Summary

Distributed systems are complex to build and operate, and there are certain primitives that are common to a majority of them. Rather then re-implement the same capabilities every time, many projects build on top of Apache Zookeeper. In this episode Patrick Hunt explains how the Apache Zookeeper project was started, how it functions, and how it is used as a building block for other distributed systems. He also explains the operational considerations for running your own cluster, how it compares to more recent entrants such as Consul and EtcD, and what is in store for the future.

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Preamble

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 Patrick Hunt about Apache Zookeeper and how it is used as a building block for distributed systems

Interview

Introduction

How did you get involved in the area of data management?

Can you start by explaining what Zookeeper is and how the project got started? What are the main motivations for using a centralized coordination service for distributed systems?

What are the distributed systems primitives that are built into Zookeeper? What are some of the higher-order capabilities that Zookeeper provides to users who are building distributed systems on top of Zookeeper? What are some of the types of system level features that application developers will need which aren’t provided by Zookeeper?

Can you discuss how Zookeeper is architected and how that design has evolved over time? What have you found to be some of the most complicated or difficult aspects of building and maintaining Zookeeper?

What are the scaling factors for Zookeeper? What are the edge cases that users should be aware of? Where does it fall on the axes of the CAP theorem?

What are the main failure modes for Zookeeper? How much of the recovery logic is left up to the end user of the Zookeeper cluster?

Since there are a number of projects that rely on Zookeeper, many of which are likely to be run in the same environment (e.g. Kafka and Flink), what would be involved in sharing a single Zookeeper cluster among those multiple services?

In recent years we have seen projects such as EtcD which is used by Kubernetes, and Consul. How does Zookeeper compare with those projects? What are some of the cases where Zookeeper is the wrong choice?

How have the needs of distributed systems engineers changed since you first began working on Zookeeper?

If you were to start the project over today, what would you do differently? Would you still use Java?

What are some of the most interesting or unexpected ways that you have seen Zookeeper used?

What do you have planned for the future of Zookeeper?

Contact Info

@phunt on Twitter

Parting Question

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

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA