Summary

Building a database engine requires a substantial amount of engineering effort and time investment. Over the decades of research and development into building these software systems there are a number of common components that are shared across implementations. When Paul Dix decided to re-write the InfluxDB engine he found the Apache Arrow ecosystem ready and waiting with useful building blocks to accelerate the process. In this episode he explains how he used the combination of Apache Arrow, Flight, Datafusion, and Parquet to lay the foundation of the newest version of his time-series database.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I'm interviewing Paul Dix about his investment in the Apache Arrow ecosystem and how it led him to create the latest PFAD in database design

Interview

Introduction
How did you get involved in the area of data management?
Can you start by describing the FDAP stack and how the components combine to provide a foundational architecture for database engines?
- This was the core of your recent re-write of the InfluxDB engine. What were the design goals and constraints that led you to this architecture?
Each of the architectural components are well engineered for their particular scope. What is the engineering work that is involved in building a cohesive platform from those components?
One of the major benefits of using open source components is the network effect of ecosystem integrations. That can also be a risk when the community vision for the project doesn't align with your own goals. How have you worked to mitigate that risk in your specific platform?
Can you describe the operational/architectural aspects of building a full data engine on top of the FDAP stack?
- What are the elements of the overall product/user experience that you had to build to create a cohesive platform?
What are some of the other tools/technologies that can benefit from some or all of the pieces of the FDAP stack?
What are the pieces of the Arrow ecosystem that are still immature or need further investment from the community?
What are the most interesting, innovative, or unexpected ways that you have seen parts or all of the FDAP stack used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on/with the FDAP stack?
When is the FDAP stack the wrong choice?
What do you have planned for the future of the InfluxDB IOx engine and the FDAP stack?

Contact Info

LinkedIn
pauldix on GitHub

Parting Question

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

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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