This commit allows for composite aggregations in datafeeds.
Composite aggs provide a much better solution for having influencers, partitions, etc. on high volume data. Instead of worrying about long scrolls in the datafeed, the calculation is distributed across cluster via the aggregations.
The restrictions for this support are as follows:
- The composite aggregation must have EXACTLY one `date_histogram` source
- The sub-aggs of the composite aggregation must have a `max` aggregation on the SAME timefield as the aforementioned `date_histogram` source
- The composite agg must be the ONLY top level agg and it cannot have a `composite` or `date_histogram` sub-agg
- If using a `date_histogram` to bucket time, it cannot have a `composite` sub-agg.
- The top-level `composite` agg cannot have a sibling pipeline agg. Pipeline aggregations are supported as a sub-agg (thus a pipeline agg INSIDE the bucket).
Some key user interaction differences:
- Speed + resources used by the cluster should be controlled by the `size` parameter in the `composite` aggregation. Previously, we said if you are using aggs, use a specific `chunking_config`. But, with composite, that is not necessary.
- Users really shouldn't use nested `terms` aggs anylonger. While this is still a "valid" configuration and MAY be desirable for some users (only wanting the top 10 of certain terms), typically when users want influencers, partition fields, etc. they want the ENTIRE population. Previously, this really wasn't possible with aggs, with `composite` it is.
- I cannot really think of a typical usecase that SHOULD ever use a multi-bucket aggregation that is NOT supported by composite.
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
This commit allows documents seen within the same time bucket to be out of order.
This is already supported within the native process.
Additionally, when recording the "latest" record timestamp, we were assuming that the latest seen document was truly the "latest". This is not really the case if latency is utilized or if documents come out of order within the same bucket.
This commit adds a new `_preview` endpoint for data frame analytics.
This allows users to see the data on which their model will be trained. This is especially useful
in the arrival of custom feature processors.
The API design is a similar to datafeed `_preview` and data frame analytics `_explain`.
Users can now specify runtime mappings as part of the source config
of a data frame analytics job. Those runtime mappings become part of
the mapping of the destination index. This ensures the fields are
accessible in the destination index even if the relevant data frame
analytics job gets deleted.
Closes#65056
A `model_alias` allows trained models to be referred by a user defined moniker.
This not only improves the readability and simplicity of numerous API calls, but it allows for simpler deployment and upgrade procedures for trained models.
Previously, if you referenced a model ID directly within an ingest pipeline, when you have a new model that performs better than an earlier referenced model, you have to update the pipeline itself. If this model was used in numerous pipelines, ALL those pipelines would have to be updated.
When using a `model_alias` in an ingest pipeline, only that `model_alias` needs to be updated. Then, the underlying referenced model will change in place for all ingest pipelines automatically.
An additional benefit is that the model referenced is not changed until it is fully loaded into cache, this way throughput is not hampered by changing models.
The PR adds early_stopping_enabled optional data frame analysis configuration parameter. The enhancement was already described in elastic/ml-cpp#1676 and so I mark it here as non-issue.
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".
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
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.
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.
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.
Relates #63163
This PR adds detail to the explanation of the soft_limit
memory_status in ML job stats. A consequence that was not
mentioned before is that examples are not added to category
definitions.
Relates elastic/ml-cpp#1590
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.
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
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
Renames data frame analytics _evaluate API results as follows:
- per class accuracy renamed from `accuracy` to `value`
- per class precision renamed from `precision` to `value`
- per class recall renamed from `recall` to `value`
- auc_roc `score` renamed to `value` for both outlier detection and classification
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 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
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>`
Adds new flag include to the get trained models API
The flag initially has two valid values: definition, total_feature_importance.
Consequently, the old include_model_definition flag is now deprecated.
When total_feature_importance is included, the total_feature_importance field is included in the model metadata object.
Including definition is the same as previously setting include_model_definition=true.
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
Adds HLRC and some docs for the new feature_processors field in Data frame analytics.
Co-authored-by: Przemysław Witek <przemyslaw.witek@elastic.co>
Co-authored-by: Lisa Cawley <lcawley@elastic.co>
Changes:
* Moves `Retrieve selected fields` to its own page and adds a title abbreviation.
* Adds existing script and stored fields content to `Retrieve selected fields`
* Adds a xref for `Retrieve selected fields` to `Search your data`
* Adds related redirects and updates existing xrefs
This commit adds the new configurable field `custom`.
`custom` indicates if the preprocessor was submitted by a user or automatically created by the analytics job.
Eventually, this field will be used in calculating feature importance. When `custom` is true, the feature importance for
the processed fields is calculated. When `false` the current behavior is the same (we calculate the importance for the originating field/feature).
This also adds new required methods to the preprocessor interface. If users are to supply their own preprocessors
in the analytics job configuration, we need to know the input and output field names.
This adds a setting to data frame analytics jobs called
`max_number_threads`. The setting expects a positive integer.
When used the user specifies the max number of threads that may
be used by the analysis. Note that the actual number of threads
used is limited by the number of processors on the node where
the job is assigned. Also, the process may use a couple more threads
for operational functionality that is not the analysis itself.
This setting may also be updated for a stopped job.
More threads may reduce the time it takes to complete the job at the cost
of using more CPU.
Adds parsing of `status` and `increased_memory_estimate_bytes`
to data frame analytics `memory_usage`. When the training surpasses
the model memory limit, the status will be set to `hard_limit` and
`increased_memory_estimate_bytes` can be used to update the job's
limit in order to restart the job.
When a local model is constructed, the cache hit miss count is incremented.
When a user calls _stats, we will include the sum cache hit miss count across ALL nodes. This statistic is important to in comparing against the inference_count. If the cache hit miss count is near the inference_count it indicates that the cache is overburdened, or inappropriately configured.
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.
When we force delete a DF analytics job, we currently first force
stop it and then we proceed with deleting the job config.
This may result in logging errors if the job config is deleted
before it is retrieved while the job is starting.
Instead of force stopping the job, it would make more sense to
try to stop the job gracefully first. So we now try that out first.
If normal stop fails, then we resort to force stopping the job to
ensure we can go through with the delete.
In addition, this commit introduces `timeout` for the delete action
and makes use of it in the child requests.
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.
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
Closes#52150
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.
Relates elastic/ml-cpp#1130
The ML info endpoint returns the max_model_memory_limit setting
if one is configured. However, it is still possible to create
a job that cannot run anywhere in the current cluster because
no node in the cluster has enough memory to accommodate it.
This change adds an extra piece of information,
limits.effective_max_model_memory_limit, to the ML info
response that returns the biggest model memory limit that could
be run in the current cluster assuming no other jobs were
running.
The idea is that the ML UI will be able to warn users who try to
create jobs with higher model memory limits that their jobs will
not be able to start unless they add a bigger ML node to their
cluster.
Relates elastic/kibana#63942
Adds a "node" field to the response from the following endpoints:
1. Open anomaly detection job
2. Start datafeed
3. Start data frame analytics job
If the job or datafeed is assigned to a node immediately then
this field will return the ID of that node.
In the case where a job or datafeed is opened or started lazily
the node field will contain an empty string. Clients that want
to test whether a job or datafeed was opened or started lazily
can therefore check for this.
Fixes#54067
This paves the data layer way so that exceptionally large models are partitioned across multiple documents.
This change means that nodes before 7.8.0 will not be able to use trained inference models created on nodes on or after 7.8.0.
I chose the definition document limit to be 100. This *SHOULD* be plenty for any large model. One of the largest models that I have created so far had the following stats:
~314MB of inflated JSON, ~66MB when compressed, ~177MB of heap.
With the chunking sizes of `16 * 1024 * 1024` its compressed string could be partitioned to 5 documents.
Supporting models 20 times this size (compressed) seems adequate for now.
Data frame analytics dynamically determines the classification field type. This field type then dictates the encoded JSON that is written to Elasticsearch.
Inference needs to know about this field type so that it may provide the EXACT SAME predicted values as analytics.
Here is added a new field `prediction_field_type` which indicates the desired type. Options are: `string` (DEFAULT), `number`, `boolean` (where close_to(1.0) == true, false otherwise).
Analytics provides the default `prediction_field_type` when the model is created from the process.
A new field called `inference_config` is now added to the trained model config object. This new field allows for default inference settings from analytics or some external model builder.
The inference processor can still override whatever is set as the default in the trained model config.
Secondary authorization headers are to be used to facilitate Kibana spaces support + ML jobs/datafeeds.
Now on PUT/Update/Preview datafeed, and PUT data frame analytics the secondary authorization is preferred over the primary (if provided).
closes https://github.com/elastic/elasticsearch/issues/53801
This adds two new fields to category definitions.
- `num_matches` indicating how many documents have been seen by this category
- `preferred_to_categories` indicating which other categories this particular category supersedes when messages are categorized.
These fields are only guaranteed to be up to date after a `_flush` or `_close`
native change: https://github.com/elastic/ml-cpp/pull/1062
This is a simple naming change PR, to fix the fact that "metadata" is a
single English word, and for too long we have not followed general
naming conventions for it. We are also not consistent about it, for
example, METADATA instead of META_DATA if we were trying to be
consistent with MetaData (although METADATA is correct when considered
in the context of "metadata"). This was a simple find and replace across
the code base, only taking a few minutes to fix this naming issue
forever.
It is possible for ML jobs to open lazily if the "allow_lazy_open"
option in the job config is set to true. Such jobs wait in the
"opening" state until a node has sufficient capacity to run them.
This commit fixes the bug that prevented datafeeds for jobs lazily
waiting assignment from being started. The state of such datafeeds
is "starting", and they can be stopped by the stop datafeed API
while in this state with or without force.
Fixes#53763
Adds a new parameter for classification that enables choosing whether to assign labels to
maximise accuracy or to maximise the minimum class recall.
Fixes#52427.
Adds a new `default_field_map` field to trained model config objects.
This allows the model creator to supply field map if it knows that there should be some map for inference to work directly against the training data.
The use case internally is having analytics jobs supply a field mapping for multi-field fields. This allows us to use the model "out of the box" on data where we trained on `foo.keyword` but the `_source` only references `foo`.
Adds reporting of memory usage for data frame analytics jobs.
This commit introduces a new index pattern `.ml-stats-*` whose
first concrete index will be `.ml-stats-000001`. This index serves
to store instrumentation information for those jobs.
This adds a new configurable field called `indices_options`. This allows users to create or update the indices_options used when a datafeed reads from an index.
This is necessary for the following use cases:
- Reading from frozen indices
- Allowing certain indices in multiple index patterns to not exist yet
These index options are available on datafeed creation and update. Users may specify them as URL parameters or within the configuration object.
closes https://github.com/elastic/elasticsearch/issues/48056
This change adds support for the following new model_size_stats
fields:
- categorized_doc_count
- total_category_count
- frequent_category_count
- rare_category_count
- dead_category_count
- categorization_status
Relates #50749
Changes the find_file_structure response to include a CSV
ingest processor in the ingest pipeline it suggests.
Previously the Kibana file upload functionality parsed CSV
in the browser, but by parsing CSV in the ingest pipeline
it makes the Kibana file upload functionality more easily
interchangable with Filebeat such that the configurations
it creates can more easily be used to import data with the
same structure repeatedly in production.
Adds a new URL parameter, `tags` to the GET _ml/inference/<model_id> endpoint.
This parameter allows the list of models to be further reduced to those who contain all the provided tags.
Object fields cannot be used as features. At the moment _explain
API includes them and even worse it allows it does not error when
an object field is excluded. This creates the expectation to the
user that all children fields will also be excluded while it's not
the case.
This commit omits object fields from the _explain API and also
adds an error if an object field is included or excluded.
Adds a new parameter to regression and classification that enables computation
of importance for the top most important features. The computation of the importance
is based on SHAP (SHapley Additive exPlanations) method.
Adds a `force` parameter to the delete data frame analytics
request. When `force` is `true`, the action force-stops the
jobs and then proceeds to the deletion. This can be used in
order to delete a non-stopped job with a single request.
Closes#48124
The docs/reference/redirects.asciidoc file stores a list of relocated or
deleted pages for the Elasticsearch Reference documentation.
This prunes several older redirects that are no longer needed and
don't require work to fix broken links in other repositories.
Co-Authored-By: Przemysław Witek <przemyslaw.witek@elastic.co>
Co-Authored-By: David Roberts <dave.roberts@elastic.co>
Co-Authored-By: Ed Savage <32410745+edsavage@users.noreply.github.com>
This adds a new `randomize_seed` for regression and classification.
When not explicitly set, the seed is randomly generated. One can
reuse the seed in a similar job in order to ensure the same docs
are picked for training.
This adds a `_source` setting under the `source` setting of a data
frame analytics config. The new `_source` is reusing the structure
of a `FetchSourceContext` like `analyzed_fields` does. Specifying
includes and excludes for source allows selecting which fields
will get reindexed and will be available in the destination index.
Closes#49531
The categorization job wizard in the ML UI will use this
information when showing the effect of the chosen categorization
analyzer on a sample of input.
This commit replaces the _estimate_memory_usage API with
a new API, the _explain API.
The API consolidates information that is useful before
creating a data frame analytics job.
It includes:
- memory estimation
- field selection explanation
Memory estimation is moved here from what was previously
calculated in the _estimate_memory_usage API.
Field selection is a new feature that explains to the user
whether each available field was selected to be included or
not in the analysis. In the case it was not included, it also
explains the reason why.
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.
Benjamin Trent
István Zoltán Szabó
James Rodewig
Lisa Cawley
Joe Gallo
Dimitris Athanasiou
Valeriy Khakhutskyy
David Roberts
David Kyle
Przemysław Witek
James Rodewig
Jason Tedor
Tom Veasey
Darren LaCasse
Orhan Toy