--- layout: default title: dissect parent: Processors grand_parent: Pipelines nav_order: 52 --- # dissect 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: ```yaml 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](#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. | | `dissect_when` | No | String | Specifies a condition for performing the `dissect` operation using a [Data Prepper expression]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/expression-syntax/). 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 `/`. 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”}`.