* EQL: case sensitivity aware integration testing (#58624)
* Add DataLoader
* Rewrite case sensitivity settings:
NULL -> run both case sensitive and insensitive tests
TRUE -> run case sensitive test only
FALSE -> run case insensitive test only
* Rename test_queries_supported
* Add more toml tests from the Python client
Co-authored-by: Ross Wolf <31489089+rw-access@users.noreply.github.com>
(cherry picked from commit 34d383421599f060a5c083b40df35f135de49e39)
Adds parsing of `status` and `memory_reestimate_bytes`
to data frame analytics `memory_usage`. When the training surpasses
the model memory limit, the status will be set to `hard_limit` and
`memory_reestimate_bytes` can be used to update the job's
limit in order to restart the job.
Backport of #58588
Implements a new histogram aggregation called `variable_width_histogram` which
dynamically determines bucket intervals based on document groupings. These
groups are determined by running a one-pass clustering algorithm on each shard
and then reducing each shard's clusters using an agglomerative
clustering algorithm.
This PR addresses #9572.
The shard-level clustering is done in one pass to minimize memory overhead. The
algorithm was lightly inspired by
[this paper](https://ieeexplore.ieee.org/abstract/document/1198387). It fetches
a small number of documents to sample the data and determine initial clusters.
Subsequent documents are then placed into one of these clusters, or a new one
if they are an outlier. This algorithm is described in more details in the
aggregation's docs.
At reduce time, a
[hierarchical agglomerative clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering)
algorithm inspired by [this paper](https://arxiv.org/abs/1802.00304)
continually merges the closest buckets from all shards (based on their
centroids) until the target number of buckets is reached.
The final values produced by this aggregation are approximate. Each bucket's
min value is used as its key in the histogram. Furthermore, buckets are merged
based on their centroids and not their bounds. So it is possible that adjacent
buckets will overlap after reduction. Because each bucket's key is its min,
this overlap is not shown in the final histogram. However, when such overlap
occurs, we set the key of the bucket with the larger centroid to the midpoint
between its minimum and the smaller bucket’s maximum:
`min[large] = (min[large] + max[small]) / 2`. This heuristic is expected to
increases the accuracy of the clustering.
Nodes are unable to share centroids during the shard-level clustering phase. In
the future, resolving https://github.com/elastic/elasticsearch/issues/50863
would let us solve this issue.
It doesn’t make sense for this aggregation to support the `min_doc_count`
parameter, since clusters are determined dynamically. The `order` parameter is
not supported here to keep this large PR from becoming too complex.
Co-authored-by: James Dorfman <jamesdorfman@users.noreply.github.com>
Backporting #58096 to 7.x branch.
Relates to #53100
* use mapping source direcly instead of using mapper service to extract the relevant mapping details
* moved assertion to TimestampField class and added helper method for tests
* Improved logic that inserts timestamp field mapping into an mapping.
If the timestamp field path consisted out of object fields and
if the final mapping did not contain the parent field then an error
occurred, because the prior logic assumed that the object field existed.
When a local model is constructed, the cache hit miss count is incremented.
When a user calls _stats, we will include the sum cache hit miss count across ALL nodes. This statistic is important to in comparing against the inference_count. If the cache hit miss count is near the inference_count it indicates that the cache is overburdened, or inappropriately configured.
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.
This change allows to use an `index_filter` in the
field capabilities API. Indices are filtered from
the response if the provided query rewrites to `match_none`
on every shard:
````
GET metrics-*
{
"index_filter": {
"bool": {
"must": [
"range": {
"@timestamp": {
"gt": "2019"
}
}
}
}
}
````
The filtering is done on a best-effort basis, it uses the can match phase
to rewrite queries to `match_none` instead of fully executing the request.
The first shard that can match the filter is used to create the field
capabilities response for the entire index.
Closes#56195
The dangling_indices.import API name could cause issues in the client
libs because import is a reserved word in many languages. Rename the
API to avoid this, and rename the other APIs for consistency.
Related to #48366.
Backport of #50920. Part of #48366. Implement an API for listing,
importing and deleting dangling indices.
Co-authored-by: David Turner <david.turner@elastic.co>
* Remove usage of deprecated testCompile configuration
* Replace testCompile usage by testImplementation
* Make testImplementation non transitive by default (as we did for testCompile)
* Update CONTRIBUTING about using testImplementation for test dependencies
* Fail on testCompile configuration usage
Previously, hidden indices were not included in snapshots by default, unless
specified using one of the usual methods for doing so: naming indices directly,
using index patterns starting with a ., or specifying expand_wildcards to
a value that includes hidden (e.g. all or hidden,open).
This commit changes the default expand_wildcards value to include hidden
indices.
Allow a field inside the data to be used as a tie breaker for events
that have the same timestamp.
The field is optional by default.
If used, the tie-breaker always requires a non-null value since it is
used inside `search_after` which requires a non-null value.
Fix#56824
(cherry picked from commit e5719ecb474b32730d93afdbb6834a32b0b2df8b)
This PR adds the initial Java side changes to enable
use of the per-partition categorization functionality
added in elastic/ml-cpp#1293.
There will be a followup change to complete the work,
as there cannot be any end-to-end integration tests
until elastic/ml-cpp#1293 is merged, and also
elastic/ml-cpp#1293 does not implement some of the
more peripheral functionality, like stop_on_warn and
per-partition stats documents.
The changes so far cover REST APIs, results object
formats, HLRC and docs.
Backport of #57683
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
As the datastream information is stored in the `ClusterState.Metadata` we exposed
the `Metadata` to the `AsyncWaitStep#evaluateCondition` method in order for
the steps to be able to identify when a managed index is part of a DataStream.
If a managed index is part of a DataStream the rollover target is the DataStream
name and the highest generation index is the write index (ie. the rolled index).
(cherry picked from commit 6b410dfb78f3676fce1b7401f1628c1ca6fbd45a)
Signed-off-by: Andrei Dan <andrei.dan@elastic.co>
* [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
Currently, the IndicesOptions set on a High Level rest client PutMappingRequest
are not correctly converted to request parameters. This change adds the missing
conversion and tests.
Closes#57045
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.
Added support for decompression at LLRC and added integration test
(cherry picked from commit 2621452473e0c236aa28db749f782a24eca6c974)
Signed-off-by: Andrei Dan <andrei.dan@elastic.co>
Co-authored-by: Hakky54 <hakangoudberg@hotmail.com>
This is another part of the breakup of the massive BuildPlugin. This PR
moves the code for configuring publications to a separate plugin. Most
of the time these publications are jar files, but this also supports the
zip publication we have for integ tests.
This merges the code for the `significant_terms` agg into the package
for the code for the `terms` agg. They are *super* entangled already,
this mostly just admits that to ourselves.
Precondition for the terms work in #56487
Backport: #55377
This commit adds the ability to auto create data streams using index templates v2.
Index templates (v2) now have a data_steam field that includes a timestamp field,
if provided and index name matches with that template then a data stream
(plus first backing index) is auto created.
Relates to #53100
This commit removes the `prefer_v2_templates` flag and setting. This was a brief setting that
allowed specifying whether V1 or V2 template should be used when an index is created. It has been
removed in favor of V2 templates always having priority.
Relates to #53101Resolves#56528
This is not a breaking change because this flag was never in a released version.
Another Jackson release is available. There are some CVEs addressed,
none of which impact us, but since we can now bump Jackson easily, let
us move along with the train to avoid the false positives from security
scanners.
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>
This PR implements the following changes to make ML model snapshot
retention more flexible in advance of adding a UI for the feature in
an upcoming release.
- The default for `model_snapshot_retention_days` for new jobs is now
10 instead of 1
- There is a new job setting, `daily_model_snapshot_retention_after_days`,
that defaults to 1 for new jobs and `model_snapshot_retention_days`
for pre-7.8 jobs
- For days that are older than `model_snapshot_retention_days`, all
model snapshots are deleted as before
- For days that are in between `daily_model_snapshot_retention_after_days`
and `model_snapshot_retention_days` all but the first model snapshot
for that day are deleted
- The `retain` setting of model snapshots is still respected to allow
selected model snapshots to be retained indefinitely
Backport of #56125
As of elastic/ml-cpp#1179, the analytics process reports phases
depending on the analysis type. This commit adjusts the phases
of current analyses from `analyzing` to the following:
- outlier_detection: [`computing_outlier`]
- regression/classification: [`feature_selection`, `coarse_parameter_search`, `fine_tuning_parameters`, `final_training`]
Backport of #56107
Backport of #56034.
Move includeDataStream flag from an IndicesOptions to IndexNameExpressionResolver.Context
as a dedicated field that callers to IndexNameExpressionResolver can set.
Also alter indices stats api to support data streams.
The rollover api uses this api and otherwise rolling over data stream does no longer work.
Relates to #53100
Adds the step of stopping all data frame analytics before
deleting them to the cleanup of the corresponding HLRC tests.
Closes#56097
Backport of #56101
* Allow Deleting Multiple Snapshots at Once (#55474)
Adds deleting multiple snapshots in one go without significantly changing the mechanics of snapshot deletes otherwise.
This change does not yet allow mixing snapshot delete and abort. Abort is still only allowed for a single snapshot delete by exact name.
Andrei Stefan
Przemysław Witek
Dimitris Athanasiou
Igor Motov
Nik Everett
Martijn van Groningen
Benjamin Trent
Jason Tedor
Jim Ferenczi
Rory Hunter
Rene Groeschke
Przemko Robakowski
Dan Hermann
David Kyle
Costin Leau
Gordon Brown
David Roberts
Andrei Dan
Lee Hinman
Christoph Büscher
Ryan Ernst
zhenxianyimeng
Mark Vieira
Hendrik Muhs
Armin Braun