Using FoundationDB As The Bedrock For Your Distributed Systems

May 7, 2019

Ryan Worl, a software engineer deeply involved with FoundationDB, dives into the intricacies of this powerful distributed key-value store. He discusses its unique architecture and how to set it up for diverse applications while ensuring ACID compliance. Worl shares insights on optimizing performance in distributed systems, handling conflicts, and the role of interoperability between data layers. The conversation also touches on effective testing and deployment strategies and the challenges companies face in modern data management.

Ask episode

Chapters

Transcript

Episode notes

Intro

00:00 • 3min

Unpacking FoundationDB: Architecture and Experiences

02:56 • 20min

Exploring FoundationDB Configuration and Operations

22:39 • 8min

Navigating Interoperability Between Data Layers

31:08 • 2min

Optimizing Performance in Distributed Systems with FoundationDB

32:40 • 3min

Navigating Conflict Resolution in FoundationDB

35:11 • 2min

Testing and Deploying FoundationDB

37:23 • 27min

Addressing the Gaps in Modern Data Management

01:04:18 • 2min

Summary

The database market continues to expand, offering systems that are suited to virtually every use case. But what happens if you need something customized to your application? FoundationDB is a distributed key-value store that provides the primitives that you need to build a custom database platform. In this episode Ryan Worl explains how it is architected, how to use it for your applications, and provides examples of system design patterns that can be built on top of it. If you need a foundation for your distributed systems, then FoundationDB is definitely worth a closer look.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support.
Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit.
You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
Your host is Tobias Macey and today I’m interviewing Ryan Worl about FoundationDB, a distributed key/value store that gives you the power of ACID transactions in a NoSQL database

Interview

Introduction
How did you get involved in the area of data management?
Can you explain what FoundationDB is and how you got involved with the project?
What are some of the unique use cases that FoundationDB enables?
Can you describe how FoundationDB is architected?
- How is the ACID compliance implemented at the cluster level?
What are some of the mechanisms built into FoundationDB that contribute to its fault tolerance?
- How are conflicts managed?
FoundationDB has an interesting feature in the form of Layers that provide different semantics on the underlying storage. Can you describe how that is implemented and some of the interesting layers that are available?
- Is it possible to apply different layers, such as relational and document, to the same underlying objects in storage?
One of the aspects of FoundationDB that is called out in the documentation and which I have heard about elsewhere is the performance that it provides. Can you describe some of the implementation mechanics of FoundationDB that allow it to provide such high throughput?
For someone who wants to run FoundationDB can you describe a typical deployment topology?
- What are the scaling factors for the underlying storage and for the Layers that are operating on the cluster?
Once you have a cluster deployed, what are some of the edge cases that users should watch out for?
- How are version upgrades managed in a cluster?
What are some of the ways that FoundationDB impacts the way that an application developer or data engineer would architect their software as compared to working with something like Postgres or MongoDB?
What are some of the more interesting/unusual/unexpected ways that you have seen FoundationDB used?
When is FoundationDB the wrong choice?
What is in store for the future of FoundationDB?

Contact Info

Parting Question

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