This changes the actions that would attempt to make the managed index read only to
check if the managed index is the write index of a data stream before proceeding.
The updated actions are shrink, readonly, freeze and forcemerge.
(cherry picked from commit c906f631833fee8628f898917a8613a1f436c6b1)
Signed-off-by: Andrei Dan <andrei.dan@elastic.co>
As part of the "ML in Spaces" project, access to the ML UI in
Kibana is migrating to being controlled by Kibana privileges.
The ML UI will check whether the logged-in user has permission
to do something ML-related using Kibana privileges, and if they
do will call the relevant ML Elasticsearch API using the Kibana
system user. In order for this to work the kibana_system role
needs to have administrative access to ML.
Backport of #58061
We don't allow converting a data stream's writeable index into a searchable
snapshot. We are currently preventing swapping a data stream's write index
with the restored index.
This adds another step that will not proceed with the searchable snapshot action
until the managed index is not the write index of a data stream anymore.
(cherry picked from commit ccd618ead7cf7f5a74b9fb34524d00024de1479a)
Signed-off-by: Andrei Dan <andrei.dan@elastic.co>
MappedFieldType is a combination of two concerns:
* an extension of lucene's FieldType, defining how a field should be indexed
* a set of query factory methods, defining how a field should be searched
We want to break these two concerns apart. This commit is a first step to doing this, breaking
the inheritance relationship between MappedFieldType and FieldType. MappedFieldType
instead has a series of boolean flags defining whether or not the field is searchable or
aggregatable, and FieldMapper has a separate FieldType passed to its constructor defining
how indexing should be done.
Relates to #56814
Allows the kibana user to collect data telemetry in a background
task by giving the kibana_system built-in role the view_index_metadata
and monitoring privileges over all indices (*).
Without this fix, users who try to use Metricbeat for Stack Monitoring today
see the following error repeatedly in their Metricbeat log. Due to this error
Metricbeat is unwilling to proceed further and, thus, no Stack Monitoring
data is indexed into the Elasticsearch cluster.
Co-authored-by: Albert Zaharovits <albert.zaharovits@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
This has `EnsembleInferenceModel` not parse feature_names from the XContent.
Instead, it will rely on `rewriteFeatureIndices` to be called ahead time.
Consequently, protections are made for a fail fast path if `rewriteFeatureIndices` has not been called before `infer`.
This type of result will store stats about how well categorization
is performing. When per-partition categorization is in use, separate
documents will be written for every partition so that it is possible
to see if categorization is working well for some partitions but not
others.
This PR is a minimal implementation to allow the C++ side changes to
be made. More Java side changes related to per-partition
categorization will be in followup PRs. However, even in the long
term I do not see a major benefit in introducing dedicated APIs for
querying categorizer stats. Like forecast request stats the
categorizer stats can be read directly from the job's results alias.
Backport of #57978
Adds support for reading in `model_size_info` objects.
These objects contain numeric values indicating the model definition size and complexity.
Additionally, these objects are not stored or serialized to any other node. They are to be used for calculating and storing model metadata. They are much smaller on heap than the true model definition and should help prevent the analytics process from using too much memory.
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
The shrink action creates a shrunken index with the target number of shards.
This makes the shrink action data stream aware. If the ILM managed index is
part of a data stream the shrink action will make sure to swap the original
managed index with the shrunken one as part of the data stream's backing
indices and then delete the original index.
(cherry picked from commit 99aeed6acf4ae7cbdd97a3bcfe54c5d37ab7a574)
Signed-off-by: Andrei Dan <andrei.dan@elastic.co>
This deprecates `Rounding#round` and `Rounding#nextRoundingValue` in
favor of calling
```
Rounding.Prepared prepared = rounding.prepare(min, max);
...
prepared.round(val)
```
because it is always going to be faster to prepare once. There
are going to be some cases where we won't know what to prepare *for*
and in those cases you can call `prepareForUnknown` and stil be faster
than calling the deprecated method over and over and over again.
Ultimately, this is important because it doesn't look like there is an
easy way to cache `Rounding.Prepared` or any of its precursors like
`LocalTimeOffset.Lookup`. Instead, we can just build it at most once per
request.
Relates to #56124
Before to determine if a field is meta-field, a static method of MapperService
isMetadataField was used. This method was using an outdated static list
of meta-fields.
This PR instead changes this method to the instance method that
is also aware of meta-fields in all registered plugins.
Related #38373, #41656Closes#24422
We want to validate the DataStreams on creation to make sure the future backing
indices would not clash with existing indices in the system (so we can
always rollover the data stream).
This changes the validation logic to allow for a DataStream to be created
with a backing index that has a prefix (eg. `shrink-foo-000001`) even if the
former backing index (`foo-000001`) exists in the system.
The new validation logic will look for potential index conflicts with indices
in the system that have the counter in the name greater than the data stream's
generation.
This ensures that the `DataStream`'s future rollovers are safe because for a
`DataStream` `foo` of generation 4, we will look for standalone indices in the
form of `foo-%06d` with the counter greater than 4 (ie. validation will fail if
`foo-000006` exists in the system), but will also allow replacing a
backing index with an index named by prefixing the backing index it replaces.
(cherry picked from commit 695b242d69f0dc017e732b63737625adb01fe595)
Signed-off-by: Andrei Dan <andrei.dan@elastic.co>
Deleting expired data can take a long time leading to timeouts if there
are many jobs. Often the problem is due to a few large jobs which
prevent the regular maintenance of the remaining jobs. This change adds
a job_id parameter to the delete expired data endpoint to help clean up
those problematic jobs.
This makes it easier to debug where such tasks come from in case they are returned from the get tasks API.
Also renamed the last occurrence of waitForCompletion to waitForCompletionTimeout in get async search request.
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
This is a major refactor of the underlying inference logic.
The main refactor is now we are separating the model configuration and
the inference interfaces.
This has the following benefits:
- we can store extra things with the model that are not
necessary for inference (i.e. treenode split information gain)
- we can optimize inference separate from model serialization and storage.
- The user is oblivious to the optimizations (other than seeing the benefits).
A major part of this commit is removing all inference related methods from the
trained model configurations (ensemble, tree, etc.) and moving them to a new class.
This new class satisfies a new interface that is ONLY for inference.
The optimizations applied currently are:
- feature maps are flattened once
- feature extraction only happens once at the highest level
(improves inference + feature importance through put)
- Only storing what we need for inference + feature importance on heap
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
rewrite config on update if either version is outdated, credentials change,
the update changes the config or deprecated settings are found. Deprecated
settings get migrated to the new format. The upgrade can be easily extended to
do any necessary re-writes.
fixes#56499
backport #57648
In #55592 and #55416, we deprecated the settings for enabling and disabling
basic license features and turned those settings into no-ops. Since doing so,
we've had feedback that this change may not give users enough time to cleanly
switch from non-ILM index management tools to ILM. If two index managers
operate simultaneously, results could be strange and difficult to
reconstruct. We don't know of any cases where SLM will cause a problem, but we
are restoring that setting as well, to be on the safe side.
This PR is not a strict commit reversion. First, we are keeping the new
xpack.watcher.use_ilm_index_management setting, introduced when
xpack.ilm.enabled was made a no-op, so that users can begin migrating to using
it. Second, the SLM setting was modified in the same commit as a group of other
settings, so I have taken just the changes relating to SLM.
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] mark forecasts for force closed/failed jobs as failed (#57143)
forecasts that are still running should be marked as failed/finished in the following scenarios:
- Job is force closed
- Job is re-assigned to another node.
Forecasts are not "resilient". Their execution does not continue after a node failure. Consequently, forecasts marked as STARTED or SCHEDULED should be flagged as failed. These forecasts can then be deleted.
Additionally, force closing a job kills the native task directly. This means that if a forecast was running, it is not allowed to complete and could still have the status of `STARTED` in the index.
relates to https://github.com/elastic/elasticsearch/issues/56419
* [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
The ssl.trust setting for Watcher provides a list of hostnames that
should be automatically trusted for SSL hostname verification. It was
accidentally broken when we added the full ssl.* settings for email
notifications (see #45272)
This commit corrects this, so the setting is once again respected,
as long as none of the other ssl settings are configured for email
notifications.
Resolves: #52153
Backport of: #56090
Fix delete_expired_data/nightly maintenance when
many model snapshots need deleting (#57041)
The queries performed by the expired data removers pull back entire
documents when only a few fields are required. For ModelSnapshots in
particular this is a problem as they contain quantiles which may be
100s of KB and the search size is set to 10,000.
This change makes the search more efficient by only requesting the
fields needed to work out which expired data should be deleted.
The original implementation utilized `bbox` as the index mapping type. This would not work as it would have to be `envelope`. But, given that `envelope` and `polygon` are tessellated in the same way, we choose to use `polygon` as the geo_shape type. This is for easier support other places in the stack (a la kibana maps)
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.
WatcherIndexTemplateRegistry as of https://github.com/elastic/elasticsearch/pull/52962
requires all nodes to be on 7.7.0 before it allows the version 11 index template to be
installed.
While in a mixed cluster, nothing prevents Watcher from running on the new
host before the all of the nodes are on 7.7.0. This will result in the
.watcher-history-11* index without the proper mappings. Without the proper
mapping a single document (for a large watch) can exceed the default 1000 field
limit and cause error to show in the logs.
This commit ensures the same logic for writing to the index is applied as for
installing the template. In a mixed cluster, the `10` index template will continue
to be written. Only once all of nodes are on 7.7.0+ will the `11` index template
be installed and used.
closes#56732
This change aims to fix our setup in CI so that we can run 7.x in
FIPS 140 mode. The major issue that we have in 7.x and did not
have in master is that we can't use the diagnostic trust manager
in FIPS mode in Java 8 with SunJSSE in FIPS approved mode as it
explicitly disallows the wrapping of X509TrustManager.
Previous attempts like #56427 and #52211 focused on disabling the
setting in all of our tests when creating a Settings object or
on setting fips_mode.enabled accordingly (which implicitly disables
the diagnostic trust manager). The attempts weren't future proof
though as nothing would forbid someone to add new tests without
setting the necessary setting and forcing this would be very
inconvenient for any other case ( see
#56427 (comment) for the full argumentation).
This change introduces a runtime check in SSLService that overrides
the configuration value of xpack.security.ssl.diagnose.trust and
disables the diagnostic trust manager when we are running in Java 8
and the SunJSSE provider is set in FIPS mode.
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 aggregation will perform normalizations of metrics
for a given series of data in the form of bucket values.
The aggregations supports the following normalizations
- rescale 0-1
- rescale 0-100
- percentage of sum
- mean normalization
- z-score normalization
- softmax normalization
To specify which normalization is to be used, it can be specified
in the normalize agg's `normalizer` field.
For example:
```
{
"normalize": {
"buckets_path": <>,
"normalizer": "percent"
}
}
```
Andrei Dan
Rene Groeschke
David Roberts
Benjamin Trent
Alan Woodward
Alejandro Fernández Haro
Shaunak Kashyap
David Kyle
Yang Wang
Nik Everett
Dan Hermann
Przemysław Witek
Mayya Sharipova
Luca Cavanna
Dimitris Athanasiou
Hendrik Muhs
William Brafford
Ioannis Kakavas
William Brafford
Mark Vieira
Tim Vernum
Jake Landis
Ryan Ernst
Tal Levy