This PR implements recursive mapping merging for composable index templates.
When creating an index, we perform the following:
* Add each component template mapping in order, merging each one in after the
last.
* Merge in the index template mappings (if present).
* Merge in the mappings on the index request itself (if present).
Some principles:
* All 'structural' changes are disallowed (but everything else is fine). An
object mapper can never be changed between `type: object` and `type: nested`. A
field mapper can never be changed to an object mapper, and vice versa.
* Generally, each section is merged recursively. This includes `object`
mappings, as well as root options like `dynamic_templates` and `meta`. Once we
reach 'leaf components' like field definitions, they always overwrite an
existing one instead of being merged.
Relates to #53101.
When per_partition_categorization.stop_on_warn is set for an analysis
config it is now passed through to the autodetect C++ process.
Also adds some end-to-end tests that exercise the functionality
added in elastic/ml-cpp#1356
Backport of #58632
* Replace compile configuration usage with api (#58451)
- Use java-library instead of plugin to allow api configuration usage
- Remove explicit references to runtime configurations in dependency declarations
- Make test runtime classpath input for testing convention
- required as java library will by default not have build jar file
- jar file is now explicit input of the task and gradle will ensure its properly build
* Fix compile usages in 7.x branch
Adds parsing of `status` and `memory_reestimate_bytes`
to data frame analytics `memory_usage`. When the training surpasses
the model memory limit, the status will be set to `hard_limit` and
`memory_reestimate_bytes` can be used to update the job's
limit in order to restart the job.
Backport of #58588
Today we have individual settings for configuring node roles such as
node.data and node.master. Additionally, roles are pluggable and we have
used this to introduce roles such as node.ml and node.voting_only. As
the number of roles is growing, managing these becomes harder for the
user. For example, to create a master-only node, today a user has to
configure:
- node.data: false
- node.ingest: false
- node.remote_cluster_client: false
- node.ml: false
at a minimum if they are relying on defaults, but also add:
- node.master: true
- node.transform: false
- node.voting_only: false
If they want to be explicit. This is also challenging in cases where a
user wants to have configure a coordinating-only node which requires
disabling all roles, a list which we are adding to, requiring the user
to keep checking whether a node has acquired any of these roles.
This commit addresses this by adding a list setting node.roles for which
a user has explicit control over the list of roles that a node has. If
the setting is configured, the node has exactly the roles in the list,
and not any additional roles. This means to configure a master-only
node, the setting is merely 'node.roles: [master]', and to configure a
coordinating-only node, the setting is merely: 'node.roles: []'.
With this change we deprecate the existing 'node.*' settings such as
'node.data'.
* [ML] make waiting for renormalization optional for internally flushing job (#58537)
When flushing, datafeeds only need the guaruntee that the latest bucket has been handled.
But, in addition to this, the typical call to flush waits for renormalization to complete. For large jobs, this can take a fair bit of time (even longer than a bucket length). This causes unnecessary delays in handling data.
This commit adds a new internal only flag that allows datafeeds (and forecasting) to skip waiting on renormalization.
closes#58395
This changes the default value for the results field of inference
applied on models that are trained via a data frame analytics job.
Previously, the results field default was `predicted_value`. This
commit makes it the same as in the training job itself. The new
default field is `<dependent_variable>_prediction`. Apart from
making inference consistent with the training job the model came
from, it is helpful to preserve the dependent variable name
by default as it provides some context to the user that may
avoid confusion as to which model results came from.
Backport of #58538
This commits allows data streams to be a valid source for analytics and transforms.
Data streams are fairly transparent and our `_search` and `_reindex` actions work without error.
For `_transforms` the check-pointing works as desired as well. Data streams are effectively treated as an `alias` and the backing index values are stored within checkpointing information.
Unlike `classification`, which is using a cross validation splitter
that produces training sets whose size is predictable and equal to
`training_percent * class_cardinality`, for regression we have been
using a random splitter that takes an independent decision for each
document. This means we cannot predict the exact size of the training
set. This poses a problem as we move towards performing test inference
on the java side as we need to be able to provide an accurate upper
bound of the training set size to the c++ process.
This commit replaces the random splitter we use for regression with
the same streaming-reservoir approach we do for `classification`.
Backport of #58331
There was a discrepancy in the implementation of flush
acknowledgements: most of the class was designed on the
basis that the "last finalized bucket time" could be null
but the wire serialization assumed that it was never
null. This works because, the C++ sends zero "last
finalized bucket time" when it is not known or not
relevant. But then the Java code will print that to
XContent as it is assuming null represents not known or
not relevant.
This change corrects the discrepancies. Internally within
the class null represents not known or not relevant, but
this is translated from/to 0 for communications from the
C++ and old nodes that have the bug.
Additionally I switched from Date to Instant for this
class and made the member variables final to modernise it
a bit.
Backport of #58413
Backporting #58096 to 7.x branch.
Relates to #53100
* use mapping source direcly instead of using mapper service to extract the relevant mapping details
* moved assertion to TimestampField class and added helper method for tests
* Improved logic that inserts timestamp field mapping into an mapping.
If the timestamp field path consisted out of object fields and
if the final mapping did not contain the parent field then an error
occurred, because the prior logic assumed that the object field existed.
When a local model is constructed, the cache hit miss count is incremented.
When a user calls _stats, we will include the sum cache hit miss count across ALL nodes. This statistic is important to in comparing against the inference_count. If the cache hit miss count is near the inference_count it indicates that the cache is overburdened, or inappropriately configured.
This commit bumps our JNA dependency from 4.5.1 to 5.5.0, so that we are
now on the latest maintained line, and pick up a large collection of bug
fixes that have accumulated.
The main improvement here is that the total expected
count of training rows in the test is calculated as the
sum of the training fraction times the cardinality of each
class (instead of the training fraction times the total doc count).
Also relaxes slightly the error bound on the uniformity test from 0.12
to 0.13.
Closes#54122
Backport of #58180
This commit adds an optional field, `description`, to all ingest processors
so that users can explain the purpose of the specific processor instance.
Closes#56000.
* Remove usage of deprecated testCompile configuration
* Replace testCompile usage by testImplementation
* Make testImplementation non transitive by default (as we did for testCompile)
* Update CONTRIBUTING about using testImplementation for test dependencies
* Fail on testCompile configuration usage
This type of result will store stats about how well categorization
is performing. When per-partition categorization is in use, separate
documents will be written for every partition so that it is possible
to see if categorization is working well for some partitions but not
others.
This PR is a minimal implementation to allow the C++ side changes to
be made. More Java side changes related to per-partition
categorization will be in followup PRs. However, even in the long
term I do not see a major benefit in introducing dedicated APIs for
querying categorizer stats. Like forecast request stats the
categorizer stats can be read directly from the job's results alias.
Backport of #57978
Adds support for reading in `model_size_info` objects.
These objects contain numeric values indicating the model definition size and complexity.
Additionally, these objects are not stored or serialized to any other node. They are to be used for calculating and storing model metadata. They are much smaller on heap than the true model definition and should help prevent the analytics process from using too much memory.
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
Now that annotations are part of the anomaly detection job results
the annotations index should be refreshed on flushing and closing
the job so that flush and close continue to fulfil their contracts
that immediately after returning all results the job generated up
to that point are searchable.
ModelLoadingService only caches models if they are referenced by an
ingest pipeline. For models used in search we want to always cache the
models and rely on TTL to evict them. Additionally when an ingest
pipeline is deleted the model it references should not be evicted if
it is used in search.
Search after is a better choice for the delete expired data iterators
where processing takes a long time as unlike scroll a context does not
have to be kept alive. Also changes the delete expired data endpoint to
404 if the job is unknown
Since we change the memory estimates for data frame analytics jobs from worst case to a realistic case, the strict less-than assertion in the test does not hold anymore. I replaced it with a less-or-equal-than assertion.
Backport or #57882
When Joni, the regex engine that powers grok emits a warning it
does so by default to System.err. System.err logs are all bucketed
together in the server log at WARN level. When Joni emits a warning,
it can be extremely verbose, logging a message for each execution
again that pattern. For ingest node that means for every document
that is run that through Grok. Fortunately, Joni provides a call
back hook to push these warnings to a custom location.
This commit implements Joni's callback hook to push the Joni warning
to the Elasticsearch server logger (logger.org.elasticsearch.ingest.common.GrokProcessor)
at debug level. Generally these warning indicate a possible issue with
the regular expression and upon creation of the Grok processor will
do a "test run" of the expression and log the result (if any) at WARN
level. This WARN level log should only occur on pipeline creation which
is a much lower frequency then every document.
Additionally, the documentation is updated with instructions for how
to set the logger to debug level.
This adds new plugin level circuit breaker for the ML plugin.
`model_inference` is the circuit breaker qualified name.
Right now it simply adds to the breaker when the model is loaded (and possibly breaking) and removing from the breaker when the model is unloaded.
Before to determine if a field is meta-field, a static method of MapperService
isMetadataField was used. This method was using an outdated static list
of meta-fields.
This PR instead changes this method to the instance method that
is also aware of meta-fields in all registered plugins.
Related #38373, #41656Closes#24422
Deleting expired data can take a long time leading to timeouts if there
are many jobs. Often the problem is due to a few large jobs which
prevent the regular maintenance of the remaining jobs. This change adds
a job_id parameter to the delete expired data endpoint to help clean up
those problematic jobs.
This PR adds the initial Java side changes to enable
use of the per-partition categorization functionality
added in elastic/ml-cpp#1293.
There will be a followup change to complete the work,
as there cannot be any end-to-end integration tests
until elastic/ml-cpp#1293 is merged, and also
elastic/ml-cpp#1293 does not implement some of the
more peripheral functionality, like stop_on_warn and
per-partition stats documents.
The changes so far cover REST APIs, results object
formats, HLRC and docs.
Backport of #57683
This is a major refactor of the underlying inference logic.
The main refactor is now we are separating the model configuration and
the inference interfaces.
This has the following benefits:
- we can store extra things with the model that are not
necessary for inference (i.e. treenode split information gain)
- we can optimize inference separate from model serialization and storage.
- The user is oblivious to the optimizations (other than seeing the benefits).
A major part of this commit is removing all inference related methods from the
trained model configurations (ensemble, tree, etc.) and moving them to a new class.
This new class satisfies a new interface that is ONLY for inference.
The optimizations applied currently are:
- feature maps are flattened once
- feature extraction only happens once at the highest level
(improves inference + feature importance through put)
- Only storing what we need for inference + feature importance on heap
When we force delete a DF analytics job, we currently first force
stop it and then we proceed with deleting the job config.
This may result in logging errors if the job config is deleted
before it is retrieved while the job is starting.
Instead of force stopping the job, it would make more sense to
try to stop the job gracefully first. So we now try that out first.
If normal stop fails, then we resort to force stopping the job to
ensure we can go through with the delete.
In addition, this commit introduces `timeout` for the delete action
and makes use of it in the child requests.
Backport of #57680
In #55592 and #55416, we deprecated the settings for enabling and disabling
basic license features and turned those settings into no-ops. Since doing so,
we've had feedback that this change may not give users enough time to cleanly
switch from non-ILM index management tools to ILM. If two index managers
operate simultaneously, results could be strange and difficult to
reconstruct. We don't know of any cases where SLM will cause a problem, but we
are restoring that setting as well, to be on the safe side.
This PR is not a strict commit reversion. First, we are keeping the new
xpack.watcher.use_ilm_index_management setting, introduced when
xpack.ilm.enabled was made a no-op, so that users can begin migrating to using
it. Second, the SLM setting was modified in the same commit as a group of other
settings, so I have taken just the changes relating to SLM.
