* [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] Add new include flag to GET inference/<model_id> API for model training metadata (#61922)
Adds new flag include to the get trained models API
The flag initially has two valid values: definition, total_feature_importance.
Consequently, the old include_model_definition flag is now deprecated.
When total_feature_importance is included, the total_feature_importance field is included in the model metadata object.
Including definition is the same as previously setting include_model_definition=true.
* fixing test
* Update x-pack/plugin/core/src/test/java/org/elasticsearch/xpack/core/ml/action/GetTrainedModelsRequestTests.java
* [ML] adding docs + hlrc for data frame analysis feature_processors (#61149)
Adds HLRC and some docs for the new feature_processors field in Data frame analytics.
Co-authored-by: Przemysław Witek <przemyslaw.witek@elastic.co>
Co-authored-by: Lisa Cawley <lcawley@elastic.co>
Changes:
* Removes narrative around URI searches. These aren't commonly used in production. The `q` param is already covered in the search API docs: https://www.elastic.co/guide/en/elasticsearch/reference/master/search-search.html#search-api-query-params-q
* Adds a common options section that highlights narrative docs for query DSL, aggregations, multi-index search, search fields, pagination, sorting, and async search.
* Adds a `Search shard routing` page. Moves narrative docs for adaptive replica selection, preference, routing , and shard limits to that section.
* Moves search timeout and cancellation content to the `Search your data` page.
* Creates a `Search multiple data streams and indices` page. Moves related narrative docs for multi-target syntax searches and `indices_boost` to that page.
* Removes narrative examples for the `search_type` parameters. Moves documentation for this parameter to the search API docs.
Plugin discovery documentation contained information about installing
Elasticsearch 2.0 and installing an oracle JDK, both of which is no
longer valid.
While noticing that the instructions used cleartext HTTP to install
packages, this commit replaces HTTPs links instead of HTTP where possible.
In addition a few community links have been removed, as they do not seem
to exist anymore.
Co-authored-by: Alexander Reelsen <alexander@reelsen.net>
Different kinds of requests may need different request options from the client
default. Users can optionally set RequestConfig on a single request's
RequestOptions to override the default. Without this, socketTimeout can only
set at RestClient initialization.
Co-authored-by: weizijun <weizijun1989@gmail.com>
This adds a setting to data frame analytics jobs called
`max_number_threads`. The setting expects a positive integer.
When used the user specifies the max number of threads that may
be used by the analysis. Note that the actual number of threads
used is limited by the number of processors on the node where
the job is assigned. Also, the process may use a couple more threads
for operational functionality that is not the analysis itself.
This setting may also be updated for a stopped job.
More threads may reduce the time it takes to complete the job at the cost
of using more CPU.
Backport of #59254 and #57274
Add caching support for application privileges to reduce number of round-trips to security index when building application privilege descriptors.
Privilege retrieving in NativePrivilegeStore is changed to always fetching all privilege documents for a given application. The caching is applied to all places including "get privilege", "has privileges" APIs and CompositeRolesStore (for authentication).
Today when creating a follower index via the put follow API, or via an
auto-follow pattern, it is not possible to specify settings overrides
for the follower index. Instead, we copy all of the leader index
settings to the follower. Yet, there are cases where a user would want
some different settings on the follower index such as the number of
replicas, or allocation settings. This commit addresses this by allowing
the user to specify settings overrides when creating follower index via
manual put follower calls, or via auto-follow patterns. Note that not
all settings can be overrode (e.g., index.number_of_shards) so we also
have detection that prevents attempting to override settings that must
be equal between the leader and follow index. Note that we do not even
allow specifying such settings in the overrides, even if they are
specified to be equal between the leader and the follower
index. Instead, the must be implicitly copied from the leader index, not
explicitly set by the user.
When we force delete a DF analytics job, we currently first force
stop it and then we proceed with deleting the job config.
This may result in logging errors if the job config is deleted
before it is retrieved while the job is starting.
Instead of force stopping the job, it would make more sense to
try to stop the job gracefully first. So we now try that out first.
If normal stop fails, then we resort to force stopping the job to
ensure we can go through with the delete.
In addition, this commit introduces `timeout` for the delete action
and makes use of it in the child requests.
Backport of #57680
* [ML] adds new for_export flag to GET _ml/inference API (#57351)
Adds a new boolean flag, `for_export` to the `GET _ml/inference/<model_id>` API.
This flag is useful for moving models between clusters.
This adds a max_model_memory setting to forecast requests.
This setting can take a string value that is formatted according to byte sizes (i.e. "50mb", "150mb").
The default value is `20mb`.
There is a HARD limit at `500mb` which will throw an error if used.
If the limit is larger than 40% the anomaly job's configured model limit, the forecast limit is reduced to be strictly lower than that value. This reduction is logged and audited.
related native change: https://github.com/elastic/ml-cpp/pull/1238
closes: https://github.com/elastic/elasticsearch/issues/56420
Throttling nightly cleanup as much as we do has been over cautious.
Night cleanup should be more lenient in its throttling. We still
keep the same batch size, but now the requests per second scale
with the number of data nodes. If we have more than 5 data nodes,
we don't throttle at all.
Additionally, the API now has `requests_per_second` and `timeout` set.
So users calling the API directly can set the throttling.
This commit also adds a new setting `xpack.ml.nightly_maintenance_requests_per_second`.
This will allow users to adjust throttling of the nightly maintenance.
This documents the index template v2 and component template APIs in the
high level rest client.
(cherry picked from commit 9bcf89b1e27613ab8887ce611ec2b0d1356cba8b)
Signed-off-by: Andrei Dan <andrei.dan@elastic.co>
Adds a "node" field to the response from the following endpoints:
1. Open anomaly detection job
2. Start datafeed
3. Start data frame analytics job
If the job or datafeed is assigned to a node immediately then
this field will return the ID of that node.
In the case where a job or datafeed is opened or started lazily
the node field will contain an empty string. Clients that want
to test whether a job or datafeed was opened or started lazily
can therefore check for this.
Backport of #55473
The usage of local parameter for GetFieldMappingRequest has been removed from the underlying transport action since v2.0.
This PR deprecates the parameter from rest layer. It will be removed in next major version.
Today when canceling a task we broadcast ban/unban requests to all nodes
in the cluster. This strategy does not scale well for hierarchical
cancellation. With this change, we will track outstanding child requests
and broadcast the cancellation to only nodes that have outstanding child
tasks. This change also prevents a parent task from sending child
requests once it got canceled.
Relates #50990
Supersedes #51157
Co-authored-by: Igor Motov <igor@motovs.org>
Co-authored-by: Yannick Welsch <yannick@welsch.lu>
* [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
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>
Benjamin Trent
Przemysław Witek
Lisa Cawley
James Rodewig
Yang Wang
Mark Tozzi
James Baiera
David Kyle
Dimitris Athanasiou
Jason Tedor
Lee Hinman
Samidh
Kamyar Ghajar
Andrei Dan
Hendrik Muhs
David Roberts
Nhat Nguyen
Christoph Büscher
Tom Veasey
Przemko Robakowski
OriGlassman
Nik Everett
Raidok
Ioannis Kakavas