Discover the future of software from the people making it happen.Listen to some of the smartest developers we know talk about what they're working on, how they're trying to move the industry forward, and what you can learn from it. You might find the solution to your next architectural headache, pick up a new programming language, or just hear some good war stories from the frontline of technology.Join your host Kris Jenkins as we try to figure out what tomorrow's computing will look like the best way we know how - by listening directly to the developers' voices.
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Algorithms + Data Structures = Programs
The Algorithms + Data Structures = Programs Podcast (aka ADSP: The Podcast) is an informal podcast inspired by Magic Read Along. We plan to talk about whatever we feel like - algorithms, data structures, programming languages, latest news in tech and more. Feel free to follow us on Twitter at @adspthepodcast.
Cuis Smalltalk and the History of Computing's Future (with Juan Vuletich)
Smalltalk is one of those programming languages that’s lived out of the mainstream, but often referenced as an influence and an important part of programming history. It’s the cornerstone of object-oriented programming, it was into message passing before actors were cool, and it blurs the line between operating system, programming language and personal notebook. But what is it?Joining us to discuss it is Juan Vuletich, the creator of one of Smalltalk’s latest incarnations, Cuis. In this episode we cover Smalltalk’s history, its design ideas, Cuis’s unique implementation and what makes this modern implementation something special.Smalltalk is over 50 years old, but its vision of how computing could work has only begun. Let’s see if we can mine some ideas from it to take us into the next generation of computing...--The Cuis Smalltalk Book: https://cuis-Smalltalk.github.io/TheCuisBook/Preface.htmlCuis on Github: https://github.com/Cuis-Smalltalk/Cuis-Smalltalk-DevThe Cuis Community: https://cuis.st/communityA Short History of Cuis: https://github.com/Cuis-Smalltalk/Cuis-Smalltalk-Dev/blob/master/Documentation/CuisHistory.mdMonticello VCS: https://wiki.squeak.org/squeak/1287Juan’s Music Research: https://www.jvuletich.org/research.htmlBack to the Future - The Story of Squeak (pdf): https://dl.acm.org/doi/pdf/10.1145/263700.263754Kris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins
The Inko Programming Language, and Life as a Language Designer (with Yorick Peterse)
This week we take a close look at the language Inko from two perspectives: The language design features that make it special, and the realities of being a language developer.Yorick Peterse joins us to discuss why he’s building Inko, and which design sweetspots he’s looking for. We begin with memory management, aiming for the kind of developer who wants control, but without the complexities of Rust. Then we look at the designing for concurrency with typed channels, and handling exceptions by removing them and leaning heavily into ADTs and pattern matching.Mixed in with all that is a discussion on the realities of being a programming language developer. How do you figure out how to implement your ideas? What tradeoffs do you make and what kind of programmer do you want to be most useful to? How do you teach people new ideas in programming, and how “different” can you make a language before it feels weird? And perhaps the hardest question of all: How do you fund a new programming language in 2024?–Inko’s Homepage: https://inko-lang.org/Yorick’s Homepage: https://yorickpeterse.com/Ownership You Can Count On (paper): https://inko-lang.org/papers/ownership.pdf“The Error Model”: https://joeduffyblog.com/2016/02/07/the-error-model/Kris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins
Building the Zed Text Editor (with Nathan Sobo)
I’ve often wondered how you build a text editor. Like many software projects, it’s a simple idea at the core with an almost infinite scope for features. How do you build a solid foundation to expand on? Which features matter for launch? And how do you hope to satisfy the needs of every programmer, working in every language?My guest for this episode is Nathan Sobo. He’s tackled this problem once before with the Atom editor, and he’s back older & wiser with Zed - a new editor written completely from scratch in Rust. It has a modern UI, a wide spread of language support, and a completely different way of looking at team collaboration. But with so much ambition, what are Zed’s priorities, and what’s been left for a future version?--Zed Homepage: https://zed.dev/Segment Trees: https://en.wikipedia.org/wiki/Segment_treeRopes: https://en.wikipedia.org/wiki/Rope_(data_structure)Rust Executors: https://rust-lang.github.io/async-book/02_execution/04_executor.htmlMore about Roc: https://youtu.be/DzhIprQan68More about TigerBeetle: https://youtu.be/ayG7ltGRRHsKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins
Reimplementing Apache Kafka with Golang and S3
This week on Developer Voices we’re talking to Ryan Worl, whose career in big data engineering has taken him from DataDog to Co-Founding WarpStream, an Apache Kafka-compatible streaming system that uses Golang for the brains and S3 for the storage. Ryan tells us about his time at DataDog, along with the things he learnt from doing large-scale systems migration bit-by-bit, before we discuss how and why he started WarpStream. Why re-implement Kafka? What are the practical challenges and cost benefits of moving all your storage to S3? And would he choose Go a second time around?--WarpStream: https://www.warpstream.com/DataDog: https://www.datadoghq.com/Ryan on Twitter: https://x.com/ryanworl Kris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins
Extending Postgres for High-Performance Analytics (with Philippe Noël)
PostgreSQL is an incredible general-purpose database, but it can’t do everything. Every design decision is a tradeoff, and inevitably some of those tradeoffs get fundamentally baked into the way it’s built. Take storage for instance - Postgres tables are row-oriented; great for row-by-row access, but when it comes to analytics, it can’t compete with a dedicated OLAP database that uses column-oriented storage. Or can it?Joining me this week is Philippe Noël of ParadeDB, who’s going to take us on a tour of Postgres’ extension mechanism, from creating custom functions and indexes to Rust code that changes the way Postgres stores data on disk. In his journey to bring Elasticsearch’s strengths to Postgres, he’s gone all the way down to raw datafiles and back through the optimiser to teach a venerable old dog some new data-access tricks. –ParadeDB: https://paradedb.comParadeDB on Twitter: https://twitter.com/paradedbParadeDB on Github: https://github.com/paradedb/paradedbpgrx (Postgres with Rust): https://github.com/pgcentralfoundation/pgrxTantivy (Rust FTS library): https://github.com/quickwit-oss/tantivyPgMQ (Queues in Postgres): https://tembo.io/blog/introducing-pgmqApache Datafusion: https://datafusion.apache.org/Lucene: https://lucene.apache.org/Kris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins
Designing Actor-Based Software (with Hugh McKee)
The actor model is a popular approach to building scalable software systems. And isn’t hard to understand when you’re just reading about the beginner’s examples. But how do you architect a complex design using the actor model? Which patterns work well? How do you think through it?Joining me to take us through it is Hugh McKee. Hugh’s a total actor-model fan, and a Developer Advocate for Lightbend (the company that created the popular actor framework Akka). He takes us from his definition of actors to the designs he’s worked on, the patterns he’s found most useful, and the interesting meeting-point between actor-based designs and event-based ones.—Wikipedia - Actor Model: https://en.wikipedia.org/wiki/Actor_modelHugh’s book, Designing Reactive Systems: https://go.lightbend.com/designing-reactive-systems-role-of-actor-modelHugh on Twitter: https://twitter.com/mckeeh3Hugh on LinkedIn: https://www.linkedin.com/in/mckeehughKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins
ByteWax: Rust's Research Meets Python's Practicalities (with Dan Herrera)
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…–The original Naiad Paper: https://dl.acm.org/doi/10.1145/2517349.2522738Timely Dataflow: https://github.com/TimelyDataflow/timely-dataflowBytewax the Library: https://github.com/bytewax/bytewaxBytewax the Service: https://bytewax.io/PyO3, for calling Rust from Python: https://pyo3.rs/v0.21.2/Kris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins--#softwaredevelopment #dataengineering #apachekafka #timelydataflow
Mojo Lang - Tomorrow's High Performance Python? (with Chris Lattner)
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…–Mojo: https://www.modular.com/max/mojoMojo’s Roadmap: https://docs.modular.com/mojo/roadmap.htmlThe Mojo Discord: https://discord.com/invite/modularMLIR: https://mlir.llvm.org/Chris’s Talks: https://nondot.org/sabre/Resume.html#talksChris on Twitter: https://twitter.com/clattner_llvmKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins–#software #podcast #mojolang #ml #pythonml
Batch Data & Streaming Data in one Atom (with Jove Zhong)
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…–Proton Docs: https://docs.timeplus.com/protonProton Source: https://github.com/timeplus-io/protonTimeplus: https://www.timeplus.com/Kris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins–#podcast #softwareengineering #databases #dataengineering
Advanced Memory Management in Vale (with Evan Ovadia)
Rust changed the discussion around memory management - this week's guest hopes to push that discussion even further.This week we're joined by Evan Ovadia, creator of the Vale programming language and collector of memory management techniques from far and wide. He takes us through his most important ones, including linear types, generation references and regions, to see what Evan hopes the future of memory management will look like.If you've been interested in Rust's borrow-check and want more (or want different!) then Evan has some big ideas for you to sink your teeth into.–Vale: https://vale.dev/The Vale Discord: https://discord.com/invite/SNB8yGHEvan’s Blog: https://verdagon.dev/homeEvan’s 7DRL Entry: https://verdagon.dev/blog/higher-raii-7drl7DRL: https://7drl.com/https://verdagon.dev/grimoire/grimoireWhat Colour Is Your Function?: https://journal.stuffwithstuff.com/2015/02/01/what-color-is-your-function/42, the language: https://forty2.is/Verona Language: https://www.microsoft.com/en-us/research/project/project-verona/Austral language: https://austral-lang.org/Surely You’re Joking, Mr Feynman! (book): https://www.goodreads.com/book/show/35167685-surely-you-re-joking-mr-feynmanEvan on Twitter: https://twitter.com/verdagonFind Evan in the Vale Discord: https://discord.com/invite/SNB8yGHKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins–#software #programming #podcast #valelang
Bringing Pure Python to Apache Kafka (with Tomáš Neubauer)
The “big data infrastructure” world is dominated by Java, but the data-analysis world is dominated by Python. So if you need to analyse and process huge amounts of data, chances are you’re in for a less-than-ideal time. The impedance mismatch will probably make your life hard somehow. So there are a lot of projects and companies trying to solve that problem. To bridge those two worlds seamlessly, and many of the popular solutions see SQL as the glue. But this week we’re going to look at another solution - ignore Java, treat Kafka as a protocol, and build up all the infrastructure tools you need with a pure Python library. It’s a lot of work, but in theory it would make Python the one language for data storage, analysis and processing, at scale. Tempting, but is it feasible? Joining me to discuss the pros, cons, and massive scope of that approach is Tomáš Neubauer. He started off doing real time data analysis for the Maclaren’s F1 team, and is now deep in the Python mines effectively rewriting Kafka Streams in Python. But how? How much work is actually involved in porting those ideas to Python-land, and how do you even get started? And perhaps most fundamental of all - even if you succeed, will that be enough to make the job easy, or will you still have to scale the mountain of teaching people how to use the new tools you’ve built? Let's find out.– Quix Streams on Github: https://github.com/quixio/quix-streamsQuix Streams getting started guide: https://quix.io/get-started-with-quix-streamsQuix: https://quix.io/ Tomáš on LinkedIn: https://www.linkedin.com/in/tom%C3%A1%C5%A1-neubauer-a10bb144Tomáš on Twitter: https://twitter.com/TomasNeubauer0Kris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins --#podcast #softwaredevelopment #datascience #apachekafka #streamprocessing
Taking Erlang to OCaml 5 (with Leandro Ostera)
Erlang wears three hats - it’s a language, it’s a platform, and it’s an approach to making software run reliably once it’s in production. Those last two are so interesting I sometimes wonder why those ideas haven’t been ported to every language going. How much work would it be?This week we’re going to dig right down into that question with Leandro Ostera. He’s been working on Riot - a project to bring the best of Erlang’s runtime system and philosophy to OCaml. But why OCaml? Is it possible to marry together OCaml’s type system with Erlang’s dynamic dispatch systems? And what is it about the recent release of OCaml5 that makes the whole project easier?–Leandro’s Blog: https://www.abstractmachines.dev/Why Typing Erlang is Hard: https://www.abstractmachines.dev/posts/am012-why-typing-erlang-is-hard/Riot: https://riot.ml/Riot source: https://github.com/riot-ml/riotReasonML: https://reasonml.github.io/ReScript: https://rescript-lang.org/Leandro on Twitter: https://twitter.com/leosteraKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins--#podcast #softwaredevelopment #erlang #ocaml #softwaredesign
How Apache Pinot Achieves 200,000 Queries per Second (with Tim Berglund)
The likes of LinkedIn and Uber use Pinot to power some astonishingly high-scale queries against realtime data. The numbers alone would make an impressive case-study. But behind the headline lies a fascinating set of architectural decisions and constraints to get there. So how does Pinot work? How does it process queries? How are the various roles split across a cluster? And equally important - what does it *not* try to achieve.Joining me to go through the nuts and bolts of how Pinot handles SQL queries is Tim Berglund, veteran technology explainer of the realtime-data world. He takes us through Pinot step-by-step, covering the roles of brokers, servers, controllers and minions as we build up the picture of a query engine that's interesting in theory and massively performant in practice.–Apache Pinot: https://pinot.apache.org/Apache Pinot Docs: https://docs.pinot.apache.org/StarTree: https://startree.ai/Event Driven Design episode with Bobby Calderwood: https://youtu.be/V7vhSHqMxusTim on Twitter: https://twitter.com/tlberglundKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins–#podcast #softwaredevelopment #apachepinot #database #dataengineering #sql
Neovim: Creating, Curating and Customising your Ideal Editor (with TJ DeVries)
TJ DeVries is a core contributor to Neovim and several of its most interesting sub-projects, and he joins us this week to go in depth into how Neovim got started, how it’s structured, and what a truly programmable editor has to offer programmers who want the perfect environment.Along the way we look at what we can learn from Neovim’s successful fork of the 30-year old codebase from Vim, how it still collaborates with the original project, and what putting Lua at the heart of the system has done for casual tinkerers and hardcore plugin writers alike.Not everyone will come away from this discussion wanting to switch editors, but I’m sure you’ll get a newfound appreciation for digging deeper into the developer tools you use everyday.–Neovim: https://neovim.io/Neovim Kickstarter: https://github.com/nvim-lua/kickstart.nvimKickstarter walkthrough video: https://www.youtube.com/watch?v=m8C0Cq9Uv9oA directory of Neovim plugins: https://dotfyle.com/Vimscript’s definition of true and false: https://vimhelp.org/eval.txt.html#BooleanTJ on Twitter: https://twitter.com/teej_dvTJ on Twitch: https://www.twitch.tv/teej_dvTJ on YouTube: https://www.youtube.com/@teej_dvKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins–#podcast #software #softwareengineering #dx
Creating Hackathons that Work (with Jon Gottfried)
Done right, a Hackathon can be a fantastic place to be a programmer - you get time and space to build and learn, in a room full of like-minded people, with swag and prizes to sweeten the deal. It’s a great way to pick up new ideas and run with them. But done wrong it can be a waste of time. What’s the difference between a good hackathon and a bad one? What do the good ones do right, and what can we learn from that?This week we’re talking about the Joy of Hacks with Major League Hacking Co-Founder Jon Gottfried. He’s got over 10 years of experience building a Hackathon network that provides the right environment for “structured mucking about with computers”, so we’re going to pick his brains.If you’re ever attending a Hackathon, organising one, or looking for a way to build or contribute to your local programming community, Jon can help guide you to events that work.--Major League Hacking: https://mlh.io/Major League Hacking’s 2024 Event Calendar: https://mlh.io/seasons/2024/eventsGames Week: https://events.mlh.io/events/10848 Jon on Mastodon: https://hachyderm.io/@jonmarkgoJon on LinkedIn: https://www.linkedin.com/in/jonmarkgoJon on Twitter: https://twitter.com/jonmarkgoKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkinsBonus link - The Great American Baking Show 2023: https://www.youtube.com/watch?v=IlWLSAKEedk--#software #podcast #programming #hackathon