Bytewax is a curious stream processing tool that blends a Python surface with a Rust core to produce something that’s in a similar vein to Kafka Streams or Apache Flink, but with a fundamentally different implementation. This week we’re going to take a look at what it does, how it works in theory, and how the marriage of Python and Rust works in practice…

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The original Naiad Paper: https://dl.acm.org/doi/10.1145/2517349.2522738

Timely Dataflow: https://github.com/TimelyDataflow/timely-dataflow

Bytewax the Library: https://github.com/bytewax/bytewax

Bytewax the Service: https://bytewax.io/

PyO3, for calling Rust from Python: https://pyo3.rs/v0.21.2/

Kris on Mastodon: http://mastodon.social/@krisajenkins

Kris on LinkedIn: https://www.linkedin.com/in/krisjenkins/

Kris on Twitter: https://twitter.com/krisajenkins

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#softwaredevelopment #dataengineering #apachekafka #timelydataflow

Mojo is the latest language from the creator of Swift and LLVM. It’s an attempt to take some of the best techniques from CPU/GPU-level programming and package them up in a Python-compatible syntax.

In this episode we explore why Mojo was created, and what it offers to Python programmers and non-Python programmers alike. How is it built for performance, and which performance features matter? What’s its take on functional programming and type systems? And can it marry the high-level programming of Python with the low-level programming of LLVM/MLIR?

If you’re a Python programmer who needs better performance, a C programmer who expects more from a ‘scripting language’, or just someone who’d be happier if Python had a first-class type system, Mojo might well be for you…

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Mojo: https://www.modular.com/max/mojo

Mojo’s Roadmap: https://docs.modular.com/mojo/roadmap.html

The Mojo Discord: https://discord.com/invite/modular

MLIR: https://mlir.llvm.org/

Chris’s Talks: https://nondot.org/sabre/Resume.html#talks

Chris on Twitter: https://twitter.com/clattner_llvm

Kris on Mastodon: http://mastodon.social/@krisajenkins

Kris on LinkedIn: https://www.linkedin.com/in/krisjenkins/

Kris on Twitter: https://twitter.com/krisajenkins

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#software #podcast #mojolang #ml #pythonml

Every database has to juggle the need to process new data and to query old data. That task falls to any system that “does stuff and remembers stuff”. But it’s quite hard to really optimise one system for both use cases. There are different constraints on new and old data, and as a system gets larger and larger, those differences multiply to breaking point. That’s something Twitter’s engineers were figuring out in the 2010s.

One solution that came up in those years was the Lambda Architecture. A two-pronged approach that recognises the divide between new and old data, and works hard to blend the two together seamlessly in userspace. But that seamless blending is easier said than done. It’s nearly all bespoke work.

What if you could get it off the shelf? Let someone else do the work of combining two different kinds of database into one neat package? That's the question of the week as we look at the recently open-sourced project Proton, and its attempt to be the Lambda Architecture in a box…

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Proton Docs: https://docs.timeplus.com/proton

Proton Source: https://github.com/timeplus-io/proton

Timeplus: https://www.timeplus.com/

Kris on Mastodon: http://mastodon.social/@krisajenkins

Kris on LinkedIn: https://www.linkedin.com/in/krisjenkins/

Kris on Twitter: https://twitter.com/krisajenkins

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#podcast #softwareengineering #databases #dataengineering