Allows geo fields (`geo_point`, `geo_shape`) to have missing values.
Fixes a bug where such missing values would result in an error.
Closes#57299
Backport of #57300
Since #51888 the ML job stats endpoint has returned entries for
jobs that have a persistent task but not job config. Such
orphaned tasks caused monitoring to fail.
This change ignores any such corrupt jobs for monitoring purposes.
Backport of #57235
If a job is NOT opened, forecasts should be able to be deleted, no matter their state.
This also fixes a bug with expanding forecast IDs. We should check for wildcard `*` and `_all` when expanding the ids
closes https://github.com/elastic/elasticsearch/issues/56419
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.
Field mapping detection is done via grok patterns.
This commit adds well-known text (WKT) formatted geometry detection.
If everything is a `POINT`, then a `geo_point` mapping is preferred.
Otherwise, if all the fields are WKT geometries a `geo_shape` mapping is preferred.
This does **NOT** detect other types of formatted geometries (geohash, comma delimited points, etc.)
closes https://github.com/elastic/elasticsearch/issues/56967
Merging logic is currently split between FieldMapper, with its merge() method, and
MappedFieldType, which checks for merging compatibility. The compatibility checks
are called from a third class, MappingMergeValidator. This makes it difficult to reason
about what is or is not compatible in updates, and even what is in fact updateable - we
have a number of tests that check compatibility on changes in mapping configuration
that are not in fact possible.
This commit refactors the compatibility logic so that it all sits on FieldMapper, and
makes it called at merge time. It adds a new FieldMapperTestCase base class that
FieldMapper tests can extend, and moves the compatibility testing machinery from
FieldTypeTestCase to here.
Relates to #56814
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.
In DF analytics classification, it is possible to use no samples
of a class if its cardinality is too low.
This commit fixes this by ensuring the target sample count can never be zero.
Backport of #56783
This is a followup to #56632. Tests that had to be changed
to mock the C++ log handler more accurately need to be more
careful about when that stream ends, as ending of that
stream is used to detect crashes in the production system.
Fixes#56796
Adds the conflicting types and an example of an index which specifies
them in order to make it easier for the user to understand the conflict.
Backport of #56700
Prior to this change the named pipes that connect the ML C++
processes to the Elasticsearch JVM were all opened before any
of them were read from or written to.
This created a problem, where if the C++ process logged more
messages between opening the log pipe and opening the last
pipe to be connected than there was space for in the named
pipe's buffer then the C++ process would block. This would
mean it never got as far as opening the last named pipe, so
the JVM would never get as far as reading from the log pipe,
hence a deadlock.
This change alters the connection order so that the JVM
starts reading from the logging pipe immediately after opening
it so that if the C++ process logs messages while opening the
other named pipes they are captured in a timely manner and
there is no danger of a deadlock.
Backport of #56632
Two spots that allow for some optimization:
* We are often creating a composite reference of just a single item in
the transport layer => special cased via static constructor to make sure we never do that
* Also removed the pointless case of an empty composite bytes ref
* `ByteBufferReference` is practically always created from a heap buffer these days so there
is no point of dealing with all the bounds checks and extra references to sliced buffers from that
and we can just use the underlying array directly
We have been using a zero timeout in the case that DF analytics
is stopped. This may cause a timeout when we cancel, for example,
the reindex task.
This commit fixes this by using the default timeout instead.
Backport of #56423
It is possible that the config document for a data frame
analytics job is deleted from the config index. If that is
the case the user is unable to stop a running job because
we attempt to retrieve the config and that will throw.
This commit changes that. When the request is forced,
we do not expand the requested ids based on the existing
configs but from the list of running tasks instead.
Backport of #56360
Due to multi-threading it is possible that phase progress
updates written from the c++ process arrive reordered.
We can address this by ensuring that progress may only increase.
Closes#56282
Backport of #56339
* [ML] lay ground work for handling >1 result indices (#55892)
This commit removes all but one reference to `getInitialResultsIndexName`.
This is to support more than one result index for a single job.
The following settings are now no-ops:
* xpack.flattened.enabled
* xpack.logstash.enabled
* xpack.rollup.enabled
* xpack.slm.enabled
* xpack.sql.enabled
* xpack.transform.enabled
* xpack.vectors.enabled
Since these settings no longer need to be checked, we can remove settings
parameters from a number of constructors and methods, and do so in this
commit.
We also update documentation to remove references to these settings.
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
In #55763 I thought I could remove the flag that marks
reindexing was finished on a data frame analytics task.
However, that exposed a race condition. It is possible that
between updating reindexing progress to 100 because we
have called `DataFrameAnalyticsManager.startAnalytics()` and
a call to the _stats API which updates reindexing progress via the
method `DataFrameAnalyticsTask.updateReindexTaskProgress()` we
end up overwriting the 100 with a lower progress value.
This commit fixes this issue by bringing back the help of
a `isReindexingFinished` flag as it was prior to #55763.
Closes#56128
Backport of #56135
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
If there are ill-formed pipelines, or other pipelines are not ready to be parsed, `InferenceProcessor.Factory::accept(ClusterState)` logs warnings. This can be confusing and cause log spam.
It might lead folks to think there an issue with the inference processor. Also, they would see logs for the inference processor even though they might not be using the inference processor. Leading to more confusion.
Additionally, pipelines might not be parseable in this method as some processors require the new cluster state metadata before construction (e.g. `enrich` requires cluster metadata to be set before creating the processor).
closes https://github.com/elastic/elasticsearch/issues/55985
Backport of #55858 to 7.x branch.
Currently the TransportBulkAction detects whether an index is missing and
then decides whether it should be auto created. The coordination of the
index creation also happens in the TransportBulkAction on the coordinating node.
This change adds a new transport action that the TransportBulkAction delegates to
if missing indices need to be created. The reasons for this change:
* Auto creation of data streams can't occur on the coordinating node.
Based on the index template (v2) either a regular index or a data stream should be created.
However if the coordinating node is slow in processing cluster state updates then it may be
unaware of the existence of certain index templates, which then can load to the
TransportBulkAction creating an index instead of a data stream. Therefor the coordination of
creating an index or data stream should occur on the master node. See #55377
* From a security perspective it is useful to know whether index creation originates from the
create index api or from auto creating a new index via the bulk or index api. For example
a user would be allowed to auto create an index, but not to use the create index api. The
auto create action will allow security to distinguish these two different patterns of
index creation.
This change adds the following new transport actions:
AutoCreateAction, the TransportBulkAction redirects to this action and this action will actually create the index (instead of the TransportCreateIndexAction). Later via #55377, can improve the AutoCreateAction to also determine whether an index or data stream should be created.
The create_index index privilege is also modified, so that if this permission is granted then a user is also allowed to auto create indices. This change does not yet add an auto_create index privilege. A future change can introduce this new index privilege or modify an existing index / write index privilege.
Relates to #53100
* Make xpack.monitoring.enabled setting a no-op
This commit turns xpack.monitoring.enabled into a no-op. Mostly, this involved
removing the setting from the setup for integration tests. Monitoring may
introduce some complexity for test setup and teardown, so we should keep an eye
out for turbulence and failures
* Docs for making deprecated setting a no-op
This commit converts the remaining isXXXAllowed methods to instead of
use isAllowed with a Feature value. There are a couple other methods
that are static, as well as some licensed features that check the
license directly, but those will be dealt with in other followups.
This commit correctly sets the maxLinesPerRow in the CsvPreference for delimited files given the file structure finder settings.
Previously, it was silently ignored.
This refactors native integ tests to assert progress without
expecting explicit phases for analyses. We can test those with
yaml tests in a single place.
Backport of #55925
* Make xpack.ilm.enabled setting a no-op
* Add watcher setting to not use ILM
* Update documentation for no-op setting
* Remove NO_ILM ml index templates
* Remove unneeded setting from test setup
* Inline variable definitions for ML templates
* Use identical parameter names in templates
* New ILM/watcher setting falls back to old setting
* Add fallback unit test for watcher/ilm setting
Fixes test by exposing the method ModelLoadingService::addModelLoadedListener()
so that the test class can be notified when a model is loaded which happens in
a background thread
On second thought, this check does not seem to be adding value.
We can test that the phases are as we expect them for each analysis
by adding yaml tests. Those would fail if we introduce new phases
from c++ accidentally or without coordination. This would achieve
the same thing. At the same time we would not have to comment out
this code each time a new phase is introduced. Instead we can just
temporarily mute those yaml tests. Note I will add those tests
right after the imminent new phases are added to the c++ side.
Backport of #55926
While it is good to not be lenient when attempting to guess the file format, it is frustrating to users when they KNOW it is CSV but there are a few ill-formatted rows in the file (via some entry error, etc.).
This commit allows for up to 10% of sample rows to be considered "bad". These rows are effectively ignored while guessing the format.
This percentage of "allows bad rows" is only applied when the user has specified delimited formatting options. As the structure finder needs some guidance on what a "bad row" actually means.
related to https://github.com/elastic/elasticsearch/issues/38890
We were previously checking at least one supported field existed
when the _explain API was called. However, in the case of analyses
with required fields (e.g. regression) we were not accounting that
the dependent variable is not a feature and thus if the source index
only contains the dependent variable field there are no features to
train a model on.
This commit adds a validation that at least one feature is available
for analysis. Note that we also move that validation away from
`ExtractedFieldsDetector` and the _explain API and straight into
the _start API. The reason for doing this is to allow the user to use
the _explain API in order to understand why they would be seeing an
error like this one.
For example, the user might be using an index that has fields but
they are of unsupported types. If they start the job and get
an error that there are no features, they will wonder why that is.
Calling the _explain API will show them that all their fields are
unsupported. If the _explain API was failing instead, there would
be no way for the user to understand why all those fields are
ignored.
Closes#55593
Backport of #55876
This change adds a new setting, daily_model_snapshot_retention_after_days,
to the anomaly detection job config.
Initially this has no effect, the effect will be added in a followup PR.
This PR gets the complexities of making changes that interact with BWC
over well before feature freeze.
Backport of #55878
Adding to #55659, we missed another way we could set the task to
failed due to task cancellation. CI revealed that we might also
get a `SearchPhaseExecutionException` whose cause is a
`TaskCancelledException`. That exception is not wrapped so
unwrapping it will not return the underlying `TaskCancelledException`.
Thus to be complete in catching this, we also need to check the
error's cause.
Closes#55068
Backport of #55797
This is a continuation from #55580.
Now that we're parsing phase progresses from the analytics process
we change `ProgressTracker` to allow for custom phases between
the `loading_data` and `writing_results` phases. Each `DataFrameAnalysis`
may declare its own phases.
This commit sets things in place for the analytics process to start
reporting different phases per analysis type. However, this is
still preserving existing behaviour as all analyses currently
declare a single `analyzing` phase.
Backport of #55763
The failed_category_count statistic records the number of times
categorization wanted to create a new category but couldn't
because the job had reached its model_memory_limit.
Backport of #55716
Since #55580 we've introduced a new format for parsing progress
from the data frame analytics process. As the process is now
writing out progress in this new way, we can remove the parsing
of the old format.
Backport of #55711
Dimitris Athanasiou
Benjamin Trent
David Roberts
David Kyle
Przemysław Witek
Alan Woodward
Armin Braun
Ryan Ernst
zhenxianyimeng
Julie Tibshirani
Dan Hermann
William Brafford
Martijn van Groningen