Elasticsearch Elasticsearch Script

By Opster Team

Updated: Jul 30, 2023

| 2 min read

Introduction to Elasticsearch Scripting

Elasticsearch scripting is a versatile feature that allows users to execute custom scripts for various purposes, such as data manipulation, filtering, and scoring. Scripts can be written in different languages, including Painless, which is Elasticsearch’s default scripting language. This article will focus on advanced usage and best practices for Elasticsearch scripting, providing examples and step-by-step instructions for specific use cases.

1. Inline vs Stored Scripts

Elasticsearch supports two types of scripts: inline and stored. Inline scripts are defined within the API request, while stored scripts are saved in the cluster state and can be reused across multiple requests.

Inline scripts are useful for quick, one-time operations, but they can be less efficient for repeated use. On the other hand, stored scripts are more efficient for repeated use, as they are compiled only once and cached for future executions. To create a stored script, use the following API request:

PUT _scripts/my_stored_script
{
  "script": {
    "lang": "painless",
    "source": "params.my_field * params.my_factor"
  }
}

To use a stored script in a query, refer to it by its ID:

GET my_index/_search
{
  "query": {
    "script_score": {
      "query": {
        "match_all": {}
      },
      "script": {
        "id": "my_stored_script",
        "params": {
          "my_field": "field_name",
          "my_factor": 2
        }
      }
    }
  }
}

2. Script Contexts

Elasticsearch provides different script contexts depending on the operation being performed. Some common contexts include:

• Filter context: Used in query clauses to filter documents.
• Score context: Used in script_score queries to modify the relevance score of documents.
• Ingest context: Used in ingest pipelines to manipulate incoming documents.

Understanding the context in which a script is executed is essential for writing efficient and effective scripts.

3. Accessing Document Fields

In Elasticsearch scripts, you can access document fields using the doc object. For example, to access a field called price, use doc['price'].value. Note that accessing fields this way is more efficient than using the _source object, as it avoids loading the entire document source.

4. Avoiding Script Compilations

Frequent script compilations can negatively impact Elasticsearch performance. To minimize compilations, use the following best practices:

• Use stored scripts instead of inline scripts for repeated operations.
• Use parameters instead of hardcoding values in scripts.
• Cache script results using the cache parameter in the query.

5. Error Handling and Debugging

Elasticsearch provides detailed error messages when a script fails to compile or execute. To debug a script, you can use the _scripts/painless/_execute API to test the script without affecting the cluster:

POST _scripts/painless/_execute
{
  "script": {
    "source": "params.field1 + params.field2",
    "params": {
      "field1": 1,
      "field2": 2
    }
  }
}

Conclusion

Elasticsearch scripting is a powerful feature that enables users to perform custom operations on their data. By understanding advanced concepts such as script contexts, field access, and error handling, you can write efficient and effective scripts to enhance your Elasticsearch experience. Remember to follow best practices, such as using stored scripts and minimizing compilations, to ensure optimal performance.