Elasticsearch Elasticsearch Query Multiple Indices: Best Practices and Performance Optimization

By Opster Team

Updated: Jul 23, 2023

| 2 min read

Introduction

When working with Elasticsearch, there may be situations where you need to query multiple indices simultaneously. This can be useful for various reasons, such as searching across different types of data or aggregating results from multiple sources. In this article, we will discuss best practices and performance optimization techniques for querying multiple indices in Elasticsearch.

1. Use Index Aliases

Index aliases are a convenient way to group multiple indices under a single name. This can simplify querying multiple indices, as you can use the alias name instead of listing all the individual index names. To create an index alias, use the following API call:

POST /_aliases
{
  "actions": [
    {
      "add": {
        "indices": ["index1","index2","index3"],
        "alias": "my_alias"
      }
    }
  ]
}

Now you can query the alias `my_alias` instead of specifying all the individual index names:

GET /my_alias/_search
{
  "query": {
    "match_all": {}
  }
}

2. Use Wildcards and Date Patterns

When querying multiple indices, you can use wildcards and date patterns to match multiple index names. For example, if you have daily indices named `logs-2021.01.01`, `logs-2021.01.02`, and so on, you can use a wildcard to query all indices starting with `logs-`:

GET /logs-*/_search
{
  "query": {
    "match_all": {}
  }
}

You can also use date patterns to match indices within a specific date range:

GET /<logs-{now/d-2d}>,<logs-{now/d-1d}>,<logs-{now/d}>/_search
{
  "query": {
    "match_all": {}
  }
}

This query will search the indices for the last three days.

3. Limit the Number of Indices

Querying a large number of indices can negatively impact performance. To avoid this, try to limit the number of indices you query by using more specific index patterns or date ranges. Additionally, consider using the `ignore_unavailable` option to ignore any missing or closed indices:

GET /logs-2021.01.*/_search?ignore_unavailable=true
{
  "query": {
    "match_all": {}
  }
}

4. Use Filtered Aliases

Filtered aliases allow you to create an alias with a predefined filter. This can be useful when querying multiple indices with a common filter condition. To create a filtered alias, use the following API call:

PUT /_aliases
{
  "actions": [
    {
      "add": {
        "indices": ["index1","index2","index3"],
        "alias": "filtered_alias",
        "filter": {
          "term": {
            "field": "value"
          }
        }
      }
    }
  ]
}

Now you can query the `filtered_alias`, and the filter will be applied automatically:

GET /filtered_alias/_search
{
  "query": {
    "match_all": {}
  }
}

5. Optimize Query Performance

When querying multiple indices, consider using the following techniques to optimize performance:

• Use the `preference` parameter to ensure that all re quests for a specific user or session are processed by the same node, reducing network overhead and leveraging any existing node-level caches.
• Use the `search_type` parameter with the `dfs_query_then_fetch` option to improve the accuracy of term and phrase queries across multiple indices.
• Use the `timeout` parameter to limit the time spent on a query, preventing long-running queries from consuming resources.

Conclusion

In conclusion, querying multiple indices in Elasticsearch can be a powerful technique for searching and aggregating data across multiple sources. By following best practices and optimizing performance, you can ensure that your multi-index queries are efficient and effective.