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

Feb 11, 2018

Ajay Kulkarni and Mike Freedman, co-founders of TimescaleDB, discuss the origins and challenges of building a scalable time series database. They explain how TimescaleDB handles out-of-order data and infrequent sensor connections. They also share insights into marketing and business aspects, including the decision to release the code base as open source, future plans for the enterprise version, and the support and investment structure for the open source business model.

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Episode notes

Introduction

00:00 • 2min

Creating a Scalable Time Series Database: The Origins of Timescale DB

01:49 • 5min

Challenges of Handling Time Series Data

06:23 • 27min

Building and Marketing Challenges

33:24 • 15min

Exploring the Business Aspects of TimescaleDB

48:16 • 9min

Discussion on Support and Investment Structure for Open Source Business

57:16 • 2min

Challenges and Solutions in Today's Data Infrastructure

59:44 • 3min

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

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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

Ajay
- LinkedIn
- @acoustik on Twitter
- Timescale Blog

Mike
- Website
- LinkedIn
- @michaelfreedman on Twitter
- Timescale Blog

Timescale
- Website
- @timescaledb on Twitter
- GitHub

Parting Question