This refactors `DataFrameAnalyticsTask` into its own class.
The task has quite a lot of functionality now and I believe it would
make code more readable to have it live as its own class rather than
an inner class of the start action class.
Backport of #46402
* [ML] waiting for ml indices before waiting task assignment testFullClusterRestart
* waiting for a stable cluster after fullrestart
* removing unused imports
The test seems to have been failing due to a race condition between
stopping the task and refreshing the destination index. In particular,
we were going forward with refreshing the destination index even
though the task stopped in the meantime. This was fixed in
request.
Closes#43960
Backport of #46271
Though we allow CCS within datafeeds, users could prevent nodes from accessing remote clusters. This can cause mysterious errors and difficult to troubleshoot.
This commit adds a check to verify that `cluster.remote.connect` is enabled on the current node when a datafeed is configured with a remote index pattern.
* [ML] Regression dependent variable must be numeric
This adds a validation that the dependent variable of a regression
analysis must be numeric.
* Address review comments and fix some problems
In addition to addressing the review comments, this
commit fixes a few issues I found during testing.
In particular:
- if there were mappings for required fields but they were
not included we were not reporting the error
- if explicitly included fields had unsupported types we were
not reporting the error
Unfortunately, I couldn't get those fixed without refactoring
the code in `ExtractedFieldsDetector`.
This commit adds support for `boolean` fields in data frame
analytics (and currently both outlier detection and regression).
The analytics process expects `boolean` fields to be encoded as
integers with 0 or 1 value.
Adds a parameter `training_percent` to regression. The default
value is `100`. When the parameter is set to a value less than `100`,
from the rows that can be used for training (ie. those that have a
value for the dependent variable) we randomly choose whether to actually
use for training. This enables splitting the data into a training set and
the rest, usually called testing, validation or holdout set, which allows
for validating the model on data that have not been used for training.
Technically, the analytics process considers as training the data that
have a value for the dependent variable. Thus, when we decide a training
row is not going to be used for training, we simply clear the row's
dependent variable.
The native process requires that there be a non-zero number of rows to analyze. If the flag --rows 0 is passed to the executable, it throws and does not start.
When building the configuration for the process we should not start the native process if there are no rows.
Adding some logging to indicate what is occurring.
Previously, the stats API reports a progress percentage
for DF analytics tasks that are running and are in the
`reindexing` or `analyzing` state.
This means that when the task is `stopped` there is no progress
reported. Thus, one cannot distinguish between a task that never
run to one that completed.
In addition, there are blind spots in the progress reporting.
In particular, we do not account for when data is loaded into the
process. We also do not account for when results are written.
This commit addresses the above issues. It changes progress
to being a list of objects, each one describing the phase
and its progress as a percentage. We currently have 4 phases:
reindexing, loading_data, analyzing, writing_results.
When the task stops, progress is persisted as a document in the
state index. The stats API now reports progress from in-memory
if the task is running, or returns the persisted document
(if there is one).
* [ML] Adding data frame analytics stats to _usage API (#45820)
* [ML] Adding data frame analytics stats to _usage API
* making the size of analytics stats 10k
* adjusting backport
Regression analysis support missing fields. Even more, it is expected
that the dependent variable has missing fields to the part of the
data frame that is not for training.
This commit allows to declare that an analysis supports missing values.
For such analysis, rows with missing values are not skipped. Instead,
they are written as normal with empty strings used for the missing values.
This also contains a fix to the integration test.
Closes#45425
* [ML] better handle empty results when evaluating regression
* adding new failure test to ml_security black list
* fixing equality check for regression results
We cannot know how long the analysis will take to complete thus we should not have
a timeout. Note that if the process crashes, the result processor will pick the
exception due to the stream closing.
Closes#45723
Changes the order of parameters in Geometries from lat, lon to lon, lat
and moves all Geometry classes are moved to the
org.elasticsearch.geomtery package.
Backport of #45332Closes#45048
* Reenable Integ Tests in native-multi-node-tests
* The tests broken here were likely fixed by #45463 => let's reenable them and see if things run fine again
* Relates #45405, #45455
This commit adds a first draft of a regression analysis
to data frame analytics. There is high probability that
the exact syntax might change.
This commit adds the new analysis type and its parameters as
well as appropriate validation. It also modifies the extractor
and the fields detector to be able to handle categorical fields
as regression analysis supports them.
In the FIPS JVM the JVM default locale seems to leak into places
where it should be overridden. This change skips assertions
in TimestampFormatFinderTests.testGuessIsDayFirstFromLocale
that may be impacted.
Fixes#45140
When doing a fieldwise Levenshtein distance comparison
between CSV rows, this change ignores all fields that
have long values, not just the longest field.
This approach works better for CSV formats that have
multiple freeform text fields rather than just a single
"message" field.
Fixes#45047
If one tries to start a DF analytics job that has already run,
the result will be that the task will fail after reindexing the
dest index from the source index. The results of the prior run
will be gone and the task state is not properly set to failed
with the failure reason.
This commit improves the behavior in this scenario. First, we
set the task state to `failed` in a set of failures that were
missed. Second, a validation is added that if the destination
index exists, it must be empty.
In case closing the process throws an exception we should be catching
it no matter its type. The process may have terminated because of a
fatal error in which case closing the process will throw a server
error, not an `IOException`. If this happens we fail to mark the
persistent task as failed and the task gets in limbo.
As data frame rows with missing values for analyzed fields are skipped,
we can be more efficient by including a query that only picks documents
that have values for all analyzed fields. Besides improving the number
of documents we go through, we also provide a more accurate measurement
of how many rows we need which reduces the memory requirements.
This also adds an integration test that runs outlier detection on data
with missing fields.
TaskListener accepts today Throwable in its onFailure method. Though
looking at where it is called (TransportAction), it can never be
notified of a Throwable.
This commit changes the signature of TaskListener#onFailure so that it
accepts an `Exception` rather than a `Throwable` as second argument.
* Switch from using docvalue_fields to extracting values from _source
where applicable. Doing this means parsing the _source and handling the
numbers parsing just like Elasticsearch is doing it when it's indexing
a document.
* This also introduces a minor limitation: aliases type of fields that
are NOT part of a tree of sub-fields will not be able to be retrieved
anymore. field_caps API doesn't shed any light into a field being an
alias or not and at _source parsing time there is no way to know if a
root field is an alias or not. Fields of the type "a.b.c.alias" can be
extracted from docvalue_fields, only if the field they point to can be
extracted from docvalue_fields. Also, not all fields in a hierarchy of
fields can be evaluated to being an alias.
(cherry picked from commit 8bf8a055e38f00df5f49c8d97f632f69d6e00c2c)
* Mute failing test
tracked in #44552
* mute EvilSecurityTests
tracking in #44558
* Fix line endings in ESJsonLayoutTests
* Mute failing ForecastIT test on windows
Tracking in #44609
* mute BasicRenormalizationIT.testDefaultRenormalization
tracked in #44613
* fix mute testDefaultRenormalization
* Increase busyWait timeout windows is slow
* Mute failure unconfigured node name
* mute x-pack internal cluster test windows
tracking #44610
* Mute JvmErgonomicsTests on windows
Tracking #44669
* mute SharedClusterSnapshotRestoreIT testParallelRestoreOperationsFromSingleSnapshot
Tracking #44671
* Mute NodeTests on Windows
Tracking #44256
