When we added support for wildcard application names, we started to build
the prefix query along with the term query but we used 'filter' clause
instead of 'should', so this would not fetch the correct application
privilege descriptor thereby failing the _has_privilege checks.
This commit changes the clause to use should and with minimum_should_match
as 1.
Backport of #45794 to 7.x. Convert most `awaitBusy` calls to
`assertBusy`, and use asserts where possible. Follows on from #28548 by
@liketic.
There were a small number of places where it didn't make sense to me to
call `assertBusy`, so I kept the existing calls but renamed the method to
`waitUntil`. This was partly to better reflect its usage, and partly so
that anyone trying to add a new call to awaitBusy wouldn't be able to find
it.
I also didn't change the usage in `TransportStopRollupAction` as the
comments state that the local awaitBusy method is a temporary
copy-and-paste.
Other changes:
* Rework `waitForDocs` to scale its timeout. Instead of calling
`assertBusy` in a loop, work out a reasonable overall timeout and await
just once.
* Some tests failed after switching to `assertBusy` and had to be fixed.
* Correct the expect templates in AbstractUpgradeTestCase. The ES
Security team confirmed that they don't use templates any more, so
remove this from the expected templates. Also rewrite how the setup
code checks for templates, in order to give more information.
* Remove an expected ML template from XPackRestTestConstants The ML team
advised that the ML tests shouldn't be waiting for any
`.ml-notifications*` templates, since such checks should happen in the
production code instead.
* Also rework the template checking code in `XPackRestTestHelper` to give
more helpful failure messages.
* Fix issue in `DataFrameSurvivesUpgradeIT` when upgrading from < 7.4
In the current implementation, the validation of the role query
occurs at runtime when the query is being executed.
This commit adds validation for the role query when creating a role
but not for the template query as we do not have the runtime
information required for evaluating the template query (eg. authenticated user's
information). This is similar to the scripts that we
store but do not evaluate or parse if they are valid queries or not.
For validation, the query is evaluated (if not a template), parsed to build the
QueryBuilder and verify if the query type is allowed.
Closes#34252
* [ML][Transforms] remove `force` flag from _start (#46414)
* [ML][Transforms] remove `force` flag from _start
* fixing expected error message
* adjusting bwc version
* Add retention to Snapshot Lifecycle Management (#46407)
This commit adds retention to the existing Snapshot Lifecycle Management feature (#38461) as described in #43663. This allows a user to configure SLM to automatically delete older snapshots based on a number of criteria.
An example policy would look like:
```
PUT /_slm/policy/snapshot-every-day
{
"schedule": "0 30 2 * * ?",
"name": "<production-snap-{now/d}>",
"repository": "my-s3-repository",
"config": {
"indices": ["foo-*", "important"]
},
// Newly configured retention options
"retention": {
// Snapshots should be deleted after 14 days
"expire_after": "14d",
// Keep a maximum of thirty snapshots
"max_count": 30,
// Keep a minimum of the four most recent snapshots
"min_count": 4
}
}
```
SLM Retention is run on a scheduled configurable with the `slm.retention_schedule` setting, which supports cron expressions. Deletions are run for a configurable time bounded by the `slm.retention_duration` setting, which defaults to 1 hour.
Included in this work is a new SLM stats API endpoint available through
``` json
GET /_slm/stats
```
That returns statistics about snapshot taken and deleted, as well as successful retention runs, failures, and the time spent deleting snapshots. #45362 has more information as well as an example of the output. These stats are also included when retrieving SLM policies via the API.
* Add base framework for snapshot retention (#43605)
* Add base framework for snapshot retention
This adds a basic `SnapshotRetentionService` and `SnapshotRetentionTask`
to start as the basis for SLM's retention implementation.
Relates to #38461
* Remove extraneous 'public'
* Use a local var instead of reading class var repeatedly
* Add SnapshotRetentionConfiguration for retention configuration (#43777)
* Add SnapshotRetentionConfiguration for retention configuration
This commit adds the `SnapshotRetentionConfiguration` class and its HLRC
counterpart to encapsulate the configuration for SLM retention.
Currently only a single parameter is supported as an example (we still
need to discuss the different options we want to support and their
names) to keep the size of the PR down. It also does not yet include version serialization checks
since the original SLM branch has not yet been merged.
Relates to #43663
* Fix REST tests
* Fix more documentation
* Use Objects.equals to avoid NPE
* Put `randomSnapshotLifecyclePolicy` in only one place
* Occasionally return retention with no configuration
* Implement SnapshotRetentionTask's snapshot filtering and delet… (#44764)
* Implement SnapshotRetentionTask's snapshot filtering and deletion
This commit implements the snapshot filtering and deletion for
`SnapshotRetentionTask`. Currently only the expire-after age is used for
determining whether a snapshot is eligible for deletion.
Relates to #43663
* Fix deletes running on the wrong thread
* Handle missing or null policy in snap metadata differently
* Convert Tuple<String, List<SnapshotInfo>> to Map<String, List<SnapshotInfo>>
* Use the `OriginSettingClient` to work with security, enhance logging
* Prevent NPE in test by mocking Client
* Allow empty/missing SLM retention configuration (#45018)
Semi-related to #44465, this allows the `"retention"` configuration map
to be missing.
Relates to #43663
* Add min_count and max_count as SLM retention predicates (#44926)
This adds the configuration options for `min_count` and `max_count` as
well as the logic for determining whether a snapshot meets this criteria
to SLM's retention feature.
These options are optional and one, two, or all three can be specified
in an SLM policy.
Relates to #43663
* Time-bound deletion of snapshots in retention delete function (#45065)
* Time-bound deletion of snapshots in retention delete function
With a cluster that has a large number of snapshots, it's possible that
snapshot deletion can take a very long time (especially since deletes
currently have to happen in a serial fashion). To prevent snapshot
deletion from taking forever in a cluster and blocking other operations,
this commit adds a setting to allow configuring a maximum time to spend
deletion snapshots during retention. This dynamic setting defaults to 1
hour and is best-effort, meaning that it doesn't hard stop a deletion
at an hour mark, but ensures that once the time has passed, all
subsequent deletions are deferred until the next retention cycle.
Relates to #43663
* Wow snapshots suuuure can take a long time.
* Use a LongSupplier instead of actually sleeping
* Remove TestLogging annotation
* Remove rate limiting
* Add SLM metrics gathering and endpoint (#45362)
* Add SLM metrics gathering and endpoint
This commit adds the infrastructure to gather metrics about the different SLM actions that a cluster
takes. These actions are stored in `SnapshotLifecycleStats` and perpetuated in cluster state. The
stats stored include the number of snapshots taken, failed, deleted, the number of retention runs,
as well as per-policy counts for snapshots taken, failed, and deleted. It also includes the amount
of time spent deleting snapshots from SLM retention.
This commit also adds an endpoint for retrieving all stats (further commits will expose this in the
SLM get-policy API) that looks like:
```
GET /_slm/stats
{
"retention_runs" : 13,
"retention_failed" : 0,
"retention_timed_out" : 0,
"retention_deletion_time" : "1.4s",
"retention_deletion_time_millis" : 1404,
"policy_metrics" : {
"daily-snapshots2" : {
"snapshots_taken" : 7,
"snapshots_failed" : 0,
"snapshots_deleted" : 6,
"snapshot_deletion_failures" : 0
},
"daily-snapshots" : {
"snapshots_taken" : 12,
"snapshots_failed" : 0,
"snapshots_deleted" : 12,
"snapshot_deletion_failures" : 6
}
},
"total_snapshots_taken" : 19,
"total_snapshots_failed" : 0,
"total_snapshots_deleted" : 18,
"total_snapshot_deletion_failures" : 6
}
```
This does not yet include HLRC for this, as this commit is quite large on its own. That will be
added in a subsequent commit.
Relates to #43663
* Version qualify serialization
* Initialize counters outside constructor
* Use computeIfAbsent instead of being too verbose
* Move part of XContent generation into subclass
* Fix REST action for master merge
* Unused import
* Record history of SLM retention actions (#45513)
This commit records the deletion of snapshots by the retention component
of SLM into the SLM history index for the purposes of reviewing operations
taken by SLM and alerting.
* Retry SLM retention after currently running snapshot completes (#45802)
* Retry SLM retention after currently running snapshot completes
This commit adds a ClusterStateObserver to wait until the currently
running snapshot is complete before proceeding with snapshot deletion.
SLM retention waits for the maximum allowed deletion time for the
snapshot to complete, however, the waiting time is not factored into
the limit on actual deletions.
Relates to #43663
* Increase timeout waiting for snapshot completion
* Apply patch
From 2374316f0d.patch
* Rename test variables
* [TEST] Be less strict for stats checking
* Skip SLM retention if ILM is STOPPING or STOPPED (#45869)
This adds a check to ensure we take no action during SLM retention if
ILM is currently stopped or in the process of stopping.
Relates to #43663
* Check all actions preventing snapshot delete during retention (#45992)
* Check all actions preventing snapshot delete during retention run
Previously we only checked to see if a snapshot was currently running,
but it turns out that more things can block snapshot deletion. This
changes the check to be a check for:
- a snapshot currently running
- a deletion already in progress
- a repo cleanup in progress
- a restore currently running
This was found by CI where a third party delete in a test caused SLM
retention deletion to throw an exception.
Relates to #43663
* Add unit test for okayToDeleteSnapshots
* Fix bug where SLM retention task would be scheduled on every node
* Enhance test logging
* Ignore if snapshot is already deleted
* Missing import
* Fix SnapshotRetentionServiceTests
* Expose SLM policy stats in get SLM policy API (#45989)
This also adds support for the SLM stats endpoint to the high level rest client.
Retrieving a policy now looks like:
```json
{
"daily-snapshots" : {
"version": 1,
"modified_date": "2019-04-23T01:30:00.000Z",
"modified_date_millis": 1556048137314,
"policy" : {
"schedule": "0 30 1 * * ?",
"name": "<daily-snap-{now/d}>",
"repository": "my_repository",
"config": {
"indices": ["data-*", "important"],
"ignore_unavailable": false,
"include_global_state": false
},
"retention": {}
},
"stats": {
"snapshots_taken": 0,
"snapshots_failed": 0,
"snapshots_deleted": 0,
"snapshot_deletion_failures": 0
},
"next_execution": "2019-04-24T01:30:00.000Z",
"next_execution_millis": 1556048160000
}
}
```
Relates to #43663
* Rewrite SnapshotLifecycleIT as as ESIntegTestCase (#46356)
* Rewrite SnapshotLifecycleIT as as ESIntegTestCase
This commit splits `SnapshotLifecycleIT` into two different tests.
`SnapshotLifecycleRestIT` which includes the tests that do not require
slow repositories, and `SLMSnapshotBlockingIntegTests` which is now an
integration test using `MockRepository` to simulate a snapshot being in
progress.
Relates to #43663Resolves#46205
* Add error logging when exceptions are thrown
* Update serialization versions
* Fix type inference
* Use non-Cancellable HLRC return value
* Fix Client mocking in test
* Fix SLMSnapshotBlockingIntegTests for 7.x branch
* Update SnapshotRetentionTask for non-multi-repo snapshot retrieval
* Add serialization guards for SnapshotLifecyclePolicy
ML users who upgrade from versions prior to 7.4 to 7.4 or later
will have ML results indices that do not have mappings for the
total_search_time_ms field. Therefore, when searching these
indices we must tolerate this field not having a mapping.
Fixes#46437
This PR merges the `vectors-optimize-brute-force` feature branch, which makes
the following changes to how vector functions are computed:
* Precompute the L2 norm of each vector at indexing time. (#45390)
* Switch to ByteBuffer for vector encoding. (#45936)
* Decode vectors and while computing the vector function. (#46103)
* Use an array instead of a List for the query vector. (#46155)
* Precompute the normalized query vector when using cosine similarity. (#46190)
Co-authored-by: Mayya Sharipova <mayya.sharipova@elastic.co>
Currently, when using script_score functions like cosineSimilarity, the query
vector is treated as an array of doubles. Since the stored document vectors use
floats, it seems like the least surprising behavior for the query vectors to
also be float arrays.
In addition to improving consistency, this change may help with some
optimizations we have been considering around vector dot product.
Adds a parameter `training_percent` to regression. The default
value is `100`. When the parameter is set to a value less than `100`,
from the rows that can be used for training (ie. those that have a
value for the dependent variable) we randomly choose whether to actually
use for training. This enables splitting the data into a training set and
the rest, usually called testing, validation or holdout set, which allows
for validating the model on data that have not been used for training.
Technically, the analytics process considers as training the data that
have a value for the dependent variable. Thus, when we decide a training
row is not going to be used for training, we simply clear the row's
dependent variable.
The existing privilege model for API keys with privileges like
`manage_api_key`, `manage_security` etc. are too permissive and
we would want finer-grained control over the cluster privileges
for API keys. Previously APIs created would also need these
privileges to get its own information.
This commit adds support for `manage_own_api_key` cluster privilege
which only allows api key cluster actions on API keys owned by the
currently authenticated user. Also adds support for retrieval of
the API key self-information when authenticating via API key
without the need for the additional API key privileges.
To support this privilege, we are introducing additional
authentication context along with the request context such that
it can be used to authorize cluster actions based on the current
user authentication.
The API key get and invalidate APIs introduce an `owner` flag
that can be set to true if the API key request (Get or Invalidate)
is for the API keys owned by the currently authenticated user only.
In that case, `realm` and `username` cannot be set as they are
assumed to be the currently authenticated ones.
The changes cover HLRC changes, documentation for the API changes.
Closes#40031
This commit introduces PKI realm delegation. This feature
supports the PKI authentication feature in Kibana.
In essence, this creates a new API endpoint which Kibana must
call to authenticate clients that use certificates in their TLS
connection to Kibana. The API call passes to Elasticsearch the client's
certificate chain. The response contains an access token to be further
used to authenticate as the client. The client's certificates are validated
by the PKI realms that have been explicitly configured to permit
certificates from the proxy (Kibana). The user calling the delegation
API must have the delegate_pki privilege.
Closes#34396
This adds a pipeline aggregation that calculates the cumulative
cardinality of a field. It does this by iteratively merging in the
HLL sketch from consecutive buckets and emitting the cardinality up
to that point.
This is useful for things like finding the total "new" users that have
visited a website (as opposed to "repeat" visitors).
This is a Basic+ aggregation and adds a new Data Science plugin
to house it and future advanced analytics/data science aggregations.
The native process requires that there be a non-zero number of rows to analyze. If the flag --rows 0 is passed to the executable, it throws and does not start.
When building the configuration for the process we should not start the native process if there are no rows.
Adding some logging to indicate what is occurring.
Since #45136, we use soft-deletes instead of translog in peer recovery.
There's no need to retain extra translog to increase a chance of
operation-based recoveries. This commit ignores the translog retention
policy if soft-deletes is enabled so we can discard translog more
quickly.
Backport of #45473
Relates #45136
* [ML] better handle empty results when evaluating regression
* adding new failure test to ml_security black list
* fixing equality check for regression results
* Update the REST API specification
This patch updates the REST API spefication in JSON files to better encode deprecated entities,
to improve specification of URL paths, and to open up the schema for future extensions.
Notably, it changes the `paths` from a list of strings to a list of objects, where each
particular object encodes all the information for this particular path: the `parts` and the `methods`.
Among the benefits of this approach is eg. encoding the difference between using the `PUT` and `POST`
methods in the Index API, to either use a specific document ID, or let Elasticsearch generate one.
Also `documentation` becomes an object that supports an `url` and also a `description` which is a
new field.
* Adapt YAML runner to new REST API specification format
The logic for choosing the path to use when running tests has been
simplified, as a consequence of the path parts being listed under each
path in the spec. The special case for create and index has been removed.
Also the parsing code has been hardened so that errors are thrown earlier
when the structure of the spec differs from what expected, and their
error messages should be more helpful.
This commit adds a first draft of a regression analysis
to data frame analytics. There is high probability that
the exact syntax might change.
This commit adds the new analysis type and its parameters as
well as appropriate validation. It also modifies the extractor
and the fields detector to be able to handle categorical fields
as regression analysis supports them.
This commit replaces task_state and indexer_state in the
data frame _stats output with a single top level state
that combines the two. It is defined as:
- failed if what's currently reported as task_state is failed
- stopped if there is no persistent task
- Otherwise what's currently reported as indexer_state
Backport of #45276
* [ML][Data Frame] Add update transform api endpoint (#45154)
This adds the ability to `_update` stored data frame transforms. All mutable fields are applied when the next checkpoint starts. The exception being `description`.
This PR contains all that is necessary for this addition:
* HLRC
* Docs
* Server side
The PutJob API accidentally used an "expert" API of CreateIndexRequest.
That API is semi-lenient to syntax; a type could be omitted and the
request would work as expected. But if a type was omitted it would
not merge with templates correctly, leading to index creation that
only has the template and not the requested mappings in the request.
This commit refactors the PutJob API to:
- Include the type name
- Use a less "expert" API in an attempt to future proof against errors
- Uses an XContentBuilder instead of string replacing, removes json template
If one tries to start a DF analytics job that has already run,
the result will be that the task will fail after reindexing the
dest index from the source index. The results of the prior run
will be gone and the task state is not properly set to failed
with the failure reason.
This commit improves the behavior in this scenario. First, we
set the task state to `failed` in a set of failures that were
missed. Second, a validation is added that if the destination
index exists, it must be empty.
In order to make it easier to interpret the output of the ILM Explain
API, this commit adds two request parameters to that API:
- `only_managed`, which causes the response to only contain indices
which have `index.lifecycle.name` set
- `only_errors`, which causes the response to contain only indices in an
ILM error state
"Error state" is defined as either being in the `ERROR` step or having
`index.lifecycle.name` set to a policy that does not exist.
* Switch from using docvalue_fields to extracting values from _source
where applicable. Doing this means parsing the _source and handling the
numbers parsing just like Elasticsearch is doing it when it's indexing
a document.
* This also introduces a minor limitation: aliases type of fields that
are NOT part of a tree of sub-fields will not be able to be retrieved
anymore. field_caps API doesn't shed any light into a field being an
alias or not and at _source parsing time there is no way to know if a
root field is an alias or not. Fields of the type "a.b.c.alias" can be
extracted from docvalue_fields, only if the field they point to can be
extracted from docvalue_fields. Also, not all fields in a hierarchy of
fields can be evaluated to being an alias.
(cherry picked from commit 8bf8a055e38f00df5f49c8d97f632f69d6e00c2c)
* Only emit deprecation warning if there was actual change of a datafeed's job_id.
* Add @Deprecated annotation to DatafeedUpdate.Builder#setJobId method
This change adjusts the data frame transforms stats
endpoint to return a structure that is easier to
understand.
This is a breaking change for clients of the data frame
transforms stats endpoint, but the feature is in beta so
stability is not guaranteed.
Backport of #44350
