BytesReference is currently an abstract class which is extended by
various implementations. This makes it very difficult to use the
delegation pattern. The implication of this is that our releasable
BytesReference is a PagedBytesReference type and cannot be used as a
generic releasable bytes reference that delegates to any reference type.
This commit makes BytesReference an interface and introduces an
AbstractBytesReference for common functionality.
The AbstractHlrcWriteableXContentTestCase was replaced by a better test
case a while ago, and this is the last two instances using it. They have
been converted and the test is now deleted.
Ref #39745
This commit removes the randomization used by every execute call in the
high level rest tests. Previously every execute call, which can be many
calls per single test, would rely on a random boolean to determine if
they should use the sync or async methods provided to the execute
method. This commit runs the tests twice, using two different clusters,
both of them providing the value one time via a sysprop. This ensures
that the whole suite of tests is run using the sync and async code
paths.
Closes#39667
All internal searches (triggered by APIs) across the .security index
must be performed while "under the security origin". Otherwise,
the search is performed in the context of the caller which most
likely does not have privileges to search .security (hopefully).
This commit fixes this in the case of two methods in the
TokenService and corrects an overly done such context switch
in the ApiKeyService.
In addition, this makes all tests from the client/rest-high-level
module execute as an all mighty administrator,
but not a literal superuser.
Closes#47151
Several links from the ILM HLRC Javadoc to the online documentation were
not updated when the ILM HLRC documentation was written. This commit
fixes those links.
Adds `GET /_script_context`, returning a `contexts` object with each
available context as a key whose value is an empty object. eg.
```
{
"contexts": {
"aggregation_selector": {},
"aggs": {},
"aggs_combine": {},
...
}
}
```
refs: #47411
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.
which is backport merge and adds a new ingest processor, named enrich processor,
that allows document being ingested to be enriched with data from other indices.
Besides a new enrich processor, this PR adds several APIs to manage an enrich policy.
An enrich policy is in charge of making the data from other indices available to the enrich processor in an efficient manner.
Related to #32789
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.
This commit adds HLRC support and documentation for the SLM Start and
Stop APIs, as well as updating existing documentation where appropriate.
This commit also ensures that the SLM APIs are properly included in the
HLRC documentation.
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
This commit adds two APIs that allow to pause and resume
CCR auto-follower patterns:
// pause auto-follower
POST /_ccr/auto_follow/my_pattern/pause
// resume auto-follower
POST /_ccr/auto_follow/my_pattern/resume
The ability to pause and resume auto-follow patterns can be
useful in some situations, including the rolling upgrades of
cluster using a bi-directional cross-cluster replication scheme
(see #46665).
This commit adds a new active flag to the AutoFollowPattern
and adapts the AutoCoordinator and AutoFollower classes so
that it stops to fetch remote's cluster state when all auto-follow
patterns associate to the remote cluster are paused.
When an auto-follower is paused, remote indices that match the
pattern are just ignored: they are not added to the pattern's
followed indices uids list that is maintained in the local cluster
state. This way, when the auto-follow pattern is resumed the
indices created in the remote cluster in the meantime will be
picked up again and added as new following indices. Indices
created and then deleted in the remote cluster will be ignored
as they won't be seen at all by the auto-follower pattern at
resume time.
Backport of #47510 for 7.x
Currently there are two issues with serializing BulkByScrollResponse.
First, when deserializing from XContent, indexing exceptions and search
exceptions are switched. Additionally, search exceptions do no retain
the appropriate RestStatus code, so you must evaluate the status code
from the exception. However, the exception class is not always correctly
retained when serialized.
This commit adds tests in the failure case. Additionally, fixes the
swapping of failure types and adds the rest status code to the search
failure.
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
Use case:
User with `create_doc` index privilege will be allowed to only index new documents
either via Index API or Bulk API.
There are two cases that we need to think:
- **User indexing a new document without specifying an Id.**
For this ES auto generates an Id and now ES version 7.5.0 onwards defaults to `op_type` `create` we just need to authorize on the `op_type`.
- **User indexing a new document with an Id.**
This is problematic as we do not know whether a document with Id exists or not.
If the `op_type` is `create` then we can assume the user is trying to add a document, if it exists it is going to throw an error from the index engine.
Given these both cases, we can safely authorize based on the `op_type` value. If the value is `create` then the user with `create_doc` privilege is authorized to index new documents.
In the `AuthorizationService` when authorizing a bulk request, we check the implied action.
This code changes that to append the `:op_type/index` or `:op_type/create`
to indicate the implied index action.
This commit adds support to retrieve all API keys if the authenticated
user is authorized to do so.
This removes the restriction of specifying one of the
parameters (like id, name, username and/or realm name)
when the `owner` is set to `false`.
Closes#46887
* Remove eclipse conditionals
We used to have some meta projects with a `-test` prefix because
historically eclipse could not distinguish between test and main
source-sets and could only use a single classpath.
This is no longer the case for the past few Eclipse versions.
This PR adds the necessary configuration to correctly categorize source
folders and libraries.
With this change eclipse can import projects, and the visibility rules
are correct e.x. auto compete doesn't offer classes from test code or
`testCompile` dependencies when editing classes in `main`.
Unfortunately the cyclic dependency detection in Eclipse doesn't seem to
take the difference between test and non test source sets into account,
but since we are checking this in Gradle anyhow, it's safe to set to
`warning` in the settings. Unfortunately there is no setting to ignore
it.
This might cause problems when building since Eclipse will probably not
know the right order to build things in so more wirk might be necesarry.
* Add API to execute SLM retention on-demand (#47405)
This is a backport of #47405
This commit adds the `/_slm/_execute_retention` API endpoint. This
endpoint kicks off SLM retention and then returns immediately.
This in particular allows us to run retention without scheduling it
(for entirely manual invocation) or perform a one-off cleanup.
This commit also includes HLRC for the new API, and fixes an issue
in SLMSnapshotBlockingIntegTests where retention invoked prior to the
test completing could resurrect an index the internal test cluster
cleanup had already deleted.
Resolves#46508
Relates to #43663
* [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
Currently the policy config is placed directly in the json object
of the toplevel `policies` array field. For example:
```
{
"policies": [
{
"match": {
"name" : "my-policy",
"indices" : ["users"],
"match_field" : "email",
"enrich_fields" : [
"first_name",
"last_name",
"city",
"zip",
"state"
]
}
}
]
}
```
This change adds a `config` field in each policy json object:
```
{
"policies": [
{
"config": {
"match": {
"name" : "my-policy",
"indices" : ["users"],
"match_field" : "email",
"enrich_fields" : [
"first_name",
"last_name",
"city",
"zip",
"state"
]
}
}
}
]
}
```
This allows us in the future to add other information about policies
in the get policy api response.
The UI will consume this API to build an overview of all policies.
The UI may in the future include additional information about a policy
and the plan is to include that in the get policy api, so that this
information can be gathered in a single api call.
An example of the information that is likely to be added is:
* Last policy execution time
* The status of a policy (executing, executed, unexecuted)
* Information about the last failure if exists