csv

If this filter is successful, add any arbitrary fields to this event. Field names can be dynamic and include parts of the event using the %{field}.

Example:

    filter {
      csv {
        add_field => { "foo_%{somefield}" => "Hello world, from %{host}" }
      }
    }
[source,ruby]
    # You can also add multiple fields at once:
    filter {
      csv {
        add_field => {
          "foo_%{somefield}" => "Hello world, from %{host}"
          "new_field" => "new_static_value"
        }
      }
    }

If the event has field "somefield" == "hello" this filter, on success, would add field foo_hello if it is present, with the value above and the %{host} piece replaced with that value from the event. The second example would also add a hardcoded field.

If this filter is successful, add arbitrary tags to the event. Tags can be dynamic and include parts of the event using the %{field} syntax.

Example:

    filter {
      csv {
        add_tag => [ "foo_%{somefield}" ]
      }
    }
[source,ruby]
    # You can also add multiple tags at once:
    filter {
      csv {
        add_tag => [ "foo_%{somefield}", "taggedy_tag"]
      }
    }

If the event has field "somefield" == "hello" this filter, on success, would add a tag foo_hello (and the second example would of course add a taggedy_tag tag).

Define whether column names should autogenerated or not. Defaults to true. If set to false, columns not having a header specified will not be parsed.

Define a list of column names (in the order they appear in the CSV, as if it were a header line). If columns is not configured, or there are not enough columns specified, the default column names are "column1", "column2", etc. In the case that there are more columns in the data than specified in this column list, extra columns will be auto-numbered: (e.g. "user_defined_1", "user_defined_2", "column3", "column4", etc.)

Define a set of datatype conversions to be applied to columns. Possible conversions are integer, float, date, date_time, boolean

Example

    filter {
      csv {
        convert => { "column1" => "integer", "column2" => "boolean" }
      }
    }

Call the filter flush method at regular interval. Optional.

Define the character used to quote CSV fields. If this is not specified the default is a double quote ". Optional.

If this filter is successful, remove arbitrary fields from this event. Fields names can be dynamic and include parts of the event using the %{field} Example:

    filter {
      csv {
        remove_field => [ "foo_%{somefield}" ]
      }
    }
[source,ruby]
    # You can also remove multiple fields at once:
    filter {
      csv {
        remove_field => [ "foo_%{somefield}", "my_extraneous_field" ]
      }
    }

If the event has field "somefield" == "hello" this filter, on success, would remove the field with name foo_hello if it is present. The second example would remove an additional, non-dynamic field.

If this filter is successful, remove arbitrary tags from the event. Tags can be dynamic and include parts of the event using the %{field} syntax.

Example:

    filter {
      csv {
        remove_tag => [ "foo_%{somefield}" ]
      }
    }
[source,ruby]
    # You can also remove multiple tags at once:
    filter {
      csv {
        remove_tag => [ "foo_%{somefield}", "sad_unwanted_tag"]
      }
    }

If the event has field "somefield" == "hello" this filter, on success, would remove the tag foo_hello if it is present. The second example would remove a sad, unwanted tag as well.

Define the column separator value. If this is not specified, the default is a comma ,. Optional.

Define whether empty columns should be skipped. Defaults to false. If set to true, columns containing no value will not get set.

The CSV data in the value of the source field will be expanded into a data structure.

Define target field for placing the data. Defaults to writing to the root of the event.