Elasticsearch Elasticsearch Dynamic Mapping: Advanced Insights and Best Practices

By Opster Team

Updated: Jun 22, 2023

| 2 min read

Aside from reading this guide, we recommend you try running the Elasticsearch Error Check-Up which analyzes 2 JSON files to detect many configuration errors.

To easily resolve issues in your deployment and locate their root cause, try AutoOps for Elasticsearch. It diagnoses problems by analyzing hundreds of metrics collected by a lightweight agent and offers guidance for resolving them.

Introduction 

Dynamic mapping is an essential feature in Elasticsearch that allows the automatic detection and mapping of new fields in the index. This feature is particularly useful when dealing with a large number of fields or when the schema is not predefined.

In this article, we will delve into the advanced aspects of dynamic mapping, its benefits, and best practices for optimizing its usage. If you want to learn about Elasticsearch mapping in general, check out this guide. You should also take a look at this guide, which contains a detailed explanation of how to define efficient mapping in Elasticsearch.

Understanding Dynamic Mapping

Dynamic mapping is enabled by default in Elasticsearch, and it works by analyzing the incoming documents and automatically creating a mapping for the new fields. Elasticsearch uses built-in rules to determine the data type of the new fields based on the content. For instance, if a field contains a valid date, Elasticsearch will map it as a date field.

Customizing Dynamic Mapping

While the default dynamic mapping rules work well in most cases, there might be situations where you need to customize the mapping rules. Elasticsearch allows you to define custom dynamic mapping templates to control the mapping of new fields. These templates can be based on field names, patterns, or data types.

To create a custom dynamic mapping template, you can use the following steps:

1. Define the template in the index mappings using the `dynamic_templates` parameter. Here’s an example of a dynamic template that maps all string fields with the prefix “user_” as keyword fields:

json
{
  "index_patterns": ["my_index"],
  "mappings": {
    "dynamic_templates": [
      {
        "user_strings": {
          "match_pattern": "regex",
          "match": "^user_.*",
          "mapping": {
            "type": "keyword"
          }
        }
      }
    ]
  }
}

2. Apply the template to the index by creating or updating the index with the new settings:

bash
PUT /my_index
{
  "mappings": {
    "dynamic_templates": [
      {
        "user_strings": {
          "match_pattern": "regex",
          "match": "^user_.*",
          "mapping": {
            "type": "keyword"
          }
        }
      }
    ]
  }
}

Disabling Dynamic Mapping

In some cases, you might want to disable dynamic mapping for specific fields or the entire index. To disable dynamic mapping for an index, you can set the `dynamic` parameter to `false` in the index mappings:

json
{
  "mappings": {
    "dynamic": false
  }
}

Best Practices for Dynamic Mapping

1. Use explicit mappings: While dynamic mapping is convenient, it’s always a good idea to define explicit mappings for fields that are known in advance. This ensures that the fields are mapped correctly and helps avoid potential issues with data type conflicts.

2. Monitor field count: Elasticsearch has a default limit of 1000 fields per index. If you have a large number of fields, you might hit this limit, causing indexing errors. Monitor the field count and consider using multi-fields or nested fields to reduce the number of fields in the index.

3. Use dynamic templates: Custom dynamic templates can help you control the mapping of new fields and ensure that they are mapped correctly. Use dynamic templates to define custom rules based on field names, patterns, or data types.

4. Test your mappings: Before deploying your mappings to production, test them with sample data to ensure that they work as expected as once a mapping is created there’s no way to modify it. This can help you identify potential issues and fine-tune your mappings.

Conclusion

In conclusion, Elasticsearch dynamic mapping is a powerful feature that simplifies the handling of new fields in the index. By understanding its advanced aspects and following best practices, you can optimize its usage and ensure that your data is mapped correctly.

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