Elasticsearch Elasticsearch Index Pattern

By Opster Team

Updated: Aug 20, 2023

| 3 min read

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What is an Elasticsearch index pattern?

An Elasticsearch index pattern allows you to define how to match and interact with multiple indices. It is a crucial component when working with data in Elasticsearch.

In this article, we will discuss the best practices and usage of index patterns in Elasticsearch.

Elasticsearch Index Patterns: Best Practices and Usage

1. Use Wildcards to Match Multiple Indices

When defining an index pattern, you can use wildcards (*) to match multiple indices. This is particularly useful when you have time-based indices, such as logs or metrics data, that are split into daily or monthly indices.

For example, if you have daily log indices like log-2023.01.01, log-2023.01.02, and so on, you can define an index pattern as log-* to match all daily log indices, or log-2023.* to match all indices created in 2023.

Using wildcards can be cumbersome because you might have to compute the dates in your own application logic. Wildcards also become suboptimal over time as the number of indexes grows and you find yourself querying more and more data as time goes by, it might not be how you want to query your data. Leveraging date math expressions can help alleviate that as we’ll see in the next section.

2. Utilize Date Math in Index Patterns

Date math expressions allow you to dynamically calculate dates based on the current date, which can be useful when working with time-based indices. You can use date math in index patterns to match indices based on a specific time range.

For example, to match the index created 7 days ago, you can use an index pattern like log-<now/d-7d{yyyy.MM.dd}>. If today is August 17th, 2023, then this index pattern will resolve to log-2023.08.10.

Note that it is not possible to express a time interval using a single index pattern with a date math expression, nor is it possible to use wildcards with date math expressions. So if you want to query all indexes of the last three days (today included), you can use the following index patterns:


It is also worth noting that when used in a request path, the index patterns need to be URL-encoded because they contain special characters:

GET log-%3Cnow%2Fd%7Byyyy.MM.dd%7D%3E%2Clog-%3Cnow%2Fd-1d%7Byyyy.MM.dd%7D%3E%2Clog-%3Cnow%2Fd-2d%7Byyyy.MM.dd%7D%3E/_search

In contrast to the previous section, you’re always going to query three days of data but you don’t need to have custom logic to compute the appropriate dates targeting the indexes you want to query.

3. Keep Index Patterns Up-to-date

As new indices are created or old ones are deleted, it’s essential to keep your index patterns up-to-date to ensure they match the correct set of indices. Regularly review your index patterns and update them as needed to maintain accurate search results and analytics.

4. Use Aliases for Flexibility

Index aliases provide a level of abstraction that allows you to refer to one or more indices using a single name. This can be particularly useful when working with index patterns, as it allows you to change the underlying indices without modifying the index pattern itself.

For example, you can create an alias called logs that points to the indices log-2023.01.01, log-2023.01.02, and so on. Then, you can define an index pattern as logs, which will match all indices pointed to by the logs alias.

5. Optimize Index Patterns for Performance

When working with large datasets, it’s essential to optimize your index patterns for performance. One way to do this is by limiting the number of indices matched by the index pattern. For example, instead of using a wildcard to match all indices, you can use a more specific pattern that only matches the most relevant indices.

For example, let’s say you are storing the logs of an application App1 into daily indexes called log-app1-2023.01.01, log-app1-2023.01.02, etc, and the logs of another application App2 in log-app2-2023.01.01, log-app2-2023.01.02, etc. If you want to only query the logs of App1, you should use the index pattern log-app1-* instead of log-* so that the query doesn’t have to visit log-app2-* indexes which you are not interested in.

Additionally, consider using filtered aliases to limit the data returned by queries that use the index pattern. This can help improve query performance by reducing the amount of data that needs to be processed.

6. Test Index Patterns Before Deployment

Before deploying an index pattern in a production environment, it’s essential to test it thoroughly to ensure it matches the correct set of indices and provides accurate search results and analytics. Use tools like Kibana or Elasticsearch APIs to test your index patterns and verify their accuracy.


In conclusion, Elasticsearch index patterns are a powerful way to interact with multiple indices, enabling you to perform searches and analytics across a wide range of data. By following the best practices and usage guidelines outlined in this article, you can effectively manage and optimize your index patterns for improved performance and accuracy.

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