Elasticsearch Elasticsearch Multiple Index Query: Advanced Techniques and Best Practices

By Opster Team

Updated: Nov 14, 2023

| 2 min read

Introduction

When working with Elasticsearch, there are situations where you need to query multiple indices simultaneously. This can be useful when you have data spread across multiple indices and want to retrieve results from all of them in a single query. In this article, we will discuss advanced techniques and best practices for querying multiple indices in Elasticsearch.

Advanced techniques & best practices for querying multiple indices

1. Multi-Index Query Syntax

To query multiple indices, you can simply provide a comma-separated list of index names or use wildcards in the index name. Here’s an example of querying two indices named “index1” and “index2”:

GET /index1,index2/_search
{
  "query": {
    "match": {
      "field": "value"
    }
  }
}

You can also use wildcards to match multiple indices. For example, if you have indices named “logs-2021-01”, “logs-2021-02”, and “logs-2021-03”, you can query all of them using a wildcard:

GET /logs-2021-*/_search
{
  "query": {
    "match": {
      "field": "value"
    }
  }
}

2. Filtering Results by Index

When querying multiple indices, you might want to filter the results based on the index they belong to. You can achieve this using the “_index” meta field in a bool query. Here’s an example of filtering results from “index1” only:

GET /index1,index2/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "field": "value"
          }
        }
      ],
      "filter": [
        {
          "term": {
            "_index": "index1"
          }
        }
      ]
    }
  }
}

3. Handling Different Field Mappings

When querying multiple indices, you might encounter situations where the same field has different mappings across indices. This can lead to errors or unexpected results. To handle this, you can use the “ignore_unmapped” option in the query. Here’s an example:

GET /index1,index2/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "field": "value"
          }
        }
      ],
      "should": [
        {
          "term": {
            "custom_field": {
              "value": "custom_value",
              "ignore_unmapped": true
            }
          }
        }
      ]
    }
  }
}

In this example, the query will not fail if “custom_field” is not mapped in one of the indices.

4. Using Field Aliases

Field aliases can be helpful when querying multiple indices with different field names but similar data. You can create field aliases in the index mappings to map different field names to a common alias. Here’s an example of creating a field alias:

PUT /index1
{
  "mappings": {
    "properties": {
      "field1": {
        "type": "text"
      },
      "common_field": {
        "type": "alias",
        "path": "field1"
      }
    }
  }
}

PUT /index2
{
  "mappings": {
    "properties": {
      "field2": {
        "type": "text"
      },
      "common_field": {
        "type": "alias",
        "path": "field2"
      }
    }
  }
}

Now you can query both indices using the “common_field” alias:

GET /index1,index2/_search
{
  "query": {
    "match": {
      "common_field": "value"
    }
  }
}

5. Controlling the Number of Shards Per Index

When querying multiple indices, the number of shards involved in the query can impact performance. To control the number of shards per index, you can use the “index.number_of_shards” setting when creating an index:

PUT /index1
{
  "settings": {
    "index.number_of_shards": 3
  }
}

Conclusion

By following these advanced techniques and best practices, you can efficiently query multiple indices in Elasticsearch and retrieve the desired results. Remember to consider the performance implications of querying multiple indices and optimize your queries accordingly.