Inference processors asynchronously usage write stats to the .ml-stats index after they used.
In tests the write can leak into the next test causing failures depending on which test follows.
This change waits for the usage stats docs to be written at the end of the test
If a TLS-protected connection closes unexpectedly then today we often
emit a `WARN` log, typically one of the following:
io.netty.handler.codec.DecoderException: javax.net.ssl.SSLHandshakeException: Insufficient buffer remaining for AEAD cipher fragment (2). Needs to be more than tag size (16)
io.netty.handler.codec.DecoderException: javax.net.ssl.SSLException: Received close_notify during handshake
We typically only report unexpectedly-closed connections at `DEBUG`
level, but these two messages don't follow that rule and generate a lot
of noise as a result. This commit adjusts the logging to report these
two exceptions at `DEBUG` level only.
Today we use `long` to represent the number of parts of a blob. There's
no need for this extra range, it forces us to do some casting elsewhere,
and indeed when snapshotting we iterate over the parts using an `int`
which would be an infinite loop in case of overflow anyway:
for (int i = 0; i < fileInfo.numberOfParts(); i++) {
This commit changes the representation of the number of parts of a blob
to an `int`.
We convert longs to ints using `Math.toIntExact` in places where we're
sure there will be no overflow, but this doesn't explain the intent of
these conversions very well. This commit introduces a dedicated method
for these conversions, and adds an assertion that we never overflow.
If a searchable snapshot shard fails (e.g. its node leaves the cluster)
we want to be able to start it up again on a different node as quickly
as possible to avoid unnecessarily blocking or failing searches. It
isn't feasible to fully restore such shards in an acceptably short time.
In particular we would like to be able to deal with the `can_match`
phase of a search ASAP so that we can skip unnecessary waiting on shards
that may still be warming up but which are not required for the search.
This commit solves this problem by introducing a system index that holds
much of the data required to start a shard. Today(*) this means it holds
the contents of every file with size <8kB, and the first 4kB of every
other file in the shard. This system index acts as a second-level cache,
behind the first-level node-local disk cache but in front of the blob
store itself. Reading chunks from the index is slower than reading them
directly from disk, but faster than reading them from the blob store,
and is also replicated and accessible to all nodes in the cluster.
(*) the exact heuristics for what we should put into the system index
are still under investigation and may change in future.
This second-level cache is populated when we attempt to read a chunk
which is missing from both levels of cache and must therefore be read
from the blob store.
We also introduce `SearchableSnapshotsBlobStoreCacheIntegTests` which
verify that we do not hit the blob store more than necessary when
starting up a shard that we've seen before, whether due to a node
restart or because a snapshot was mounted multiple times.
Backport of #60522
Co-authored-by: Tanguy Leroux <tlrx.dev@gmail.com>
If a search failure occurs during data frame extraction we catch
the error and retry once. However, we retry another search that is
identical to the first one. This means we will re-fetch any docs
that were already processed. This may result either to training
a model using duplicate data or in the case of outlier detection to
an error message that the process received more records than it
expected.
This commit fixes this issue by tracking the latest doc's sort key
and then using that in a range query in case we restart the search
due to a failure.
Backport of #61544
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
Backports the following commits to 7.x:
[ML] write warning if configured memory limit is too low for analytics job (#61505)
Having `_start` fail when the configured memory limit is too low can be frustrating.
We should instead warn the user that their job might not run properly if their configured limit is too low.
It might be that our estimate is too high, and their configured limit works just fine.
DeprecationLogger's constructor should not create two loggers. It was
taking parent logger instance, changing its name with a .deprecation
prefix and creating a new logger.
Most of the time parent logger was not needed. It was causing Log4j to
unnecessarily cache the unused parent logger instance.
depends on #61515
backports #58435
Backport to add case insensitive support for regex queries.
Forks a copy of Lucene’s RegexpQuery and RegExp from Lucene master.
This can be removed when 8.7 Lucene is released.
Closes#59235
Splitting DeprecationLogger into two. HeaderWarningLogger - responsible for adding a response warning headers and ThrottlingLogger - responsible for limiting the duplicated log entries for the same key (previously deprecateAndMaybeLog).
Introducing A ThrottlingAndHeaderWarningLogger which is a base for other common logging usages where both response warning header and logging throttling was needed.
relates #55699
relates #52369
backports #55941
The building block of the eql response is currently the SearchHit. This
is a problem since it is tied to an actual search, and thus has scoring,
highlighting, shard information and a lot of other things that are not
relevant for EQL.
This becomes a problem when doing sequence queries since the response is
not generated from one search query and thus there are no SearchHits to
speak of.
Emulating one is not just conceptually incorrect but also problematic
since most of the data is missed or made-up.
As such this PR introduces a simple class, Event, that maps nicely to
the terminology while hiding the ES internals (the use of SearchHit or
GetResult/GetResponse depending on the API used).
Fix#59764Fix#59779
Co-authored-by: Igor Motov <igor@motovs.org>
(cherry picked from commit 997376fbe6ef2894038968842f5e0635731ede65)
* Faster `equals` for `BytesArray` which is nice since with this change we use it for the search cache
* Lighter `StreamInput` for `BytesArray` that should save memory and some indirection relative to the one on the abstract bytes reference
* Lighter `writeTo` implementation
* Build a `BytesArray` instead of a PagedBytesReference whenever possible to save indirection and memory
This is mostly motivated by the performance issues we are seeing around the GET mappings
REST API which (in case of a large number of indices) will create decompressing streams in a hot loop
which takes a significant amount of time for the system calls involved in instantiating deflaters
and inflaters.
Also, this fixes a leaked deflater when deserializing cached repository data.
This commit removes the log info message "Created ML annotations index and aliases".
The message comes in addition to elasticsearch's index creation logging and it does
not add to it. In addition, since #61107 that message may be logged multiple times.
Backport of #61461
Report anonymous roles in response to "GET _security/_authenticate" API call when:
* Anonymous role is enabled
* User is not the anonymous user
* Credentials is not an API Key
There are warnings about unlicense realms when user lookup fails. This PR adds
similar warnings for when no authentication token can be extracted from the request.
The API key document currently doesn't include the user's full_name or email attributes,
and as a result, when those attributes return `null` when hitting `GET`ing `/_security/_authenticate`,
and in the SAML response from the [IdP Plugin](https://github.com/elastic/elasticsearch/pull/54046).
This changeset adds those fields to the document and extracts them to fill in the User when
authenticating. They're effectively going to be a snapshot of the User from when the key was
created, but this is in line with roles and metadata as well.
Signed-off-by: lloydmeta <lloydmeta@gmail.com>
Before when a value was copied to a field through a parent field or `copy_to`,
we parsed it using the `FieldMapper` from the source field. Instead we should
parse it using the target `FieldMapper`. This ensures that we apply the
appropriate mapping type and options to the copied value.
To implement the fix cleanly, this PR refactors the value parsing strategy. Now
instead of looking up values directly, field mappers produce a helper object
`ValueFetcher`. The value fetchers are responsible for almost all aspects of
fetching, including looking up the right paths in the _source.
The PR is fairly big but each commit can be reviewed individually.
Fixes#61033.
In addition, this commit converts ScaledFloatFieldMapper as it was relying
on a number of static values taken from NumberFieldMapper that had changed
or been removed.
This switches a few tests for field mappers from `ESSingleNodeTestCase`
to `ESTestCase` because, in general, we prefer to avoid
`ESSingleNodeTestCase` when we can because it is slow and "big". "Big"
here means that it pulls in an entire node, making it difficult to
reason about what you are testing.
The test didn't take into account the case where 0 documents are
indexed into the shard, meaning that files aren't loaded during
the pre-warm phase. The test injects FileSystem failures, if
the snapshot doesn't contain any files, pre-warm doesn't read
any files and the recovery completes normally.
Closes#61295
Backport of #61317
Adds a method to make a random date `DateFormatter` pattern. We expect
this'll be useful for runtime fields to compate their formatting with
the standard date field.
feature_processors allow users to create custom features from
individual document fields.
These `feature_processors` are the same object as the trained model's pre_processors.
They are passed to the native process and the native process then appends them to the
pre_processor array in the inference model.
closes https://github.com/elastic/elasticsearch/issues/59327
When the ML annotations index was first added, only the
ML UI wrote to it, so the code to create it was designed
with this in mind. Now the ML backend also creates
annotations, and those mappings can change between
versions.
In this change:
1. The code that runs on the master node to create the
annotations index if it doesn't exist but another ML
index does also now ensures the mappings are up-to-date.
This is good enough for the ML UI's use of the
annotations index, because the upgrade order rules say
that the whole Elasticsearch cluster must be upgraded
prior to Kibana, so the master node should be on the
newer version before Kibana tries to write an
annotation with the new fields.
2. We now also check whether the annotations index exists
with the correct mappings before starting an autodetect
process on a node. This is necessary because ML nodes
can be upgraded before the master node, so could write
an annotation with the new fields before the master node
knows about the new fields.
Backport of #61107
When a user upgrades between versions, they may stop their ML jobs.
Then when the upgrade is complete, they will want to open the jobs again.
But, when opening a job, we attempt to clear out the jobs finished_time. If the job configuration has adjusted between the versions (i.e. added a new field), it will dynamically update the .ml-config index.
We should instead manually change the mapping to be the updated version.
Fixes a test failure in which we allocated some shards and then
relocated them elsewhere, invalidating an assertion about the recovery
statistics which assumed that the shards stayed where they were
originally allocated.
Closes#61067.
This commit adds the `data_hot`, `data_warm`, `data_cold`, and `data_frozen` node roles to the
x-pack plugin. These roles are intended to be the base for the formalization of data tiers in
Elasticsearch.
These roles all act as data nodes (meaning shards can be allocated to them). Nodes with the existing
`data` role acts as though they have all of the roles configured (it is a hot, warm, cold, and
frozen node).
This also includes a custom `AllocationDecider` that allows the user to configure the following
settings on a cluster level:
- `cluster.routing.allocation.require._tier`
- `cluster.routing.allocation.include._tier`
- `cluster.routing.allocation.exclude._tier`
And in index settings:
- `index.routing.allocation.require._tier`
- `index.routing.allocation.include._tier`
- `index.routing.allocation.exclude._tier`
Relates to #60848
This adds a frozen phase to ILM that will allow the execution of the
set_priority, unfollow, allocate, freeze and searchable_snapshot actions.
The frozen phase will be executed after the cold and before the delete phase.
(cherry picked from commit 6d0148001c3481290ed7e60dab588e0191346864)
Signed-off-by: Andrei Dan <andrei.dan@elastic.co>
Today a snapshot repository verification ensures that all master-eligible and data nodes have write access to the
snapshot repository (and can see each other's data) since taking a snapshot requires data nodes and the currently
elected master to write to the repository. However, a dedicated voting-only master-eligible node is not a data node and
will never be the elected master so we should not require it to have write access to the repository.
Closes#59649
David Kyle
David Turner
Ioannis Kakavas
Dimitris Athanasiou
Benjamin Trent
Przemyslaw Gomulka
Przemysław Witek
Igor Motov
markharwood
Costin Leau
Armin Braun
Yang Wang
Lloyd
Julie Tibshirani
Alan Woodward
Nik Everett
Francisco Fernández Castaño
Andrei Stefan
Martijn van Groningen
Mark Tozzi
David Roberts
Nhat Nguyen
Lee Hinman
Andrei Dan
Yannick Welsch