This API call in most implementations is fairly IO heavy and slow
so it is more natural to be async in the first place.
Concretely though, this change is a prerequisite of #49060 since
determining the repository generation from the cluster state
introduces situations where this call would have to wait for other
operations to finish. Doing so in a blocking manner would break
`SnapshotResiliencyTests` and waste a thread.
Also, this sets up the possibility to in the future make use of async IO
where provided by the underlying Repository implementation.
In a follow-up `SnapshotsService#getRepositoryData` will be made async
as well (did not do it here, since it's another huge change to do so).
Note: This change for now does not alter the threading behaviour in any way (since `Repository#getRepositoryData` isn't forking) and is purely mechanical.
The following edge cases were fixed:
1. A request to force-stop a stopping datafeed is no longer
ignored. Force-stop is an important recovery mechanism
if normal stop doesn't work for some reason, and needs
to operate on a datafeed in any state other than stopped.
2. If the node that a datafeed is running on is removed from
the cluster during a normal stop then the stop request is
retried (and will likely succeed on this retry by simply
cancelling the persistent task for the affected datafeed).
3. If there are multiple simultaneous force-stop requests for
the same datafeed we no longer fail the one that is
processed second. The previous behaviour was wrong as
stopping a stopped datafeed is not an error, so stopping
a datafeed twice simultaneously should not be either.
Backport of #49191
* [ML] ML Model Inference Ingest Processor (#49052)
* [ML][Inference] adds lazy model loader and inference (#47410)
This adds a couple of things:
- A model loader service that is accessible via transport calls. This service will load in models and cache them. They will stay loaded until a processor no longer references them
- A Model class and its first sub-class LocalModel. Used to cache model information and run inference.
- Transport action and handler for requests to infer against a local model
Related Feature PRs:
* [ML][Inference] Adjust inference configuration option API (#47812)
* [ML][Inference] adds logistic_regression output aggregator (#48075)
* [ML][Inference] Adding read/del trained models (#47882)
* [ML][Inference] Adding inference ingest processor (#47859)
* [ML][Inference] fixing classification inference for ensemble (#48463)
* [ML][Inference] Adding model memory estimations (#48323)
* [ML][Inference] adding more options to inference processor (#48545)
* [ML][Inference] handle string values better in feature extraction (#48584)
* [ML][Inference] Adding _stats endpoint for inference (#48492)
* [ML][Inference] add inference processors and trained models to usage (#47869)
* [ML][Inference] add new flag for optionally including model definition (#48718)
* [ML][Inference] adding license checks (#49056)
* [ML][Inference] Adding memory and compute estimates to inference (#48955)
* fixing version of indexed docs for model inference
improve error handling for script errors, treating it as irrecoverable errors which puts the task
immediately into failed state, also improves the error extraction to properly report the script
error.
fixes#48467
This commit fixes a NPE problem as reported in #49150.
But this problem uncovered that we never added proper handling
of state for data frame analytics tasks.
In this commit we improve the `MlTasks.getDataFrameAnalyticsState`
method to handle null tasks and state tasks properly.
Closes#49150
Backport of #49186
Backport of #48849. Update `.editorconfig` to make the Java settings the
default for all files, and then apply a 2-space indent to all `*.gradle`
files. Then reformat all the files.
We can have a race here where `scheduleNextRun` executes concurrently to `stop`
and so we run into a `RejectedExecutionException` that we don't catch and thus it
fails tests.
=> Fixed by ignoring these so long as they coincide with a scheduler shutdown
When using the move-to-step API, we should reread the phase JSON from
the latest version of the ILM policy. This allows a user to move to the
same step while re-reading the policy's latest version. For example,
when changing rollover criteria.
While manually messing around with some other things I discovered that
we only reread the policy when using the retry API, not the move-to-step
API. This commit changes the move-to-step API to always read the latest
version of the policy.
* [ML] Add new geo_results.(actual_point|typical_point) fields for `lat_long` results (#47050)
[ML] Add new geo_results.(actual_point|typical_point) fields for `lat_long` results (#47050)
Related PR: https://github.com/elastic/ml-cpp/pull/809
* adjusting bwc version
decouple TransformTask and ClientTransformIndexer. Interaction between the 2 classes are
now moved into a context class which holds shared information.
relates #45369
Previous behavior while copying HTTP headers to the ThreadContext,
would allow multiple HTTP headers with the same name, handling only
the first occurrence and disregarding the rest of the values. This
can be confusing when dealing with multiple Headers as it is not
obvious which value is read and which ones are silently dropped.
According to RFC-7230, a client must not send multiple header fields
with the same field name in a HTTP message, unless the entire field
value for this header is defined as a comma separated list or this
specific header is a well-known exception.
This commits changes the behavior in order to be more compliant to
the aforementioned RFC by requiring the classes that implement
ActionPlugin to declare if a header can be multi-valued or not when
registering this header to be copied over to the ThreadContext in
ActionPlugin#getRestHeaders.
If the header is allowed to be multivalued, then all such headers
are read from the HTTP request and their values get concatenated in
a comma-separated string.
If the header is not allowed to be multivalued, and the HTTP
request contains multiple such Headers with different values, the
request is rejected with a 400 status.
This adds the infrastructure to be able to retry the execution of retryable
steps and makes the `check-rollover-ready` retryable as an initial step to
make the rollover action more resilient to transient errors.
(cherry picked from commit 454020ac8acb147eae97acb4ccd6fb470d1e5f48)
Signed-off-by: Andrei Dan <andrei.dan@elastic.co>
* Un-AwaitsFix and enhance logging for testPolicyCRUD
This removes the `AwaitsFix` and increases the test logging for
`SnapshotLifecycleServiceTests.testPolicyCRUD` in an effort to track
down the cause of #44997.
* Remove unused import
* [ML][Inference] separating definition and config object storage (#48651)
This separates out the `definition` object from being stored within the configuration object in the index.
This allows us to gather the config object without decompressing a potentially large definition.
Additionally, `input` is moved to the TrainedModelConfig object and out of the definition. This is so the trained input fields are accessible outside the potentially large model definition.
The open and close follower steps didn't check if the index is open,
closed respectively, before executing the open/close request.
This changes the steps to check the index state and only perform the
open/close operation if the index is not already open/closed.
This commit ensures that the creation of a DocumentSubsetReader does not
eagerly resolve the role query and the number of docs that match.
We want to delay this expensive operation in order to ensure that we really
need this information when we build it. For this reason the role query and the
number of docs are now resolved on demand. This commit also depends on
https://issues.apache.org/jira/browse/LUCENE-9003 that will also compute the global
number of docs lazily.
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
7.5+ for SLM requires [stats] object to exist in the cluster state.
When doing an in-place upgrade from 7.4 to 7.5+ [stats] does not exist
in cluster state, result in an exception on startup [1].
This commit moves the [stats] to be an optional object in the parser
and if not found will default to an empty stats object.
[1] Caused by: java.lang.IllegalArgumentException: Required [stats]
Reverting the change introducing IsoLocal.ROOT and introducing IsoCalendarDataProvider that defaults start of the week to Monday and requires minimum 4 days in first week of a year. This extension is using java SPI mechanism and defaults for Locale.ROOT only.
It require jvm property java.locale.providers to be set with SPI,COMPAT
closes#41670
backport #48209
This change adds a new field `"shards"` to `RepositoryData` that contains a mapping of `IndexId` to a `String[]`. This string array can be accessed by shard id to get the generation of a shard's shard folder (i.e. the `N` in the name of the currently valid `/indices/${indexId}/${shardId}/index-${N}` for the shard in question).
This allows for creating a new snapshot in the shard without doing any LIST operations on the shard's folder. In the case of AWS S3, this saves about 1/3 of the cost for updating an empty shard (see #45736) and removes one out of two remaining potential issues with eventually consistent blob stores (see #38941 ... now only the root `index-${N}` is determined by listing).
Also and equally if not more important, a number of possible failure modes on eventually consistent blob stores like AWS S3 are eliminated by moving all delete operations to the `master` node and moving from incremental naming of shard level index-N to uuid suffixes for these blobs.
This change moves the deleting of the previous shard level `index-${uuid}` blob to the master node instead of the data node allowing for a safe and consistent update of the shard's generation in the `RepositoryData` by first updating `RepositoryData` and then deleting the now unreferenced `index-${newUUID}` blob.
__No deletes are executed on the data nodes at all for any operation with this change.__
Note also: Previous issues with hanging data nodes interfering with master nodes are completely impossible, even on S3 (see next section for details).
This change changes the naming of the shard level `index-${N}` blobs to a uuid suffix `index-${UUID}`. The reason for this is the fact that writing a new shard-level `index-` generation blob is not atomic anymore in its effect. Not only does the blob have to be written to have an effect, it must also be referenced by the root level `index-N` (`RepositoryData`) to become an effective part of the snapshot repository.
This leads to a problem if we were to use incrementing names like we did before. If a blob `index-${N+1}` is written but due to the node/network/cluster/... crashes the root level `RepositoryData` has not been updated then a future operation will determine the shard's generation to be `N` and try to write a new `index-${N+1}` to the already existing path. Updates like that are problematic on S3 for consistency reasons, but also create numerous issues when thinking about stuck data nodes.
Previously stuck data nodes that were tasked to write `index-${N+1}` but got stuck and tried to do so after some other node had already written `index-${N+1}` were prevented form doing so (except for on S3) by us not allowing overwrites for that blob and thus no corruption could occur.
Were we to continue using incrementing names, we could not do this. The stuck node scenario would either allow for overwriting the `N+1` generation or force us to continue using a `LIST` operation to figure out the next `N` (which would make this change pointless).
With uuid naming and moving all deletes to `master` this becomes a non-issue. Data nodes write updated shard generation `index-${uuid}` and `master` makes those `index-${uuid}` part of the `RepositoryData` that it deems correct and cleans up all those `index-` that are unused.
Co-authored-by: Yannick Welsch <yannick@welsch.lu>
Co-authored-by: Tanguy Leroux <tlrx.dev@gmail.com>
FIPS 140 bootstrap checks should not be bootstrap checks as they
are always enforced. This commit moves the validation logic within
the security plugin.
The FIPS140SecureSettingsBootstrapCheck was not applicable as the
keystore was being loaded on init, before the Bootstrap checks
were checked, so an elasticsearch keystore of version < 3 would
cause the node to fail in a FIPS 140 JVM before the bootstrap check
kicked in, and as such hasn't been migrated.
Resolves: #34772
The enrich stats api picked the wrong task to be displayed
in the executing stats section.
In case `wait_for_completion` was set to `false` then no task
was being displayed and if that param was set to `true` then
the wrong task was being displayed (transport action task instead
of enrich policy executor task).
Testing executing policies in enrich stats api is tricky.
I have verified locally that this commit fixes the bug.
This PR adds an origin for the Enrich feature, and modifies the background
maintenance task to use the origin when executing client operations.
Without this fix, the maintenance task fails to execute when security is
enabled.
There is no reason to still resolve the
fallback `IndexId` here. It only applies to
`2.x` repos and those we can't read anymore
anyway because they use an `/index` instead of
an `/index-N` blob at the repo root for which
at least 7.x+ does not contain the logic to find
it.
* Add SLM support to xpack usage and info APIs
This is a backport of #48096
This adds the missing xpack usage and info information into the
`/_xpack` and `/_xpack/usage` APIs. The output now looks like:
```
GET /_xpack/usage
{
...
"slm" : {
"available" : true,
"enabled" : true,
"policy_count" : 1,
"policy_stats" : {
"retention_runs" : 0,
...
}
}
```
and
```
GET /_xpack
{
...
"features" : {
...
"slm" : {
"available" : true,
"enabled" : true
},
...
}
}
```
Relates to #43663
* Fix missing license
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
max_empty_searches = -1 in a datafeed update implies
max_empty_searches will be unset on the datafeed when
the update is applied. The isNoop() method needs to
take this -1 to null equivalence into account.
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 PR adds the ability to run the enrich policy execution task in the background,
returning a task id instead of waiting for the completed operation.
Currently, partial snapshots will eventually build up unless they are
manually deleted. Partial snapshots may be useful if there is not a more
recent successful snapshot, but should eventually be deleted if they are
no longer useful.
With this change, partial snapshots are deleted using the following
strategy: PARTIAL snapshots will be kept until the configured
expire_after period has passed, if present, and then be deleted. If
there is no configured expire_after in the retention policy, then they
will be deleted if there is at least one more recent successful snapshot
from this policy (as they may otherwise be useful for troubleshooting
purposes). Partial snapshots are not counted towards either min_count or
max_count.
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
Armin Braun
David Roberts
Julie Tibshirani
Benjamin Trent
Przemysław Witek
Hendrik Muhs
Dimitris Athanasiou
Rory Hunter
Lee Hinman
Ioannis Kakavas
Andrei Dan
Jim Ferenczi
Tim Brooks
Michael Basnight
Jake Landis
Przemyslaw Gomulka
Martijn van Groningen
James Baiera
rsarawgi
Gordon Brown