Using FoundationDB As The Bedrock For Your Distributed Systems
Data Engineering Podcast
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Testing and Deploying FoundationDB
This chapter explores the comprehensive testing strategies implemented for FoundationDB, particularly focusing on handling complexities in distributed systems through deterministic simulations. It also examines deployment configurations and the importance of effective maintenance, monitoring, and disaster recovery strategies in cluster environments. Additionally, the chapter discusses upgrade challenges, unique use cases, and upcoming features that enhance FoundationDB's flexibility and performance across various applications.
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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.
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- 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?
Links
- FoundationDB
- Jepsen
- Andy Pavlo
- Archive.org – The Internet Archive
- FoundationDB Summit
- Flow Language
- C++
- Actor Model
- Erlang
- Zookeeper
- PAXOS consensus algorithm
- Multi-Version Concurrency Control (MVCC) AKA Optimistic Locking
- ACID
- CAP Theorem
- Redis
- Record Layer
- CloudKit
- Document Layer
- Segment
- 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
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