Elasticsearch Resolving Conflicting Field Types in Elasticsearch

By Opster Team

Updated: Nov 8, 2023

| 2 min read

Overview

In the world of Elasticsearch, conflicting field types can be a common issue that developers encounter. This problem arises when the same field in different documents is indexed with different data types. For example, a field could be indexed as a string in one document and as an integer in another. This inconsistency can lead to unexpected errors and issues when indexing your data. In this article, we will delve into the causes of this problem and provide detailed solutions to resolve it.

Understanding the Cause of Conflicting Field Types

The root cause of conflicting field types lies in the dynamic mapping feature of Elasticsearch. When a new document is indexed, Elasticsearch automatically infers the data types of the fields based on the first document it encounters. If subsequent documents have the same field but with a different data type, Elasticsearch will throw a mapping exception.

For instance, consider two documents:

Document 1:

PUT my_index/_doc/1
{
  "user": {
    "id": 123,
    "name": "John Doe"
  }
}

Document 2:

PUT my_index/_doc/2
{
  "user": {
    "id": “abc”,
    "name": "Jane Doe"
  }
}

In this case, the ‘id’ field is a long in Document 1 and a string in Document 2. This discrepancy will lead to a mapping conflict and the Document 2 won’t be indexed.

Resolving Conflicting Field Types

There are several strategies to resolve conflicting field types in Elasticsearch. If you want to learn specifically how to resolve the conflict error: dropping index due to conflict with, check out this guide

1. Explicit Mapping:

 One of the most effective ways to prevent mapping conflicts is to define explicit mappings. Instead of relying on Elasticsearch to infer the data types, you can specify the data types for your fields. For example:

PUT /my_index
{
  "mappings": {
    "properties": {
      "user": {
        "properties": {
          "id": { "type": "keyword" },
          "name": { "type": "text" }
        }
      }
    }
  }
}

In this mapping, the ‘id’ field is explicitly set as a keyword, and the ‘name’ field is set as text. This will ensure that all documents indexed into ‘my_index’ will have to conform to these data types.

2. Coercion: 

Elasticsearch allows you to enable coercion for fields. This means that Elasticsearch will try to convert the data to the specified type. If the conversion is not possible, an error will be thrown. You can enable coercion in your mapping like this:

PUT /my_index
{
  "mappings": {
    "properties": {
      "user": {
        "properties": {
          "id": { "type": "integer", "coerce": true },
          "name": { "type": "text" }
        }
      }
    }
  }
}

Coercion aims to transform unclean values so that they conform to the data type required by a specific field. As an example:

• Strings will be converted into numeric values.
• Decimal points will be removed when converting to integers.

You can check the official elasticsearch coerce article for more information.

3. Ignoring Malformed Fields: 

Another option is to ignore malformed fields. This means that if a document contains a field with the wrong data type, Elasticsearch will ignore the field and index the rest of the document. This can be enabled in your mapping like this:

PUT /my_index
{
  "mappings": {
    "properties": {
      "user": {
        "properties": {
          "id": { "type": "integer", "ignore_malformed": true },
          "name": { "type": "text" }
        }
      }
    }
  }
}

When the ignore_malformed parameter is configured as true, it permits the exception to be disregarded. In this scenario, the problematic field is not indexed in the index, but the rest of the document’s fields are handled as usual.

By default, “index.mapping.coerce”: “false” which means attempting to index a wrong data type into a field will result in an exception if you not change any default settings.

Conclusion

While these strategies can help resolve conflicting field types, it’s important to note that they are not a substitute for good data hygiene. Ensuring that your data is consistent and correctly formatted before indexing can save you a lot of trouble down the line. 

In conclusion, conflicting field types in Elasticsearch can be a tricky issue to navigate. However, with a clear understanding of the problem and the right strategies, you can effectively resolve these conflicts and ensure the integrity of your data.