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Dissect processor

The dissect processor extracts values from an event and maps them to individual fields based on user-defined dissect patterns. The processor is well suited for field extraction from log messages with a known structure.

Basic usage

To use the dissect processor, create the following pipeline.yaml file:

dissect-pipeline:
  source:
    file:
      path: "/full/path/to/logs_json.log"
      record_type: "event"
      format: "json"
  processor:
    - dissect:
        map:
          log: "%{Date} %{Time} %{Log_Type}: %{Message}"
  sink:
    - stdout:

Then create the following file named logs_json.log and replace the path in the file source of your pipeline.yaml file with the path of a file containing the following JSON data:

{"log": "07-25-2023 10:00:00 ERROR: error message"}

The dissect processor will retrieve the fields (Date, Time, Log_Type, and Message) from the log message, based on the pattern %{Date} %{Time} %{Type}: %{Message} configured in the pipeline.

After running the pipeline, you should receive the following standard output:

{
    "log" : "07-25-2023 10:00:00 ERROR: Some error",
    "Date" : "07-25-2023"
    "Time" : "10:00:00"
    "Log_Type" : "ERROR"
    "Message" : "error message"
}

Configuration

You can configure the dissect processor with the following options.

Option Required Type Description
map Yes Map Defines the dissect patterns for specific keys. For details on how to define fields in the dissect pattern, see Field notations.
target_types No Map Specifies the data types for extract fields. Valid options are integer, double, string, and boolean. By default, all fields are of the string type.
delete_source No Boolean Whether to delete the source field after successful parsing. Default is false.
dissect_when No String Specifies a condition for performing the dissect operation using a Data Prepper expression. If specified, the dissect operation will only run when the expression evaluates to true.

Field notations

You can define dissect patterns with the following field types.

Normal field

A field without a suffix or prefix. The field will be directly added to the output event. The format is %{field_name}.

Skip field

A field that will not be included in the event. The format is %{} or %{?field_name}.

Append field

A field that will be combined with other fields. To append multiple values and include the final value in the field, use + before the field name in the dissect pattern. The format is %{+field_name}.

For example, with the pattern %{+field_name}, %{+field_name}, log message "foo, bar" will parse into {"field_name": "foobar"}.

You can also define the order of the concatenation with the help of the suffix /<integer>.

For example, with a pattern "%{+field_name/2}, %{+field_name/1}", log message "foo, bar" will parse into {"field_name": "barfoo"}.

If the order is not mentioned, the append operation will occur in the order of the fields specified in the dissect pattern.

Indirect field

A field that uses the value from another field as its field name. When defining a pattern, prefix the field with a & to assign the value found in the field as the key in the key-value pair.

For example, with a pattern "%{?field_name}, %{&field_name}", the log message "foo, bar" will parse into {“foo”: “bar”}. In the log message, foo is captured from the skip field %{?field_name}. foo then serves as the key to the value captured from the field %{&field_name}.

Padded field

A field with the paddings to the right removed. The -> operator can be used as a suffix to indicate that white spaces after this field can be ignored.

For example, with a pattern %{field1->} %{field2}, log message “firstname lastname” will parse into {“field1”: “firstname”, “field2”: “lastname”}.

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