Additional Settings for Optimizing Elasticsearch Cluster Performance – Source: socprime.com

When managing an Elasticsearch cluster, fine-tuning certain settings can enhance stability and performance, especially under high data loads or cluster transitions. Below are some advanced settings and their descriptions to help improve cluster efficiency.

Adjusting Timeout for Unassigned Shards

Command:

PUT _all/_settings {   "settings": {     "index.unassigned.node_left.delayed_timeout": "5m"   } }

Purpose:

The refresh_interval controls how often Elasticsearch refreshes its index to make newly indexed documents searchable. Increasing the interval to 30 seconds reduces the frequency of refresh operations, improving indexing throughput for write-heavy workloads.

Use Case:

This is particularly useful for logs or metrics data where immediate visibility isn’t critical.

Optimizing Recovery Speed

Command:

PUT _cluster/settings {   "transient": {     "indices.recovery.max_bytes_per_sec": "200mb"   } }

Purpose:

The indices.recovery.max_bytes_per_sec setting limits the speed of shard recovery operations. Setting this to 200mb ensures faster recovery times when nodes rejoin the cluster, reducing downtime while avoiding excessive resource usage.

Use Case:

This is beneficial when dealing with large clusters or high data volumes where shard recovery might otherwise bottleneck cluster performance.

Recommendations

1. Monitor the Cluster:
   Regularly monitor the cluster using the _cat APIs or monitoring tools to understand the impact of these changes.
2. Test in Staging:
   Always test these settings in a staging environment before applying them to a production cluster.
3. Customize for Workload:
   Adjust these parameters based on your specific workload requirements and resource constraints.

Summary

These additional settings provide targeted optimizations for shard allocation, indexing performance, and recovery speed. By implementing these configurations, administrators can improve cluster resilience and efficiency, especially in environments with dynamic data flows or large datasets.

For further details, refer to the official Elasticsearch documentation.

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