Link Search Menu Expand Document Documentation Menu
Documentation

You're viewing version 2.9 of the OpenSearch documentation. This version is no longer maintained. For the latest version, see the current documentation. For information about OpenSearch version maintenance, see Release Schedule and Maintenance Policy.

CSV

The csv processor is used to parse CSVs and store them as individual fields in a document. The processor ignores empty fields. The following is the syntax for the csv processor:

{
  "csv": {
    "field": "field_name",
    "target_fields": ["field1, field2, ..."]
  }
}

Configuration parameters

The following table lists the required and optional parameters for the csv processor.

Parameter Required Description
field Required The name of the field that contains the data to be converted. Supports template snippets.
target_fields Required The name of the field in which to store the parsed data.
description Optional A brief description of the processor.
empty_value Optional Represents optional parameters that are not required or are not applicable.
if Optional A condition for running this processor.
ignore_failure Optional If set to true, failures are ignored. Default is false.
ignore_missing Optional If set to true, the processor will not fail if the field does not exist. Default is true.
on_failure Optional A list of processors to run if the processor fails.
quote Optional The character used to quote fields in the CSV data. Default is ".
separator Optional The delimiter used to separate the fields in the CSV data. Default is ,.
tag Optional An identifier tag for the processor. Useful for debugging to distinguish between processors of the same type.
trim Optional If set to true, the processor trims white space from the beginning and end of the text. Default is false.

Using the processor

Follow these steps to use the processor in a pipeline.

Step 1: Create a pipeline.

The following query creates a pipeline, named csv-processor, that splits resource_usage into three new fields named cpu_usage, memory_usage, and disk_usage:

PUT _ingest/pipeline/csv-processor
{
  "description": "Split resource usage into individual fields",
  "processors": [
    {
      "csv": {
        "field": "resource_usage",
        "target_fields": ["cpu_usage", "memory_usage", "disk_usage"],
        "separator": ","
      }
    }
  ]
}

Step 2 (Optional): Test the pipeline.

It is recommended that you test your pipeline before you ingest documents.

To test the pipeline, run the following query:

POST _ingest/pipeline/csv-processor/_simulate
{
  "docs": [
    {
      "_index": "testindex1",
      "_id": "1",
      "_source": {
        "resource_usage": "25,4096,10",
        "memory_usage": "4096",
        "disk_usage": "10",
        "cpu_usage": "25"
      }
    }
  ]
}

Response

The following example response confirms that the pipeline is working as expected:

{
  "docs": [
    {
      "doc": {
        "_index": "testindex1",
        "_id": "1",
        "_source": {
          "memory_usage": "4096",
          "disk_usage": "10",
          "resource_usage": "25,4096,10",
          "cpu_usage": "25"
        },
        "_ingest": {
          "timestamp": "2023-08-22T16:40:45.024796379Z"
        }
      }
    }
  ]
}

Step 3: Ingest a document.

The following query ingests a document into an index named testindex1:

PUT testindex1/_doc/1?pipeline=csv-processor
{
  "resource_usage": "25,4096,10"
}

Step 4 (Optional): Retrieve the document.

To retrieve the document, run the following query:

GET testindex1/_doc/1

350 characters left

Have a question? .

Want to contribute? or .