* [ML] Add validation that rejects duplicate detectors in PutJobAction
Closes#39704
* Add YML integration test for duplicate detectors fix.
* Use "== false" comparison rather than "!" operator.
* Refine error message to sound more natural.
* Put job description in square brackets in the error message.
* Use the new validation in ValidateJobConfigAction.
* Exclude YML tests for new validation from permission tests.
* Replace usages RandomizedTestingTask with built-in Gradle Test (#40978)
This commit replaces the existing RandomizedTestingTask and supporting code with Gradle's built-in JUnit support via the Test task type. Additionally, the previous workaround to disable all tasks named "test" and create new unit testing tasks named "unitTest" has been removed such that the "test" task now runs unit tests as per the normal Gradle Java plugin conventions.
(cherry picked from commit 323f312bbc829a63056a79ebe45adced5099f6e6)
* Fix forking JVM runner
* Don't bump shadow plugin version
The invalid license enforced is exposed to the cluster state update
thread (via the license state listener) before the constructor has
finished. This violates the JLS for safe publication of an object, and
means there is a concurrency bug lurking here. This commit addresses
this by avoiding publication of the invalid license enforcer before the
constructor has returned.
This change adds information about which UI path
(if any) created ML anomaly detector jobs to the
stats returned by the _xpack/usage endpoint.
Counts for the following possibilities are expected:
* ml_module_apache_access
* ml_module_apm_transaction
* ml_module_auditbeat_process_docker
* ml_module_auditbeat_process_hosts
* ml_module_nginx_access
* ml_module_sample
* multi_metric_wizard
* population_wizard
* single_metric_wizard
* unknown
The "unknown" count is for jobs that do not have a
created_by setting in their custom_settings.
Closes#38403
Ensure that there is at least a 1s delay between the time that state
is persisted by each of the two jobs in the test.
Model snapshot IDs use the current time in epoch seconds to
distinguish themselves, hence snapshots will be overwritten
by another if it occurs in the same 1s window.
Closes#40347
If multiple jobs are created together and the anomaly
results index does not exist then some of the jobs could
fail to update the mappings of the results index. This
lead them to fail to write their results correctly later.
Although this scenario sounds rare, it is exactly what
happens if the user creates their first jobs using the
Nginx module in the ML UI.
This change fixes the problem by updating the mappings
of the results index if it is found to exist during a
creation attempt.
Fixes#38785
* [ML] Refactor common utils out of ML plugin to XPack.Core
* implementing GET filters with abstract transport
* removing added rest param
* adjusting how defaults can be supplied
The problem here was that `DatafeedJob` was updating the last end time searched
based on the `now` even though when there are aggregations, the extactor will
only search up to the floor of `now` against the histogram interval.
This commit fixes the issue by using the end time as calculated by the extractor.
It also adds an integration test that uses aggregations. This test would fail
before this fix. Unfortunately the test is slow as we need to wait for the
datafeed to work in real time.
Closes#39842
* [ML] refactoring lazy query and agg parsing
* Clean up and addressing PR comments
* removing unnecessary try/catch block
* removing bad call to logger
* removing unused import
* fixing bwc test failure due to serialization and config migrator test
* fixing style issues
* Adjusting DafafeedUpdate class serialization
* Adding todo for refactor in v8
* Making query non-optional so it does not write a boolean byte
This change does the following:
1. Makes the per-node setting xpack.ml.max_open_jobs
into a cluster-wide dynamic setting
2. Changes the job node selection to continue to use the
per-node attributes storing the maximum number of open
jobs if any node in the cluster is older than 7.1, and
use the dynamic cluster-wide setting if all nodes are on
7.1 or later
3. Changes the docs to reflect this
4. Changes the thread pools for native process communication
from fixed size to scaling, to support the dynamic nature
of xpack.ml.max_open_jobs
5. Renames the autodetect thread pool to the job comms
thread pool to make clear that it will be used for other
types of ML jobs (data frame analytics in particular)
Backport of #39320
Investigating how to make DeleteExpiredDataIT faster, it was
revealed that the security audit trail threads were quite hot.
Disabling that seems to be helping quite a bit with making this
test faster. This commit also unmutes the test to see how it goes
with the audit trail disabled.
Relates #39658Closes#39575
ML has historically used doc as the single mapping type but reindex in 7.x
will change the mapping to _doc. Switching to the typeless APIs handles
case where the mapping type is either doc or _doc. This change removes
deprecated typed usages.
This commit parallelizes some parts of the test
and its remove an unnecessary refresh call.
On my local machine it shaves off about 15 seconds
for a test execution time of ~64s (down from ~80s).
This test is still slow but progress over perfection.
Relates #37339
The ScheduledEvent class has never preserved the time
zone so it makes more sense for it to store the start and
end time using Instant rather than ZonedDateTime.
Closes#38620
The assertBusy() that waits the default 10 seconds for a
datafeed to complete very occasionally times out on slow
machines. This commit increases the timeout to 60 seconds.
It will almost never actually take this long, but it's
better to have a timeout that will prevent time being
wasted looking at spurious test failures.
This test failed once in a very long time with the assertion
that there is no document for the `non_existing_job` in the
state index. I could not see how that is possible and I cannot
reproduce. With this commit the failure message will reveal
some examples of the left behind docs which might shed a light
about what could go wrong.
The ML memory tracker does searches against ML results
and config indices. These searches can be asynchronous,
and if they are running while the node is closing then
they can cause problems for other components.
This change adds a stop() method to the MlMemoryTracker
that waits for in-flight searches to complete. Once
stop() has returned the MlMemoryTracker will not kick
off any new searches.
The MlLifeCycleService now calls MlMemoryTracker.stop()
before stopping stopping the node.
Fixes#37117
These two changes are interlinked.
Before this change unsetting ML upgrade mode would wait for all
datafeeds to be assigned and not waiting for their corresponding
jobs to initialise. However, this could be inappropriate, if
there was a reason other that upgrade mode why one job was unable
to be assigned or slow to start up. Unsetting of upgrade mode
would hang in this case.
This change relaxes the condition for considering upgrade mode to
be unset to simply that an assignment attempt has been made for
each ML persistent task that did not fail because upgrade mode
was enabled. Thus after unsetting upgrade mode there is no
guarantee that every ML persistent task is assigned, just that
each is not unassigned due to upgrade mode.
In order to make setting upgrade mode work immediately after
unsetting upgrade mode it was then also necessary to make it
possible to stop a datafeed that was not assigned. There was
no particularly good reason why this was not allowed in the past.
It is trivial to stop an unassigned datafeed because it just
involves removing the persistent task.
The .ml-annotations index is created asynchronously when
some other ML index exists. This can interfere with the
post-test index deletion, as the .ml-annotations index
can be created after all other indices have been deleted.
This change adds an ML specific post-test cleanup step
that runs before the main cleanup and:
1. Checks if any ML indices exist
2. If so, waits for the .ml-annotations index to exist
3. Deletes the other ML indices found in step 1.
4. Calls the super class cleanup
This means that by the time the main post-test index
cleanup code runs:
1. The only ML index it has to delete will be the
.ml-annotations index
2. No other ML indices will exist that could trigger
recreation of the .ml-annotations index
Fixes#38952
* Fix#38623 remove xpack namespace REST API
Except for xpack.usage and xpack.info API's, this moves the last remaining API's out of the xpack namespace
* rename xpack api's inside inside the files as well
* updated yaml tests references to xpack namespaces api's
* update callsApi calls in the IT subclasses
* make sure docs testing does not use xpack namespaced api's
* fix leftover xpack namespaced method names in docs/build.gradle
* found another leftover reference
(cherry picked from commit ccb5d934363c37506b76119ac050a254fa80b5e7)
* ML: update set_upgrade_mode, add logging
* Attempt to fix datafeed isolation
Also renamed a few methods/variables for clarity and added
some comments
Elasticsearch has long [supported](https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-index_.html#index-versioning) compare and set (a.k.a optimistic concurrency control) operations using internal document versioning. Sadly that approach is flawed and can sometime do the wrong thing. Here's the relevant excerpt from the resiliency status page:
> When a primary has been partitioned away from the cluster there is a short period of time until it detects this. During that time it will continue indexing writes locally, thereby updating document versions. When it tries to replicate the operation, however, it will discover that it is partitioned away. It won’t acknowledge the write and will wait until the partition is resolved to negotiate with the master on how to proceed. The master will decide to either fail any replicas which failed to index the operations on the primary or tell the primary that it has to step down because a new primary has been chosen in the meantime. Since the old primary has already written documents, clients may already have read from the old primary before it shuts itself down. The version numbers of these reads may not be unique if the new primary has already accepted writes for the same document
We recently [introduced](https://www.elastic.co/guide/en/elasticsearch/reference/6.x/optimistic-concurrency-control.html) a new sequence number based approach that doesn't suffer from this dirty reads problem.
This commit removes support for internal versioning as a concurrency control mechanism in favor of the sequence number approach.
Relates to #1078
With this change we no longer support pluggable discovery implementations. No
known implementations of `DiscoveryPlugin` actually override this method, so in
practice this should have no effect on the wider world. However, we were using
this rather extensively in tests to provide the `test-zen` discovery type. We
no longer need a separate discovery type for tests as we no longer need to
customise its behaviour.
Relates #38410
If a job cannot be assigned to a node because an index it
requires is unavailable and there are lazy ML nodes then
index unavailable should be reported as the assignment
explanation rather than waiting for a lazy ML node.
X-Pack security supports built-in authentication service
`token-service` that allows access tokens to be used to
access Elasticsearch without using Basic authentication.
The tokens are generated by `token-service` based on
OAuth2 spec. The access token is a short-lived token
(defaults to 20m) and refresh token with a lifetime of 24 hours,
making them unsuitable for long-lived or recurring tasks where
the system might go offline thereby failing refresh of tokens.
This commit introduces a built-in authentication service
`api-key-service` that adds support for long-lived tokens aka API
keys to access Elasticsearch. The `api-key-service` is consulted
after `token-service` in the authentication chain. By default,
if TLS is enabled then `api-key-service` is also enabled.
The service can be disabled using the configuration setting.
The API keys:-
- by default do not have an expiration but expiration can be
configured where the API keys need to be expired after a
certain amount of time.
- when generated will keep authentication information of the user that
generated them.
- can be defined with a role describing the privileges for accessing
Elasticsearch and will be limited by the role of the user that
generated them
- can be invalidated via invalidation API
- information can be retrieved via a get API
- that have been expired or invalidated will be retained for 1 week
before being deleted. The expired API keys remover task handles this.
Following are the API key management APIs:-
1. Create API Key - `PUT/POST /_security/api_key`
2. Get API key(s) - `GET /_security/api_key`
3. Invalidate API Key(s) `DELETE /_security/api_key`
The API keys can be used to access Elasticsearch using `Authorization`
header, where the auth scheme is `ApiKey` and the credentials, is the
base64 encoding of API key Id and API key separated by a colon.
Example:-
```
curl -H "Authorization: ApiKey YXBpLWtleS1pZDphcGkta2V5" http://localhost:9200/_cluster/health
```
Closes#34383
The explanation so far can be invaluable for troubleshooting
as incorrect decisions made early on in the structure analysis
can result in seemingly crazy decisions or timeouts later on.
Relates elastic/kibana#29821
Today the following settings in the `discovery.zen` namespace are still used:
- `discovery.zen.no_master_block`
- `discovery.zen.hosts_provider`
- `discovery.zen.ping.unicast.concurrent_connects`
- `discovery.zen.ping.unicast.hosts.resolve_timeout`
- `discovery.zen.ping.unicast.hosts`
This commit deprecates all other settings in this namespace so that they can be
removed in the next major version.
In 7.x Java timestamp formats are the default timestamp format and
there is no need to prefix them with "8". (The "8" prefix was used
in 6.7 to distinguish Java timestamp formats from Joda timestamp
formats.)
This change removes the "8" prefixes from timestamp formats in the
output of the ML file structure finder.
Scheduler.schedule(...) would previously assume that caller handles
exception by calling get() on the returned ScheduledFuture.
schedule() now returns a ScheduledCancellable that no longer gives
access to the exception. Instead, any exception thrown out of a
scheduled Runnable is logged as a warning.
This is a continuation of #28667, #36137 and also fixes#37708.
Doc-value fields now return a value that is based on the mappings rather than
the script implementation by default.
This deprecates the special `use_field_mapping` docvalue format which was added
in #29639 only to ease the transition to 7.x and it is not necessary anymore in
7.0.
Runnables can be submitted to
AutodetectProcessManager.AutodetectWorkerExecutorService
without error after it has been shutdown which can lead
to requests timing out as their handlers are never called
by the terminated executor.
This change throws an EsRejectedExecutionException if a
runnable is submitted after after the shutdown and calls
AbstractRunnable.onRejection on any tasks not run.
Closes#37108
Przemysław Witek
Mark Vieira
Jason Tedor
David Roberts
Dimitris Athanasiou
David Kyle
Ed Savage
David Turner
Benjamin Trent
Mehran Koushkebaghi
Martijn Laarman
Boaz Leskes
Yogesh Gaikwad
Henning Andersen
Adrien Grand
Henrique Gonçalves