You can create and apply Index lifecycle management (ILM) policies to automatically manage your indices according to your performance, resiliency, and retention requirements.
Index lifecycle policies can trigger actions such as:
- Rollover: Creates a new write index when the current one reaches a certain size, number of docs, or age.
- Shrink: Reduces the number of primary shards in an index.
- Force merge: Triggers a force merge to reduce the number of segments in an index’s shards.
- Delete: Permanently remove an index, including all of its data and metadata.
ILM makes it easier to manage indices in hot-warm-cold architectures, which are common when you’re working with time series data such as logs and metrics.
You can specify:
- The maximum shard size, number of documents, or age at which you want to roll over to a new index.
- The point at which the index is no longer being updated and the number of primary shards can be reduced.
- When to force a merge to permanently remove documents marked for deletion.
- The point at which the index can be moved to less performant hardware.
- The point at which the availability is not as critical and the number of replicas can be reduced.
- When the index can be safely deleted.
For example, if you are indexing metrics data from a fleet of ATMs into Elasticsearch, you might define a policy that says:
- When the total size of the index’s primary shards reaches 50GB, roll over to a new index.
- Move the old index into the warm phase, mark it read only, and shrink it down to a single shard.
- After 7 days, move the index into the cold phase and move it to less expensive hardware.
- Delete the index once the required 30 day retention period is reached.
To use ILM, all nodes in a cluster must run the same version. Although it might be possible to create and apply policies in a mixed-version cluster, there is no guarantee they will work as intended. Attempting to use a policy that contains actions that aren’t supported on all nodes in a cluster will cause errors.
Intro to Kibana
ELK for Logs & Metrics