Data Engineering Podcast


This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

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06 November 2023

Shining Some Light In The Black Box Of PostgreSQL Performance - E399

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Summary

Databases are the core of most applications, but they are often treated as inscrutable black boxes. When an application is slow, there is a good probability that the database needs some attention. In this episode Lukas Fittl shares some hard-won wisdom about the causes and solution of many performance bottlenecks and the work that he is doing to shine some light on PostgreSQL to make it easier to understand how to keep it running smoothly.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
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  • This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold
  • Your host is Tobias Macey and today I'm interviewing Lukas Fittl about optimizing your database performance and tips for tuning Postgres

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • What are the different ways that database performance problems impact the business?
  • What are the most common contributors to performance issues?
  • What are the useful signals that indicate performance challenges in the database?
    • For a given symptom, what are the steps that you recommend for determining the proximate cause?
  • What are the potential negative impacts to be aware of when tuning the configuration of your database?
  • How does the database engine influence the methods used to identify and resolve performance challenges?
  • Most of the database engines that are in common use today have been around for decades. How have the lessons learned from running these systems over the years influenced the ways to think about designing new engines or evolving the ones we have today?
  • What are the most interesting, innovative, or unexpected ways that you have seen to address database performance?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on databases?
  • What are your goals for the future of database engines?

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.
  • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
  • If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
  • To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

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

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