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.
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>
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
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 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>
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
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>
`foreach` processors store information within the `_ingest` metadata object.
This commit adds the contents of the `_ingest` metadata (if it is not empty).
And will append new inference results if the result field already exists.
This allows a `foreach` to execute and multiple inference results being written to the same result field.
closes https://github.com/elastic/elasticsearch/issues/60867
Use thread-local buffers and deflater and inflater instances to speed up
compressing and decompressing from in-memory bytes.
Not manually invoking `end()` on these should be safe since their off-heap memory
will eventually be reclaimed by the finalizer thread which should not be an issue for thread-locals
that are not instantiated at a high frequency.
This significantly reduces the amount of byte copying and object creation relative to the previous approach
which had to create a fresh temporary buffer (that was then resized multiple times during operations), copied
bytes out of that buffer to a freshly allocated `byte[]`, used 4k stream buffers needlessly when working with
bytes that are already in arrays (`writeTo` handles efficient writing to the compression logic now) etc.
Relates #57284 which should be helped by this change to some degree.
Also, I expect this change to speed up mapping/template updates a little as those make heavy use of these
code paths.
This adds a force-merge step to the searchable snapshot action, enabled by default,
but parameterizable using the `force_merge-index" optional boolean.
eg.
```
PUT _ilm/policy/my_policy
{
"policy": {
"phases": {
"cold": {
"actions": {
"searchable_snapshot" : {
"snapshot_repository" : "backing_repo",
"force_merge_index": true
}
}
}
}
}
}
```
(cherry picked from commit d0a17b2d35f1b083b574246bdbf3e1929471a4a9)
Signed-off-by: Andrei Dan <andrei.dan@elastic.co>
remove test, scripts are excluded in the change collector, the test is a leftover from a previous
solution of #57332, which has been discarded
relates #60724fixes#60794
This pull request adds recovery state tracking for Searchable Snapshots.
In order to track recoveries for searchable snapshot backed indices, this pull
request adds a new type of RecoveryState.
This newRecoveryState instance is able to deal with the
small differences that arise during Searchable snapshots recoveries.
Those differences can be summarized as follows:
- The Directory implementation that's provided by SearchableSnapshots mark the
snapshot files as reused during recovery. In order to keep track of the
recovery process as the cache is pre-warmed, those files shouldn't be marked
as reused.
- Once the shard is created, the cache starts its pre-warming phase, meaning that
we should keep track of those downloads during that process and tie the recovery
to this pre-warming phase. The shard is considered recovered once this pre-warming
phase has finished.
Backport of #60505
disable optimizations when using scripts in group_by, when scripts using scripts we can not predict
the outcome and we have no query counterpart. Other optimizations for other group_by's are not
affected.
fixes#57332
Implements license degradation behavior for searchable snapshots. Snapshot-backed shards are failed when the license becomes invalid, and shards won't be reallocated. After valid license is put in place again, shards are allocated again.
We have various ways of copying between two streams and handling thread-local
buffers throughout the codebase. This commit unifies a number of them and
removes buffer allocations in many spots.
- Replace immediate task creations by using task avoidance api
- One step closer to #56610
- Still many tasks are created during configuration phase. Tackled in separate steps
In order to unify model inference and analytics results we
need to write the same fields.
prediction_probability and prediction_score are now written
for inference calls against classification models.
If a feature is created via a custom pre-processor,
we should return the importance for that feature.
This means we will not return the importance for the
original document field for custom processed features.
closes https://github.com/elastic/elasticsearch/issues/59330
Putting an ingest pipeline used to require that the user calling
it had permission to get nodes info as well as permission to
manage ingest. This was due to an internal implementaton detail
that was not visible to the end user.
This change alters the behaviour so that a user with the
manage_pipeline cluster privilege can put an ingest pipeline
regardless of whether they have the separate privilege to get
nodes info. The internal implementation detail now runs as
the internal _xpack user when security is enabled.
Backport of #60106
This PR removes the expand_wildcards and forbid_closed_indices parameters from the Data
Streams Stats REST endpoint. These options are required for broadcast requests, but are not
needed for anything in terms of resolving data streams. Instead, we just set a default set of
IndicesOptions on the transport request.
* Adding new `require_alias` option to indexing requests (#58917)
This commit adds the `require_alias` flag to requests that create new documents.
This flag, when `true` prevents the request from automatically creating an index. Instead, the destination of the request MUST be an alias.
When the flag is not set, or `false`, the behavior defaults to the `action.auto_create_index` settings.
This is useful when an alias is required instead of a concrete index.
closes https://github.com/elastic/elasticsearch/issues/55267
* [ML] add new `custom` field to trained model processors (#59542)
This commit adds the new configurable field `custom`.
`custom` indicates if the preprocessor was submitted by a user or automatically created by the analytics job.
Eventually, this field will be used in calculating feature importance. When `custom` is true, the feature importance for
the processed fields is calculated. When `false` the current behavior is the same (we calculate the importance for the originating field/feature).
This also adds new required methods to the preprocessor interface. If users are to supply their own preprocessors
in the analytics job configuration, we need to know the input and output field names.
David Turner
Benjamin Trent
Przemyslaw Gomulka
Igor Motov
David Kyle
Yang Wang
Lloyd
Mark Tozzi
David Roberts
Lee Hinman
Andrei Dan
Armin Braun
Dimitris Athanasiou
Hendrik Muhs
Ryan Ernst
Francisco Fernández Castaño
Przemysław Witek
Yannick Welsch
Adrien Grand
Rene Groeschke
Jim Ferenczi
Dan Hermann
Larry Gregory
James Baiera