InfluxDB

InfluxDB enables real-time analytics by serving as a purpose-built database that optimizes processing and scaling

In the Cloud

For startups and small teams
Start small, pay for what you use

For enterprises and organizations
Dedicated resources, scale, and advanced security

On-Prem

For enterprises and organizations
Scale, enterprise features, control where you run

Try InfluxDB Cloud

The most powerful time series database as a service is a click away.

Stop flying blind

Be the first to get the latest tutorials, trainings, and all things InfluxDB, Telegraf, and more—right in your inbox.

With billions of devices and applications producing time series data every nanosecond, InfluxDB is the leading way to store and analyze this data. With the enormous variety of data sources, InfluxDB provides multiple ways for users to get data into InfluxDB. One of the most common data formats of this data is CSV, comma-separated values.

This blog post demonstrates how to take CSV data, translate it into line protocol, and send it to InfluxDB using the InfluxDB CLI and InfluxDB Client libraries.

What are CSV and Line Protocol?

CSV is data delimited by a comma to separate values. Each row is a different record, where the record consists of one or more fields. CSV data is historically used for exporting data or recording events over time, which makes it a great fit for a time series database like InfluxDB.

Here is a basic example of CSV data. It starts with a header that provides the names of each column, followed by each record in a separate row:

name,building,temperature,humidity,time
iot-devices,5a,72.3,34.1,2022-10-01T12:01:00Z
iot-devices,5a,72.1,33.8,2022-10-02T12:01:00Z
iot-devices,5a,72.2,33.7,2022-10-03T12:01:00Z

To get this data into InfluxDB, the following tools show how to translate these rows into line protocol. Line protocol consists of the following items:

Measurement name: the name data to store and later query

Fields: The actual data that will be queried

Tags: optional string fields that are used to index and help filter data

Timestamp: optional, but very common in CSV data to specify when the data record was collected or produced

Using the CSV example above, the goal state is to translate the data into something like the following line protocol:

iot-devices,building=5a temperature=72.3,humidity=34.1 1664647260000000000
iot-devices,building=5a temperature=72.1,humidity=33.8 1664733660000000000
iot-devices,building=5a temperature=72.2,humidity=33.7 1664820060000000000

In the above, the iot-devices are the measurement name, and the building is a tag. The temperature and humidity values are our fields. Finally, the timestamp is saved as a nanosecond precision UNIX timestamp.

Influx CLI

The Influx CLI tool provides commands to manage and interact with InfluxDB. With this tool, users can set up, configure, and interact with many of the capabilities of InfluxDB. From setting up new buckets and orgs to querying data, to even pushing data, the CLI can do it all.

One of the subcommands users can use is the write command. Write allows users to inject data directly into InfluxDB from annotated CSV .

CSV annotations

Annotations, either in the CSV file itself or provided as CLI options, are properties of the columns in the CSV file. They describe how to translate each column into either a measurement name, tag, field, or timestamp.

The following demonstrates adding annotations to our example data to a file:

#datatype measurement,tag,double,double,dateTime:RFC3339
name,building,temperature,humidity,time
iot-devices,5a,72.3,34.1,2022-10-01T12:01:00Z
iot-devices,5a,72.1,33.8,2022-10-02T12:01:00Z
iot-devices,5a,72.2,33.7,2022-10-03T12:01:00Z

The datatypes in this example are specified as follows:

measurement : states which column to use as the measurement name. If no column exists, this can also be specified as a header via the CLI.

tag : specifies which column or columns are to be treated as string tag data. These are optional, but help with querying and indexing data in InfluxDB.

double : is used on two columns to specify that they contain double data types.

dateTime : specifies that the final column contains the timestamp of the record and goes further to state that the format used is RFC3339.

Users can also specify additional data types for fields:

double

unsignedLong

boolean

string

ignored : used if a column is not useful or required, and this will not include the column in the final data

Finally, for timestamps , there are built-in parsing capabilities for:

RFC3339 (e.g. 2020-01-01T00:00:00Z )

RFC3339Nano (e.g. 2020-01-01T00:00:00.000000000Z )

Unix timestamps (e.g. 1577836800000000000 ).

If the timestamp is not in one of these formats, then users need to specify the format of the timestamp themselves (e.g. dateTime:2006-01-02 ) as part of the annotation using Go reference time .

CLI examples

Once annotations exist, it is time to send the data to InfluxDB using the CLI. Below is an example of sending the data contained in a file:

influx write --bucket bucketName --format=csv --file /path/to/data.csv

If the CSV itself does not have the annotations, then a user can add them as part of the CLI command:

influx write --bucket bucketName --format=csv --file /path/to/data.csv \
    --header “#datatype measurement,tag,double,double,dateTime:RFC3339”

Finally, if a CSV file does not have a relevant column for the measurement name, that too can be included as a header:

influx write --bucket bucketName --format=csv --file /path/to/data.csv \
    --header “#constant measurement,iot-devices”
    --header “#datatype tag,double,double,dateTime:RFC3339”

To get started with the Influx CLI tool, visit the docs site where users can find steps to install and get started with it. Check out the Write CSV data to InfluxDB docs for more details and examples. This includes examples with skipper header rows, different encodings, and error handling. Additionally, see this previous blog post to learn more about annotated CSV and how you can write the data directly with Flux queries.

The Influx CLI provides a simple and fast way to get started, but what if the user’s files are much larger, not annotated, or need to have some preprocessing done to them before pushing to InfluxDB? In these scenarios, users should look to the InfluxDB Client Libraries.

InfluxDB Client Libraries

The InfluxDB Client Libraries provide language-specific packages to interact with the InfluxDB v2 API quickly. This allows users to use a programming language of their choice to create, process, and package data quickly and easily and then send it to InfluxDB. The libraries are available in many languages, including Python , JavaScript , Go, C#, Java, and many others.

The following provides two examples of parsing CSV data with Python and Java and then sending that data to InfluxDB.

Python + Pandas

The Python programming language has enabled many to learn and start programming easily. Pandas , a Python data analysis library, is a fast and powerful tool for data analysis and manipulation. Together the two make for a powerful combination to easily process data to send to InfluxDB with the InfluxDB client library.

If a user has a very large CSV file or files they want to push to InfluxDB, Pandas provides an easy way to read a CSV file with headers quickly. Combined with the built-in functionality of the InfluxDB client libraries to write Pandas DataFrames , a user can read a CSV in chunks and then send those chunks into InfluxDB.

In the following example, a user is reading a CSV containing thousands of rows containing VIX stock data:

symbol,open,high,low,close,timestamp
vix,13.290000,13.910000,13.290000,13.570000,135935640000000000
vix,13.870000,13.880000,13.040000,13.310000,135944280000000000
vix,13.640000,14.330000,13.600000,14.320000,135952920000000000

To avoid reading the entire file into memory, the user can take advantage of Pandas’ read_csv function, which will read the column names based on the CSV header and chunk the file into 1,000-row chunks. Finally, use the InfluxDB client library to send those groups of 1,000 rows to InfluxDB after specifying the measurement, tag, and timestamp columns:

from influxdb_client import InfluxDBClient, WriteOptions
import pandas as pd
with InfluxDBClient.from_env_properties() as client:
    for df in pd.read_csv("vix.csv", chunksize=1_000):
        with client.write_api() as write_api:
                write_api.write(
                    record=df,
                    bucket="my-bucket",
                    data_frame_measurement_name="stocks",
                    data_frame_tag_columns=["symbol"],
                    data_frame_timestamp_column="date",
            except InfluxDBError as e:
                print(e)

The Java programming language sees use from a wide range of sources from Android devices to enterprise applications. Java users can look to opencsv to get started quickly with CSV data parsing.

This example makes use of a plain old Java object (aka POJO) along with annotations to tell opencsv which CSV columns belong to what object variable and the InfluxDB client library what the measurement name should be for the class as well as what variables should become tags, fields, or timestamps.

@Measurement(name = "stock")
public class StockData {
    @Column(tag = true)
    @CsvBindByName(column = "symbol")
    private String symbol;
    @Column
    @CsvBindByName(column = "open")
    private String open;
    @Column
    @CsvBindByName(column = "high")
    private String high;
    @Column
    @CsvBindByName(column = "low")
    private String low;
    @Column
    @CsvBindByName(column = "close")
    private String close;
    @Column(timestamp = true)
    @CsvBindByName(column = "timestamp")
    private String timestamp;
Then a user can iterate through a CSV file and create a StockData object for each line. This variable can then be manipulated, if required, before sending it to InfluxDB:
InfluxDBClient influxDBClient = InfluxDBClientFactory.create();
WriteApi writeApi = influxDBClient.getWriteApi();
FileReader reader = new FileReader("vix.csv");
CsvToBean"StockData" csvToBean = new CsvToBeanBuilder(reader)
	.withType(StockData.class)
	.build();
Iterator"StockData" stockIterator = csvToBean.iterator();
while (stockIterator.hasNext()) {
	StockData data = stockIterator.next();
	writeApi.writeMeasurement(WritePrecision.S, data);
influxDBClient.close();
Check out the InfluxDB CLI & Client Libraries today
This post has shown how quick, easy, and flexible the InfluxDB client libraries are to use. While the above only demonstrates CSV data, it starts to demonstrate the great power users can have when sending data to InfluxDB. Combined with the other APIs, users have even more options and potential.
Consider where you might be able to use InfluxDB, Influx CLI, and the client libraries, and give them a shot today!