Data stream lifecycle in Elasticsearch

Serverless Stack

A data stream lifecycle in Elasticsearch is a built-in automation mechanism for managing data retention, performance, and storage optimization. By configuring rollover, retention, and downsampling rules for your data streams, you can ensure that time-series data is efficiently maintained and that older data is removed when no longer needed. This page explains how the lifecycle works, its key features, and how to configure it for both new and existing data streams.

Data stream lifecycle manages your data streams according to your retention requirements. For example, you can configure the lifecycle to:

• Ensure that data indexed in the data stream is kept at least for the retention time you defined.
• Ensure that data older than the retention period is deleted automatically by Elasticsearch at a later time.

To achieve these, data stream lifecycle supports:

• Automatic rollover, which chunks your incoming data in smaller pieces to facilitate better performance and backwards incompatible mapping changes.
• Configurable retention, which allows you to configure the time period for which your data is guaranteed to be stored. Elasticsearch is allowed at a later time to delete data older than this time period. Retention can be configured on the data stream level or on a global level. Read more about the different options in this tutorial.

Data stream lifecycle also supports downsampling the data stream backing indices. Refer to the downsampling example for more details.

Note the availability of data stream lifecycle to ensure that it's applicable for your use case:

• Data stream lifecycle is supported only for data streams and cannot be used with individual indices.

• Data stream lifecycle is supported for all deployment types on the versioned Elastic Stack as well as for Elasticsearch Serverless.

In intervals configured by data_streams.lifecycle.poll_interval, Elasticsearch goes over each data stream and performs the following steps:

1. Checks if the data stream has a data stream lifecycle configured, skipping any indices not part of a managed data stream.
2. Rolls over the write index of the data stream, if it fulfills the conditions defined by cluster.lifecycle.default.rollover.
3. After an index is not the write index anymore (that is, the data stream has been rolled over), automatically tail merges the index. Data stream lifecycle executes a merge operation that only targets the long tail of small segments instead of the whole shard. As the segments are organised into tiers of exponential sizes, merging the long tail of small segments is only a fraction of the cost of force merging to a single segment. The small segments would usually hold the most recent data so tail merging will focus the merging resources on the higher-value data that is most likely to keep being queried.
4. If downsampling is configured it will execute all the configured downsampling rounds.
5. Applies retention to the remaining backing indices. This means deleting the backing indices whose generation_time is longer than the effective retention period (read more about the effective retention calculation). The generation_time is only applicable to rolled over backing indices and it is either the time since the backing index got rolled over, or the time optionally configured in the index.lifecycle.origination_date setting.

Since the lifecycle is configured on the data stream level, the process to configure a lifecycle on a new data stream and on an existing one differ.

Four tutorials are available to help you set up and manage data streams with data stream lifecycle:

• To create a new data stream with a lifecycle, add the data stream lifecycle as part of the index template that matches the name of your data stream. Refer to Creating a data stream with a lifecycle for the detailed steps. When a write operation with the name of your data stream reaches Elasticsearch then the data stream is created with the respective data stream lifecycle.
• To update the lifecycle settings for an individual, existing data stream, use the data stream lifecycle APIs. Refer to Update the lifecycle of a data stream for details.
• Retention settings for data streams can be configured both individually, at the data stream level, and globally, for all data streams in a cluster. To learn more, refer to Setting retention for Elasticsearch data streams.
• To migrate an existing ILM managed data stream to data stream lifecycle, follow the steps in Migrating ILM-managed data streams to data stream lifecycle.