Removing the deprecated "xpack.monitoring.enabled" setting introduced
log spam and potentially some failures in ML tests. It's possible to use
a different, non-deprecated setting to disable monitoring, so we do that
here.
We believe there's no longer a need to be able to disable basic-license
features completely using the "xpack.*.enabled" settings. If users don't
want to use those features, they simply don't need to use them. Having
such features always available lets us build more complex features that
assume basic-license features are present.
This commit deprecates settings of the form "xpack.*.enabled" for
basic-license features, excluding "security", which is a special case.
It also removes deprecated settings from integration tests and unit
tests where they're not directly relevant; e.g. monitoring and ILM are
no longer disabled in many integration tests.
This fixes the long muted testHRDSplit. Some minor adjustments for modern day elasticsearch changes :).
The cause of the failure is that a new `by` field entering the model with an exceptionally high count does not cause an anomaly. We have since stopped combining the `rare` and `by` in this manner. New entries in a `by` field are not anomalous because we have no history on them yet.
closes https://github.com/elastic/elasticsearch/issues/32966
When a datafeed transitions from lookback to real-time we request
that state is persisted from the autodetect process in the
background.
This PR adds a test to prove that for a categorization job the
state that is persisted includes the categorization state.
Without the fix from elastic/ml-cpp#1137 this test fails. After
that C++ fix is merged this test should pass.
Backport of #55243
I've noticed that a lot of our tests are using deprecated static methods
from the Hamcrest matchers. While this is not a big deal in any
objective sense, it seems like a small good thing to reduce compilation
warnings and be ready for a new release of the matcher library if we
need to upgrade. I've also switched a few other methods in tests that
have drop-in replacements.
We needlessly send documents to be persisted. If there are no stats added, then we should not attempt to persist them.
Also, this PR fixes the race condition that caused issue: https://github.com/elastic/elasticsearch/issues/54786
* [ML] Start gathering and storing inference stats (#53429)
This PR enables stats on inference to be gathered and stored in the `.ml-stats-*` indices.
Each node + model_id will have its own running stats document and these will later be summed together when returning _stats to the user.
`.ml-stats-*` is ILM managed (when possible). So, at any point the underlying index could change. This means that a stats document that is read in and then later updated will actually be a new doc in a new index. This complicates matters as this means that having a running knowledge of seq_no and primary_term is complicated and almost impossible. This is because we don't know the latest index name.
We should also strive for throughput, as this code sits in the middle of an ingest pipeline (or even a query).
Guava was removed from Elasticsearch many years ago, but remnants of it
remain due to transitive dependencies. When a dependency pulls guava
into the compile classpath, devs can inadvertently begin using methods
from guava without realizing it. This commit moves guava to a runtime
dependency in the modules that it is needed.
Note that one special case is the html sanitizer in watcher. The third
party dep uses guava in the PolicyFactory class signature. However, only
calling a method on the PolicyFactory actually causes the class to be
loaded, a reference alone does not trigger compilation to look at the
class implementation. There we utilize a MethodHandle for invoking the
relevant method at runtime, where guava will continue to exist.
The test results are affected by the off-by-one error that is
fixed by https://github.com/elastic/ml-cpp/pull/1122
This test can be unmuted once that fix is merged and has been
built into ml-cpp snapshots.
This adds training_percent parameter to the analytics process for Classification and Regression. This parameter is then used to give more accurate memory estimations.
See native side pr: elastic/ml-cpp#1111
* [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
* [ML] prefer secondary authorization header for data[feed|frame] authz (#54121)
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
* fixing for backport
* [ML] add num_matches and preferred_to_categories to category defintion objects (#54214)
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
* adjusting for backport
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.
This PR:
1. Fixes the bug where a cardinality estimate of zero could cause
a 500 status
2. Adds tests for that scenario and a few others
3. Adds sensible estimates for the cases that were previously TODO
Backport of #54462
When get filters is called without setting the `size`
paramter only up to 10 filters are returned. However,
100 filters should be returned. This commit fixes this
and adds an integ test to guard it.
It seems this was accidentally broken in #39976.
Closes#54206
Backport of #54207
This commit instruments data frame analytics
with stats for the data that are being analyzed.
In particular, we count training docs, test docs,
and skipped docs.
In order to account docs with missing values as skipped
docs for analyses that do not support missing values,
this commit changes the extractor so that it only ignores
docs with missing values when it collects the data summary,
which is used to estimate memory usage.
Backport of #53998
Adds multi-class feature importance calculation.
Feature importance objects are now mapped as follows
(logistic) Regression:
```
{
"feature_name": "feature_0",
"importance": -1.3
}
```
Multi-class [class names are `foo`, `bar`, `baz`]
```
{
“feature_name”: “feature_0”,
“importance”: 2.0, // sum(abs()) of class importances
“foo”: 1.0,
“bar”: 0.5,
“baz”: -0.5
},
```
For users to get the full benefit of aggregating and searching for feature importance, they should update their index mapping as follows (before turning this option on in their pipelines)
```
"ml.inference.feature_importance": {
"type": "nested",
"dynamic": true,
"properties": {
"feature_name": {
"type": "keyword"
},
"importance": {
"type": "double"
}
}
}
```
The mapping field name is as follows
`ml.<inference.target_field>.<inference.tag>.feature_importance`
if `inference.tag` is not provided in the processor definition, it is not part of the field path.
`inference.target_field` is defaulted to `ml.inference`.
//cc @lcawl ^ Where should we document this?
If this makes it in for 7.7, there shouldn't be any feature_importance at inference BWC worries as 7.7 is the first version to have it.
Feature importance storage format is changing to encompass multi-class.
Feature importance objects are now mapped as follows
(logistic) Regression:
```
{
"feature_name": "feature_0",
"importance": -1.3
}
```
Multi-class [class names are `foo`, `bar`, `baz`]
```
{
“feature_name”: “feature_0”,
“importance”: 2.0, // sum(abs()) of class importances
“foo”: 1.0,
“bar”: 0.5,
“baz”: -0.5
},
```
This change adjusts the mapping creation for analytics so that the field is mapped as a `nested` type.
Native side change: https://github.com/elastic/ml-cpp/pull/1071
Prepares classification analysis to support more than just
two classes. It introduces a new parameter to the process config
which dictates the `num_classes` to the process. It also
changes the max classes limit to `30` provisionally.
Backport of #53539
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`.
A previous change (#53029) is causing analysis jobs to wait for certain indices to be made available. While this it is good for jobs to wait, they could fail early on _start.
This change will cause the persistent task to continually retry node assignment when the failure is due to shards not being available.
If the shards are not available by the time `timeout` is reached by the predicate, it is treated as a _start failure and the task is canceled.
For tasks seeking a new assignment after a node failure, that behavior is unchanged.
closes#53188
William Brafford
Przemysław Witek
Benjamin Trent
David Roberts
Dimitris Athanasiou
Mark Vieira
Ryan Ernst
Jason Tedor
Jake Landis
Tom Veasey
Mayya Sharipova
James Baiera