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Fingerprint processor

Computes a hash of the document’s content. You can use this hash for content fingerprinting.

Table 19. Fingerprint Options

Name	Required	Default	Description
`fields`	yes	n/a	Array of fields to include in the fingerprint. For objects, the processor hashes both the field key and value. For other fields, the processor hashes only the field value.
`target_field`	no	`fingerprint`	Output field for the fingerprint.
`salt`	no	<none>	Salt value for the hash function.
`method`	no	`SHA-1`	The hash method used to compute the fingerprint. Must be one of `MD5`, `SHA-1`, `SHA-256`, `SHA-512`, or `MurmurHash3`.
`ignore_missing`	no	`false`	If `true`, the processor ignores any missing `fields`. If all fields are missing, the processor silently exits without modifying the document.
`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.

Example

edit

The following example illustrates the use of the fingerprint processor:

resp = client.ingest.simulate(
    pipeline={
        "processors": [
            {
                "fingerprint": {
                    "fields": [
                        "user"
                    ]
                }
            }
        ]
    },
    docs=[
        {
            "_source": {
                "user": {
                    "last_name": "Smith",
                    "first_name": "John",
                    "date_of_birth": "1980-01-15",
                    "is_active": True
                }
            }
        }
    ],
)
print(resp)

response = client.ingest.simulate(
  body: {
    pipeline: {
      processors: [
        {
          fingerprint: {
            fields: [
              'user'
            ]
          }
        }
      ]
    },
    docs: [
      {
        _source: {
          user: {
            last_name: 'Smith',
            first_name: 'John',
            date_of_birth: '1980-01-15',
            is_active: true
          }
        }
      }
    ]
  }
)
puts response

const response = await client.ingest.simulate({
  pipeline: {
    processors: [
      {
        fingerprint: {
          fields: ["user"],
        },
      },
    ],
  },
  docs: [
    {
      _source: {
        user: {
          last_name: "Smith",
          first_name: "John",
          date_of_birth: "1980-01-15",
          is_active: true,
        },
      },
    },
  ],
});
console.log(response);

POST _ingest/pipeline/_simulate
{
  "pipeline": {
    "processors": [
      {
        "fingerprint": {
          "fields": ["user"]
        }
      }
    ]
  },
  "docs": [
    {
      "_source": {
        "user": {
          "last_name": "Smith",
          "first_name": "John",
          "date_of_birth": "1980-01-15",
          "is_active": true
        }
      }
    }
  ]
}

Which produces the following result:

{
  "docs": [
    {
      "doc": {
        ...
        "_source": {
          "fingerprint" : "WbSUPW4zY1PBPehh2AA/sSxiRjw=",
          "user" : {
            "last_name" : "Smith",
            "first_name" : "John",
            "date_of_birth" : "1980-01-15",
            "is_active" : true
          }
        }
      }
    }
  ]
}

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