In addition to reading this guide, we recommend you run the Elasticsearch Health Check-Up. It will detect issues and improve your Elasticsearch performance by analyzing your shard sizes, threadpools, memory, snapshots, disk watermarks and more.The Elasticsearch Check-Up is free and requires no installation.
Before you begin reading this guide, we recommend you try running the Elasticsearch Error Check-Up which analyzes 2 JSON files to detect many configuration errors.
To evaluate your use of index patterns in Elasticsearch, we recommend you try AutoOps. AutoOps will also prevent & resolve Elasticsearch issues, cut-down administration time and reduce hardware costs. Try AutoOps for free.
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Introduction
An Elasticsearch index pattern is a crucial component when working with data in Elasticsearch, as it allows you to define how to match and interact with multiple indices.
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-2022.01.01, log-2022.01.02, and so on, you can define an index pattern as log-* to match all daily log indices.
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 indices for the last 7 days, you can use an index pattern like log-<now/d-7d{yyyy.MM.dd}>.
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-2022.01.01, log-2022.01.02, 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.
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.
Conclusion
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.
