Developer Voices

Jeremy Saenz explores NATS, a versatile message-passing system that defies messaging norms. Topics include NATS vs. Kafka, IoT benefits, CLI features, data integrity, microservices communication, and new tech like Ballerina Lang.

Juan Vuletich discusses Smalltalk's history, Cuis's unique implementation, and the future of computing. Topics include object-oriented programming, message passing, blurring OS and language, visionary ideas, empowerment through computing, iPad evolution, Smalltalk's features, VM optimization, community collaboration, and experiments with Git in Cuis Smalltalk.

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

Nathan Sobo talks about building Zed, a text editor in Rust, focusing on UI design, performance optimization, and collaboration features. He discusses challenges in editor development, using Rust for Zed, high frame rate rendering, WebAssembly, and testing concurrent networking code. The podcast explores innovative features like TreeSitter, Language Server Protocol, and future visions for Zed, including open-sourcing components and CRDB integration.

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

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

Hugh McKee, Developer Advocate for Lightbend, discusses the actor model in software design, focusing on patterns, event-driven approaches, and architectural comparisons. He explores the evolution of event-driven systems, highlights scalability benefits, and emphasizes the importance of picking the right tools for robust system design.

Dan Herrera, an expert at blending Rust's research with Python's practicalities, talks about Bytewax, a stream processing tool merging Python and Rust. They discuss the marriage of Python and Rust in practice, the challenges in data engineering, integration of Rust into Python ecosystem, timely data flow library design challenges, data flow management with Bytewax and Timely, and cluster recovery and rescaling in PyLax.

Chris Lattner, the mastermind behind Swift and LLVM, discusses Mojo, a new programming language that merges Python's syntax with high-performance capabilities. They delve into Mojo's innovative type system and memory management, which aim to enhance programming for AI and high-performance computing. Lattner explains how Mojo addresses language divides in the AI landscape and streamlines code optimization with compile-time techniques. Discover how this language could be a game-changer for developers seeking Python-like familiarity with the power of lower-level programming.

In this engaging discussion, Jove Zhong, a contributor to the open-source database Proton, shares insights on the challenges of managing both batch and streaming data. He reveals the innovative Lambda Architecture and how Proton aims to simplify data integration. Jove dives into stream processing, addressing issues like out-of-order events and data consistency. He also explores architectural strategies for massive datasets, highlighting the use of ClickHouse for efficient querying and data handling. This conversation is a treasure trove for data enthusiasts!