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.
This is a partial implementation of an endpoint for anomaly
detector model memory estimation.
It is not complete, lacking docs, HLRC and sensible numbers
for many anomaly detector configurations. These will be
added in a followup PR in time for 7.7 feature freeze.
A skeleton endpoint is useful now because it allows work on
the UI side of the change to commence. The skeleton endpoint
handles the same cases that the old UI code used to handle,
and produces very similar estimates for these cases.
Backport of #53333
This field is a specialization of the `keyword` field for the case when all
documents have the same value. It typically performs more efficiently than
keywords at query time by figuring out whether all or none of the documents
match at rewrite time, like `term` queries on `_index`.
The name is up for discussion. I liked including `keyword` in it, so that we
still have room for a `singleton_numeric` in the future. However I'm unsure
whether to call it `singleton`, `constant` or something else, any opinions?
For this field there is a choice between
1. accepting values in `_source` when they are equal to the value configured
in mappings, but rejecting mapping updates
2. rejecting values in `_source` but then allowing updates to the value that
is configured in the mapping
This commit implements option 1, so that it is possible to reindex from/to an
index that has the field mapped as a keyword with no changes to the source.
Backport of #49713
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 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.
These tests didn't work properly when run against multi-shard indices.
The `_score` based sorting test expects fairly specific scores which
isn't going to happen with multiple shards so this disables multiple
shards for that test. The other tests were failing due to a fairly
sneaky race condition around `_bulk` and type inference. This fixes them
by always sending metric values as floating point numbers so
Elasticsearch always infers them to be doubles.
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 commit updates the enrich.get_policy API to specify name
as a list, in line with other URL parts that accept a comma-separated
list of values.
In addition, update the get enrich policy API docs
to align the URL part name in the documentation with
the name used in the REST API specs.
(cherry picked from commit 94f6f946ef283dc93040e052b4676c5bc37f4bde)
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
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>
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
This change adds support for the following new model_size_stats
fields:
- categorized_doc_count
- total_category_count
- frequent_category_count
- rare_category_count
- dead_category_count
- categorization_status
Backport of #51879
The main purpose of this commit is to add a single autoscaling REST
endpoint skeleton, for the purpose of starting to build out the build
and testing infrastructure that will surround it. For example, rather
than commiting a fully-functioning autoscaling API, we introduce here
the skeleton so that we can start wiring up the build and testing
infrastructure, establish security roles/permissions, an so on. This
way, in a forthcoming PR that introduces actual functionality, that PR
will be smaller and have less distractions around that sort of
infrastructure.
Not all clients support this e.g if the java high level rest client were
to map this it would look like `client.cat().ml().api()` which hinders
discoverability.
(cherry picked from commit 21cdabf09dc8305ce2f5e3b6cb193f67137d8bdb)
* [DOCS] Align with ILM API docs (#48705)
* [DOCS] Reconciled with Snapshot/Restore reorg
* [DOCS] Split off ILM overview to a separate topic. (#51287)
* [DOCS} Split off overview to a separate topic.
* [DOCS] Incorporated feedback from @jrodewig.
* [DOCS] Edit ILM GS tutorial (#51513)
* [DOCS] Edit ILM GS tutorial
* [DOCS] Incorporated review feedback from @andreidan.
* [DOCS] Removed test link & fixed anchor & title.
* Update docs/reference/ilm/getting-started-ilm.asciidoc
Co-Authored-By: James Rodewig <james.rodewig@elastic.co>
* Fixed glossary merge error.
Co-authored-by: James Rodewig <james.rodewig@elastic.co>
