Use InfluxDB time series database

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In today’s data-driven world, data is being produced at an unprecedented rate. One of the biggest challenges for organizations is to store and analyze this data efficiently. Time series databases have emerged as a popular solution for managing and analyzing large amounts of time-stamped data. In this article, we’ll discuss what time series databases are, what they are used for, the different time series databases available, and a detailed guide on how to install, configure, and use InfluxDB time series database.

What are time series databases?

Time series databases are specialized databases designed for handling time-stamped or time series data. Time-stamped data is data that is tagged with a time stamp or a time interval. Examples of time-stamped data include financial market data, sensor data, log data, and IoT data.

What are time series databases used for?

Time series databases are commonly used for monitoring and analyzing data in real-time. They are also used for storing and analyzing historical time-stamped data. Some common use cases of time series databases include:

  • Monitoring IoT devices and sensors
  • Analyzing stock market data
  • Analyzing log data for debugging and performance optimization
  • Analyzing weather data
  • Analyzing social media data

Different time series databases available

There are several time series databases available in the market today. Some of the popular ones include:

  • InfluxDB
  • TimescaleDB
  • OpenTSDB
  • Graphite
  • Prometheus

Each of these databases has its pros and cons, and the choice of database depends on the specific requirements of the use case.


InfluxDB is a popular open-source time series database. It is designed for high write and query performance, making it ideal for use cases that require real-time monitoring and analytics. InfluxDB has a SQL-like query language called InfluxQL, which makes it easy to query and analyze data. It also has a HTTP API, which makes it easy to interact with the database programmatically.

Installation and configuration

InfluxDB can be installed on Windows, Linux, and macOS. In this section, we’ll discuss how to install and configure InfluxDB on each of these operating systems.

Windows To install InfluxDB on Windows, follow these steps:

  1. Download the InfluxDB installer from the InfluxDB website.
  2. Run the installer and follow the installation wizard.
  3. Once the installation is complete, open a command prompt and navigate to the InfluxDB installation directory.
  4. Run the following command to start the InfluxDB server:
  1. influxd.exe
  2. By default, InfluxDB listens on port 8086. You can test the installation by opening a web browser and navigating to http://localhost:8086. You should see the InfluxDB web interface.

Linux To install InfluxDB on Linux, follow these steps:

Add the InfluxDB repository to your package manager:bash

curl -sL | sudo apt-key add -
source /etc/os-release
echo "deb $VERSION_ID stable" | sudo tee /etc/apt/sources.list.d/influxdb.list

Update your package manager:

sudo apt-get update

Install InfluxDB:

sudo apt-get install influxdb

Start the InfluxDB server:

sudo service influxdb start

Writing data into InfluxDB

Now that we have our InfluxDB instance up and running and a database created, let’s write some data to it. InfluxDB provides various ways to write data to the database such as via HTTP API or through various client libraries. Here, we will see how to interact using the influx shell

Interacting with InfluxDB shell

Now that you have successfully installed and started InfluxDB, you can start interacting with it. Here are some common commands you can use to interact with InfluxDB:

  • To start the InfluxDB shell, type the following command in a terminal:

This will open the InfluxDB shell, where you can create databases, create users, insert data, and execute queries.

To create a new database, use the CREATE DATABASE command followed by the name of the database. For example:


This will create a new database called mydb.

To create a new user, use the CREATE USER command followed by the username and password. For example:

CREATE USER myuser WITH PASSWORD 'mypassword'

This will create a new user called myuser with the password mypassword.

To grant permissions to a user, use the GRANT command followed by the permissions and the username. For example:

GRANT ALL ON mydb TO myuser

This will grant all permissions to the user myuser on the database mydb.

To insert data into a database, use the INSERT command followed by the name of the measurement, the fields, and the tags. For example:

INSERT mymeasurement,tag1=value1,tag2=value2 field1=value3,field2=value4 timestamp

This will insert a new record into the mymeasurement measurement with two tags (tag1 and tag2) and two fields (field1 and field2). The timestamp value is optional and will default to the current time if not specified.

To execute a query, use the SELECT command followed by the fields and tags you want to query. For example:

SELECT field1,field2 FROM mymeasurement WHERE tag1='value1'

This will query the mymeasurement measurement and return the values of field1 and field2 for records where the tag1 tag equals value1.

To delete data from a database, use the DELETE command followed by the measurement and the conditions. For example:

DELETE from mymesaurement WHERE time < now() - 1h

This will delete all records from the mymeasurement measurement where the timestamp is more than one hour old.

In addition to these commands, InfluxDB also supports more advanced features like data aggregation, continuous queries, and retention policies. You can learn more about these features in the InfluxDB documentation.

Interacting with the Influx API

You can also interact with InfluxDB using HTTP requests or using the Flux query language. To write data to InfluxDB using HTTP, you can use a tool like curl to send POST requests to the InfluxDB API. Here’s an example:

curl -XPOST 'http://localhost:8086/write?db=mydb' --data-binary 'mymeasurement,tag1=value1,tag2=value2 field1=value3,field2=value4'

This will write a new record to the mymeasurement measurement with the specified tags and fields. To execute queries using HTTP, you can send GET requests to the InfluxDB API. Here’s an example:

curl -G 'http://localhost:8086/query?db=mydb

To write data to the database using the HTTP API, you can make an HTTP POST request to the /write endpoint with your data in the request body. InfluxDB uses a line protocol format to write data, which is a text-based format that consists of a measurement, a set of tags, a set of fields, and a timestamp. The basic syntax for writing data in line protocol format is as follows:

<measurement>[,<tag_key>=<tag_value>...] <field_key>=<field_value>[,<field_key>=<field_value>...] [<timestamp>]

Here’s an example of how to write data using the HTTP API:

import requests

data = 'cpu,host=myhost value=0.64'
url = 'http://localhost:8086/write?db=mydb&precision=s'

response =, data=data)

In this example, we’re writing a single data point to the cpu measurement with a tag host set to myhost and a field value set to 0.64. We’re making a POST request to the /write endpoint with our data in the request body. We’re also specifying the database name and the precision of the timestamp using the db and precision parameters in the URL.

To query data from InfluxDB using the HTTP API, you can make an HTTP GET request to the /query endpoint with your query in the q parameter. The query language used by InfluxDB is called Flux, and it provides a powerful set of functions for querying and manipulating time series data.

Here’s an example of how to query data using the HTTP API:

import requests

query = 'from(bucket: "mybucket") |> range(start: -1h) |> filter(fn: (r) => r._measurement == "cpu")'

url = 'http://localhost:8086/api/v2/query?org=myorg'
params = {'query': query}

response = requests.get(url, params=params)
data = response.json()

In this example, we’re querying data from the mybucket bucket using Flux. We’re selecting data from the past hour and filtering for data with a measurement of cpu. We’re making a GET request to the /query endpoint with our query in the q parameter. We’re also specifying the organization name using the org parameter in the URL. The response from the server is in JSON format, which we can parse using the json() method of the response object.


InfluxDB is a powerful time series database that can be used for a variety of use cases such as monitoring, analytics, and IoT. It provides a scalable and efficient storage solution for time series data and a powerful query language for retrieving and manipulating that data. In this article, we covered the basics of installing and configuring InfluxDB on Windows, Linux, and Mac, as well as creating a database, writing data to it, and querying data from it. I hope this article has provided you with a solid foundation for working with InfluxDB and time series data in general.

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