[role="xpack"] [[dataframe-limitations]] == {transform-cap} limitations [subs="attributes"] ++++ Limitations ++++ beta[] The following limitations and known problems apply to the 7.4 release of the Elastic {dataframe} feature: [float] [[df-compatibility-limitations]] === Beta {transforms} do not have guaranteed backwards or forwards compatibility Whilst {transforms} are beta, it is not guaranteed that a {transform} created in a previous version of the {stack} will be able to start and operate in a future version. Neither can support be provided for {transform} tasks to be able to operate in a cluster with mixed node versions. Please note that the output of a {transform} is persisted to a destination index. This is a normal {es} index and is not affected by the beta status. [float] [[df-ui-limitation]] === {dataframe-cap} UI will not work during a rolling upgrade from 7.2 If your cluster contains mixed version nodes, for example during a rolling upgrade from 7.2 to a newer version, and {transforms} have been created in 7.2, the {dataframe} UI will not work. Please wait until all nodes have been upgraded to the newer version before using the {dataframe} UI. [float] [[df-datatype-limitations]] === {dataframe-cap} data type limitation {dataframes-cap} do not (yet) support fields containing arrays – in the UI or the API. If you try to create one, the UI will fail to show the source index table. [float] [[df-ccs-limitations]] === {ccs-cap} is not supported {ccs-cap} is not supported for {transforms}. [float] [[df-kibana-limitations]] === Up to 1,000 {transforms} are supported A single cluster will support up to 1,000 {transforms}. When using the {ref}/get-data-frame-transform.html[GET {transforms} API] a total `count` of {transforms} is returned. Use the `size` and `from` parameters to enumerate through the full list. [float] [[df-aggresponse-limitations]] === Aggregation responses may be incompatible with destination index mappings When a {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 {ref}/search-aggregations-metrics-scripted-metric-aggregation.html[`scripted_metrics`] or {ref}/search-aggregations-pipeline-bucket-script-aggregation.html[`bucket_scripts`], {ref}/dynamic-mapping.html[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 {es} logs if you think this may have occurred. As a workaround, you may define custom mappings prior to starting the {transform}. For example, {ref}/indices-create-index.html[create a custom destination index] or {ref}/indices-templates.html[define an index template]. [float] [[df-batch-limitations]] === Batch {transforms} may not account for changed documents A batch {transform} uses a {ref}/search-aggregations-bucket-composite-aggregation.html[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 {dataframe} is in progress, then the results may not include these changes. [float] [[df-consistency-limitations]] === {cdataframe-cap} consistency does not account for deleted or updated documents 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 {dataframe} 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 {dataframe} destination index. [float] [[df-deletion-limitations]] === Deleting a {transform} does not delete the {dataframe} destination index or {kib} index pattern When deleting a {transform} using `DELETE _data_frame/transforms/index` neither the {dataframe} destination index nor the {kib} index pattern, should one have been created, are deleted. These objects must be deleted separately. [float] [[df-aggregation-page-limitations]] === Handling dynamic adjustment of aggregation page size 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 {ref}/circuit-breaker.html[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 {cdataframes}, 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 {es} 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. [float] [[df-dynamic-adjustments-limitations]] === Handling dynamic adjustments for many terms 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 {ref}/query-dsl-terms-query.html[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. The page `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 {ref}/index-modules.html#dynamic-index-settings[`index.max_terms_count`] defines the maximum number of terms that can be used in a terms query. The default value is 65536. If `max_page_search_size` exceeds `index.max_terms_count` the {transform} will fail. Using smaller values for `max_page_search_size` may result in a longer duration for the {transform} checkpoint to complete. [float] [[df-scheduling-limitations]] === {cdataframe-cap} scheduling limitations A {cdataframe} periodically checks for changes to source data. The functionality of the scheduler is currently limited to a basic periodic timer which can be within the `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 occur at `frequency` interval. [float] [[df-failed-limitations]] === Handling of failed {transforms} 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. If starting a failed {transform}, after the root cause has been resolved, the `_start?force=true` parameter must be specified. [float] [[df-availability-limitations]] === {cdataframes-cap} may give incorrect results if documents are not yet available to search After a document is indexed, there is a very small delay until it is available to search. A {ctransform} periodically checks for changed entities between the time since it last checked and `now` minus `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.