The HLRC will no longer be published from 8.0.0 and onwards.
Also the HLRC docs are no longer published, so these can be removed now.
The HLRC is currently used in a number of java rest tests and
for this reason it can't be removed completely.
Closes#81299
The functionality to enroll a new node to a cluster was
introduced in #77292 as a CLI tool. This change replaces this
CLI tool with the option to trigger the enrollment functionality
on startup of elasticsearch via a named argument that can be
passed to the elasticsearch startup script (--enrollment-token)
so that the users that want to enroll a node to a cluster can do
this with one command instead of two.
In a followup PR we are introducing a CLI tool version of this
functionality, that can be used to reconfigure packaged
installations.
This PR adds a new "encoded" field to the response of CreateApiKey API.
It is the base64 encoded value of "id:api_key". The field is added for
the convenience of the API consumers so that no extra
computation/encoding is needed. Sometimes the extra computation/encoding
can be error prone or not feasible for simple clients.
Resolves: #50235
This commit changes the Enroll Kibana API to create and return
a token for this service account, instead of setting and returning the
password of the kibana_system built-in user. Both the token name and
value are returned in the response of the API.
The Get service account credentials API now returns file-backed tokens from all
nodes instead of only the local node. For each file-backed service token, we
list names of the nodes where this token is found. The response for node-local
credentials (currently only file-backed tokens) is place inside the
"nodes_credentials.file_tokens" field. There is also a nodes_credentials._nodes
field containing information about the overall request execution (it works the
same way as the _nodes field of Nodes info API, etc.) Detailed response sample
can be found in #74530
This PR also removes the beta label from the API's documentation page.
Resolves: #74530
During implementation we discovered that the clusters should not
necessarily have a unique name and thus we don't need to convey
this information in the response of the Enroll Node API.
This commit adds the ability to specify exclusion patterns in Auto-Follow patterns. This allows excluding indices that match any of the inclusion patterns and also match some of the exclusion patterns giving more fine grained control in scenarios where this is important.
Related #67686
The feature branch contains changes to configure PyTorch models with a
TrainedModelConfig and defines a format to store the binary models.
The _start and _stop deployment actions control the model lifecycle
and the model can be directly evaluated with the _infer endpoint.
2 Types of NLP tasks are supported: Named Entity Recognition and Fill Mask.
The feature branch consists of these PRs: #73523, #72218, #71679#71323, #71035, #71177, #70713
Categorization jobs created once the entire cluster is upgraded to
version 7.14 or higher will default to using the new ml_standard
tokenizer rather than the previous default of the ml_classic
tokenizer, and will incorporate the new first_non_blank_line char
filter so that categorization is based purely on the first non-blank
line of each message.
The difference between the ml_classic and ml_standard tokenizers
is that ml_classic splits on slashes and colons, so creates multiple
tokens from URLs and filesystem paths, whereas ml_standard attempts
to keep URLs, email addresses and filesystem paths as single tokens.
It is still possible to config the ml_classic tokenizer if you
prefer: just provide a categorization_analyzer within your
analysis_config and whichever tokenizer you choose (which could be
ml_classic or any other Elasticsearch tokenizer) will be used.
To opt out of using first_non_blank_line as a default char filter,
you must explicitly specify a categorization_analyzer that does not
include it.
If no categorization_analyzer is specified but categorization_filters
are specified then the categorization filters are converted to char
filters applied that are applied after first_non_blank_line.
Closeselastic/ml-cpp#1724
Enroll node API can be used by new nodes in order to join an
existing cluster that has security features enabled. The response
of a call to this API contains all the necessary information that
the new node requires in order to configure itself and bootstrap
trust with the existing cluster.
Changes:
* Renames 'full copy searchable snapshot' to 'fully mounted index.'
* Renames 'shared cache searchable snapshot' to 'partially mounted index.'
* Removes some unneeded cache setup instructions for the frozen tier. We added a default cache size with #71844.
In #71701 we added a new REST API that provides statistics
about the searchable snapshots cache on Frozen Tier.
This commit adds the necessary plumbing to expose this API
in the High Level REST Client. It also exposes the documentation
of the Mount Snapshot API that was created in #68949 but not
made accessible.
Users can now specify runtime mappings as part of the source config
of a data frame analytics job. Those runtime mappings become part of
the mapping of the destination index. This ensures the fields are
accessible in the destination index even if the relevant data frame
analytics job gets deleted.
Closes#65056
The PR adds early_stopping_enabled optional data frame analysis configuration parameter. The enhancement was already described in elastic/ml-cpp#1676 and so I mark it here as non-issue.
