Elasticsearch Elasticsearch Multi Index Search

By Opster Team

Updated: Jan 28, 2024

| 2 min read

Introduction

Elasticsearch provides the ability to search across multiple indices in a single query, which can be a powerful way to retrieve relevant data from various sources. This article will discuss advanced techniques and best practices for performing multi-index searches in Elasticsearch.

Using Wildcards and Aliases for Multi-Index Search

One way to perform a multi-index search is by using wildcards (*) and aliases. Wildcards allow you to search across indices that match a specific pattern, while aliases can be used to group multiple indices under a single name.

1. Wildcards

To search across multiple indices using wildcards, you can use the asterisk (*) symbol in the index name. For example, to search across all indices starting with “logs-“, you can use the following query:

GET /logs-*/_search
{
  "query": {
    "match": {
      "message": "error"
    }
  }
}

This query will be executed against every index whose name matches the given pattern, in this case indices like “logs-2023.05.18”, “logs-today”, “logs-apache-2021” would match.

2. Aliases

Aliases can be used to group multiple indices under a single name, making it easier to search across them. To create an alias, use the following command:

POST /_aliases
{
  "actions": [
    {
      "add": {
        "index": "logs-2021-*",
        "alias": "logs-2021"
      }
    }
  ]
}

Now you can search across all indices with the “logs-2021” alias using the following query:

GET /logs-2021/_search
{
  "query": {
    "match": {
      "message": "error"
    }
  }
}

Filtering Results by Index

When performing a multi-index search, you may want to filter results by the index they belong to. To do this, you can use the “_index” field in your query. For example, to search for documents with the term “error” in the “message” field and filter results by index, you can use the following query:

GET /logs-*/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "message": "error"
          }
        }
      ],
      "filter": [
        {
          "terms": {
            "_index": ["logs-2021-01", "logs-2021-02"]
          }
        }
      ]
    }
  }
}

Optimizing Multi-Index Search Performance

When performing a multi-index search, it’s essential to optimize performance to ensure fast and efficient results. Here are some best practices to follow:

1. Use targeted index patterns: Be specific with your index patterns to avoid searching across unnecessary indices. This can significantly improve search performance.
2. Limit the number of shards: Searching across a large number of shards can negatively impact performance. Consider reducing the number of shards per index or using the shrink API to merge smaller indices.
3. Use the “preference” parameter: By setting the “preference” parameter in your search request, you can control the shard allocation for your search, which can help improve performance.
4. Monitor and optimize your cluster: Regularly monitor your Elasticsearch cluster’s performance and optimize it by adjusting settings, such as JVM heap size, thread pools, and cache sizes.

Conclusion

Elasticsearch multi-index search is a powerful way to retrieve data from various sources in a single query. By using wildcards, aliases, and filtering results by index, you can create efficient and targeted search queries. Additionally, following best practices for optimizing performance will ensure that your multi-index searches are fast and efficient.

If you want to learn more about aliases, take a look at this guide. You can also learn how to manage your shards, including how to shrink them, so your multi-index search can perform better. Finally, if you are wondering which tool would suit you better when it comes to monitoring your cluster, take a look at this guide and also consider running a free trial of Opster AutoOps.