The following limitations and known problems apply to the 8.2.3 release of the Elastic transform feature. The limitations are grouped into the following categories:
Field names prefixed with underscores are omitted from latest transformsedit
If you use the
latest type of transform and the source index has field names
that start with an underscore (_) character, they are assumed to be internal
fields. Those fields are omitted from the documents in the destination index.
Transforms support cross-cluster search if the remote cluster is configured properlyedit
If you use cross-cluster search, the remote cluster must support the search and aggregations you use in your transforms. Transforms validate their configuration; if you use cross-cluster search and the validation fails, make sure that the remote cluster supports the query and aggregations you use.
Using scripts in transformsedit
Transforms support scripting in every case when aggregations support them. However, there are certain factors you might want to consider when using scripts in transforms:
- Transforms cannot deduce index mappings for output fields when the fields are created by a script. In this case, you might want to create the mappings of the destination index yourself prior to creating the transform.
- Scripted fields may increase the runtime of the transform.
Transforms cannot optimize queries when you use scripts for all the
groupings defined in
group_by, you will receive a warning message when you use scripts this way.
Deprecation warnings for Painless scripts in transformsedit
If a transform contains Painless scripts that use deprecated syntax, deprecation warnings are displayed when the transform is previewed or started. However, it is not possible to check for deprecation warnings across all transforms as a bulk action because running the required queries might be a resource intensive process. Therefore any deprecation warnings due to deprecated Painless syntax are not available in the Upgrade Assistant.
Transforms perform better on indexed fieldsedit
Transforms sort data by a user-defined time field, which is frequently accessed. If the time field is a runtime field, the performance impact of calculating field values at query time can significantly slow the transform. Use an indexed field as a time field when using transforms.
Continuous transform scheduling limitationsedit
A continuous transform periodically checks for changes to source data. The functionality
of the scheduler is currently limited to a basic periodic timer which can be
frequency range from 1s to 1h. The default is 1m. This is designed
to run little and often. When choosing a
frequency for this timer consider
your ingest rate along with the impact that the transform
search/index operations has other users in your cluster. Also note that retries
Aggregation responses may be incompatible with destination index mappingsedit
When a pivot transform is first started, it will deduce the mappings
required for the destination index. This process is based on the field types of
the source index and the aggregations used. If the fields are derived from
dynamic mappings will be used. In some instances the
deduced mappings may be incompatible with the actual data. For example, numeric
overflows might occur or dynamically mapped fields might contain both numbers
and strings. Please check Elasticsearch logs if you think this may have occurred.
You can view the deduced mappings by using the
preview transform API.
generated_dest_index object in the API response.
If it’s required, you may define custom mappings prior to starting the transform by creating a custom destination index using the create index API. As deduced mappings cannot be overwritten by an index template, use the create index API to define custom mappings. The index templates only apply to fields derived from scripts that use dynamic mappings.
Batch transforms may not account for changed documentsedit
A batch transform uses a composite aggregation which allows efficient pagination through all buckets. Composite aggregations do not yet support a search context, therefore if the source data is changed (deleted, updated, added) while the batch data frame is in progress, then the results may not include these changes.
Continuous transform consistency does not account for deleted or updated documentsedit
While the process for transforms allows the continual recalculation of the transform as new data is being ingested, it does also have some limitations.
Changed entities will only be identified if their time field has also been updated and falls within the range of the action to check for changes. This has been designed in principle for, and is suited to, the use case where new data is given a timestamp for the time of ingest.
If the indices that fall within the scope of the source index pattern are removed, for example when deleting historical time-based indices, then the composite aggregation performed in consecutive checkpoint processing will search over different source data, and entities that only existed in the deleted index will not be removed from the data frame destination index.
Depending on your use case, you may wish to recreate the transform entirely after deletions. Alternatively, if your use case is tolerant to historical archiving, you may wish to include a max ingest timestamp in your aggregation. This will allow you to exclude results that have not been recently updated when viewing the destination index.
Deleting a transform does not delete the destination index or Kibana index patternedit
When deleting a transform using
neither the destination index nor the Kibana index pattern, should one have been
created, are deleted. These objects must be deleted separately.
Handling dynamic adjustment of aggregation page sizeedit
During the development of transforms, control was favoured over performance. In the design considerations, it is preferred for the transform to take longer to complete quietly in the background rather than to finish quickly and take precedence in resource consumption.
Composite aggregations are well suited for high cardinality data enabling pagination through results. If a circuit breaker memory exception occurs when performing the composite aggregated search then we try again reducing the number of buckets requested. This circuit breaker is calculated based upon all activity within the cluster, not just activity from transforms, so it therefore may only be a temporary resource availability issue.
For a batch transform, the number of buckets requested is only ever adjusted downwards. The lowering of value may result in a longer duration for the transform checkpoint to complete. For continuous transforms, the number of buckets requested is reset back to its default at the start of every checkpoint and it is possible for circuit breaker exceptions to occur repeatedly in the Elasticsearch logs.
The transform retrieves data in batches which means it calculates several
buckets at once. Per default this is 500 buckets per search/index operation. The
default can be changed using
max_page_search_size and the minimum value is 10.
If failures still occur once the number of buckets requested has been reduced to
its minimum, then the transform will be set to a failed state.
Handling dynamic adjustments for many termsedit
For each checkpoint, entities are identified that have changed since the last time the check was performed. This list of changed entities is supplied as a terms query to the transform composite aggregation, one page at a time. Then updates are applied to the destination index for each page of entities.
size is defined by
max_page_search_size which is also used to
define the number of buckets returned by the composite aggregation search. The
default value is 500, the minimum is 10.
The index setting
the maximum number of terms that can be used in a terms query. The default value
is 65536. If
transform will fail.
Using smaller values for
max_page_search_size may result in a longer duration
for the transform checkpoint to complete.
Handling of failed transformsedit
Failed transforms remain as a persistent task and should be handled appropriately, either by deleting it or by resolving the root cause of the failure and re-starting.
When using the API to delete a failed transform, first stop it using
_stop?force=true, then delete it.
Continuous transforms may give incorrect results if documents are not yet available to searchedit
After a document is indexed, there is a very small delay until it is available to search.
A continuous transform periodically checks for changed entities between the time since
it last checked and
sync.time.delay. This time window moves
without overlapping. If the timestamp of a recently indexed document falls
within this time window but this document is not yet available to search then
this entity will not be updated.
If using a
sync.time.field that represents the data ingest time and using a
zero second or very small
sync.time.delay, then it is more likely that this
issue will occur.
Support for date nanoseconds data typeedit
If your data uses the date nanosecond data type, aggregations are nonetheless on millisecond resolution. This limitation also affects the aggregations in your transforms.
Data streams as destination indices are not supportededit
Transforms update data in the destination index which requires writing into the destination. Data streams are designed to be append-only, which means you cannot send update or delete requests directly to a data stream. For this reason, data streams are not supported as destination indices for transforms.
ILM as destination index may cause duplicated documentsedit
ILM is not recommended to use as a transform destination index. Transforms update documents in the current destination, and cannot delete documents in the indices previously used by ILM. This may lead to duplicated documents when you use transforms combined with ILM in case of a rollover.
If you use ILM to have time-based indices, please consider using the
Date index name instead. The processor works without duplicated
documents if your transform contains a
group_by based on
Limitations in Kibanaedit
Transforms are visible in all Kibana spacesedit
Spaces enable you to organize your source and destination indices and other saved objects in Kibana and to see only the objects that belong to your space. However, a transform is a long running task which is managed on cluster level and therefore not limited in scope to certain spaces. Space awareness can be implemented for a data view under Stack Management > Kibana which allows privileges to the transform destination index.
Up to 1,000 transforms are listed in Kibanaedit
The transforms management page in Kibana lists up to 1000 transforms.
Kibana might not support every transform configuration optionedit
There might be configuration options available via the transform APIs that are not supported in Kibana. For an exhaustive list of configuration options, refer to the documentation.