Adds a warning admonition stating that the `index_options` mapping
parameter is intended only for `text` fields.
Removes an outdated statement regarding default values for numeric
and other datatypes.
Adds reporting of memory usage for data frame analytics jobs.
This commit introduces a new index pattern `.ml-stats-*` whose
first concrete index will be `.ml-stats-000001`. This index serves
to store instrumentation information for those jobs.
Backport of #52778 and #52958
Closes#43990. Describe how to change the default GC settings without changing
the default `jvm.options`. Give examples using `jvm.options.d`, and
`ES_JAVA_OPTS` with Docker.
Backport of #51233 to the seven dot x branch.
Tries to load a `Mapper` instance for the mapping snippet of a dynamic template.
This should catch things like using an analyzer that is undefined or mapping attributes that are unused.
This is best effort:
* If `{{name}}` placeholder is used in the mapping snippet then validation is skipped.
* If `match_mapping_type` is not specified then validation is performed for all mapping types.
If parsing succeeds with a single mapping type then this the dynamic mapping is considered valid.
If is detected that a dynamic template mapping snippet is invalid at mapping update time then the mapping update is failed for indices created on 8.0.0-alpha1 and later. For indices created on prior version a deprecation warning is omitted instead. In 7.x clusters the mapping update will never fail in case of an invalid dynamic template mapping snippet and a deprecation warning will always be omitted.
Closes#17411Closes#24419
Co-authored-by: Adrien Grand <jpountz@gmail.com>
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 change adds the recall@k metric and refactors precision@k to match
the new metric.
Recall@k is an important metric to use for learning to rank (LTR)
use-cases. Candidate generation or first ranking phase ranking functions
are often optimized for high recall, in order to generate as many
relevant candidates in the top-k as possible for a second phase of
ranking. Adding this metric allows tuning that base query for LTR.
See: https://github.com/elastic/elasticsearch/issues/51676
Backports: https://github.com/elastic/elasticsearch/pull/52577
Add query execution and return actual results returned from
Elasticsearch inside the tests
(cherry picked from commit 3e039282bf991af87604a6d4f8eada19d5e33842)
The introductory sections of the reference manual contains some simplified
instructions for adding a node to the cluster. Unfortunately they are a little
too simplified and only really work for clusters running on `localhost`. If you
try and follow these instructions for a distributed cluster then the new node
will, confusingly, auto-bootstrap itself into a distinct one-node cluster.
Multiple nodes running on localhost is a valid config, of course, but we should
spell out that these instructions are really only for experimentation and that
it takes a bit more work to add nodes to a distributed cluster. This commit
does so.
Also, the "important config" instructions for discovery say that you MUST set
`discovery.seed_hosts` whereas in fact it is fine to ignore this setting and
use a dynamic discovery mechanism instead. This commit weakens this statement
and links to the docs for dynamic discovery mechanisms.
Finally, this section is also overloaded with some technical details that are
not important for this context and are adequately covered elsewhere, and
completely fails to note that the default discovery port is 9300. This commit
addresses this.
Adds the `?refresh=wait_for` query argument to an index API snippet in
the term vectors API docs.
This should ensure the document is indexed and available before a
subsequent term vectors API request executes.
Fixes#52814.
Remove reference to an "SQL API" which could suggest that one needs to
treat this in a special way when configuring the ODBC driver.
(cherry picked from commit 451c341e0193b542409e8891ec2a31e62529a5e7)
Adds an explicit "important" admonition discouraging apps from using
cat APIs.
cat APIs are intended for human consumption via the command line or
Kibana console only. They are not intended for consumption by
applications.
Indices open with the `niofs` store type load much more data on-heap than
indices open with the `mmapfs` store type. This limitation is now documented
and examples have been updated to show how to update settings to use the
`mmapfs` store type rather than `niofs`.
We should be more explicit about the downsides of disabling replicas and
explain that users should be ready to re-do the entire load in case of
issues mid-way.
One architecture that we have recommended to several users to speed up
indexing involved using CCR to prevent searching from stealing resources
from indexing.
Before boost in script_score query was wrongly applied only to the subquery.
This commit makes sure that the boost is applied to the whole score
that comes out of script.
Closes#48465
Explicitly notes the Elasticsearch API endpoints that support CCS.
This should deter users from attempting to use CCS with other API
endpoints, such as `GET <index>/_doc/<_id>`.
* Adds an example request to the top of the page.
* Relocates several parameters erroneously listed under "Request body"
to the appropriate "Query parameters" section.
* Updates the "Request body" section to better document the NDJSON
structure of msearch requests.
Add default value to each one of the usages of `allow_no_indices`
since it differs between different APIs.
Relates to: #52534
(cherry picked from commit 2eb986488ac326d6da6ab8ad0203a94e08684a36)
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
Re-adds several redirects removed with #50510.
These redirects were related to the relocation of several API docs to
new pages under the 'REST APIs' chapter.
We've since decided to only remove such redirects with major releases.
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)
