Using Stored Scripts in Elasticsearch Ingestion Pipelines for Base64 and Hex Decoding

This article demonstrates how to use stored scripts in Elasticsearch to implement non-standard data processing workflows. Stored scripts allow you to save scripts independently in Elasticsearch and invoke them on-demand within ingestion pipelines, enabling code reuse without duplication.

The first part introduces the concept of stored scripts in Elasticsearch, explaining how they provide a modular and manageable approach to data processing pipelines. The second part details the implementation of a Base64 decoding stored script named "decodebase64", which accepts a field name parameter, checks for null values, and decodes the Base64 content to a target field using the Painless scripting language.

The script is created using the PUT /_scripts/decodebase64 API with the following source code:

PUT /_scripts/decodebase64
{
  "script": {
    "description": "Decode base64",
    "lang": "painless",
    "source": "def src=ctx[params['field']]; if (src == null) { return; } def target=params['target_field']; ctx[target]=src.decodeBase64();"
  }
}

Then, an ingestion pipeline is created to use this stored script with configurable parameters for field and target_field, allowing flexible reuse across different use cases. The pipeline is demonstrated with a bulk indexing operation that decodes Base64-encoded fruit names.

The third part shows a similar implementation for hexadecimal decoding using a stored script named "decodehex", which manually converts hex strings to text by iterating through pairs of characters and converting them using Integer.parseInt().

PUT /_scripts/decodehex
{
  "script": {
    "description": "Decode HEX",
    "lang": "painless",
    "source": "def src=ctx[params['field']]; if (src == null) { return; } def target=params['target_field']; StringBuilder sb = new StringBuilder(); for (int i = 0; i < src.length(); i += 2) { String byteStr = src.substring(i, i + 2); char byteChar = (char) Integer.parseInt(byteStr, 16); sb.append(byteChar) } ctx[target] = sb.toString();"
  }
}

The article also demonstrates combining multiple stored scripts in a single pipeline for processing different encoding formats simultaneously. The final section summarizes the benefits of using stored scripts: creating reusable scripts for common data transformation tasks, saving development time while maintaining code consistency, and improving pipeline readability and maintainability by avoiding inline script clutter.