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csv

The csv processor parses comma-separated values (CSVs) from the event into columns.

Configuration

The following table describes the options you can use to configure the csv processor.

Option Required Type Description
source No String The field in the event that will be parsed. Default value is message.
quote_character No String The character used as a text qualifier for a single column of data. Default value is ".
delimiter No String The character separating each column. Default value is ,.
delete_header No Boolean If specified, the event header (column_names_source_key) is deleted after the event is parsed. If there is no event header, no action is taken. Default value is true.
column_names_source_key No String The field in the event that specifies the CSV column names, which will be automatically detected. If there need to be extra column names, the column names are automatically generated according to their index. If column_names is also defined, the header in column_names_source_key can also be used to generate the event fields. If too few columns are specified in this field, the remaining column names are automatically generated. If too many column names are specified in this field, the CSV processor omits the extra column names.
column_names No List User-specified names for the CSV columns. Default value is [column1, column2, ..., columnN] if there are no columns of data in the CSV record and column_names_source_key is not defined. If column_names_source_key is defined, the header in column_names_source_key generates the event fields. If too few columns are specified in this field, the remaining column names are automatically generated. If too many column names are specified in this field, the CSV processor omits the extra column names.

Usage

Add the following examples to your pipelines.yaml file, depending on how you your CSV columns are formatted.

User-specified column names

The following example pipelines.yaml configuration points to a file named ingest.csv as the source. Then, the csv processor parses the data from the .csv file using the column names specified in the column_names setting, as shown in the following example:

csv-pipeline:
  source:
    file:
      path: "/full/path/to/ingest.csv"
      record_type: "event"
  processor:
    - csv:
        column_names: ["col1", "col2"]
  sink:
    - stdout:

When run, the processor will parse the message. Although only two column names are specified in processor settings, a third column name is automatically generated because the data contained in ingest.csv includes three columns, 1,2,3:

{"message": "1,2,3", "col1": "1", "col2": "2", "column3": "3"}

Automatically detect column names

The following configuration automatically detects the header of a CSV file ingested through an s3 source:

csv-s3-pipeline:
  source:
    s3:
      notification_type: "sqs"
      codec:
        newline:
          skip_lines: 1
          header_destination: "header"
      compression: none
      sqs:
        queue_url: "https://sqs.<region>.amazonaws.com/<account id>/<queue name>"
      aws:
        region: "<region>"
  processor:
    - csv:
        column_names_source_key: "header"
  sink:
    - stdout:

For example, if the ingest.csv file in the Amazon Simple Storage Service (Amazon S3) bucket that the Amazon Simple Queue Service (SQS) queue is attached to contains the following data:

Should,skip,this,line
a,b,c
1,2,3

Then the csv processor will take the following event:

{"header": "a,b,c", "message": "1,2,3"}

Then, the processor parses the event into the following output. Because delete_header is true by default, the header a,b,c is deleted from the output:

{"message": "1,2,3", "a": "1", "b": "2", "c": "3"}

Metrics

The following table describes common Abstract processor metrics.

Metric name Type Description
recordsIn Counter Metric representing the ingress of records to a pipeline component.
recordsOut Counter Metric representing the egress of records from a pipeline component.
timeElapsed Timer Metric representing the time elapsed during execution of a pipeline component.

The csv processor includes the following custom metrics.

Counter

The csv processor includes the following counter metrics:

  • csvInvalidEvents: The number of invalid events, usually caused by an unclosed quotation mark in the event itself. Data Prepper throws an exception when an invalid event is parsed.
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