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Attachment processor

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Attachment processor

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The attachment processor lets Elasticsearch extract file attachments in common formats (such as PPT, XLS, and PDF) by using the Apache text extraction library Tika.

The source field must be a base64 encoded binary. If you do not want to incur the overhead of converting back and forth between base64, you can use the CBOR format instead of JSON and specify the field as a bytes array instead of a string representation. The processor will skip the base64 decoding then.

Using the attachment processor in a pipeline

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Table 4. Attachment options

Name	Required	Default	Description
`field`	yes	-	The field to get the base64 encoded field from
`target_field`	no	attachment	The field that will hold the attachment information
`indexed_chars`	no	100000	The number of chars being used for extraction to prevent huge fields. Use `-1` for no limit.
`indexed_chars_field`	no	`null`	Field name from which you can overwrite the number of chars being used for extraction. See `indexed_chars`.
`properties`	no	all properties	Array of properties to select to be stored. Can be `content`, `title`, `name`, `author`, `keywords`, `date`, `content_type`, `content_length`, `language`
`ignore_missing`	no	`false`	If `true` and `field` does not exist, the processor quietly exits without modifying the document
`remove_binary`	no	`false`	If `true`, the binary `field` will be removed from the document
`resource_name`	no		Field containing the name of the resource to decode. If specified, the processor passes this resource name to the underlying Tika library to enable Resource Name Based Detection.

Example

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If attaching files to JSON documents, you must first encode the file as a base64 string. On Unix-like systems, you can do this using a base64 command:

base64 -in myfile.rtf

The command returns the base64-encoded string for the file. The following base64 string is for an .rtf file containing the text Lorem ipsum dolor sit amet: e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0=.

Use an attachment processor to decode the string and extract the file’s properties:

response = client.ingest.put_pipeline(
  id: 'attachment',
  body: {
    description: 'Extract attachment information',
    processors: [
      {
        attachment: {
          field: 'data',
          remove_binary: false
        }
      }
    ]
  }
)
puts response

response = client.index(
  index: 'my-index-000001',
  id: 'my_id',
  pipeline: 'attachment',
  body: {
    data: 'e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0='
  }
)
puts response

response = client.get(
  index: 'my-index-000001',
  id: 'my_id'
)
puts response

PUT _ingest/pipeline/attachment
{
  "description" : "Extract attachment information",
  "processors" : [
    {
      "attachment" : {
        "field" : "data",
        "remove_binary": false
      }
    }
  ]
}
PUT my-index-000001/_doc/my_id?pipeline=attachment
{
  "data": "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0="
}
GET my-index-000001/_doc/my_id

The document’s attachment object contains extracted properties for the file:

{
  "found": true,
  "_index": "my-index-000001",
  "_id": "my_id",
  "_version": 1,
  "_seq_no": 22,
  "_primary_term": 1,
  "_source": {
    "data": "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0=",
    "attachment": {
      "content_type": "application/rtf",
      "language": "ro",
      "content": "Lorem ipsum dolor sit amet",
      "content_length": 28
    }
  }
}

Keeping the binary as a field within the document might consume a lot of resources. It is highly recommended to remove that field from the document. Set remove_binary to true to automatically remove the field.

Exported fields

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The fields which might be extracted from a document are:

content,
title,
author,
keywords,
date,
content_type,
content_length,
language,
modified,
format,
identifier,
contributor,
coverage,
modifier,
creator_tool,
publisher,
relation,
rights,
source,
type,
description,
print_date,
metadata_date,
latitude,
longitude,
altitude,
rating,
comments

To extract only certain attachment fields, specify the properties array:

response = client.ingest.put_pipeline(
  id: 'attachment',
  body: {
    description: 'Extract attachment information',
    processors: [
      {
        attachment: {
          field: 'data',
          properties: [
            'content',
            'title'
          ],
          remove_binary: false
        }
      }
    ]
  }
)
puts response

PUT _ingest/pipeline/attachment
{
  "description" : "Extract attachment information",
  "processors" : [
    {
      "attachment" : {
        "field" : "data",
        "properties": [ "content", "title" ],
        "remove_binary": false
      }
    }
  ]
}

Extracting contents from binary data is a resource intensive operation and consumes a lot of resources. It is highly recommended to run pipelines using this processor in a dedicated ingest node.

Use the attachment processor with CBOR

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To avoid encoding and decoding JSON to base64, you can instead pass CBOR data to the attachment processor. For example, the following request creates the cbor-attachment pipeline, which uses the attachment processor.

response = client.ingest.put_pipeline(
  id: 'cbor-attachment',
  body: {
    description: 'Extract attachment information',
    processors: [
      {
        attachment: {
          field: 'data',
          remove_binary: false
        }
      }
    ]
  }
)
puts response

PUT _ingest/pipeline/cbor-attachment
{
  "description" : "Extract attachment information",
  "processors" : [
    {
      "attachment" : {
        "field" : "data",
        "remove_binary": false
      }
    }
  ]
}

The following Python script passes CBOR data to an HTTP indexing request that includes the cbor-attachment pipeline. The HTTP request headers use a content-type of application/cbor.

Not all Elasticsearch clients support custom HTTP request headers.

import cbor2
import requests

file = 'my-file'
headers = {'content-type': 'application/cbor'}

with open(file, 'rb') as f:
  doc = {
    'data': f.read()
  }
  requests.put(
    'http://localhost:9200/my-index-000001/_doc/my_id?pipeline=cbor-attachment',
    data=cbor2.dumps(doc),
    headers=headers
  )

Limit the number of extracted chars

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To prevent extracting too many chars and overload the node memory, the number of chars being used for extraction is limited by default to 100000. You can change this value by setting indexed_chars. Use -1 for no limit but ensure when setting this that your node will have enough HEAP to extract the content of very big documents.

You can also define this limit per document by extracting from a given field the limit to set. If the document has that field, it will overwrite the indexed_chars setting. To set this field, define the indexed_chars_field setting.

For example:

response = client.ingest.put_pipeline(
  id: 'attachment',
  body: {
    description: 'Extract attachment information',
    processors: [
      {
        attachment: {
          field: 'data',
          indexed_chars: 11,
          indexed_chars_field: 'max_size',
          remove_binary: false
        }
      }
    ]
  }
)
puts response

response = client.index(
  index: 'my-index-000001',
  id: 'my_id',
  pipeline: 'attachment',
  body: {
    data: 'e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0='
  }
)
puts response

response = client.get(
  index: 'my-index-000001',
  id: 'my_id'
)
puts response

PUT _ingest/pipeline/attachment
{
  "description" : "Extract attachment information",
  "processors" : [
    {
      "attachment" : {
        "field" : "data",
        "indexed_chars" : 11,
        "indexed_chars_field" : "max_size",
        "remove_binary": false
      }
    }
  ]
}
PUT my-index-000001/_doc/my_id?pipeline=attachment
{
  "data": "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0="
}
GET my-index-000001/_doc/my_id

Returns this:

{
  "found": true,
  "_index": "my-index-000001",
  "_id": "my_id",
  "_version": 1,
  "_seq_no": 35,
  "_primary_term": 1,
  "_source": {
    "data": "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0=",
    "attachment": {
      "content_type": "application/rtf",
      "language": "is",
      "content": "Lorem ipsum",
      "content_length": 11
    }
  }
}

response = client.ingest.put_pipeline(
  id: 'attachment',
  body: {
    description: 'Extract attachment information',
    processors: [
      {
        attachment: {
          field: 'data',
          indexed_chars: 11,
          indexed_chars_field: 'max_size',
          remove_binary: false
        }
      }
    ]
  }
)
puts response

response = client.index(
  index: 'my-index-000001',
  id: 'my_id_2',
  pipeline: 'attachment',
  body: {
    data: 'e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0=',
    max_size: 5
  }
)
puts response

response = client.get(
  index: 'my-index-000001',
  id: 'my_id_2'
)
puts response

PUT _ingest/pipeline/attachment
{
  "description" : "Extract attachment information",
  "processors" : [
    {
      "attachment" : {
        "field" : "data",
        "indexed_chars" : 11,
        "indexed_chars_field" : "max_size",
        "remove_binary": false
      }
    }
  ]
}
PUT my-index-000001/_doc/my_id_2?pipeline=attachment
{
  "data": "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0=",
  "max_size": 5
}
GET my-index-000001/_doc/my_id_2

Returns this:

{
  "found": true,
  "_index": "my-index-000001",
  "_id": "my_id_2",
  "_version": 1,
  "_seq_no": 40,
  "_primary_term": 1,
  "_source": {
    "data": "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0=",
    "max_size": 5,
    "attachment": {
      "content_type": "application/rtf",
      "language": "sl",
      "content": "Lorem",
      "content_length": 5
    }
  }
}

Using the attachment processor with arrays

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To use the attachment processor within an array of attachments the foreach processor is required. This enables the attachment processor to be run on the individual elements of the array.

For example, given the following source:

{
  "attachments" : [
    {
      "filename" : "ipsum.txt",
      "data" : "dGhpcyBpcwpqdXN0IHNvbWUgdGV4dAo="
    },
    {
      "filename" : "test.txt",
      "data" : "VGhpcyBpcyBhIHRlc3QK"
    }
  ]
}

In this case, we want to process the data field in each element of the attachments field and insert the properties into the document so the following foreach processor is used:

response = client.ingest.put_pipeline(
  id: 'attachment',
  body: {
    description: 'Extract attachment information from arrays',
    processors: [
      {
        foreach: {
          field: 'attachments',
          processor: {
            attachment: {
              target_field: '_ingest._value.attachment',
              field: '_ingest._value.data',
              remove_binary: false
            }
          }
        }
      }
    ]
  }
)
puts response

response = client.index(
  index: 'my-index-000001',
  id: 'my_id',
  pipeline: 'attachment',
  body: {
    attachments: [
      {
        filename: 'ipsum.txt',
        data: 'dGhpcyBpcwpqdXN0IHNvbWUgdGV4dAo='
      },
      {
        filename: 'test.txt',
        data: 'VGhpcyBpcyBhIHRlc3QK'
      }
    ]
  }
)
puts response

response = client.get(
  index: 'my-index-000001',
  id: 'my_id'
)
puts response

PUT _ingest/pipeline/attachment
{
  "description" : "Extract attachment information from arrays",
  "processors" : [
    {
      "foreach": {
        "field": "attachments",
        "processor": {
          "attachment": {
            "target_field": "_ingest._value.attachment",
            "field": "_ingest._value.data",
            "remove_binary": false
          }
        }
      }
    }
  ]
}
PUT my-index-000001/_doc/my_id?pipeline=attachment
{
  "attachments" : [
    {
      "filename" : "ipsum.txt",
      "data" : "dGhpcyBpcwpqdXN0IHNvbWUgdGV4dAo="
    },
    {
      "filename" : "test.txt",
      "data" : "VGhpcyBpcyBhIHRlc3QK"
    }
  ]
}
GET my-index-000001/_doc/my_id

Returns this:

{
  "_index" : "my-index-000001",
  "_id" : "my_id",
  "_version" : 1,
  "_seq_no" : 50,
  "_primary_term" : 1,
  "found" : true,
  "_source" : {
    "attachments" : [
      {
        "filename" : "ipsum.txt",
        "data" : "dGhpcyBpcwpqdXN0IHNvbWUgdGV4dAo=",
        "attachment" : {
          "content_type" : "text/plain; charset=ISO-8859-1",
          "language" : "en",
          "content" : "this is\njust some text",
          "content_length" : 24
        }
      },
      {
        "filename" : "test.txt",
        "data" : "VGhpcyBpcyBhIHRlc3QK",
        "attachment" : {
          "content_type" : "text/plain; charset=ISO-8859-1",
          "language" : "en",
          "content" : "This is a test",
          "content_length" : 16
        }
      }
    ]
  }
}

Note that the target_field needs to be set, otherwise the default value is used which is a top level field attachment. The properties on this top level field will contain the value of the first attachment only. However, by specifying the target_field on to a value on _ingest._value it will correctly associate the properties with the correct attachment.

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