When Joni, the regex engine that powers grok emits a warning it
does so by default to System.err. System.err logs are all bucketed
together in the server log at WARN level. When Joni emits a warning,
it can be extremely verbose, logging a message for each execution
again that pattern. For ingest node that means for every document
that is run that through Grok. Fortunately, Joni provides a call
back hook to push these warnings to a custom location.
This commit implements Joni's callback hook to push the Joni warning
to the Elasticsearch server logger (logger.org.elasticsearch.ingest.common.GrokProcessor)
at debug level. Generally these warning indicate a possible issue with
the regular expression and upon creation of the Grok processor will
do a "test run" of the expression and log the result (if any) at WARN
level. This WARN level log should only occur on pipeline creation which
is a much lower frequency then every document.
Additionally, the documentation is updated with instructions for how
to set the logger to debug level.
* Changes for #52239.
* Incorporating review feedback from Julie T. Also single-sourcing nexted options in the Mapping page and referencing them in the Nested page.
* Moving tip after the introduction and clarifying limits.
* Update docs/reference/mapping.asciidoc
Co-authored-by: James Rodewig <james.rodewig@elastic.co>
* Update docs/reference/mapping/types/nested.asciidoc
Co-authored-by: James Rodewig <james.rodewig@elastic.co>
Co-authored-by: James Rodewig <james.rodewig@elastic.co>
Co-authored-by: James Rodewig <james.rodewig@elastic.co>
* [ML] adding prediction_field_type to inference config (#55128)
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.
* [ML] add new inference_config field to trained model config (#54421)
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.
* fixing for backport
Restructures the 'Update an enrich policy' section to:
* Migrate the content to the section. It was previously stored in the
Put Enrich Policy API docs.
* Remove the warning tag admonition from the section content.
* Replace a reused section earlier in the "Set up an enrich processor"
page with a link.
No substantive changes were made to the content.
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`.
This adds machine learning model feature importance calculations to the inference processor.
The new flag in the configuration matches the analytics parameter name: `num_top_feature_importance_values`
Example:
```
"inference": {
"field_mappings": {},
"model_id": "my_model",
"inference_config": {
"regression": {
"num_top_feature_importance_values": 3
}
}
}
```
This will write to the document as follows:
```
"inference" : {
"feature_importance" : {
"FlightTimeMin" : -76.90955548511226,
"FlightDelayType" : 114.13514762158526,
"DistanceMiles" : 13.731580450792187
},
"predicted_value" : 108.33165831875137,
"model_id" : "my_model"
}
```
This is done through calculating the [SHAP values](https://arxiv.org/abs/1802.03888).
It requires that models have populated `number_samples` for each tree node. This is not available to models that were created before 7.7.
Additionally, if the inference config is requesting feature_importance, and not all nodes have been upgraded yet, it will not allow the pipeline to be created. This is to safe-guard in a mixed-version environment where only some ingest nodes have been upgraded.
NOTE: the algorithm is a Java port of the one laid out in ml-cpp: https://github.com/elastic/ml-cpp/blob/master/lib/maths/CTreeShapFeatureImportance.cc
usability blocked by: https://github.com/elastic/ml-cpp/pull/991
This commit updates the enrich.get_policy API to specify name
as a list, in line with other URL parts that accept a comma-separated
list of values.
In addition, update the get enrich policy API docs
to align the URL part name in the documentation with
the name used in the REST API specs.
(cherry picked from commit 94f6f946ef283dc93040e052b4676c5bc37f4bde)
The changes add more granularity for identiying the data ingestion user.
The ingest pipeline can now be configure to record authentication realm and
type. It can also record API key name and ID when one is in use.
This improves traceability when data are being ingested from multiple agents
and will become more relevant with the incoming support of required
pipelines (#46847)
Resolves: #49106
* Add empty_value parameter to CSV processor
This change adds `empty_value` parameter to the CSV processor.
This value is used to fill empty fields. Fields will be skipped
if this parameter is ommited. This behavior is the same for both
quoted and unquoted fields.
* docs updated
* Fix compilation problem
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
Backport: #50467
This commit adds the name of the current pipeline to ingest metadata.
This pipeline name is accessible under the following key: '_ingest.pipeline'.
Example usage in pipeline:
PUT /_ingest/pipeline/2
{
"processors": [
{
"set": {
"field": "pipeline_name",
"value": "{{_ingest.pipeline}}"
}
}
]
}
Closes#42106
* CSV ingest processor (#49509)
This change adds new ingest processor that breaks line from CSV file into separate fields.
By default it conforms to RFC 4180 but can be tweaked.
Closes#49113
* Allow list of IPs in geoip ingest processor
This change lets you use array of IPs in addition to string in geoip processor source field.
It will set array containing geoip data for each element in source, unless first_only parameter
option is enabled, then only first found will be returned.
Closes#46193
The documentation contained a small error, as bytes and duration was not
properly converted to a number and thus remained a string.
The documentation is now also properly tested by providing a full blown
simulate pipeline example.
When the enrich processor appends enrich data to an incoming document,
it adds a `target_field` to contain the enrich data.
This `target_field` contains both the `match_field` AND `enrich_fields`
specified in the enrich policy.
Previously, this was reflected in the documented example but not
explicitly stated. This adds several explicit statements to the docs.
Backport of #49076
In case an exception occurs inside a pipeline processor,
the pipeline stack is kept around as header in the exception.
Then in the on_failure processor the id of the pipeline the
exception occurred is made accessible via the `on_failure_pipeline`
ingest metadata.
Closes#44920
For the user agent ingest processor, custom regex files must end
with the `.yml` file extension.
This corrects the docs which said the `.yaml` extension was required.
Prior to this change the `target_field` would always be a json array
field in the document being ingested. This to take into account that
multiple enrich documents could be inserted into the `target_field`.
However the default `max_matches` is `1`. Meaning that by default
only a single enrich document would be added to `target_field` json
array field.
This commit changes this; if `max_matches` is set to `1` then the single
document would be added as a json object to the `target_field` and
if it is configured to a higher value then the enrich documents will be
added as a json array (even if a single enrich document happens to be
enriched).
István Zoltán Szabó
Jake Landis
Dan Hermann
Lisa Cawley
Adam Locke
James Rodewig
Thiago Souza
Benjamin Trent
AndyHunt66
Orhan Toy
David Pilato
bellengao
Russ Cam
Yang Wang
Przemko Robakowski
David Kyle
Florian Kelbert
Martijn van Groningen
Igor Motov
Alexander Reelsen
Shaunak Kashyap