Cover art for podcast Data Engineering Podcast

Data Engineering Podcast

419 EpisodesProduced by Tobias MaceyWebsite

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

1:02:40

TimescaleDB: Fast And Scalable Timeseries with Ajay Kulkarni and Mike Freedman - Episode 18

Summary

As communications between machines become more commonplace the need to store the generated data in a time-oriented manner increases. The market for timeseries data stores has many contenders, but they are not all built to solve the same problems or to scale in the same manner. In this episode the founders of TimescaleDB, Ajay Kulkarni and Mike Freedman, discuss how Timescale was started, the problems that it solves, and how it works under the covers. They also explain how you can start using it in your infrastructure and their plans for the future.

Preamble
  • Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • You can help support the show by checking out the Patreon page which is linked from the site.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • Your host is Tobias Macey and today I’m interviewing Ajay Kulkarni and Mike Freedman about Timescale DB, a scalable timeseries database built on top of PostGreSQL
Interview
  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what Timescale is and how the project got started?
  • The landscape of time series databases is extensive and oftentimes difficult to navigate. How do you view your position in that market and what makes Timescale stand out from the other options?
  • In your blog post that explains the design decisions for how Timescale is implemented you call out the fact that the inserted data is largely append only which simplifies the index management. How does Timescale handle out of order timestamps, such as from infrequently connected sensors or mobile devices?
  • How is Timescale implemented and how has the internal architecture evolved since you first started working on it?
    • What impact has the 10.0 release of PostGreSQL had on the design of the project?
    • Is timescale compatible with systems such as Amazon RDS or Google Cloud SQL?


  • For someone who wants to start using Timescale what is involved in deploying and maintaining it?

  • What are the axes for scaling Timescale and what are the points where that scalability breaks down?

    • Are you aware of anyone who has deployed it on top of Citus for scaling horizontally across instances?


  • What has been the most challenging aspect of building and marketing Timescale?

  • When is Timescale the wrong tool to use for time series data?

  • One of the use cases that you call out on your website is for systems metrics and monitoring. How does Timescale fit into that ecosystem and can it be used along with tools such as Graphite or Prometheus?

  • What are some of the most interesting uses of Timescale that you have seen?

  • Which came first, Timescale the business or Timescale the database, and what is your strategy for ensuring that the open source project and the company around it both maintain their health?

  • What features or improvements do you have planned for future releases of Timescale?

Contact Info

Parting Question
  • From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

Educational emoji reaction

Educational

Interesting emoji reaction

Interesting

Funny emoji reaction

Funny

Agree emoji reaction

Agree

Love emoji reaction

Love

Wow emoji reaction

Wow

Listen to Data Engineering Podcast

RadioPublic

A free podcast app for iPhone and Android

  • User-created playlists and collections
  • Download episodes while on WiFi to listen without using mobile data
  • Stream podcast episodes without waiting for a download
  • Queue episodes to create a personal continuous playlist
RadioPublic on iOS and Android
Or by RSS
RSS feed
https://www.dataengineeringpodcast.com/rss

Connect with listeners

Podcasters use the RadioPublic listener relationship platform to build lasting connections with fans

Yes, let's begin connecting
Browser window

Find new listeners

  • A dedicated website for your podcast
  • Web embed players designed to convert visitors to listeners in the RadioPublic apps for iPhone and Android
Clicking mouse cursor

Understand your audience

  • Capture listener activity with affinity scores
  • Measure your promotional campaigns and integrate with Google and Facebook analytics
Graph of increasing value

Engage your fanbase

  • Deliver timely Calls To Action, including email acquistion for your mailing list
  • Share exactly the right moment in an episode via text, email, and social media
Icon of cellphone with money

Make money

  • Tip and transfer funds directly to podcastsers
  • Earn money for qualified plays in the RadioPublic apps with Paid Listens