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Write CSV data to InfluxDB

https://docs.influxdata.com/influxdb/v2.0/write-data/developer-tools/csv/

Annotated CSV

https://docs.influxdata.com/influxdb/v2.0/reference/syntax/annotated-csv/

Tables

A table may have the following rows and columns.

Rows

  • Annotation rows: describe column properties.
  • Header row: defines column labels (one header row per table).
  • Record row: describes data in the table (one record per row).
Example

Encoding of a table with and without a header row.

result,table,_start,_stop,_time,region,host,_value
my-result,0,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:00Z,east,A,15.43
my-result,0,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:20Z,east,B,59.25
my-result,0,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:40Z,east,C,52.62

Columns

In addition to the data columns, a table may include the following columns:

  • Annotation column: Only used in annotation rows. Always the first column. Displays the name of an annotation. Value can be empty or a supported annotation. You’ll notice a space for this column for the entire length of the table, so rows appear to start with ,.
  • Result column: Contains the name of the result specified by the query.
  • Table column: Contains a unique ID for each table in a result.

Multiple tables and results

If a file or data stream contains multiple tables or results, the following requirements must be met:

  • A table column indicates which table a row belongs to.
  • All rows in a table are contiguous.
  • An empty row delimits a new table boundary in the following cases:
    • Between tables in the same result that do not share a common table schema.
    • Between concatenated CSV files.
  • Each new table boundary starts with new annotation and header rows.
Example

Encoding of two tables in the same result with the same schema (header row) and different schema.

Same schema Different schema

result,table,_start,_stop,_time,region,host,_value
my-result,0,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:00Z,east,A,15.43
my-result,0,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:20Z,east,B,59.25
my-result,0,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:40Z,east,C,52.62
my-result,1,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:00Z,west,A,62.73
my-result,1,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:20Z,west,B,12.83
my-result,1,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:40Z,west,C,51.62

Annotated CSV in Flux

Flux requires all annotation and header rows in annotated CSV. The example below illustrates how to use the csv.from() function to read annotated CSV in Flux:

import "csv"

csvData = "#datatype,string,long,dateTime:RFC3339,dateTime:RFC3339,dateTime:RFC3339,string,string,double
#group,false,false,false,false,false,false,false,false
#default,,,,,,,,
,result,table,_start,_stop,_time,region,host,_value
,,0,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:00Z,east,A,15.43
,,0,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:20Z,east,B,59.25
,,0,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:40Z,east,C,52.62
,,1,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:00Z,west,A,62.73
,,1,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:20Z,west,B,12.83
,,1,2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:40Z,west,C,51.62
"

csv.from(csv: csvData)
Query a raw CSV string
import "csv"

csvData = "
_start,_stop,_time,region,host,_value
2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:00Z,east,A,15.43
2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:20Z,east,B,59.25
2018-05-08T20:50:00Z,2018-05-08T20:51:00Z,2018-05-08T20:50:40Z,east,C,52.62
"

csv.from(
  csv: csvData,
  mode: "raw"
)