This commit changes the ThreadContext to just use a regular ThreadLocal
over the lucene CloseableThreadLocal. The CloseableThreadLocal solves
issues with ThreadLocals that are no longer needed during runtime but
in the case of the ThreadContext, we need it for the runtime of the
node and it is typically not closed until the node closes, so we miss
out on the benefits that this class provides.
Additionally by removing the close logic, we simplify code in other
places that deal with exceptions and tracking to see if it happens when
the node is closing.
Closes#42577
This commit wraps the calls to retrieve the current step in a try/catch
so that the exception does not bubble up. Instead, step info is added
containing the exception to the existing step.
Semi-related to #49128
(cherry picked from commit 72530f8a7f40ae1fca3704effb38cf92daf29057)
Signed-off-by: Andrei Dan <andrei.dan@elastic.co>
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.
Previously, DATEDIFF for minutes and hours was doing a
rounding calculation using all the time fields (secs, msecs/micros/nanos).
Instead it should first truncate the 2 dates to the respective field (mins or hours)
zeroing out all the more detailed time fields and then make the subtraction.
(cherry picked from commit 124cd18e20429e19d52fd8dc383827ea5132d428)
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
The logic for `cleanupInProgress()` was backwards everywhere (method itself and
all but one user). Also, we weren't checking it when removing a repository.
This lead to a bug (in the one spot that didn't use the method backwards) that prevented
the cleanup cluster state entry from ever being removed from the cluster state if master
failed over during the cleanup process.
This change corrects the backwards logic, adds a test that makes sure the cleanup
is always removed and adds a check that prevents repository removal during cleanup
to the repositories service.
Also, the failure handling logic in the cleanup action was broken. Repeated invocation would lead to the cleanup being removed from the cluster state even if it was in progress. Fixed by adding a flag that indicates whether or not any removal of the cleanup task from the cluster state must be executed. Sorry for mixing this in here, but I had to fix it in the same PR, as the first test (for master-failover) otherwise would often just delete the blocked cleanup action as a result of a transport master action retry.
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
AutoFollowIT relies on assertBusy() calls to wait for a given number of
leader indices to be created but this is prone to failures on CI. Instead,
we should use latches to indicate when auto-follow patterns must be
paused and resumed.
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
If CCR encounters a rejected execution exception, today we treat this as
fatal. This is not though, as the stuffed queue could drain. Requiring
an administrator to manually restart the follow tasks that faced such an
exception is a burden. This commit addresses this by making CCR
auto-retry on rejected execution exceptions.
We can't guarantee expected request failures if search request is across
many indexes, as if expected shards fail, some indexes may return 200.
closes#47743
The JdbcHttpClientRequestTests and HttpClientRequestTests classes both
hold a static reference to a mock web server that internally uses the
JDKs built-in HttpServer, which resides in a sun package that the
RamUsageEstimator does not have access to. This causes builds that use
a runtime of Java 8 to fail since the StaticFieldsInvariantRule is run
when Java 8 is used.
Relates #41526
Relates #49105
When triggered either by becoming master, a new cluster state, or a
periodic schedule, an ILM policy execution through
`maybeRunAsyncAction`, `runPolicyAfterStateChange`, or
`runPeriodicStep` throwing an exception will cause the loop the
terminate. This means that any indices that would have been processed
after the index where the exception was thrown will not be processed by
ILM.
For most execution this is not a problem because the actual running of
steps is protected by a try/catch that moves the index to the ERROR step
in the event of a problem. If an exception occurs prior to step
execution (for example, in fetching and parsing the current
policy/step) however, it causes the loop termination previously
mentioned.
This commit wraps the invocation of the methods specified above in a
try/catch block that provides better logging and does not bubble the
exception up.
Backport of #47468 to 7.x
This PR adds a new metric aggregation called string_stats that operates on string terms of a document and returns the following:
min_length: The length of the shortest term
max_length: The length of the longest term
avg_length: The average length of all terms
distribution: The probability distribution of all characters appearing in all terms
entropy: The total Shannon entropy value calculated for all terms
This aggregation has been implemented as an analytics plugin.
The rollover action is now a retryable step (see #48256)
so ILM will keep retrying until it succeeds as opposed to stopping and
moving the execution in the ERROR step.
Fixes#49073
(cherry picked from commit 3ae90898121b43032ec8f3b50514d93a86e14d0f)
Signed-off-by: Andrei Dan <andrei.dan@elastic.co>
# Conflicts:
# x-pack/plugin/ilm/qa/multi-node/src/test/java/org/elasticsearch/xpack/ilm/TimeSeriesLifecycleActionsIT.java
Ensures that methods that are called from different threads ( i.e.
from the callbacks of org.apache.http.concurrent.FutureCallback )
catch `Exception` instead of only the expected checked exceptions.
This resolves a bug where OpenIdConnectAuthenticator#mergeObjects
would throw an IllegalStateException that was never caught causing
the thread to hang and the listener to never be called. This would
in turn cause Kibana requests to authenticate with OpenID Connect
to timeout and fail without even logging anything relevant.
This also guards against unexpected Exceptions that might be thrown
by invoked library methods while performing the necessary operations
in these callbacks.
The cluster state is obtained twice in the EnrichPolicyRunner when updating
the final alias. There is a possibility for the state to be slightly different
between those two calls. This PR just has the function get the cluster state
once and reuse it for the life of the function call.
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.
Temporarily "mute" the testReplaceChildren for Pivot since it leads to
failing tests for some seeds, since the new child doesn't respond to a
valid data type.
Relates to #48900
(cherry picked from commit 6200a2207b9a4264d2f3fc976577323c7e084317)
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 a node shuts down, `TransportService` moves to stopped state and
then closes connections. If a request is done in between, an exception
was thrown that was not retried in replication actions. Now throw a
wrapped `NodeClosedException` exception instead, which is correctly
handled in replication action. Fixed other usages too.
Relates #42612
This commit fixes an off-by-one bug in the AutoFollowIT test that causes
failures because the leaderIndices counter is incremented during the evaluation
of the leaderIndices.incrementAndGet() < 20 condition but the 20th index is
not created, making the final assertion not verified.
It also gives a bit more time for cluster state updates to be processed on the
follower cluster.
Closes#48982
Ensures that we always use the primary term established by the primary to index docs on the
replica. Makes the logic around replication less brittle by always using the operation primary
term on the replica that is coming from the primary.
Our documentation regarding FIPS 140 claimed that when using SAML
in a JVM that is configured in FIPS approved only mode, one could
not use encrypted assertions. This stemmed from a wrong
understanding regarding the compliance of RSA-OAEP which is used
as the key wrapping algorithm for encrypting the key with which the
SAML Assertion is encrypted.
However, as stated for instance in
https://downloads.bouncycastle.org/fips-java/BC-FJA-SecurityPolicy-1.0.0.pdf
RSA-OAEP is approved for key transport, so this limitation is not
effective.
This change removes the limitation from our FIPS 140 related
documentation.
This PR makes the following two fixes around updating flattened fields:
* Make sure that the new value for ignore_above is immediately taken into
affect. Previously we recorded the new value but did not use it when parsing
documents.
* Allow depth_limit to be updated dynamically. It seems plausible that a user
might want to tweak this setting as they encounter more data.
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.
Backport of #48908
The enrich project doesn't have much history as all the other gradle projects,
so it makes sense to enable spotless for this gradle project.
The `HttpExportBulk` exporter is using a lot more memory than it needs to
by allocating buffers for serialization and IO:
* Remove copying of all bytes when flushing, instead use the stream wrapper
* Remove copying step turning the BAOS into a `byte[]`
* This also avoids the allocation of a single huge `byte[]` and instead makes use of the internal paging logic of the `BytesStreamOutput`
* Don't allocate a new BAOS for every document, just keep appending to a single BAOS
Currently the BulkProcessor class uses a single scheduler to schedule
flushes and retries. Functionally these are very different concerns but
can result in a dead lock. Specifically, the single shared scheduler
can kick off a flush task, which only finishes it's task when the bulk
that is being flushed finishes. If (for what ever reason), any items in
that bulk fails it will (by default) schedule a retry. However, that retry
will never run it's task, since the flush task is consuming the 1 and
only thread available from the shared scheduler.
Since the BulkProcessor is mostly client based code, the client can
provide their own scheduler. As-is the scheduler would require
at minimum 2 worker threads to avoid the potential deadlock. Since the
number of threads is a configuration option in the scheduler, the code
can not enforce this 2 worker rule until runtime. For this reason this
commit splits the single task scheduler into 2 schedulers. This eliminates
the potential for the flush task to block the retry task and removes this
deadlock scenario.
This commit also deprecates the Java APIs that presume a single scheduler,
and updates any internal code to no longer use those APIs.
Fixes#47599
Note - #41451 fixed the general case where a bulk fails and is retried
that can result in a deadlock. This fix should address that case as well as
the case when a bulk failure *from the flush* needs to be retried.
* [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
Jay Modi
Lisa Cawley
Andrei Dan
Armin Braun
Marios Trivyzas
Benjamin Trent
Przemysław Witek
David Roberts
Julie Tibshirani
Hendrik Muhs
Tanguy Leroux
Dimitris Athanasiou
Jason Tedor
Mayya Sharipova
Lee Hinman
Albert Zaharovits
Christos Soulios
Ioannis Kakavas
James Baiera
Dan Hermann
Rory Hunter
Henning Andersen
Yannick Welsch
Martijn van Groningen
Jake Landis