to clone an index. This is similar to the index split and shrink APIs, just with the
difference that the number of primary shards is kept the same. In case where the filesystem
provides hard-linking capabilities, this is a very cheap operation.
Indexing cloning can be done by running `POST my_source_index/_clone/my_target_index` and it
supports the same options as the split and shrink APIs.
Closes#44128
This change adjusts the data frame transforms stats
endpoint to return a structure that is easier to
understand.
This is a breaking change for clients of the data frame
transforms stats endpoint, but the feature is in beta so
stability is not guaranteed.
Backport of #44350
Christoph Büscher
David Roberts
Tom Veasey
Przemko Robakowski
Benjamin Trent
OriGlassman
Nik Everett
Raidok
Ioannis Kakavas
Martijn van Groningen
Dimitris Athanasiou
Jake
Przemysław Witek
Henning Andersen
Henning Andersen
Lisa Cawley
Michael Basnight
James Rodewig
Alexandre Fonseca
Gordon Brown
Tanguy Leroux
Yogesh Gaikwad
Lee Hinman
István Zoltán Szabó
Hendrik Muhs
Jilles van Gurp
Luca Cavanna
Albert Zaharovits
markharwood
Jim Ferenczi
Yannick Welsch