Real-time visibility into stacks, sensors and systems

InfluxDB is the open source time series database
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Built for developers

InfluxDB is easy to start and easy to scale; purpose-built and optimized for time to awesome.

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Trusted by Ops

Acting on time series data is easy with InfluxDB — deep insights for unified metrics and events.

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Vital to business

Capture and analyze untapped data from virtual and physical assets to seize new opportunities.

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394,500

InfluxDB databases running right now

Time series is the fastest growing database category

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Infrastructure and application monitoring

Hit your most demanding SLAs and deliver improved experiences.

Coupa Software

"InfluxDB Cloud is providing improved visibility across areas where we previously couldn’t see, allowing us to proactively identify and fix issues before customers find them."

Sanket Naik
VP of Cloud Operations and Security • Coupa
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"InfluxDB is a high-speed read and write database. So think of it. The data is being written in real-time, you can read in real-time, and when you’re reading it, you can apply your machine learning model. So, in real-time, you can forecast, and you can detect anomalies."

Rajeev Tomer
Sr. Manager of Data Engineering • Capital One
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"Measurements help us make educated, data-driven decisions quickly. They are what keeps us in business. It drives the need for products like InfluxDB. If you can’t measure something to get results, you can’t possibly get better at it. Worse yet, you won’t know what you should be focusing on."

Dennis Brazil
SRE Monitoring • PayPal
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IoT monitoring and analytics

Chart a path to automation and autonomy with InfluxDB.

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“As more people populate cities and miss nature, nature is moving to the city. But for nature cities to be a reality, we need to understand greenery performance from data. That’s why Spiio is using InfluxData to accelerate the green revolution.”

Jens-Ole Graulund
CTO • Spiio
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"Customers realize that the new user report generated with InfluxDB is cool and very fast, so they will use it more."

Florian Rampp
Technical Lead • tado°
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"On scalability, we wanted to grow from monitoring about 1,000 units at first to 20 million units by 2020. So we also needed the system to be reliable and stable while growing between those two points. And we needed it to be fast, both for data collection and querying."

David McLean
Senior Developer • BBOXX
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Innovator spotlight

Real-time analytics produces real business value for Houghton Mifflin Harcourt.

Our technical services team is now able to align performance with the fiduciary aspects of infrastructure operation.

Robert Allen
Director of Engineering • Houghton Mifflin Harcourt

  • July ’20
  • 15

    Event Details

    Register to join us for our monthly Community Office Hours. This is your chance to interact directly with Influxers who are available to answer your questions about all things InfluxDB and time series.

    Time

    (Wednesday) 10:00am

    Location

    via Zoom

  • 16

    Event Details

    The Time Series Data Virtual Summit is a virtual event focused on the impact of time series data. You will also gain firsthand knowledge and inspiration from a variety of developers and open source project members on how time series data can be used to help you build and optimize your solutions for real-time visibility into stacks, sensors, and systems.
    Register now

    Time

    (Thursday) 07:00am

    Location

    Virtual event

  • 21

    Event Details

    Register to join us for Time Series Meetup: The Virtual Edition - event for everyone who is passionate or curious about time series data and how it can be used.
    Register now

    Time

    (Tuesday) 10:00am

    Location

    via Zoom

From the blog

Release Announcement: InfluxDB 2.0.0 Beta 14
A new release of InfluxDB 2.0 Beta is available now. We will be shipping regular updates as we add new features and fix issues. Please keep in mind that these beta builds are not meant for testing performance or production usage. Please join us in our InfluxDB Community Slack and ...
Anomaly Detection with Median Absolute Deviation
When you want to spot hosts, applications, containers, plant equipment, or sensors that are behaving differently from others, you can use the Median Absolute Deviation (MAD) algorithm to identify when a time series is “deviating from the pack”. In this tutorial, we’ll identify anomalous hosts using mad() — the Flux implementation ...

Available as InfluxDB open source, InfluxDB Cloud & InfluxDB Enterprise

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