What if your database worked more like Git? Every change captured as an immutable event you can replay, instead of a single mutating row that quietly forgets its own history. That's event sourcing, and Chris May is back on Talk Python, fresh off our Datastar panel, to walk us through what it actually looks like in Python. We'll cover the core patterns, the libraries to reach for, when not to use it, and why event sourcing turns out to be a surprisingly good fit for AI-assisted coding.

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Links from the show Guest
Chris May: everydaysuperpowers.dev

Intro to event sourcing e-book: everydaysuperpowers.gumroad.com

Domain-Driven Design: The Power of CQRS and Event Sourcing: How CQRS/ES Redefine Building Scalable System: ricofritzsche.me
DDD: www.amazon.com
Understanding Eventsourcing (Martin Dilger): www.amazon.com
Event Sourcing Explained using Football Video: www.youtube.com
Why I finally embraced event sourcing and why you should too article: everydaysuperpowers.dev
valkey: valkey.io
diskcache: talkpython.fm
eventsourcing package: github.com
eventsourcing docs: eventsourcing.readthedocs.io
John Bywater: github.com
Datastar: data-star.dev
Microconf: microconf.com
Event Modeling & Event Sourcing Podcast: podcast.eventmodeling.org
Python Package Guides for AI Agents: github.com
Iodine tablets AI joke: x.com
KurrentDb: www.kurrent.io

Watch this episode on YouTube: youtube.com
Episode #548 deep-dive: talkpython.fm/548
Episode transcripts: talkpython.fm

Theme Song: Developer Rap
🥁 Served in a Flask 🎸: talkpython.fm/flasksong

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