Rust in Production Matthias Endler

For our very first episode, we welcome a special guest, Paul Dix, the CTO of InfluxData.He starts by giving us an overview of InfluxDB, an open source time series database used by developers to track server and application data. He takes us back to the early days of InfluxDB and explains how it came into existence, starting with the challenges they faced with their initial SaaS application and how they made the decision to repurpose their infrastructure and create this open source database. Paul also sheds light on the popularity of the programming language Go, which had a significant influence on their decision to use it for their project.He takes us through the journey of InfluxDB's development and the improvements that have been made over the years. He emphasizes the enhancements made in versions 0.11 and 1.0 to improve performance and query capabilities. Moreover, he shares their decision to explore using Rust for certain parts of the project and the positive impact it has had. Moving forward, the conversation delves into the challenges of managing high volumes of data in time series databases.Paul talks about the solutions they implemented, such as using BoltDB and developing the time-structured merge tree storage engine. We then dive into the decision to rewrite InfluxDB in Rust and the benefits it offers. He explains the improved performance, concurrency, and error handling that Rust brings to the table. Paul goes on to discuss the development process and how the engineering team has embraced Rust across their projects.As the conversation progresses, we touch on the performance improvements in InfluxDB 3 and the future plans for the database. Paul shares their vision of incorporating additional features and integrating with other tools and languages. He also mentions InfluxDB's involvement in open-source projects like Apache Aero Rust and Data Fusion, highlighting their ambition to extend beyond metric data. Paul concludes the conversation by discussing the standards and libraries in analytics, the role of Apache Iceberg, and the collaboration among data and analytics companies. He provides advice for getting started with Rust and InfluxDB, urging listeners to engage in hands-on projects and learn from books and online documentation.Thank you, Paul, for sharing your insights and expertise.

In this episode, we are joined by Steven, the CTO of PubNub, a company that has developed an edge net messaging network with over a billion connected devices. Steven explains that while message buses like Kafka or RabbitMQ are suitable for smaller scales, PubNub focuses on the challenges of connecting mobile devices and laptops at a web scale. They aim to provide instant signal delivery at a massive scale, prioritizing low latency for a seamless user experience. To achieve this, PubNub has architected their system to be globally distributed, running on AWS with Kubernetes clusters spread across all of Amazon's zones. They utilize GeoDNS to ensure users connect to the closest region for the lowest latency possible. Steven goes on to discuss the challenges they faced in building their system, particularly in terms of memory management and cleanup. They had to deal with issues such as segmentation faults and memory leaks, which caused runtime problems, outages, and potential data loss. PubNub had to invest in additional memory to compensate for these leaks and spend time finding and fixing the problems. While C was efficient, it came with significant engineering costs. As a solution, PubNub started adopting Rust, which helped alleviate some of these challenges. When they replaced a service with Rust, they observed a 5x improvement in memory and performance. Steven also talks about choosing programming languages for their platform and the difficulties in finding and retaining C experts. They didn't consider Java due to its perceived academic nature, and Go didn't make the list of options at the time. However, they now have services in production written in Go, though rewriting part of their PubSub bus in Go performed poorly compared to their existing C system. Despite this, they are favoring Rust as their language of choice for new services, citing its popularity and impressive results. The conversation delves into performance considerations with Python and the use of PyPy as a just-in-time compiler for optimization. While PyPy improved performance, it also required a lot of memory, which could be expensive. On the other hand, Rust provided a significant boost in both memory and performance, making it a favorable choice for PubNub. They also discuss provisioning, taking into account budget and aiming to be as close to what they need as possible. Kubernetes and auto scaling with HPAs (Horizontal Pod Autoscaling) are used to dynamically adjust resources based on usage. Integrating new services into PubNub's infrastructure involves both API-based communication and event-driven approaches. They use frameworks like Axiom for API-based communication and leverage Kafka with Protobuf for event sourcing. JSON is also utilized in some cases. Steven explains that they chose Protobuf for high-traffic topics and where stability is crucial. While the primary API for customers is JSON-based, PubNub recognizes the superior performance of Protobuf and utilizes it for certain cases, especially for shrinking down large character strings like booleans. They also discuss the advantages of compression enabled with Protobuf. The team reflects on the philosophy behind exploring Rust's potential for profit and its use in infrastructure and devices like IoT. Rust's optimization for smaller binaries is highlighted, and PubNub sees it as their top choice for reliability and performance. They mention developing a Rust SDK for customers using IoT devices. The open-source nature of Rust and its ability to integrate into projects and develop open standards are also praised. While acknowledging downsides like potential instabilities and longer compilation time, they remain impressed with Rust's capabilities. The conversation covers stability and safety in Rust, with the speaker expressing confidence in the compiler's ability to handle alpha software and packages. Relying on native primitives for concurrency in Rust adds to the speaker's confidence in the compi

In this episode, Nicolas, a staff software engineer at Apollo GraphQL, discusses the company's use of GraphQL API technologies. Apollo GraphQL specializes in open-source libraries for both client and server-side applications, with a focus on integrating Rust into their main offerings: the Apollo router and GraphOS cloud. Nicolas explains how the Apollo router consolidates multiple microservices into a single API, efficiently routing requests to appropriate services.
He delves into GraphQL's role as an effective query language for APIs, highlighting its ability to provide a comprehensive description of API data and its compatibility with existing data systems. The shift from the JavaScript-based Apollo Gateway to the Rust-based Apollo Router is a key topic, with an emphasis on the performance and safety improvements Rust brings to the table.
The conversation covers the use of Rust for the router and GraphQL parser, alongside Kotlin for the management plane and GraphQL for the API. Challenges in stability and reliability are discussed, as well as Rust's advantages in safety and type system consistency. Nicolas shares insights on Async Rust, particularly its impact on productivity and application in CLI tools like Rover.
The episode also addresses learning Rust in stages, from basic language concepts to advanced internal mechanisms. It touches on functional patterns in Rust and strategies for effective dependency management. Closing the discussion, Nicolas highlights the inclusive and supportive nature of the Rust community.

In this episode, we have Micah Wylde from Arroyo as our guest. Micah introduces us to Arroyo, a real-time data processing engine that simplifies stream processing for data engineers using Rust. They explain how Arroyo enables users to write SQL queries with Rust user-defined functions on top of streaming data, highlighting the advantages of real-time data processing and discussing the challenges posed by competitors like Apache Flink. Moving on, we dive into the use of Rust in Arroyo and its benefits in terms of performance and memory safety. We explore the complementarity of workflow engines and stream processors and examine Arroyo's approach to real-time SQL and its compatibility with Postgres. Micah delves into memory and lifetime concerns and elaborates on how Arroyo manages them in its storage layer. Shifting gears, we explore the use of the Tokyo framework in the Arroyo system and how it has enhanced speed and efficiency. Micah shares insights into the challenges and advantages of utilizing Rust, drawing from their experiences with Arroyo projects. Looking ahead, we discuss the future of the Rust ecosystem, addressing the current state of the Rust core and standard library, as well as the challenges of interacting with other languages using FFI or dynamically loading code. We touch upon Rust's limitations regarding a stable ABI and explore potential solutions like WebAssembly. We also touch upon industry perceptions of Rust, investor perspectives, and the hiring process for Rust engineers. The conversation takes us through the crates used in the Arroyo system, our wishlist for Rust ecosystem improvements, and the cost-conscious nature of companies that make Rust an attractive choice in the current macroeconomic environment. As we wrap up, we discuss the challenges Rust faces in competing with slower Java systems and ponder the potential for new languages to disrupt the trend in the future. We touch upon efficiency challenges in application software and the potential for a new language to emerge in this space. We delve into the increasing interest in using Rust in data science and the promising prospects of combining Rust with higher-level languages. Finally, we discuss the importance of fostering a welcoming and drama-free Rust community. I would like to thank Micah for joining us today and sharing their insights. To find more resources related to today's discussion, please refer to the show notes. Stay tuned for our next episode, and thank you for listening!

Have you ever wondered how computers stay in sync with the time? That is theresponsibility of the Network Time Protocol (NTP). Around since 1985, NTP is oneof the oldest protocols still in use on the internet and its referenceimplementation, ntpd, written in C, is still widely used today.That's a problem. C is a language that is not memory safe, and ntpd has had itsshare of security vulnerabilities. Here is a list of CVEs.As part of Project Pendulum, Folkert de Vries and his colleagues from TweedeGolf have been working on a Rust implementation of NTP. I sit down with Folkertto talk about the project, the challenges they faced, and the benefits of usingRust for this kind of project.Along the way, we learn about funding open source projects, the challenges ofestablishing a new implementation of a protocol, and all sorts of otherinteresting things that might or might not be related to NTP.

In an ever-expanding world of microservices, APIs, and devices, maintaining an overview of application states and the myriad errors that can occur is challenging. For years, Sentry has been a go-to choice for developers to monitor their applications and receive notifications about issues within their code.Traditionally, Sentry was predominantly a Python shop, but they became one of the early adopters of Rust in their technology stack. They have been utilizing Rust for a few years now (since at least 2017), starting with sentry-cli, a command-line utility to work with Sentry, and continuing with their source map parsing pipeline, which reduced processing times from 20 seconds to less than 500 milliseconds.More recently, they have been developing two new projects in Rust: relay and symbolicator. Relay acts as a proxy for sending events to Sentry, while symbolicator is a service for handling the symbolication of stack traces. Both projects are open source and available on GitHub.Arpad Borsos (swatinem), Senior Native Platform Engineer at Sentry, sat down with me to discuss their journey with Rust and how it has enabled them to build a cutting-edge monitoring platform for large-scale applications.Our conversation covered topics such as 100x speedups, the Rust-Python interface, and the comparison between actor-based and task-based concurrency.