Elasticsearch Elasticsearch Msearch

By Opster Team - May 2023

Updated: May 22, 2023

| 2 min read

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Optimizing Multi-Search in Elasticsearch

Introduction to Multi-Search API

Elasticsearch provides a powerful and efficient way to execute multiple search queries in a single request using the Multi-Search API (_msearch). This feature allows you to send multiple search requests within a single HTTP request, reducing the overhead of multiple round-trips to the server. In this article, we will discuss the benefits, use cases, and best practices for optimizing _msearch in Elasticsearch.

Benefits of Using Multi-Search API

1. Improved performance: By bundling multiple search queries into a single request, you can reduce the latency associated with multiple round-trips to the server.

2. Simplified client-side code: Instead of managing multiple search requests and their responses, you can handle a single _msearch request and response, simplifying your client-side code.

3. Consistent response format: The _msearch API returns a consistent response format, making it easier to parse and process the results.

Use Cases for Multi-Search API

1. Dashboard applications: When building a dashboard that requires data from multiple indices or multiple queries, using the _msearch API can help improve performance and simplify your code.

2. Batch processing: If you need to process multiple search queries in parallel, the _msearch API can help you execute these queries more efficiently.

3. Real-time analytics: For real-time analytics applications that require data from multiple sources or queries, the _msearch API can provide a more efficient way to retrieve the required data.

Optimizing Multi-Search API Usage

1. Limit the number of queries: While the _msearch API allows you to send multiple queries in a single request, it’s essential to limit the number of queries to avoid overloading your Elasticsearch cluster. Consider using pagination or limiting the number of results returned per query to manage the amount of data returned.

2. Use filters and caching: To improve performance, use filters where possible, as filters are fast and can be cached. Additionally, consider enabling query caching for frequently used queries to reduce the load on your Elasticsearch cluster.

3. Monitor and adjust timeouts: Keep an eye on the Elasticsearch logs and monitor the response times of your _msearch requests. If you notice timeouts or slow response times, consider adjusting the timeout settings for your _msearch requests.

4. Optimize index design: Ensure that your indices are optimized for search performance by using appropriate mappings, sharding, and replica settings.

Example of Multi-Search API Request

Here’s an example of an _msearch request that includes two search queries:

POST _msearch
{"query": {"match": {"field1": "value1"}}}
{"query": {"match": {"field2": "value2"}}}

In this example, the first line is an empty header, followed by the first search query, another empty header, and the second search query. The response will include the results for both queries in a consistent format.


The Multi-Search API in Elasticsearch is a powerful tool that allows you to execute multiple search queries in a single request, improving performance and simplifying client-side code. By following the best practices and optimization techniques outlined in this article, you can make the most of this feature and enhance your Elasticsearch search performance.

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