Write CSV data with the influx CLI

Use the influx write command to write CSV data to InfluxDB. Include Extended annotated CSV annotations to specify how the data translates into line protocol. Include annotations in the CSV file or inject them using the --header flag of the influx write command.

Example write command

influx write -b example-bucket -f path/to/example.csv

example.csv

#datatype measurement,tag,double,dateTime:RFC3339
m,host,used_percent,time
mem,host1,64.23,2020-01-01T00:00:00Z
mem,host2,72.01,2020-01-01T00:00:00Z
mem,host1,62.61,2020-01-01T00:00:10Z
mem,host2,72.98,2020-01-01T00:00:10Z
mem,host1,63.40,2020-01-01T00:00:20Z
mem,host2,73.77,2020-01-01T00:00:20Z

Resulting line protocol

mem,host=host1 used_percent=64.23 1577836800000000000
mem,host=host2 used_percent=72.01 1577836800000000000
mem,host=host1 used_percent=62.61 1577836810000000000
mem,host=host2 used_percent=72.98 1577836810000000000
mem,host=host1 used_percent=63.40 1577836820000000000
mem,host=host2 used_percent=73.77 1577836820000000000

• CSV Annotations
• Inject annotation headers
• Skip annotation headers
• Process input as CSV
• Specify CSV character encoding
• Skip rows with errors
• Advanced examples

CSV Annotations

Use CSV annotations to specify which element of line protocol each CSV column represents and how to format the data. CSV annotations are rows at the beginning of a CSV file that describe column properties.

The influx write command supports Extended annotated CSV which provides options for specifying how CSV data should be converted into line protocol and how data is formatted.

To write data to InfluxDB, data must include the following:

• measurement
• field set
• timestamp (Optional but recommended)
• tag set (Optional)

Use CSV annotations to specify which of these elements each column represents.

Write raw query results back to InfluxDB

Flux returns query results in annotated CSV. These results include all annotations necessary to write the data back to InfluxDB.

Inject annotation headers

If the CSV data you want to write to InfluxDB does not contain the annotations required to properly convert the data to line protocol, use the --header flag to inject annotation rows into the CSV data.

influx write -b example-bucket \
  -f path/to/example.csv \
  --header "#constant measurement,birds" \
  --header "#datatype dateTime:2006-01-02,long,tag"

date,sighted,loc
2020-01-01,12,Boise
2020-06-01,78,Boise
2020-01-01,54,Seattle
2020-06-01,112,Seattle
2020-01-01,9,Detroit
2020-06-01,135,Detroit

birds,loc=Boise sighted=12i 1577836800000000000
birds,loc=Boise sighted=78i 1590969600000000000
birds,loc=Seattle sighted=54i 1577836800000000000
birds,loc=Seattle sighted=112i 1590969600000000000
birds,loc=Detroit sighted=9i 1577836800000000000
birds,loc=Detroit sighted=135i 1590969600000000000

Use files to inject headers

The influx write command supports importing multiple files in a single command. Include annotations and header rows in their own file and import them with the write command. Files are read in the order in which they’re provided.

influx write -b example-bucket \
  -f path/to/headers.csv \
  -f path/to/example.csv

#constant measurement,birds
#datatype dateTime:2006-01-02,long,tag

date,sighted,loc
2020-01-01,12,Boise
2020-06-01,78,Boise
2020-01-01,54,Seattle
2020-06-01,112,Seattle
2020-01-01,9,Detroit
2020-06-01,135,Detroit

Resulting line protocol

birds,loc=Boise sighted=12i 1577836800000000000
birds,loc=Boise sighted=78i 1590969600000000000
birds,loc=Seattle sighted=54i 1577836800000000000
birds,loc=Seattle sighted=112i 1590969600000000000
birds,loc=Detroit sighted=9i 1577836800000000000
birds,loc=Detroit sighted=135i 1590969600000000000

Skip annotation headers

Some CSV data may include header rows that conflict with or lack the annotations necessary to write CSV data to InfluxDB. Use the --skipHeader flag to specify the number of rows to skip at the beginning of the CSV data.

influx write -b example-bucket \
  -f path/to/example.csv \
  --skipHeader=2

You can then inject new header rows to rename columns and provide the necessary annotations.

Process input as CSV

The influx write command automatically processes files with the .csv extension as CSV files. If your CSV file uses a different extension, use the --format flat to explicitly declare the format of the input file.

influx write -b example-bucket \
  -f path/to/example.txt \
  --format csv

Specify CSV character encoding

The influx write command assumes CSV files contain UTF-8 encoded characters. If your CSV data uses different character encoding, specify the encoding with the --encoding.

influx write -b example-bucket \
  -f path/to/example.csv \
  --encoding "UTF-16"

Skip rows with errors

If a row in your CSV data is missing an element required to write to InfluxDB or data is incorrectly formatted, when processing the row, the influx write command returns an error and cancels the write request. To skip rows with errors, use the --skipRowOnError flag.

influx write -b example-bucket \
  -f path/to/example.csv \
  --skipRowOnError

Use the --errors-file flag to record errors to a file. The error file identifies all rows that cannot be imported and includes error messages for debugging. For example:

cpu,1.1

Advanced examples

• Define constants
• Annotation shorthand
• Ignore columns
• Use alternate numeric formats
• Use alternate boolean format
• Use different timestamp formats

Define constants

Use the Extended annotated CSV #constant annotation to add a column and value to each row in the CSV data.

#constant measurement,example
#constant tag,source,csv
#datatype long,dateTime:RFC3339
count,time
1,2020-01-01T00:00:00Z
4,2020-01-02T00:00:00Z
9,2020-01-03T00:00:00Z
18,2020-01-04T00:00:00Z

example,source=csv count=1 1577836800000000000
example,source=csv count=4 1577923200000000000
example,source=csv count=9 1578009600000000000
example,source=csv count=18 1578096000000000000

Annotation shorthand

Extended annotated CSV supports annotation shorthand, which lets you define the column label, datatype, and default value in the column header.

m|measurement,count|long|0,time|dateTime:RFC3339
example,1,2020-01-01T00:00:00Z
example,4,2020-01-02T00:00:00Z
example,,2020-01-03T00:00:00Z
example,18,2020-01-04T00:00:00Z

example count=1 1577836800000000000
example count=4 1577923200000000000
example count=0 1578009600000000000
example count=18 1578096000000000000

Replace column header with annotation shorthand

It’s possible to replace the column header row in a CSV file with annotation shorthand without modifying the CSV file. This lets you define column data types and default values while writing to InfluxDB.

To replace an existing column header row with annotation shorthand:

1. Use the --skipHeader flag to ignore the existing column header row.
2. Use the --header flag to inject a new column header row that uses annotation shorthand.

influx write -b example-bucket \
  -f example.csv \
  --skipHeader=1
  --header="m|measurement,count|long|0,time|dateTime:RFC3339"

m,count,time
example,1,2020-01-01T00:00:00Z
example,4,2020-01-02T00:00:00Z
example,,2020-01-03T00:00:00Z
example,18,2020-01-04T00:00:00Z

example count=1i 1577836800000000000
example count=4i 1577923200000000000
example count=0i 1578009600000000000
example count=18i 1578096000000000000

Ignore columns

Use the Extended annotated CSV #datatype ignored annotation to ignore columns when writing CSV data to InfluxDB.

#datatype measurement,long,time,ignored
m,count,time,foo
example,1,2020-01-01T00:00:00Z,bar
example,4,2020-01-02T00:00:00Z,bar
example,9,2020-01-03T00:00:00Z,baz
example,18,2020-01-04T00:00:00Z,baz

m count=1i 1577836800000000000
m count=4i 1577923200000000000
m count=9i 1578009600000000000
m count=18i 1578096000000000000

Use alternate numeric formats

If your CSV data contains numeric values that use a non-default fraction separator (.) or contain group separators, define your numeric format in the double, long, and unsignedLong datatype annotations.

#datatype measurement,"double:.,",dateTime:RFC3339
m,lbs,time
example,"1,280.7",2020-01-01T00:00:00Z
example,"1,352.5",2020-01-02T00:00:00Z
example,"1,862.8",2020-01-03T00:00:00Z
example,"2,014.9",2020-01-04T00:00:00Z

example lbs=1280.7 1577836800000000000
example lbs=1352.5 1577923200000000000
example lbs=1862.8 1578009600000000000
example lbs=2014.9 1578096000000000000

#datatype measurement,"long:.,",dateTime:RFC3339
m,lbs,time
example,"1,280.0",2020-01-01T00:00:00Z
example,"1,352.0",2020-01-02T00:00:00Z
example,"1,862.0",2020-01-03T00:00:00Z
example,"2,014.9",2020-01-04T00:00:00Z

example lbs=1280i 1577836800000000000
example lbs=1352i 1577923200000000000
example lbs=1862i 1578009600000000000
example lbs=2014i 1578096000000000000

#datatype measurement,"unsignedLong:.,",dateTime:RFC3339
m,lbs,time
example,"1,280.0",2020-01-01T00:00:00Z
example,"1,352.0",2020-01-02T00:00:00Z
example,"1,862.0",2020-01-03T00:00:00Z
example,"2,014.9",2020-01-04T00:00:00Z

example lbs=1280u 1577836800000000000
example lbs=1352u 1577923200000000000
example lbs=1862u 1578009600000000000
example lbs=2014u 1578096000000000000

Use alternate boolean format

Line protocol supports only specific boolean values. If your CSV data contains boolean values that line protocol does not support, define your boolean format in the boolean datatype annotation.

#datatype measurement,"boolean:y,Y,1:n,N,0",dateTime:RFC3339
m,verified,time
example,y,2020-01-01T00:00:00Z
example,n,2020-01-02T00:00:00Z
example,1,2020-01-03T00:00:00Z
example,N,2020-01-04T00:00:00Z

example verified=true 1577836800000000000
example verified=false 1577923200000000000
example verified=true 1578009600000000000
example verified=false 1578096000000000000

Use different timestamp formats

The influx write command automatically detects RFC3339 and number formatted timestamps when converting CSV to line protocol. If using a different timestamp format, define your timestamp format in the dateTime datatype annotation.

#datatype measurement,dateTime:2006-01-02,field
m,time,lbs
example,2020-01-01,1280.7
example,2020-01-02,1352.5
example,2020-01-03,1862.8
example,2020-01-04,2014.9

example lbs=1280.7 1577836800000000000
example lbs=1352.5 1577923200000000000
example lbs=1862.8 1578009600000000000
example lbs=2014.9 1578096000000000000