A majority of the scalable data processing platforms that we rely on are built as distributed systems. This brings with it a vast number of subtle ways that errors can creep in. Kyle Kingsbury created the Jepsen framework for testing the guarantees of distributed data processing systems and identifying when and why they break. In this episode he shares his approach to testing complex systems, the common challenges that are faced by engineers who build them, and why it is important to understand their limitations. This was a great look at some of the underlying principles that power your mission critical workloads.
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- 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 Kyle Kingsbury about his work on the Jepsen testing framework and the failure modes of distributed systems
- How did you get involved in the area of data management?
- Can you start by describing what the Jepsen project is?
- What was your inspiration for starting the project?
- What other methods are available for evaluating and stress testing distributed systems?
- What are some of the common misconceptions or misunderstanding of distributed systems guarantees and how they impact real world usage of things like databases?
- How do you approach the design of a test suite for a new distributed system?
- What is your heuristic for determining the completeness of your test suite?
- What are some of the common challenges of setting up a representative deployment for testing?
- Can you walk through the workflow of setting up, running, and evaluating the output of a Jepsen test?
- How is Jepsen implemented?
- How has the design evolved since you first began working on it?
- What are the pros and cons of using Clojure for building Jepsen?
- If you were to start over today on the Jepsen framework what would you do differently?
- What are some of the most common failure modes that you have identified in the platforms that you have tested?
- What have you found to be the most difficult to resolve distributed systems bugs?
- What are some of the interesting developments in distributed systems design that you are keeping an eye on?
- How do you perceive the impact that Jepsen has had on modern distributed systems products?
- What have you found to be the most interesting, unexpected, or challenging lessons learned while building Jepsen and evaluating mission critical systems?
- What do you have planned for the future of the Jepsen framework?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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- Distributed Systems
- Cassandra DTest
- CRDT == Conflict-free Replicated Data-type
- JVM == Java Virtual Machine
- Raft consensus algorithm
- Leslie Lamport
- Heidi Howard
- CALM Conjecture
- Causal Consistency
- Hillel Wayne
- Christopher Meiklejohn
- Distsys Class
- Distributed Systems For Fun And Profit by Mikito Takada
- Christopher Meiklejohn Reading List