This is a quality of life improvement for typical users. Almost all anomaly jobs will receive their data through a datafeed.
The datafeed config can now be supplied and is available in the datafeed field in the job config for creation and getting jobs.
Previously it was a requirement of the close job API that if the
job had an associated datafeed that that datafeed was stopped
before the job could be closed. Experience has shown that this
is just a pedantic nuisance. If a user closes the job without
first stopping the datafeed then it's just a mistake, and they
then have to make two further calls, to stop the datafeed and
then attempt to close the job again.
This PR changes the behaviour so that if you ask to close a job
whose datafeed is running then the datafeed gets stopped first
as part of the same call. Datafeeds are stopped with the same
level of force as the job close request specified.
Backport of #74257
Adds a new API that allows a user to reset
an anomaly detection job.
To use the API do:
```
POST _ml/anomaly_detectors/<job_id>_reset
```
The API removes all data associated to the job.
In particular, it deletes model state, results and stats.
However, job notifications and user annotations are not removed.
Also, the API can be called asynchronously by setting the parameter
`wait_for_completion` to `false` (defaults to `true`). When run
that way the API returns the task id for further monitoring.
In order to prevent the job from opening while it is resetting,
a new job field has been added called `blocked`. It is an object
that contains a `reason` and the `task_id`. `reason` can take
a value from ["delete", "reset", "revert"] as all these
operations should block the job from opening. The `task_id` is also
included in order to allow tracking the task if necessary.
Finally, this commit also sets the `blocked` field when
the revert snapshot API is called as a job should not be opened
while it is reverted to a different model snapshot.
Backport of #73908
It is useful to know the following information when reading datafeed stats:
- Is the datafeed a "real-time" datafeed, i.e. a datafeed without a configured `end` time
- Has the datafeed processed all past data available at the time of starting.
This object is only available if the datafeed task has been created.
It has the form:
```
"running_state": {
"is_real_time": <boolean>,
"look_back_finished": <boolean>
}
```
Categorization jobs created once the entire cluster is upgraded to
version 7.14 or higher will default to using the new ml_standard
tokenizer rather than the previous default of the ml_classic
tokenizer, and will incorporate the new first_non_blank_line char
filter so that categorization is based purely on the first non-blank
line of each message.
The difference between the ml_classic and ml_standard tokenizers
is that ml_classic splits on slashes and colons, so creates multiple
tokens from URLs and filesystem paths, whereas ml_standard attempts
to keep URLs, email addresses and filesystem paths as single tokens.
It is still possible to config the ml_classic tokenizer if you
prefer: just provide a categorization_analyzer within your
analysis_config and whichever tokenizer you choose (which could be
ml_classic or any other Elasticsearch tokenizer) will be used.
To opt out of using first_non_blank_line as a default char filter,
you must explicitly specify a categorization_analyzer that does not
include it.
If no categorization_analyzer is specified but categorization_filters
are specified then the categorization filters are converted to char
filters applied that are applied after first_non_blank_line.
Backport of #72805
This commit increases the xpack.ml.max_open_jobs from 20 to 512. Additionally, it ignores nodes that cannot provide an accurate view into their native memory.
If a node does not have a view into its native memory, we ignore it for assignment.
This effectively fixes a bug with autoscaling. Autoscaling relies on jobs with adequate memory to assign jobs to nodes. If that is hampered by the xpack.ml.max_open_jobs scaling decisions are hampered.
Previously, a datafeed and job must already exist for the `_preview` API to work.
With this change, users can get an accurate preview of the data that will be sent to the anomaly detection job
without creating either of them.
closes https://github.com/elastic/elasticsearch/issues/70264
The text structure finder API documentation had many references to the "files". While this is one use of the API, the API now has a more generic name. This commit replaces many references to the word "file" to the more generic word "text".
* [ML] move find file structure to a new API endpoint (#67123)
This introduces a new `text-structure` plugin. This is the new home of the find file structure API.
The old REST URL is still available but is deprecated.
The new URL is: `_text_structure/find_structure`. All parameters and behavior are unchanged.
Changes to the high-level REST client and docs will be in separate commit.
related to: https://github.com/elastic/elasticsearch/issues/67001
This commit is fixing a potential bug if we support anomaly detection
results index rollover in the future.
In particular, we determine the current `data_counts` by sorting on the
latest record time. However, this is not correct if the job reverts
to an older model snapshot. To fix this we add `log_time` to `data_counts`
(similarly to `model_size_stats`) and sort on `log_time` to figure
out the current counts for the job.
Backport of #66343
There is little evidence of this endpoint being used
and there is quite a lot of code complexity associated
with the various formats that can be used to upload
data and the different errors that can occur when direct
data upload is open to end users.
In a future release we can make this endpoint internal
so that only datafeeds can use it, and remove all the
options and formats that are not used by datafeeds.
End users will have to store their input data for
anomaly detection in Elasticsearch indices (which we
believe all do today) and use a datafeed to feed it
to anomaly detection jobs.
Backport of #66347
At present the Java code makes a decision on whether to
use current model memory or model memory limit to calculate
how much memory a job requires to be assigned.
The plan is to move this decision to the C++ code, which will
report it via a new field in the model size stats. An
additional change will be that once we have made the switch
from using model memory limit to using current model memory
we will never switch back, as this causes large fluctuations
up and down in memory requirement which will be much more
noticeable when autoscaling is in use.
Although the only two options at present are model memory
limit and current model memory, the new enum includes a
third possibility, peak model memory. To switch to this
now would be tricky, as there have been two bugs in the
implementation of peak model memory which render its value
unreliable in 7.x. However, in 8.x it might make sense to
switch to using peak model memory instead of current model
memory and it's much easier from a BWC perspective if the
enum contains all the values from the start.
Backport of #65561
This change adds an extra piece of information,
limits.total_ml_memory, to the ML info response.
This returns the total amount of memory that ML
is permitted to use for native processes across
all ML nodes in the cluster. Some of this may
already be in use; the value returned is total,
not available ML memory.
Backport of #65195
* [ML] add new snapshot upgrader API for upgrading older snapshots (#64665)
This new API provides a way for users to upgrade their own anomaly job
model snapshots.
To upgrade a snapshot the following is done:
- Open a native process given the job id and the desired snapshot id
- load the snapshot to the process
- write the snapshot again from the native task (now updated via the
native process)
relates #64154
* [ML] adding for_export flag for ml plugin GET resource APIs (#63092)
This adds the new `for_export` flag to the following APIs:
- GET _ml/anomaly_detection/<job_id>
- GET _ml/datafeeds/<datafeed_id>
- GET _ml/data_frame/analytics/<analytics_id>
The flag is designed for cloning or exporting configuration objects to later be put into the same cluster or a separate cluster.
The following fields are not returned in the objects:
- any field that is not user settable (e.g. version, create_time)
- any field that is a calculated default value (e.g. datafeed chunking_config)
- any field that would effectively require changing to be of use (e.g. datafeed job_id)
- any field that is automatically set via another Elastic stack process (e.g. anomaly job custom_settings.created_by)
closes https://github.com/elastic/elasticsearch/issues/63055
* [ML] adding new flag exclude_generated that removes generated fields in GET config APIs (#63899)
When exporting and cloning ml configurations in a cluster it can be
frustrating to remove all the fields that were generated by
the plugin. Especially as the number of these fields change
from version to version.
This flag, exclude_generated, allows the GET config APIs to return
configurations with these generated fields removed.
APIs supporting this flag:
- GET _ml/anomaly_detection/<job_id>
- GET _ml/datafeeds/<datafeed_id>
- GET _ml/data_frame/analytics/<analytics_id>
The following fields are not returned in the objects:
- any field that is not user settable (e.g. version, create_time)
- any field that is a calculated default value (e.g. datafeed chunking_config)
- any field that is automatically set via another Elastic stack process (e.g. anomaly job custom_settings.created_by)
relates to #63055
The original comment mentioned issue #48583, but issue #48941
is specifically open for this mute. However, this is
inappropriate, as the underlying reason the test cannot be
unmuted is the same as for all the other tests skipped with the
comment "Kibana sample data": issues #51572, #51576 and #51678.
Closes#48941
This commit adjusts the following APIs so now they not only support an `_all` case, but wildcard patterned Ids as well.
- `GET _ml/calendars/<calendar_id>/events`
- `GET _ml/calendars/<calendar_id>`
- `GET _ml/anomaly_detectors/<job_id>/model_snapshots/<snapshot_id>`
- `DELETE _ml/anomaly_detectors/<job_id>/_forecast/<forecast_id>`
Previously the "mappings" field of the response from the
find_file_structure endpoint was not a drop-in for the
mappings format of the create index endpoint - the
"properties" layer was missing. The reason for omitting
it initially was that the assumption was that the
find_file_structure endpoint would only ever return very
simple mappings without any nested objects. However,
this will not be true in the future, as we will improve
mappings detection for complex JSON objects. As a first
step it makes sense to move the returned mappings closer
to the standard format.
This is a small building block towards fixing #55616
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 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 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
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.
