Machine learning limitationsedit

The following limitations and known problems apply to the 7.3.0 release of the Elastic machine learning features:

CPUs must support SSE4.2edit

Machine learning uses Streaming SIMD Extensions (SSE) 4.2 instructions, so it works only on machines whose CPUs support SSE4.2. If you run Elasticsearch on older hardware you must disable machine learning by setting to false. See Machine learning settings in Elasticsearch.

Categorization uses English dictionary wordsedit

Categorization identifies static parts of unstructured logs and groups similar messages together. The default categorization tokenizer assumes English language log messages. For other languages you must define a different categorization_analyzer for your job. For more information, see Categorizing log messages.

Additionally, a dictionary used to influence the categorization process contains only English words. This means categorization might work better in English than in other languages. The ability to customize the dictionary will be added in a future release.

Pop-ups must be enabled in browsersedit

The machine learning features in Kibana use pop-ups. You must configure your web browser so that it does not block pop-up windows or create an exception for your Kibana URL.

Anomaly Explorer omissions and limitationsedit

In Kibana, Anomaly Explorer charts are not displayed for anomalies that were due to categorization, time_of_day functions, or time_of_week functions. Those particular results do not display well as time series charts.

The charts are also not displayed for detectors that use script fields. In that case, the original source data cannot be easily searched because it has been somewhat transformed by the script.

The Anomaly Explorer charts can also look odd in circumstances where there is very little data to plot. For example, if there is only one data point, it is represented as a single dot. If there are only two data points, they are joined by a line.

Jobs close on the datafeed end dateedit

If you start a datafeed and specify an end date, it will close the job when the datafeed stops. This behavior avoids having numerous open one-time jobs.

If you do not specify an end date when you start a datafeed, the job remains open when you stop the datafeed. This behavior avoids the overhead of closing and re-opening large jobs when there are pauses in the datafeed.

Jobs created in Kibana must use datafeedsedit

If you create jobs in Kibana, you must use datafeeds. If the data that you want to analyze is not stored in Elasticsearch, you cannot use datafeeds and therefore you cannot create your jobs in Kibana. You can, however, use the machine learning APIs to create jobs and to send batches of data directly to the jobs. For more information, see Datafeeds and API quick reference.

Post data API requires JSON formatedit

The post data API enables you to send data to a job for analysis. The data that you send to the job must use the JSON format.

For more information about this API, see Post Data to Jobs.

Misleading high missing field countsedit

One of the counts associated with a machine learning job is missing_field_count, which indicates the number of records that are missing a configured field.

Since jobs analyze JSON data, the missing_field_count might be misleading. Missing fields might be expected due to the structure of the data and therefore do not generate poor results.

For more information about missing_field_count, see Data Counts Objects.

Terms aggregation size affects data analysisedit

By default, the terms aggregation returns the buckets for the top ten terms. You can change this default behavior by setting the size parameter.

If you are send pre-aggregated data to a job for analysis, you must ensure that the size is configured correctly. Otherwise, some data might not be analyzed.

Fields named "by", "count", or "over" cannot be used to split dataedit

You cannot use the following field names in the by_field_name or over_field_name properties in a job: by; count; over. This limitation also applies to those properties when you create advanced jobs in Kibana.

Jobs created in Kibana use model plot config and pre-aggregated dataedit

If you create single or multi-metric jobs in Kibana, it might enable some options under the covers that you’d want to reconsider for large or long-running jobs.

For example, when you create a single metric job in Kibana, it generally enables the model_plot_config advanced configuration option. That configuration option causes model information to be stored along with the results and provides a more detailed view into anomaly detection. It is specifically used by the Single Metric Viewer in Kibana. When this option is enabled, however, it can add considerable overhead to the performance of the system. If you have jobs with many entities, for example data from tens of thousands of servers, storing this additional model information for every bucket might be problematic. If you are not certain that you need this option or if you experience performance issues, edit your job configuration to disable this option.

For more information, see Model Plot Config.

Likewise, when you create a single or multi-metric job in Kibana, in some cases it uses aggregations on the data that it retrieves from Elasticsearch. One of the benefits of summarizing data this way is that Elasticsearch automatically distributes these calculations across your cluster. This summarized data is then fed into machine learning instead of raw results, which reduces the volume of data that must be considered while detecting anomalies. However, if you have two jobs, one of which uses pre-aggregated data and another that does not, their results might differ. This difference is due to the difference in precision of the input data. The machine learning analytics are designed to be aggregation-aware and the likely increase in performance that is gained by pre-aggregating the data makes the potentially poorer precision worthwhile. If you want to view or change the aggregations that are used in your job, refer to the aggregations property in your datafeed.

For more information, see Datafeed Resources.

Security integrationedit

When the Elasticsearch security features are enabled, a datafeed stores the roles of the user who created or updated the datafeed at that time. This means that if those roles are updated then the datafeed subsequently runs with the new permissions that are associated with the roles. However, if the user’s roles are adjusted after creating or updating the datafeed, the datafeed continues to run with the permissions that were associated with the original roles. For more information, see Datafeeds.

Forecasts cannot be created for population jobsedit

If you use an over_field_name property in your job (that is to say, it’s a population job), you cannot create a forecast. If you try to create a forecast for this type of job, an error occurs. For more information about forecasts, see Forecasting the futureedit.

Forecasts cannot be created for jobs that use geographic, rare, or time functionsedit

If you use any of the following analytical functions in your job, you cannot create a forecast:

  • lat_long
  • rare and freq_rare
  • time_of_day and time_of_week

If you try to create a forecast for this type of job, an error occurs. For more information about any of these functions, see Function reference.

Jobs must be stopped before upgradesedit

You must stop any machine learning jobs that are running before you start the upgrade process. For more information, see Stopping machine learning and Upgrading the Elastic Stack.

Rollup indices and index patterns are not supportededit

Rollup indices and index patterns cannot be used in machine learning jobs or datafeeds. This limitation applies irrespective of whether you create the jobs in Kibana or by using APIs. In Kibana, if you select an index, saved search, or index pattern that uses the Rollup feature, the machine learning job creation wizards fail.

See Rolling up historical data.

Machine learning objects do not belong to Kibana spacesedit

If you create spaces in Kibana, you see only the saved objects that belong to that space. This limited scope does not apply to machine learning objects; they are visible in all of your spaces.

However, the Kibana machine learning features interact with some saved objects (such as index patterns, dashboards, and visualizations) that might not be available in all spaces. For example:

  • [experimental] This functionality is experimental and may be changed or removed completely in a future release. Elastic will take a best effort approach to fix any issues, but experimental features are not subject to the support SLA of official GA features. If you upload a file on the Machine Learning page in Kibana, the machine learning features identify the file format and field mappings. You can then optionally import that data into an Elasticsearch index and create an index pattern. This index pattern belongs to the space that is active when you import the file. If you want to use the index pattern in other spaces, you must create it as needed.
  • Likewise, if you use the single-metric, multi-metric, or population job wizards to create a job in Kibana, you can encounter problems if you subsequently try to clone the job in a different space. In particular, problems occur when you try to clone the job in a space that does not contain appropriate index patterns.
  • If you used a supplied configuration to create jobs (for example, for Apache or NGINX web access logs), visualizations and dashboards are automatically generated. These objects belong to the space that was active when you created the job. If you change your active space, custom URLs from the machine learning results to the dashboards or visualizations might fail.

Job and datafeed APIs have a maximum search sizeedit

In 6.6 and later releases, the get jobs API and the get job statistics API return a maximum of 10,000 jobs. Likewise, the get datafeeds API and the get datafeed statistics API return a maximum of 10,000 datafeeds.