10 Elasticsearch Performance Tuning Tips for 2025

10 Elasticsearch Performance Tuning Tips for 2025

Elasticsearch performance optimization is crucial for maintaining fast, reliable search experiences. Here are the top 10 tips for 2025:

1. Optimize Your Index Settings

Start with the right index settings for your use case. Consider the following:

• Number of shards: Don't over-shard your indices. A good rule of thumb is to keep shard sizes between 10-50GB.
• Number of replicas: Balance between search performance and storage costs.
• Refresh interval: Increase the refresh interval for indices that don't need real-time search.

{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 1,
    "refresh_interval": "30s"
  }
}

2. Use Appropriate Field Types

Choose the right field types for your data:

• Use keyword for exact matches and aggregations
• Use text for full-text search
• Use date for temporal data
• Consider dense_vector for semantic search

3. Implement Proper Mapping

Design your mappings carefully:

{
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "analyzer": "standard"
      },
      "category": {
        "type": "keyword"
      },
      "created_at": {
        "type": "date",
        "format": "yyyy-MM-dd HH:mm:ss"
      }
    }
  }
}

4. Optimize Query Performance

• Use filters instead of queries when possible
• Implement query caching
• Use bool queries efficiently
• Avoid wildcard queries on large datasets

5. Monitor Cluster Health

Regular monitoring is essential:

• Track cluster status and node health
• Monitor query performance metrics
• Set up alerts for critical thresholds
• Use Elasticsearch monitoring tools

6. Implement Index Lifecycle Management (ILM)

Automate index management:

• Hot phase: Active indexing and searching
• Warm phase: Read-only, less frequent access
• Cold phase: Infrequent access, compressed storage
• Delete phase: Automatic cleanup

7. Optimize Hardware Resources

• Ensure adequate RAM (heap size should be 50% of available RAM, max 32GB)
• Use SSDs for better I/O performance
• Distribute load across multiple nodes
• Consider dedicated master nodes for large clusters

8. Use Bulk Operations

Batch your operations for better performance:

const body = documents.flatMap(doc => [
  { index: { _index: 'my-index' } },
  doc
]);

await client.bulk({ body });

9. Implement Proper Security

• Enable authentication and authorization
• Use TLS encryption
• Implement field-level security
• Regular security audits

10. Plan for Scaling

• Design for horizontal scaling
• Use cross-cluster replication for disaster recovery
• Implement proper backup strategies
• Plan capacity based on growth projections

Conclusion

Following these performance tuning tips will help you build a robust, scalable Elasticsearch implementation. Remember to test changes in a staging environment before applying them to production.

For more advanced optimization strategies, consider working with Elasticsearch experts who can provide tailored solutions for your specific use case.