Using FoundationDB As The Bedrock For Your Distributed Systems
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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. 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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 LinkedIn @ryanworl on Twitter Website Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links FoundationDB Jepsen Andy Pavlo Archive.org – The Internet Archive FoundationDB Summit Flow Language C++ Actor Model Erlang Zookeeper Podcast Episode PAXOS consensus algorithm Multi-Version Concurrency Control (MVCC) AKA Optimistic Locking ACID CAP Theorem Redis Record Layer CloudKit Document Layer Segment Podcast Episode NVMe SnowflakeDB FlatBuffers Protocol Buffers Ryan Worl FoundationDB Summit Presentation Google F1 Google Spanner WaveFront EtcD B+ Tree Michael Stonebraker Three Vs Confluent The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast
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