Data frame analytics limitations

The following limitations and known problems apply to the 7.3 release of the Elastic data frame analytics feature:

Cross-cluster search is not supported in 7.3 for data frame analytics.

The delete data frame analytics job API does not delete the destination index that contains the annotated data of the data frame analytics. That index must be deleted separately.

You cannot update Data frame analytics configurations. Instead, delete the data frame analytics job and create a new one.

Data frame analytics can analyze data frames that fit into the memory limit dedicated for machine learning processes. For general machine learning settings, see Machine learning settings in Elasticsearch.

Currently, outlier detection only supports numeric features. Non-numeric fields will be excluded from the analysis by default. If such a field is explicitly included in the analysis, starting the data frame analytics should result in failure. Arrays are not supported either. Documents that contain analyzed fields with unsupported values will be skipped entirely.

If there are missing values in feature fields (fields that are subjects of the data frame analytics), then the document that contains the fields with the missing values will be skipped during the analysis.

Outlier detection requires numeric or boolean data to analyze. The algorithms don’t support missing values (see also Documents with missing values in analyzed fields are skipped), therefore fields that have data types other than numeric or boolean are ignored. Documents where included fields contain missing values, null values, or an array are also ignored. Therefore a destination index may contain documents that don’t have an outlier score. These documents are still reindexed from the source index to the destination index, but they are not included in the outlier detection analysis and therefore no outlier score is computed.