This template was added for 7.0 for what I am guessing is a BWC issue related to deprecation
warnings. It unfortunately seems to cause failures because templates for these tests are not cleared
after the test (because these are upgrade tests).
Resolves#56363
This need some reorg of BinaryDV field data classes to allow specialisation of scripted doc values.
Moved common logic to a new abstract base class and added a new subclass to return string-based representations to scripts.
Closes#58044
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
Previously we excluded requiring licenses for dependencies with the
group name org.elasticsearch under the assumption that these use the
top-level Elasticsearch license. This is not always correct, for
example, for the org.elasticsearch:jna dependency as this is merely a
wrapper around the upstream JNA project, and that is the license that we
should be including. A recent change modified this check from using the
group name to checking only if the dependency is a project
dependency. This exposed the use of JNA in SQL CLI to this check, but
the license for it was not added. This commit addresses this by adding
the license.
Relates #58015
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`.
We were previously configuring BWC testing tasks by matching on task
name prefix. This naive approach breaks down when you have versions like
1.0.1 and 1.0.10 since they both share a common prefix. This commit
makes the pattern matching more specific so we won't inadvertently
spin up the wrong cluster version.
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>
Now that annotations are part of the anomaly detection job results
the annotations index should be refreshed on flushing and closing
the job so that flush and close continue to fulfil their contracts
that immediately after returning all results the job generated up
to that point are searchable.
ModelLoadingService only caches models if they are referenced by an
ingest pipeline. For models used in search we want to always cache the
models and rely on TTL to evict them. Additionally when an ingest
pipeline is deleted the model it references should not be evicted if
it is used in search.
Search after is a better choice for the delete expired data iterators
where processing takes a long time as unlike scroll a context does not
have to be kept alive. Also changes the delete expired data endpoint to
404 if the job is unknown
Since we change the memory estimates for data frame analytics jobs from worst case to a realistic case, the strict less-than assertion in the test does not hold anymore. I replaced it with a less-or-equal-than assertion.
Backport or #57882
Adds assertions to Netty to make sure that its threads are not polluted by thread contexts (and
also that thread contexts are not leaked). Moves the ClusterApplierService to use the system
context (same as we do for MasterService), which allows to remove a hack from
TemplateUgradeService and makes it clearer that applying CS updates is fully executing under
system context.
When Joni, the regex engine that powers grok emits a warning it
does so by default to System.err. System.err logs are all bucketed
together in the server log at WARN level. When Joni emits a warning,
it can be extremely verbose, logging a message for each execution
again that pattern. For ingest node that means for every document
that is run that through Grok. Fortunately, Joni provides a call
back hook to push these warnings to a custom location.
This commit implements Joni's callback hook to push the Joni warning
to the Elasticsearch server logger (logger.org.elasticsearch.ingest.common.GrokProcessor)
at debug level. Generally these warning indicate a possible issue with
the regular expression and upon creation of the Grok processor will
do a "test run" of the expression and log the result (if any) at WARN
level. This WARN level log should only occur on pipeline creation which
is a much lower frequency then every document.
Additionally, the documentation is updated with instructions for how
to set the logger to debug level.
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)
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
Adds assertions to Netty to make sure that its threads are not polluted by thread contexts (and
also that thread contexts are not leaked). Moves the ClusterApplierService to use the system
context (same as we do for MasterService), which allows to remove a hack from
TemplateUgradeService and makes it clearer that applying CS updates is fully executing under
system context.
* Convert to date/datetime the result of numeric aggregations (min, max)
in Painless scripts
(cherry picked from commit f1de99e2a6fbf3806c4f2b6b809738aa8faa2d75)
This adds new plugin level circuit breaker for the ML plugin.
`model_inference` is the circuit breaker qualified name.
Right now it simply adds to the breaker when the model is loaded (and possibly breaking) and removing from the breaker when the model is unloaded.
Fix broken numeric shard generations when reading them from the wire
or physically from the physical repository.
This should be the cheapest way to clean up broken shard generations
in a BwC and safe-to-backport manner for now. We can potentially
further optimize this by also not doing the checks on the generations
based on the versions we see in the `RepositoryData` but I don't think
it matters much since we will read `RepositoryData` from cache in almost
all cases.
Closes#57798
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
#47711 and #47246 helped to validate that monitoring settings are
rejected at time of setting the monitoring settings. Else an invalid
monitoring setting can find it's way into the cluster state and result
in an exception thrown [1] on the cluster state application (there by
causing significant issues). Some additional monitoring settings have
been identified that can result in invalid cluster state that also
result in exceptions thrown on cluster state application.
All settings require a type of either http or local to be
applicable. When a setting is changed, the exporters are automatically
updated with the new settings. However, if the old or new settings lack
of a type setting an exception will be thrown (since exporters are
always of type 'http' or 'local'). Arguably we shouldn't blindly create
and destroy new exporters on each monitoring setting update, but the
lifecycle of the exporters is abit out the scope this PR is trying to
address.
This commit introduces a similar methodology to check for validity as
#47711 and #47246 but this time for ALL (including non-http) settings.
Monitoring settings are not useful unless there an exporter with a type
defined. The type is used as dependent setting, such that it must
exist to set the value. This ensures that when any monitoring settings
changes that they can only get added to cluster state if the type
exists. If the type exists (and the other validations pass) then the
exporters will get re-built and the cluster state remains valid.
Tests have been included to ensure that all dynamic monitoring settings
have the type as dependent settings.
[1]
org.elasticsearch.common.settings.SettingsException: missing exporter type for [found-user-defined] exporter
at org.elasticsearch.xpack.monitoring.exporter.Exporters.initExporters(Exporters.java:126) ~[?:?]
Lisa Cawley
Lee Hinman
markharwood
Alejandro Fernández Haro
Shaunak Kashyap
Rene Groeschke
Jason Tedor
Dan Hermann
Benjamin Trent
Mark Vieira
Ignacio Vera
Martijn van Groningen
David Roberts
David Kyle
Mark Tozzi
Alan Woodward
Costin Leau
Albert Zaharovits
Valeriy Khakhutskyy
Aleksandr Maus
Andrei Dan
Yannick Welsch
Hendrik Muhs
Yang Wang
Jake Landis
Nik Everett
Hossein Dehghan
Andrei Stefan
Armin Braun
Przemysław Witek
Mayya Sharipova
Luca Cavanna