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On today’s podcast, Wes Reisz talks to Matt Klein about Envoy. Envoy is a modern, high performance, small footprint edge and service proxy. While it was originally developed at Lyft (and still drives much of their architecture), it is a fully open source driven project. Matt addresses on this podcast what he sees as the major design goals of Envoy, answers questions about a sidecar performance impact, discusses observability, and thinks out loud on the future of Envoy.

Why listen to this podcast:

- Envoy’s goal is to abstract the network from application programmers. It’s really about helping application developers focus on building business logic and not on the application plumbing.
- Envoy is a large community driven project, not a cohesive product that does one thing. It can be used as a foundational building blocks to extend into a variety of use cases, including as an edge proxy, as a service mesh sidecar, and as a substrate for building new products.
- While there is performance cost for using sidecar proxies, the rich featureset is often a worthwhile tradeoff. With that said, there is work being done that is greatly improving Envoy’s performance.
- Envoy is built to run Lyft. There were no features that were in Envoy when it was open sourced that were not used at Lyft.
- Envoy emits a rich set of logs and has a plugable tracing system. The goal is observability first and one of the main project goals.
- Lyft deploys Envoy master twice per week.
- Envoy’s roadmap includes work on automating settings (rate limits and retries), focus on ease of operation (such as where things got routed what the internal timings), and additional protocol support such as Kafka.

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Matt Klein on Lyft’s Envoy, Including Edge Proxy, Service Mesh, & Potential AI Use Cases

Probabilistic Programming has been discussed as a programming paradigm that uses statistical approaches to dealing with uncertainty in data as a first class construct. On today’s podcast, Wes talks with Mike Lee Williams of Cloudera’s Fast Forward Labs about Probabilistic Programming. The two discusses how Bayesian Inference works, how it’s used in Probabilistic Programming, production-level languages in the space, and some of the implementations/libraries that we’re seeing.

Key Takeaways
* Federated machine learning is an approach of developing models at an edge device and returning just the model to a centralized location. By taking the averages of the edge models, you can protect privacy and distribute processing of building models.
*Probabilistic Programming is a family of programming languages that make statistical problems easier to describe and solve.
*It is heavily influenced by Bayesian Inference or an approach to experimentation that turns what you know before the experiment and the results of the experiment into concrete answers on what you should do next.
* The Bayesian approach to unsupervised learning comes with the ability to measure uncertainty (or the ability to quantify risk).
* Most of the tooling used for Probabilistic Programming today is highly declarative. “You simply describe the world and press go.”
* If you have a practical, real-world problem today for Probabilistic Programming, Stan and PyMC3 are two languages to consider. Both are relatively mature languages with great documentation.
* Prophet, a time-series forecasting library built at Facebook as a wrapper around Stan, is a particularly approachable place to use Bayesian Inference for forecasting use cases general purpose.

Show Notes

Mike Lee Williams on Probabilistic Programming, Bayesian Inference, and Languages like PyMC3

On today’s podcast, Justin Cormack discusses how the modern operating system is being decomposed with toolkits and libraries such as LinuxKit, eBPF, XDP, and what the kernel space service mesh Cilium is doing. Wes Reisz and Justin Cormack also discuss how Cilium differs from service meshes like an Istio, Linkerd2 (previously Conduit), or Envoy. Justin is a systems engineer at Docker. He previously was working with unikernels at Unikernel Systems in Cambridge before being acquired by Docker. (edited)

*Key Takeaways:*
* LinuxKit is an appliance way of thinking about your operating system and is gaining adoption. There are contributions now from Oracle, Cloudflare, Intel, etc. Docker has seen interesting use cases such as customers running LinuxKit on large cloud providers directly on bare metal (more on this coming soon).
* The operating system of today is really unchanged since the Sun workstation of the 90’s. Yet everything else about software has really changed such as automation, build pipelines, and delivery.
* XDP (eXpress Data Path) is a packet processing layer for Linux that lets you run fast in kernel compiled safe program in kernel called eBPF. It’s used for things like packet filtering and encapsulation/decapsulation.
* Cilium is an in-kernel, high performance service mesh that leverages eBPF. Cilium is very good at layer 4 processing, but doesn’t really do the layer 7 things that some of the other services meshes can offer (such as proxying http/1 to http/2)

Justin Cormack on Decomposing the Modern Operating System

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