Script processoredit

Runs an inline or stored script on incoming documents. The script runs in the ingest context.

The script processor uses the script cache to avoid recompiling the script for each incoming document. To improve performance, ensure the script cache is properly sized before using a script processor in production.

Table 39. Script options

Name Required Default Description

lang

no

"painless"

Script language.

id

no

-

ID of a stored script. If no source is specified, this parameter is required.

source

no

-

Inline script. If no id is specified, this parameter is required.

params

no

-

Object containing parameters for the script.

description

no

-

Description of the processor. Useful for describing the purpose of the processor or its configuration.

if

no

-

Conditionally execute the processor. See Conditionally run a processor.

ignore_failure

no

false

Ignore failures for the processor. See Handling pipeline failures.

on_failure

no

-

Handle failures for the processor. See Handling pipeline failures.

tag

no

-

Identifier for the processor. Useful for debugging and metrics.

Access source fieldsedit

The script processor parses each incoming document’s JSON source fields into a set of maps, lists, and primitives. To access these fields with a Painless script, use the map access operator: ctx['my-field']. You can also use the shorthand ctx.<my-field> syntax.

The script processor does not support the ctx['_source']['my-field'] or ctx._source.<my-field> syntaxes.

The following processor uses a Painless script to extract the tags field from the env source field.

response = client.ingest.simulate(
  body: {
    pipeline: {
      processors: [
        {
          script: {
            description: "Extract 'tags' from 'env' field",
            lang: 'painless',
            source: "\n            String[] envSplit = ctx['env'].splitOnToken(params['delimiter']);\n            ArrayList tags = new ArrayList();\n            tags.add(envSplit[params['position']].trim());\n            ctx['tags'] = tags;\n          ",
            params: {
              delimiter: '-',
              position: 1
            }
          }
        }
      ]
    },
    docs: [
      {
        _source: {
          env: 'es01-prod'
        }
      }
    ]
  }
)
puts response
POST _ingest/pipeline/_simulate
{
  "pipeline": {
    "processors": [
      {
        "script": {
          "description": "Extract 'tags' from 'env' field",
          "lang": "painless",
          "source": """
            String[] envSplit = ctx['env'].splitOnToken(params['delimiter']);
            ArrayList tags = new ArrayList();
            tags.add(envSplit[params['position']].trim());
            ctx['tags'] = tags;
          """,
          "params": {
            "delimiter": "-",
            "position": 1
          }
        }
      }
    ]
  },
  "docs": [
    {
      "_source": {
        "env": "es01-prod"
      }
    }
  ]
}

The processor produces:

{
  "docs": [
    {
      "doc": {
        ...
        "_source": {
          "env": "es01-prod",
          "tags": [
            "prod"
          ]
        }
      }
    }
  ]
}

Access metadata fieldsedit

You can also use a script processor to access metadata fields. The following processor uses a Painless script to set an incoming document’s _index.

response = client.ingest.simulate(
  body: {
    pipeline: {
      processors: [
        {
          script: {
            description: 'Set index based on `lang` field and `dataset` param',
            lang: 'painless',
            source: "\n            ctx['_index'] = ctx['lang'] + '-' + params['dataset'];\n          ",
            params: {
              dataset: 'catalog'
            }
          }
        }
      ]
    },
    docs: [
      {
        _index: 'generic-index',
        _source: {
          lang: 'fr'
        }
      }
    ]
  }
)
puts response
POST _ingest/pipeline/_simulate
{
  "pipeline": {
    "processors": [
      {
        "script": {
          "description": "Set index based on `lang` field and `dataset` param",
          "lang": "painless",
          "source": """
            ctx['_index'] = ctx['lang'] + '-' + params['dataset'];
          """,
          "params": {
            "dataset": "catalog"
          }
        }
      }
    ]
  },
  "docs": [
    {
      "_index": "generic-index",
      "_source": {
        "lang": "fr"
      }
    }
  ]
}

The processor changes the document’s _index to fr-catalog from generic-index.

{
  "docs": [
    {
      "doc": {
        ...
        "_index": "fr-catalog",
        "_source": {
          "lang": "fr"
        }
      }
    }
  ]
}