Category Archives: Community

Beta3 of Chronograf

As promised, we released an update to the Chronograf beta that includes bug fixes and some updates to the remaining features. In particular, we have added the ability to create your own queries outside of the query builder. This is useful if you want to build queries in time intervals outside of the standard set or if you prefer to type in your queries manually.

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Announcing the new Chronograf, a UI for the TICK stack & a complete open source monitoring solution

Today we’re releasing the first open source version of Chronograf, the user interface of the TICK stack. With this release we can now provide the entire stack as a complete open source monitoring solution. It’s part of our vision to enable users to own their monitoring on pure open source software that is as easy to setup and use as commercial SaaS offerings. It’s a continuation of our two primary drives as we build software: optimize for developer happiness by giving our users the fastest time to value with tools and solutions that are a joy to use.

Continue reading Announcing the new Chronograf, a UI for the TICK stack & a complete open source monitoring solution

A Shoutout to our contributors!

Hopefully you have already read Nathan’s blog on the exciting news about the new version of Telegraf and Kapacitor. We are especially proud of the added Kubernetes metrics that can now be collected and acted upon. As an open source platform, we are able to quickly add key functionality like this because of the effort of the collective community. This is one of the reasons why the community means so much to us; and without you, our product would not be as great as it is. The proof is in the numbers – just check out the key stats on Github like Stars & number of Contributors!

Continue reading A Shoutout to our contributors!

We’re going to GrafanaCon 2016

We are excited to let you know that we will be at GrafanaCon2016 which will be held at the Intrepid Sea, Air & Space Museum on the Hudson River in NYC. Paul Dix, our CTO, will be there to meet with any attendee currently using InfluxData or have plans to do so. In preparation for this event, we wanted to hear from some of our users about how they are using InfluxDB and Grafana. Share with us your projects on twitter and we’ll place your entry in a raffle with a chance to win a free pass to GrafanaCon. We will select the winner on November 18, 2016 giving you plenty of time to get your questions ready for Paul!

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InfluxData’s Q&A Session for Oct 20: Intro to Kapacitor for Alerting and Anomaly Detection

We have posted the video archive for the Oct 13: Intro to Telegraf and the Plugin Ecosystem and the following is a recap of the Q&A session during the training.

Q: What determines choosing batch or stream?

A: Choosing batch vs stream depends on how you would like to process the data. Batching is the equivalent of using InfluxDB’s “GROUP BY” function, where data is segmented into time bound buckets. Streaming would be for processing data in real-time.

Continue reading InfluxData’s Q&A Session for Oct 20: Intro to Kapacitor for Alerting and Anomaly Detection

InfluxData’s Q&A Session for Oct 13: Intro to Telegraf and the Plugin Ecosystem

We have posted the video archive for the Oct 13: Intro to Telegraf and the Plugin Ecosystem and the following is a recap of the Q&A session during the training.

Q: Is Raspberry Pi supported? I had a problem with memory issues using InfluxDB version 0.8.

A: We have completely changed the product and build pipeline since v0.8. We now have ARM support. In addition, if you’re looking to upgrade to v1.0 from v0.8 I recommend checking out one of our technical papers which goes into detail about the differences between the versions and the migration steps.

Continue reading InfluxData’s Q&A Session for Oct 13: Intro to Telegraf and the Plugin Ecosystem

Monitoring and alerting with Kapacitor now available on InfluxCloud

Today we’ve made Kapacitor, the InfluxData project for monitoring and alerting on time series data, available on our AWS backed InfluxCloud offering. Existing and new InfluxCloud customers can now add a fully managed instance of Kapacitor starting at $200 per month.

Using Kapacitor’s API, users can create and enable TICKscripts on our cloud. Here’s an example that will send an alert to Slack if CPU utilization is > 95% for more than two minutes. It performs this check every 10 seconds.

        .crit(lambda: "value" > 95)
        // Only alert if all points in the window match the criteria.

Alerts can be configured based on moving averages, outliers, missing data (known as a dead man’s switch) and many other criteria. See the Kapacitor documentation for more examples and details on how it works.

Continue reading Monitoring and alerting with Kapacitor now available on InfluxCloud

InfluxDB is 27x Faster vs MongoDB for Time-Series Workloads

This is the third in a series of detailed benchmarking tests comparing InfluxDB vs Elasticsearch, Cassandra, MongoDB and other databases for time-series data and metrics workloads.

At InfluxData, one of the common questions we’ve been getting asked by developers and architects alike the last few months is, “How does InfluxDB compare to MongoDB for time-series workloads?” This question might be prompted for a few reasons. First, if they’re starting a brand new project and doing the due diligence of evaluating a few solutions head-to-head, it can be helpful in creating their comparison grid. Second, they might already be using MongoDB for ingesting data in an existing application, but would like to now see how they can integrate metrics collection into their system and believe there might be a better solution than MongoDB for this task.

Over the last few weeks a few members of the InfluxData engineering and QA teams set out to compare the performance and features of InfluxDB and MongoDB for common time-series workloads, specifically looking at the rates of data ingestion, on-disk data compression, and query performance. InfluxDB outperformed MongoDB in all three tests with 27x greater write throughput, while using 84x less disk space, and delivering relatively equal performance when it came to query speed.

To read the complete details of the benchmarks and methodology, download the “Benchmarking InfluxDB vs. MongoDB for Time-Series Data & Metrics Management” technical paper.

Our overriding goal was to create a consistent, up-to-date comparison that reflects the latest developments in both InfluxDB and MongoDB with later coverage of other databases and time-series solutions. We will periodically re-run these benchmarks and update our detailed technical paper with our findings. All of the code for these benchmarks are available on Github. Feel free to open up issues or pull requests on that repository or if you have any questions, comments, or suggestions.

Now, let’s take a look at the results…

Versions Tested

InfluxDB v1.0.0

InfluxDB is an open-source time-series database written in Go. At its core is a custom-built storage engine called the Time-Structured Merge (TSM) Tree, which is optimized for time-series data. Controlled by a custom SQL-like query language named InfluxQL, InfluxDB provides out-of-the-box support for mathematical and statistical functions across time ranges and is perfect for custom monitoring and metrics collection, real-time analytics, plus IoT and sensor data workloads.

MongoDB v3.3.11

MongoDB is an open-source, document-oriented database, colloquially known as a NoSQL database, written in C and C++. Though it’s not generally considered a true time series database per se, its creators often promote its use for time-series workloads. It offers modeling primitives in the form of timestamps and bucketing, which give users the ability to store and query time series data.

About the Benchmarks

In building a representative benchmark suite, we identified the most commonly evaluated characteristics for working with time-series data. We looked at performance across three vectors:

  • Data ingest performance – measured in values per second
  • On-disk storage requirements – measured in MBs
  • Mean query response time – measured in milliseconds

About the Dataset

For this benchmark, we focused on a dataset that models a common DevOps monitoring and metrics use case, where a fleet of servers are periodically reporting system and application metrics at a regular time interval. We sampled 100 values across 9 subsystems (CPU, memory, disk, disk I/O, kernel, network, Redis, PostgreSQL, and Nginx) every 10 seconds. For the key comparisons, we looked at a dataset that represents 100 servers over a 6-hour period, which represents a relatively modest deployment.

  • Number of Servers: 1000
  • Values measured per Server: 100
  • Measurement Interval: 10s
  • Dataset duration(s): 6h
  • Total values in dataset: 216,000,000

This is only a subset of the entire benchmark suite, but it’s a representative example. If you’re interested in additional detail, you can read more about the testing methodology on GitHub.

Write Performance

InfluxDB outperformed MongoDB by 27x when it came to data ingestion.


On-Disk Compression

InfluxDB outperformed MongoDB by delivering 84x better compression.


Query Performance

InfluxDB and MongoDB had relatively equal performance characteristics when it came to query speed.



The benchmarking tests and resulting data demonstrated that InfluxDB outperformed MongoDB in data ingestion and on-disk storage by a significant margin. Specifically:

  • InfluxDB outperformed MongoDB by 27x when it came to data ingestion
  • InfluxDB outperformed MongoDB by delivering 84x better compression
  • InfluxDB and MongoDB performed similarly on query response time as concurrency increased.

It’s also important to note that configuring MongoDB to work with time series data wasn’t trivial. It requires up-front decisions about how to structure your collections and data types, which can be very time consuming and will have long-lasting impacts on how you can interact with your data and what types of queries you can run. InfluxDB, on the other hand, is ready to use for time series workloads out-of-the-box with no additional configuration.

In conclusion, we highly encourage developers and architects to run these benchmarks themselves to independently verify the results on their hardware and data sets of choice. However, for those looking for a valid starting point on which technology will give better time-series data ingestion, compression and query performance “out-of-the-box”, InfluxDB is the clear winner across many dimensions, especially when the data sets become larger and the system runs over a longer period of time.

What’s next

  • Download: 1.0 GA downloads for the TICK-stack are live on our “downloads” page
  • Deploy on the Cloud: Get started with a FREE trial of InfluxCloud featuring fully-managed clusters, Kapacitor and Grafana.
  • Deploy on Your Servers: Want to run InfluxDB clusters on your servers? Try a FREE 14-day trial of InfluxEnterprise featuring an intuitive UI for deploying, monitoring and rebalancing clusters, plus managing backups and restores.
  • Tell Your Story: Over 100 companies have shared their story on how InfluxDB is helping them succeed. Submit your testimonial and get a limited edition InfluxDB hoodie as a thank you.

InfluxDB 1.0 GA Released: A Retrospective and What’s Next

Today we’re excited to announce the 1.0 release of the open source time series database, InfluxDB and our commercial offering, InfluxEnterprise, which supports high availability deployments and scale out clustering for increased throughput. This makes today the biggest day in our company’s history. This release has been almost 3 years in the making and on this occasion I’d like to take a look back at the project’s history, let users know what compatibility guarantees the 1.x line of releases will have and talk about what’s next for InfluxDB.

As we announce the release today, there are tens of thousands of organizations around the globe using InfluxDB. They’re using it to monitor their network infrastructure, security, container infrastructure, solar panels, agriculture, scientific experiments, user analytics, business intelligence, home automation, and countless other specific use cases. To learn more about how companies big and small are using InfluxDB to manage their time-series data, checkout our testimonials page which currently has over 100 companies listed. What’s your story?

Why a time-series database?

In September of 2013 Todd Persen, John Shahid, and I were working on Errplane, a SaaS application for doing real-time metrics and monitoring. Todd and I had started the company in 2012 and despite getting a leg up from taking it through the Winter 2013 batch of Y Combinator, it wasn’t working as we’d hoped. We had raised a modest seed round of funding so we weren’t in danger of imminent demise, but we weren’t having success gaining real customer traction. Continue reading InfluxDB 1.0 GA Released: A Retrospective and What’s Next

Announcing Kapacitor 1.0 – A Data Processing Engine for InfluxDB

Kapacitor 1.0 GA is here. Kapacitor is the brains of the TICK stack. You can leverage Kapacitor to process your data for various business needs and use it to find changes or anomalies within your time series data. We have come a long way since the 0.13.1 release of Kapacitor back in mid May 2016. Since then, we’ve added 33 new features and 42 bug fixes. We had many PRs from the community, from simple bug fixes to major features. Thanks to everyone for helping improve Kapacitor!

What’s new?

Of the new features in 1.0 there are eight we want to highlight, three of which are community contributions…

  • HTTP based subscriptions: Goodbye to the complexity of managing a unique UDP port per database, HTTP subscription are here and provide a simpler, more reliable transport from InfluxDB to Kapacitor.
  • Template Tasks: You can now define templates so that multiple tasks that share common behavior can easily be managed together.
  • Holt-Winters Forecasting: Start using Kapacitor for more complex anomaly detection tasks, Holt-Winters is the first of many powerful algorithms to be added to the TICK stack.
  • Alert Reset Expressions: Noise from alerts is a plague. Thanks to @minhdanh, you can now define reset expressions for your alert levels to reduce alert noise.
  • Group By Fields: Convert any field into a tag so that you can use it to group your data streams.
  • Telegram Alerting: You can now send messages to Telegram thanks to @burdandrei.
  • Better and Faster Lambda Expressions: @yosiat greatly improved the performance of lambda expressions and added support for conditional logic.
  • Live Replays: Replay data directly from InfluxDB without going through any intermediate steps.

Continue reading Announcing Kapacitor 1.0 – A Data Processing Engine for InfluxDB