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Data Engineering Podcast

Troubleshooting Kafka In Production

Summary

Kafka has become a ubiquitous technology, offering a simple method for coordinating events and data across different systems. Operating it at scale, however, is notoriously challenging. Elad Eldor has experienced these challenges first-hand, leading to his work writing the book "Kafka: : Troubleshooting in Production". In this episode he highlights the sources of complexity that contribute to Kafka's operational difficulties, and some of the main ways to identify and mitigate potential sources of trouble.

Announcements

  • 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 Elad Eldor about operating Kafka in production and how to keep your clusters stable and performant

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you describe your experiences with Kafka?
    • What are the operational challenges that you have had to overcome while working with Kafka?
    • What motivated to write a book about how to manage Kafka in production?
  • There are many options now for persistent data queues. What are the factors to consider when determining whether Kafka is the right choice?
    • In the case where Kafka is the appropriate tool, there are many ways to run it now. What are the considerations that teams need to work through when determining whether/where/how to operate a cluster?
  • When provisioning a Kafka cluster, what are the requirements that need to be considered when determining the sizing?
    • What are the axes along which size/scale need to be determined?
  • The core promise of Kafka is that it is a durable store for continuous data. What are the mechanisms that are available for preventing data loss?
    • Under what circumstances can data be lost?
  • What are the different failure conditions that cluster operators need to be aware of?
    • What are the monitoring strategies that are most helpful for identifying (proactively or reactively) those errors?
  • In the event of these different cluster errors, what are the strategies for mitigating and recovering from those failures?
  • When a cluster's usage expands beyond the original designed capacity, what are the options/procedures for expanding that capacity?
    • When a cluster is underutilized, how can it be scaled down to reduce cost?
  • What are the most interesting, innovative, or unexpected ways that you have seen Kafka used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working with Kafka?
  • When is Kafka the wrong choice?
  • What are the changes that you would like to see in Kafka to make it easier to operate?

Contact Info

Parting Question

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

Closing Announcements

  • Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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