Elasticsearch Optimizing Query Performance in Elasticsearch

By Opster Team

Updated: Jun 22, 2023

| 2 min read


Elasticsearch is a widely used search and analytics engine that provides fast and scalable search capabilities. As the volume of data and the complexity of queries increase, it becomes crucial to optimize query performance to ensure efficient resource utilization and maintain a responsive system. In this article, we will discuss various techniques and best practices to optimize query performance in Elasticsearch. If you want to learn about Elasticsearch boolean queries, check out this guide.

1. Use Appropriate Data Types and Mappings

Choosing the right data types and mappings for your indices can significantly impact query performance. Ensure that you use appropriate data types for your fields, such as keyword for exact matches and text for full-text search. Additionally, use custom analyzers and tokenizers to optimize the indexing process for your specific use case.

2. Optimize Index Settings

Index settings play a crucial role in query performance. Some key settings to consider are:

  • `number_of_shards`: Choose an appropriate number of primary shards based on your data volume, the number of nodes and query patterns. Too few shards can lead to hotspots, while too many can cause overhead and slow down queries.
  • `number_of_replicas`: Set the number of replicas based on your read-heavy or write-heavy workload. More replicas can improve read performance but may slow down indexing.
  • `refresh_interval`: Adjust the refresh interval based on your indexing and search requirements. A higher interval can improve indexing performance but may delay search results.

3. Use Filter Context

When possible, use filter context in your queries to improve performance. Filter context allows Elasticsearch to cache the results of filters, which can be reused across multiple queries. This can significantly speed up query execution, especially for frequently used filters.


  "query": {
    "bool": {
      "filter": [
        { "term": { "category": "electronics" } },
        { "range": { "price": { "gte": 100, "lte": 500 } } }

4. Use Source Filtering

Source filtering allows you to limit the fields returned in the search results, reducing the amount of data transferred and parsed. This can improve query performance, especially when dealing with large documents.


  "_source": ["title", "price"],
  "query": {
    "match": { "description": "smartphone" }

5. Optimize Query Structure

Optimizing the structure of your queries can have a significant impact on performance. Some tips include:

  • Use `bool` queries with `must`, `should`, and `must_not` clauses to combine multiple conditions efficiently.
  • Avoid using `wildcard` and `regexp` queries when possible, as they can be resource-intensive.
  • Use `match_phrase` for phrase searches instead of `match` with `operator: “and”` to reduce the number of terms considered.

6. Use Pagination Wisely

When dealing with large result sets, use pagination to limit the number of results returned per request. However, avoid using deep pagination, as it can be resource-intensive. Instead, consider using the `search_after` parameter to paginate through results more efficiently.

7. Monitor and Optimize Slow Queries

Monitor your Elasticsearch cluster using tools like the Slow Log and the Search Profiler to identify slow queries. Analyze the slow queries and optimize them by following the best practices mentioned above.

8. Use Index Aliases and Routing

Index aliases and routing can help distribute the query load across multiple nodes and shards, improving query performance. Use index aliases to group indices with similar data and query patterns, and use routing to direct queries to specific shards based on a routing key.

9. Optimize Hardware and Cluster Configuration

Ensure that your Elasticsearch cluster is running on appropriate hardware and is configured correctly. Some key considerations include:

  • Use SSDs for faster disk I/O.
  • Allocate sufficient heap memory to the JVM, but not more than 50% of the available RAM (up to 32GB).
  • Configure the Elasticsearch thread pool settings based on your workload.

10. Upgrade to the Latest Elasticsearch Version

Regularly upgrade your Elasticsearch cluster to the latest version, as newer versions often include performance improvements and bug fixes.


In conclusion, optimizing query performance in Elasticsearch involves a combination of best practices, such as using appropriate data types, optimizing index settings, and structuring queries efficiently. By following these guidelines and monitoring your cluster’s performance, you can ensure that your Elasticsearch deployment remains fast and responsive even as your data and query complexity grow.

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