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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 compiler's safety. The Rust ecosystem is seen as providing adequate coverage, although packages like libRDKafka, which are pre-1.0, can be challenging to set up or deploy. The speaker emphasizes simplicity in code and avoiding excessive abstractions, although they acknowledge the benefits of features like generics and traits in Rust. They suggest resources like a book by David McCloyd that focuses on learning Rust without overwhelming complexity. Expanding on knowledge sharing within the team, Stephen discusses how Rust advocates within the team have encouraged its use and the possibilities it holds for AI infrastructure platforms. They believe Rust could improve performance and reduce latency, particularly for CPU tasks in AI. They mention the adoption of Rust in the data science field, such as its use in the Parquet data format. The importance of tooling improvements, setting strict standards, and eliminating unsafe code is highlighted. The speaker expresses the desire for a linter that enforces a simplified version of Rust to enhance code readability, maintainability, and testability. They discuss the balance between functional and object-oriented programming in Rust, suggesting object-oriented programming for larger-scale code structure and functional paradigms within functions. Onboarding Rust engineers is also addressed, considering whether to prioritize candidates with prior Rust experience or train individuals skilled in another language on the job. Recognizing the shortage of Rust engineers, Stephen encourages those interested in Rust to pursue a career at PubNub, pointing to resources like their website and LinkedIn page for tutorials and videos. They emphasize the importance of latency in their edge messaging technology and invite users to try it out.

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 compiler's safety. The Rust ecosystem is seen as providing adequate coverage, although packages like libRDKafka, which are pre-1.0, can be challenging to set up or deploy. The speaker emphasizes simplicity in code and avoiding excessive abstractions, although they acknowledge the benefits of features like generics and traits in Rust. They suggest resources like a book by David McCloyd that focuses on learning Rust without overwhelming complexity. Expanding on knowledge sharing within the team, Stephen discusses how Rust advocates within the team have encouraged its use and the possibilities it holds for AI infrastructure platforms. They believe Rust could improve performance and reduce latency, particularly for CPU tasks in AI. They mention the adoption of Rust in the data science field, such as its use in the Parquet data format. The importance of tooling improvements, setting strict standards, and eliminating unsafe code is highlighted. The speaker expresses the desire for a linter that enforces a simplified version of Rust to enhance code readability, maintainability, and testability. They discuss the balance between functional and object-oriented programming in Rust, suggesting object-oriented programming for larger-scale code structure and functional paradigms within functions. Onboarding Rust engineers is also addressed, considering whether to prioritize candidates with prior Rust experience or train individuals skilled in another language on the job. Recognizing the shortage of Rust engineers, Stephen encourages those interested in Rust to pursue a career at PubNub, pointing to resources like their website and LinkedIn page for tutorials and videos. They emphasize the importance of latency in their edge messaging technology and invite users to try it out.

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.

This is Rust in Production, a podcast about companies who use Rust to shape the future of infrastructure. We follow their journey in pursuit of more reliable and efficient software as they solve some of the most challenging technical problems in the world. I'm your host, Matthias Endler, and I'm a software engineer at corrode, a consultancy that helps companies make the most of Rust. I've been using Rust since 2015, have been a member of the Rust Cologne meetup since Rust 1.0 and ran a YouTube channel called "Hello Rust". There are plenty of great podcasts about Rust, but I felt that there was a missing piece. I wanted to hear more about how companies who use Rust in production. What are the challenges they face? How do they overcome them? What are the benefits of using Rust? How does the company find and hire Rust developers? And what advice would they give to other companies who want to use Rust. I sit down with decision-makers from companies that bet big on Rust and ask them in-depth questions about what they learned along the way. New episodes air every two weeks on Thursdays at 4pm UTC. If you don't want to miss out, please subscribe on Spotify, Apple Podcasts, or wherever you listen to podcasts. This helps other people find the show and supports our work. If you want to learn more about the show, please visit corrode.dev/podcast. Stay tuned for the first episode, where I talk to Paul Dix from InfluxData about how they use Rust in the latest version of InfluxDB.

This is Rust in Production, a podcast about companies who use Rust to shape the future of infrastructure. We follow their journey in pursuit of more reliable and efficient software as they solve some of the most challenging technical problems in the world. I'm your host, Matthias Endler, and I'm a software engineer at corrode, a consultancy that helps companies make the most of Rust. I've been using Rust since 2015, have been a member of the Rust Cologne meetup since Rust 1.0 and ran a YouTube channel called "Hello Rust". There are plenty of great podcasts about Rust, but I felt that there was a missing piece. I wanted to hear more about how companies who use Rust in production. What are the challenges they face? How do they overcome them? What are the benefits of using Rust? How does the company find and hire Rust developers? And what advice would they give to other companies who want to use Rust. I sit down with decision-makers from companies that bet big on Rust and ask them in-depth questions about what they learned along the way. New episodes air every two weeks on Thursdays at 4pm UTC. If you don't want to miss out, please subscribe on Spotify, Apple Podcasts, or wherever you listen to podcasts. This helps other people find the show and supports our work. If you want to learn more about the show, please visit corrode.dev/podcast. Stay tuned for the first episode, where I talk to Paul Dix from InfluxData about how they use Rust in the latest version of InfluxDB.