Data frame analytics limitations
editData frame analytics limitations
editThe following limitations and known problems apply to the 8.0.1 release of the Elastic data frame analytics feature. The limitations are grouped into four categories:
- Platform limitations are related to the platform that hosts the machine learning feature of the Elastic Stack.
- Configuration limitations apply to the configuration process of the data frame analytics jobs.
- Operational limitations affect the behavior of the data frame analytics jobs that are running.
- Limitations in Kibana only apply to data frame analytics jobs managed via the user interface.
Platform limitations
editCPU scheduling improvements apply to Linux and MacOS only
editWhen there are many machine learning jobs running at the same time and there are insufficient CPU resources, the JVM performance must be prioritized so search and indexing latency remain acceptable. To that end, when CPU is constrained on Linux and MacOS environments, the CPU scheduling priority of native analysis processes is reduced to favor the Elasticsearch JVM. This improvement does not apply to Windows environments.
Configuration limitations
editCross-cluster search is not supported
editCross-cluster search is not supported for data frame analytics.
Nested fields are not supported
editNested fields are not supported for data frame analytics jobs. These fields are ignored during the analysis. If a nested field is selected as the dependent variable for classification or regression analysis, an error occurs.
Data frame analytics jobs cannot be updated
editYou cannot update data frame analytics configurations. Instead, delete the data frame analytics job and create a new one.
Data frame analytics memory limitation
editData frame analytics can only perform analyses that fit into the memory available for machine learning. Overspill to disk is not currently possible. For general machine learning settings, see Machine learning settings in Elasticsearch.
When you create a data frame analytics job and the inference step of the process fails due to the model is too large to fit into JVM, follow the steps in this GitHub issue for a workaround.
Data frame analytics jobs cannot use more than 232 documents for training
editA data frame analytics job that would use more than 232 documents for training cannot
be started. The limitation applies only for documents participating in training
the model. If your source index contains more than 232 documents, set the
training_percent
to a value that represents less than 232 documents.
Trained models created in 7.8 are not backwards compatible
editTrained models created in version 7.8.0 are not backwards compatible with older node versions. In a mixed cluster environment, all nodes must be at least 7.8.0 to use a model created on a 7.8.0 node.
Operational limitations
editDeleting a data frame analytics job does not delete the destination index
editThe 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.
Data frame analytics jobs runtime may vary
editThe runtime of data frame analytics jobs depends on numerous factors, such as the number of data points in the data set, the type of analytics, the number of fields that are included in the analysis, the supplied hyperparameters, the type of analyzed fields, and so on. For this reason, a general runtime value that applies to all or most of the situations does not exist. The runtime of a data frame analytics job may take from a couple of minutes up to many hours in extreme cases.
The runtime increases with an increasing number of analyzed fields in a nearly linear fashion. For data sets of more than 100,000 points, start with a low training percent. Run a few data frame analytics jobs to see how the runtime scales with the increased number of data points and how the quality of results scales with an increased training percentage.
Data frame analytics jobs may restart after an Elasticsearch upgrade
editA data frame analytics job may be restarted from the beginning in the following cases:
- the job is in progress during an Elasticsearch update,
- the job resumes on a node with a higher version,
- the results format has changed requiring different mappings in the destination index.
If any of these conditions applies, the destination index of the data frame analytics job is deleted and the job starts again from the beginning – regardless of the phase where the job was in.
Documents with values of multi-element arrays in analyzed fields are skipped
editIf the value of an analyzed field (field that is subect of the data frame analytics) in a document is an array with more than one element, the document that contains this field is skipped during the analysis.
Outlier detection field types
editOutlier detection requires numeric or boolean data to analyze. The algorithms don’t support missing values, 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.
Regression field types
editRegression supports fields that are numeric, boolean, text, keyword and ip. It is also tolerant of missing values. Fields that are supported are included in the analysis, other fields are ignored. Documents where included fields contain an array are also ignored. Documents in the destination index that don’t contain a results field are not included in the regression analysis.
Classification field types
editClassification supports fields that have numeric, boolean, text, keyword, or ip data types. It is also tolerant of missing values. Fields that are supported are included in the analysis, other fields are ignored. Documents where included fields contain an array are also ignored. Documents in the destination index that don’t contain a results field are not included in the classification analysis.
Imbalanced class sizes affect classification performance
editIf your training data is very imbalanced, classification analysis may not provide good predictions. Try to avoid highly imbalanced situations. We recommend having at least 50 examples of each class and a ratio of no more than 10 to 1 for the majority to minority class labels in the training data. If your training data set is very imbalanced, consider downsampling the majority class, upsampling the minority class, or gathering more data.
Deeply nested objects affect inference performance
editIf the data that you run inference against contains documents that have a series
of combinations of dot delimited and nested fields (for example:
{"a.b": "c", "a": {"b": "c"},...}
), the performance of the operation might be
slightly slower. Consider using as simple mapping as possible for the best
performance profile.
Analytics runtime performance may significantly slow down with feature importance computation
editFor complex models (such as those with many deep trees), the calculation of feature importance takes significantly more time. If a reduction in runtime is important to you, try strategies such as disabling feature importance, reducing the amount of training data (for example by decreasing the training percentage), setting hyperparameter values, or only selecting fields that are relevant for analysis.
Limitations in Kibana
editTrained models are visible in all Kibana spaces
editSpaces enable you to organize your data frame analytics jobs in Kibana and to see only the jobs and other saved objects that belong to your space. However, this limited scoped does not apply to trained models; they are are visible in all spaces.