In the case multi-fields exist in the source index, we pick
all variants of them in our extracted fields detection for
data frame analytics. This means we may have multiple instances
of the same feature. The worse consequence of this is when the
dependent variable (for regression or classification) is also
duplicated which means we train a model on the dependent variable
itself.
Now that #48770 is merged, this commit is adding logic to
only select one variant of multi-fields.
Closes#48756
Backport of #48799
Aggregatable mutli-fields are at the moment wrongly mapped
as normal doc_value fields and thus they support fetching from
source. However, they do not exist in the source. This results
to failure to extract such fields.
This commit fixes this bug. While a fix could be worked out
on top of the existing code, it is evident the extraction logic
has become difficult to understand and maintain. As we also
want to deduplicate multi-fields for data frame analytics,
it seemed appropriate to refactor the code to simplify and
better handle the extraction of multi-fields.
Relates #48756
Backport of #48770
At present the ML C++ artifact is always downloaded from
S3. This change adds an option to configure the location.
(The intention is to use a file:/// URL to pick up the
artifact built in a Docker container in ml-cpp PR builds
so that C++ changes that will break Java integration tests
can be detected before the ml-cpp PRs are merged.)
Relates elastic/ml-cpp#766
Moves common field extraction logic to its own package so that it can
be used both for anomaly detection and data frame analytics.
In preparation for refactoring extraction fields to be simpler and to
support multi-fields properly.
Backport of #48709
* [ML][Inference] separating definition and config object storage (#48651)
This separates out the `definition` object from being stored within the configuration object in the index.
This allows us to gather the config object without decompressing a potentially large definition.
Additionally, `input` is moved to the TrainedModelConfig object and out of the definition. This is so the trained input fields are accessible outside the potentially large model definition.
There is a watchdog in order to avoid long running (and expensive)
grok expressions. Currently the watchdog is thread based, threads
that run grok expressions are registered and after completion unregister.
If these threads stay registered for too long then the watch dog interrupts
these threads. Joni (the library that powers grok expressions) has a
mechanism that checks whether the current thread is interrupted and
if so abort the pattern matching.
Newer versions have an additional method to abort long running pattern
matching inside joni. Instead of checking the thread's interrupted flag,
joni now also checks a volatile field that can be set via a `Matcher`
instance. This is more efficient method for aborting long running matches.
(joni checks each 30k iterations whether interrupted flag is set vs.
just checking a volatile field)
Recently we upgraded to a recent joni version (#47374), and this PR
is a followup of that PR.
This change should also fix#43673, since it appears when unit tests
are ran the a test runner thread's interrupted flag may already have
been set, due to some thread reuse.
Reverting the change introducing IsoLocal.ROOT and introducing IsoCalendarDataProvider that defaults start of the week to Monday and requires minimum 4 days in first week of a year. This extension is using java SPI mechanism and defaults for Locale.ROOT only.
It require jvm property java.locale.providers to be set with SPI,COMPAT
closes#41670
backport #48209
If a job stops right after reindexing is finished but before
we refresh the destination index, we don't refresh at all.
If the job is started again right after, it jumps into the analyzing state.
However, the data is still not searchable.
This is why we were seeing test failures that we start the process
expecting X rows (where X is lower than the expected number of docs)
and we end up getting X+.
We fix this by moving the refresh of the dest index right before
we start the process so it always ensures the data is searchable.
Closes#47612
Backport of #48090
This adds parsing an inference model as a possible
result of the analytics process. When we do parse such a model
we persist a `TrainedModelConfig` into the inference index
that contains additional metadata derived from the running job.
Audit messages are stored with millisecond timestamps. If two
messages have the same millisecond timestamp then asserting on
their order is impossible given the information available.
This PR changes the assertion on audit messages in the native
data frame analytics tests to assert that the expected audit
messages exist in any order.
Fixes#48035
This change adds:
- A new option, allow_lazy_open, to anomaly detection jobs
- A new option, allow_lazy_start, to data frame analytics jobs
Both work in the same way: they allow a job to be
opened/started even if no ML node exists that can
accommodate the job immediately. In this situation
the job waits in the opening/starting state until ML
node capacity is available. (The starting state for data
frame analytics jobs is new in this change.)
Additionally, the ML nightly maintenance tasks now
creates audit warnings for ML jobs that are unassigned.
This means that jobs that cannot be assigned to an ML
node for a very long time will show a yellow warning
triangle in the UI.
A final change is that it is now possible to close a job
that is not assigned to a node without using force.
This is because previously jobs that were open but
not assigned to a node were an aberration, whereas
after this change they'll be relatively common.
Adds a new datafeed config option, max_empty_searches,
that tells a datafeed that has never found any data to stop
itself and close its associated job after a certain number
of real-time searches have returned no data.
Backport of #47922
Usually syslog timestamps have two spaces before a single
digit day-of-month. However, in some non-syslog cases
where syslog-like timestamps are used there is only one
space. The grok pattern supports this, so the timestamp
parser should too. This change makes the
find_file_structure endpoint do this.
Also fixes another problem that the same test case
exposed in the find_file_structure endpoint, which was
that the exclude_lines_pattern for delimited files was
always created on the assumption the delimiter was a
comma. Now it is based on the actual delimiter.
* [ML][Analytics] fix bug where regression deleted early does not delete state (#47885)
* [ML][Analytics] fix bug where regression deleted early does not delete state
* Fixing ml with security test failure
* fixing for older java
When exceptions could be returned from another node, the exception
might be wrapped in a `RemoteTransportException`. In places where
we handled specific exceptions using `instanceof` we ought to unwrap
the cause first.
This commit attempts to fix this issue after searching code in the ML
plugin.
Backport of #47676
Adds the following parameters to `outlier_detection`:
- `compute_feature_influence` (boolean): whether to compute or not
feature influence scores
- `outlier_fraction` (double): the proportion of the data set assumed
to be outlying prior to running outlier detection
- `standardization_enabled` (boolean): whether to apply standardization
to the feature values
Backport of #47600
When an ML job runs the memory required can be
broken down into:
1. Memory required to load the executable code
2. Instrumented model memory
3. Other memory used by the job's main process or
ancilliary processes that is not instrumented
Previously we added a simple fixed overhead to
account for 1 and 3. This was 100MB for anomaly
detection jobs (large because of the completely
uninstrumented categorization function and
normalize process), and 20MB for data frame
analytics jobs.
However, this was an oversimplification because
the executable code only needs to be loaded once
per machine. Also the 100MB overhead for anomaly
detection jobs was probably too high in most cases
because categorization and normalization don't use
_that_ much memory.
This PR therefore changes the calculation of memory
requirements as follows:
1. A per-node overhead of 30MB for _only_ the first
job of any type to be run on a given node - this
is to account for loading the executable code
2. The established model memory (if applicable) or
model memory limit of the job
3. A per-job overhead of 10MB for anomaly detection
jobs and 5MB for data frame analytics jobs, to
account for the uninstrumented memory usage
This change will enable more jobs to be run on the
same node. It will be particularly beneficial when
there are a large number of small jobs. It will
have less of an effect when there are a small number
of large jobs.
While it seemed like the PUT data frame analytics action did not
have to be a master node action as the config is stored in an index
rather than the cluster state, there are other subtle nuances which
make it worthwhile to convert it. In particular, it helps maintain
order of execution for put actions which are anyhow user driven and
are expected to have low volume.
This commit converts `TransportPutDataFrameAnalyticsAction` from
a handled transport action to a master node action.
Note this means that the action might fail in a mixed cluster
but as the API is still experimental and not widely used there will
be few moments more suitable to make this change than now.
Due to #47003 many clusters will have built up a
large backlog of expired results. On upgrading to
a version where that bug is fixed users could find
that the first ML daily maintenance task deletes
a very large amount of documents.
This change introduces throttling to the
delete-by-query that the ML daily maintenance uses
to delete expired results to limit it to deleting an
average 200 documents per second. (There is no
throttling for state/forecast documents as these
are expected to be lower volume.)
Additionally a rough time limit of 8 hours is applied
to the whole delete expired data action. (This is only
rough as it won't stop part way through a single
operation - it only checks the timeout between
operations.)
Relates #47103
This commit restores the model state if available in data
frame analytics jobs.
In addition, this changes the start API so that a stopped job
can be restarted. As we now store the progress in the state index
when the task is stopped, we can use it to determine what state
the job was in when it got stopped.
Note that in order to be able to distinguish between a job
that runs for the first time and another that is restarting,
we ensure reindexing progress is reported to be at least 1
for a running task.
* [ML][Inference] adding .ml-inference* index and storage (#47267)
* [ML][Inference] adding .ml-inference* index and storage
* Addressing PR comments
* Allowing null definition, adding validation tests for model config
* fixing line length
* adjusting for backport
A refactoring in 6.6 meant that the ML daily
maintenance actions have not been run at all
since then. This change installs the local
master listener that schedules the ML daily
maintenance, and also defends against some
subtle race conditions that could occur in the
future if a node flipped very quickly between
master and non-master.
Fixes#47003
Backport of #45794 to 7.x. Convert most `awaitBusy` calls to
`assertBusy`, and use asserts where possible. Follows on from #28548 by
@liketic.
There were a small number of places where it didn't make sense to me to
call `assertBusy`, so I kept the existing calls but renamed the method to
`waitUntil`. This was partly to better reflect its usage, and partly so
that anyone trying to add a new call to awaitBusy wouldn't be able to find
it.
I also didn't change the usage in `TransportStopRollupAction` as the
comments state that the local awaitBusy method is a temporary
copy-and-paste.
Other changes:
* Rework `waitForDocs` to scale its timeout. Instead of calling
`assertBusy` in a loop, work out a reasonable overall timeout and await
just once.
* Some tests failed after switching to `assertBusy` and had to be fixed.
* Correct the expect templates in AbstractUpgradeTestCase. The ES
Security team confirmed that they don't use templates any more, so
remove this from the expected templates. Also rewrite how the setup
code checks for templates, in order to give more information.
* Remove an expected ML template from XPackRestTestConstants The ML team
advised that the ML tests shouldn't be waiting for any
`.ml-notifications*` templates, since such checks should happen in the
production code instead.
* Also rework the template checking code in `XPackRestTestHelper` to give
more helpful failure messages.
* Fix issue in `DataFrameSurvivesUpgradeIT` when upgrading from < 7.4
When the ML native multi-node tests use _cat/indices/_all
and the request goes to a non-master node, _all is
translated to a list of concrete indices by the authz layer
on the coordinating node before the request is forwarded
to the master node. Then it is possible for the master
node to return an index_not_found_exception if one of
the concrete indices that was expanded on the
coordinating node has been deleted in the meantime.
(#47159 has been opened to track the underlying problem.)
It has been observed that the index that gets deleted when
the problem affects the ML native multi-node tests is
always the ML notifications index. The tests that fail are
only interested in the presence or absense of ML results
indices. Therefore the workaround is to only _cat indices
that match the ML results index pattern.
Fixes#45652
Przemysław Witek
Christoph Büscher
Dimitris Athanasiou
David Roberts
Benjamin Trent
Martijn van Groningen
Przemyslaw Gomulka
rsarawgi
Igor Motov
Rory Hunter
Tanguy Leroux