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This documentation describes using the dissect processor in OpenSearch ingest pipelines. Consider using the Data Prepper dissect processor, which runs on the OpenSearch cluster, if your use case involves large or complex datasets.

Dissect

The dissect processor extracts values from a document text field and maps them to individual fields based on dissect patterns. The processor is well suited for field extractions from log messages with a known structure. Unlike the grok processor, dissect does not use regular expressions and has a simpler syntax.

Syntax

The following is the syntax for the dissect processor:

{
  "dissect": {
    "field": "source_field",
    "pattern": "%{dissect_pattern}"
  }
}

Configuration parameters

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

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 dissect-test, that uses the dissect processor to parse the log line:

PUT /_ingest/pipeline/dissect-test
{
  "description": "Pipeline that dissects web server logs",
  "processors": [
    {
      "dissect": {
        "field": "message",
        "pattern": "%{client_ip} - - [%{timestamp}] \"%{http_method} %{url} %{http_version}\" %{response_code} %{response_size}" 
      }
    }
  ]
}

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/dissect-test/_simulate
{
  "docs": [
    {
      "_index": "testindex1",
      "_id": "1",
      "_source": {
        "message": "192.168.1.10 - - [03/Nov/2023:15:20:45 +0000] \"POST /login HTTP/1.1\" 200 3456"
      }
    }
  ]
}

Response

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

{
  "docs": [
    {
      "doc": {
        "_index": "testindex1",
        "_id": "1",
        "_source": {
          "response_code": "200",
          "http_method": "POST",
          "http_version": "HTTP/1.1",
          "client_ip": "192.168.1.10",
          "message": """192.168.1.10 - - [03/Nov/2023:15:20:45 +0000] "POST /login HTTP/1.1" 200 3456""",
          "url": "/login",
          "response_size": "3456",
          "timestamp": "03/Nov/2023:15:20:45 +0000"
        },
        "_ingest": {
          "timestamp": "2023-11-03T22:28:32.830244044Z"
        }
      }
    }
  ]
}

Step 3: Ingest a document

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

PUT testindex1/_doc/1?pipeline=dissect-test
{
   "message": "192.168.1.10 - - [03/Nov/2023:15:20:45 +0000] \"POST /login HTTP/1.1\" 200 3456"
}

Step 4 (Optional): Retrieve the document

To retrieve the document, run the following query:

GET testindex1/_doc/1

Dissect patterns

A dissect pattern is a method of telling the dissect processor how to parse a string into a structured format. The pattern is defined by the parts of the string that you want to discard. For example, the %{client_ip} - - [%{timestamp}] dissect pattern parses the string "192.168.1.10 - - [03/Nov/2023:15:20:45 +0000] \"POST /login HTTP/1.1\" 200 3456" into the following fields:

client_ip: "192.168.1.1"
@timestamp: "03/Nov/2023:15:20:45 +0000"

A dissect pattern works by matching a string against a set of rules. For example, the first rule discards a single space. The dissect processor will find this space and then assign the value of client_ip to all the characters before that space. The next rule matches the [ and ] characters and then assigns the value of @timestamp to everything in between.

Building successful dissect patterns

When building a dissect pattern, it is important to pay attention to the parts of the string that you want to discard. If you discard too much of the string, then the dissect processor may not be able to successfully parse the remaining data. Conversely, if you do not discard enough of the string, then the processor may create unnecessary fields.

If any %{keyname} defined in the pattern does not have a value, then an exception is thrown. You can handle this exception by providing error handling steps in the on_failure parameter.

Empty and named skip keys

An empty key %{} or a named skip key can be used to match values but exclude the value from the final document. This can be useful if you want to parse a string but do not need to store all of its parts.

Converting matched values to a non-string data type

By default, all matched values are represented as string data types. If you need to convert a value to a different data type, you can use the convert processor.

Key modifiers

The dissect processor supports key modifiers that can change the default processor behavior. These modifiers are always placed to the left or right of the %{keyname} and are always enclosed within %{}. For example, the %{+keyname->} modifier includes the append and right padding modifiers. Key modifiers are useful for combining multiple fields into a single line of output, creating formatted lists of data items, or aggregating values from multiple sources.

The following table lists the primary modifiers for the dissect processor.

Detailed descriptions of each key modifier, along with usage examples, are provided in the following sections.

Right padding modifier (->)

The dissection algorithm is precise and requires that every character in the pattern exactly match the source string. For example, the pattern %{hellokey} %{worldkey} (one space) will match the string “Hello world” (one space) but not the string “Hello world” (two spaces) because the pattern only has one space while the source string has two.

The right padding modifier can be used to address this issue. When added to the pattern %{helloworldkey->} %{worldkey}, the right padding modifier will match Hello world (1 space), Hello  world (2 spaces), and even Hello          world (10 spaces).

The right padding modifier is used to allow for the repetition of characters following a %{keyname->}. The right padding modifier can be applied to any key along with any other modifiers. It should always be the rightmost modifier, for example, %{+keyname/1->} or %{}.

Example usage

The following is an example of how to use a right padding modifier:

%{city->}, %{state} %{zip}

In this pattern, the right padding modifier -> is applied to the %{city} key. Both addresses contain the same information, but the second entry has an extra word, City, in the city field. The right padding modifier allows the pattern to match both of these address entries even though they have slightly different formats:

New York, NY 10017
New York City, NY 10017

The following example pipeline uses the right padding modifier with an empty key %{->}:

PUT /_ingest/pipeline/dissect-test
{
  "description": "Pipeline that dissects web server logs",
  "processors": [
    {
      "dissect": {
        "field": "message",
        "pattern": "[%{client_ip}]%{->}[%{timestamp}]" 
      }
    }
  ]
}

You can test the pipeline by using the following example pipeline:

POST _ingest/pipeline/dissect-test/_simulate
{
  "docs": [
    {
      "_index": "testindex1",
      "_id": "1",
      "_source": {
        "message": "[192.168.1.10]   [03/Nov/2023:15:20:45 +0000]"
      }
    }
  ]
}

Your response should appear similar to the following:

{
  "docs": [
    {
      "doc": {
        "_index": "testindex1",
        "_id": "1",
        "_source": {
          "client_ip": "192.168.1.10",
          "message": "[192.168.1.10]   [03/Nov/2023:15:20:45 +0000]",
          "timestamp": "03/Nov/2023:15:20:45 +0000"
        },
        "_ingest": {
          "timestamp": "2024-01-22T22:55:42.090569297Z"
        }
      }
    }
  ]
}

Append modifier (+)

The append modifier combines the values of two or more values into a single output value. The values are appended from left to right. You can also specify an optional separator to be inserted between the values.

Example usage

The following is an example pipeline with an append modifier:

PUT /_ingest/pipeline/dissect-test
{
  "description": "Pipeline that dissects web server logs",
  "processors": [
    {
      "dissect": {
        "field": "message",
        "pattern": "%{+address}, %{+address} %{+address}",
        "append_separator": "|"
      }
    }
  ]
}

You can test the pipeline by using the following example pipeline:

POST _ingest/pipeline/dissect-test/_simulate
{
  "docs": [
    {
      "_index": "testindex1",
      "_id": "1",
      "_source": {
        "message": "New York, NY 10017"
      }
    }
  ]
}

The substrings are appended to the address field, as shown in the following response:

{
  "docs": [
    {
      "doc": {
        "_index": "testindex1",
        "_id": "1",
        "_source": {
          "address": "New York|NY|10017",
          "message": "New York, NY 10017"
        },
        "_ingest": {
          "timestamp": "2024-01-22T22:30:54.516284637Z"
        }
      }
    }
  ]
}

Append with order modifier (+ and /n)

The append with order modifier combines the values of two or more keys into a single output value based on the order specified after /. You have the flexibility to customize the separator that separates the appended values. The append modifier is useful for compiling multiple fields into a single formatted output line, constructing structured lists of data items, and consolidating values from various sources.

Example usage

The following example pipeline uses the append with order modifier to reverse the pattern order defined in the preceding pipeline. This pipeline specifies a separator to be inserted between the appended fields. If you don’t specify a separator, all values will be appended without a separator.

PUT /_ingest/pipeline/dissect-test
{
  "description": "Pipeline that dissects web server logs",
  "processors": [
    {
      "dissect": {
        "field": "message",
        "pattern": "%{+address/3}, %{+address/2} %{+address/1}",
        "append_separator": "|"
      }
    }
  ]
}

You can test the pipeline using the following example pipeline:

POST _ingest/pipeline/dissect-test/_simulate
{
  "docs": [
    {
      "_index": "testindex1",
      "_id": "1",
      "_source": {
        "message": "New York, NY 10017"
      }
    }
  ]
}

The substrings are appended to the address field in reverse order, as shown in the following response:

{
  "docs": [
    {
      "doc": {
        "_index": "testindex1",
        "_id": "1",
        "_source": {
          "address": "10017|NY|New York",
          "message": "New York, NY 10017"
        },
        "_ingest": {
          "timestamp": "2024-01-22T22:38:24.305974178Z"
        }
      }
    }
  ]
}

Named skip key modifier

The named skip key modifier excludes specific matches from the final output by using an empty key {} or ? modifier within the pattern. For example, the following patterns are equivalent: %{firstName} %{lastName} %{?ignore} and %{firstName} %{lastName} %{}. The named skip key modifier is useful for excluding irrelevant or unnecessary fields from the output.

Example usage

The following pattern uses a named skip key to exclude a field (in this case, ignore) from the output. You can assign a descriptive name to the empty key, for example, %{?ignore}, to clarify that the corresponding value should be excluded from the final output:

PUT /_ingest/pipeline/dissect-test
{
  "description": "Pipeline that dissects web server logs",
  "processors": [
    {
      "dissect": {
        "field": "message",
        "pattern": "%{firstName} %{lastName} %{?ignore}"
      }
    }
  ]
}

You can test the pipeline using the following example pipeline:

POST _ingest/pipeline/dissect-test/_simulate
{
  "docs": [
    {
      "_index": "testindex1",
      "_id": "1",
      "_source": {
        "message": "John Doe M.D."
      }
    }
  ]
}

Your response should appear similar to the following:

{
  "docs": [
    {
      "doc": {
        "_index": "testindex1",
        "_id": "1",
        "_source": {
          "firstName": "John",
          "lastName": "Doe",
          "message": "John Doe M.D."
        },
        "_ingest": {
          "timestamp": "2024-01-22T22:41:58.161475555Z"
        }
      }
    }
  ]
}

Reference keys (* and &)

Reference keys use parsed values as key-value pairings for structured content. This can use useful when handling systems that partially log data in key-value pairs. By using reference keys, you can preserve the key-value relationship and maintain the integrity of the extracted information.

Example usage

The following pattern uses a reference key to extract data into a structured format. In this example, client_ip and two key-value pairs are extracted for the next values:

PUT /_ingest/pipeline/dissect-test
{
  "description": "Pipeline that dissects web server logs",
  "processors": [
    {
      "dissect": {
        "field": "message",
        "pattern": "%{client_ip} %{*a}:%{&a} %{*b}:%{&b}"
      }
    }
  ]
}

You can test the pipeline using the following example pipeline:

POST _ingest/pipeline/dissect-test/_simulate
{
  "docs": [
    {
      "_index": "testindex1",
      "_id": "1",
      "_source": {
        "message": "192.168.1.10 response_code:200 response_size:3456"
      }
    }
  ]
}

The two key-value pairs were extracted into fields, as shown in the following response:

{
  "docs": [
    {
      "doc": {
        "_index": "testindex1",
        "_id": "1",
        "_source": {
          "client_ip": "192.168.1.10",
          "response_code": "200",
          "message": "192.168.1.10 response_code:200 response_size:3456",
          "response_size": "3456"
        },
        "_ingest": {
          "timestamp": "2024-01-22T22:48:51.475535635Z"
        }
      }
    }
  ]
}