csv

CSV

The csv parser creates metrics from a document containing comma separated values.

Configuration

[[inputs.file]]
  files = ["example"]

  ## Data format to consume.
  ## Each data format has its own unique set of configuration options, read
  ## more about them here:
  ##   https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md
  data_format = "csv"

  ## Indicates how many rows to treat as a header. By default, the parser assumes
  ## there is no header and will parse the first row as data. If set to anything more
  ## than 1, column names will be concatenated with the name listed in the next header row.
  ## If `csv_column_names` is specified, the column names in header will be overridden.
  csv_header_row_count = 0

  ## For assigning custom names to columns
  ## If this is specified, all columns should have a name
  ## Unnamed columns will be ignored by the parser.
  ## If `csv_header_row_count` is set to 0, this config must be used
  csv_column_names = []

  ## For assigning explicit data types to columns.
  ## Supported types: "int", "float", "bool", "string".
  ## Specify types in order by column (e.g. `["string", "int", "float"]`)
  ## If this is not specified, type conversion will be done on the types above.
  csv_column_types = []

  ## Indicates the number of rows to skip before looking for header information.
  csv_skip_rows = 0

  ## Indicates the number of columns to skip before looking for data to parse.
  ## These columns will be skipped in the header as well.
  csv_skip_columns = 0

  ## The separator between csv fields
  ## By default, the parser assumes a comma (",")
  csv_delimiter = ","

  ## The character reserved for marking a row as a comment row
  ## Commented rows are skipped and not parsed
  csv_comment = ""

  ## If set to true, the parser will remove leading whitespace from fields
  ## By default, this is false
  csv_trim_space = false

  ## Columns listed here will be added as tags. Any other columns
  ## will be added as fields.
  csv_tag_columns = []

  ## The column to extract the name of the metric from
  csv_measurement_column = ""

  ## The column to extract time information for the metric
  ## `csv_timestamp_format` must be specified if this is used
  csv_timestamp_column = ""

  ## The format of time data extracted from `csv_timestamp_column`
  ## this must be specified if `csv_timestamp_column` is specified
  csv_timestamp_format = ""

csv_timestamp_column, csv_timestamp_format

By default the current time will be used for all created metrics, to set the time using the JSON document you can use the csv_timestamp_column and csv_timestamp_format options together to set the time to a value in the parsed document.

The csv_timestamp_column option specifies the key containing the time value and csv_timestamp_format must be set to unix, unix_ms, unix_us, unix_ns, or a format string in using the Go "reference time" which is defined to be the specific time: Mon Jan 2 15:04:05 MST 2006.

Consult the Go [time][time parse] package for details and additional examples on how to set the time format.

Metrics

One metric is created for each row with the columns added as fields. The type of the field is automatically determined based on the contents of the value.

In addition to the options above, you can use metric filtering to skip over columns and rows.

Examples

Config:

[[inputs.file]]
  files = ["example"]
  data_format = "csv"
  csv_header_row_count = 1
  csv_timestamp_column = "time"
  csv_timestamp_format = "2006-01-02T15:04:05Z07:00"

Input:

measurement,cpu,time_user,time_system,time_idle,time
cpu,cpu0,42,42,42,2018-09-13T13:03:28Z

Output:

cpu cpu=cpu0,time_user=42,time_system=42,time_idle=42 1536869008000000000