Summary

Kubernetes is a framework that aims to simplify the work of running applications in production, but it forces you to adopt new patterns for debugging and resolving issues in your systems. Robusta is aimed at making that a more pleasant experience for developers and operators through pre-built automations, easy debugging, and a simple means of creating your own event-based workflows to find, fix, and alert on errors in production. In this episode Natan Yellin explains how the project got started, how it is architected and tested, and how you can start using it today to keep your Python projects running reliably.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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Your host as usual is Tobias Macey and today I’m interviewing Natan Yellin about Robusta,

Interview

Introductions
How did you get introduced to Python?
Can you describe what Robusta is and the story behind it?
What are some of the challenges that teams face when running their systems in Kubernetes?
- How does Robusta help address those difficulties?
How does Robusta compare to e.g. Rookout?
What are some of the ways that Robusta is able to provide specific insights for Python applications?
Can you describe how Robusta is implemented?
- What are some of the most challenging engineering tasks that you have had to work through while building Robusta?
- How have the capabilities and components evolved from when you started working on it?
What is the workflow for integrating Robusta into a Kubernetes environment and a team’s maintenance processes?
What are some examples of the kinds of questions that Robusta can help answer out of the box?
- What are some tasks that Robusta facilitates which require manual exploration?
What are the interfaces available for customizing and extending the functionality of Robusta?
- What is involved in adding a new automation capability to Robusta?
How have you approached the design of the tool to make it ergonomic and intuitive so that it doesn’t contribute to the stresses of dealing with errors in production?
Given that it is a tool to help resolve problems in production infrastructure, how have you worked to ensure its reliability and resilience?
What is the governance and sustainability model for Robusta?
What are the most interesting, innovative, or unexpected ways that you have seen Robusta used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Robusta?
When is Robusta the wrong choice?
What do you have planned for the future of Robusta?

Keep In Touch

LinkedIn
@aantn on Twitter
aantn on GitHub
Website

Picks

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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Links

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Run Your Applications Reliably On Kubernetes Without Losing Sleep With Robusta 53:43 Share

The Python Podcast.__init__

Shownotes Transcript

Run Your Applications Reliably On Kubernetes Without Losing Sleep With Robusta

The Python Podcast.init