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Dask Kubernetes A tool for deploying Dask clusters on Kubernetes 

The Problem Dask Kubernetes had grown to use many different Kubernetes libraries because none of them individually had all the features we needed. Some libraries are also much more verbose than others and most have poor documentation. Using many Kubernetes libraries made things hard to maintain. 

Proliferation of libraries kubernetes kubernetes_asyncio pykube-ng kubectl (via subprocess) kubectl (via pytest-kind) kopf 

Library pros/cons Pros Official, lots of examples Semi-official, asyncio Pleasant API Pleasant API Familiar to k8s users Familiar, nice Python API kubernetes kubernetes_asyncio pykube-ng kopf kubectl (via subprocess) kubectl (via pytest-kind) Cons Autogenerated, no asyncio Autogenerated, poor docs No asyncio, no port-forward Only useful for controller Adds binary dependency Can only be used in tests 

Can I bridge the gap by contributing? kubernetes and kubernetes_asyncio are both auto-generated libraries using the OpenAPI specification. They are generally not open to Pull Requests that fundamentally change the design as it would conflict with the auto-generation. pykube-ng is built with requests and has no asyncio support. Adding async support would mean rebuilding the entire internals of the library. kopf is designed for building operators in an event driven manner. It’s components would be hard to factor out into an imperative Kubernetes client. kubectl (via subprocess) is a widely-adopted Go application that would be hard to modify if needed and unpleasant to expose in Python via subprocess. kubectl (via pytest-kind) adds some syntactic sugar around subprocess in pytest-kind fixtures but would need to be extracted and released separately for use in our code. 

What about a wrapper? If I can’t add what I need upstream can I wrap or shim my dependencies to fill in gaps and expose a consistent internal API? 

I Enhanced pykube-ng with a wrapper called aiopykube 

Blocker aiopykube was a wrapper around pykube-ng that made HTTP requests to the Kubernetes API using requests, but to work with asyncio it ran each call via a threadpool executor. When it came to implementing a pure-Python port forward using websockets it was clear this would need low-level access to requests and would be a huge challenge with this design. aiopykube as a wrapper was a dead end. 

“I wish I had something that feels like kubectl, but in pure Python.” Me, 6 months ago 

Should I build a new library? Fortunately, the charging one has been solved now that we've all standardized on mini-USB. Or is it micro-USB? Shit. https://xkcd.com/927/ 

Wishlist for a new library ● The existing libraries all expose the user experience of the Kubernetes API in Python. I want to expose the user experience of kubectl. ● I want a library that supports both sync and async usage. ● I want to stop using auto-generated code and make strict client-side schema validation optional. ● I want authentication and client creation to be implicit. ● I want pure Python implementations of quality-of-life features like port forwarding but with APIs as human as kubectl. 

Goals ● Make Dask Kubernetes more maintainable and reduce contributor friction. ● Build the library that I want to use. ● Learn more about how the Kubernetes API works, but shield others from it’s complexity. Non-Goals ● Create a new standard. ● Solve everyone’s problems. ● Get everyone to adopt it. 

https://github.com/kr8s-org/kr8s A simple, batteries-included, Python client library for Kubernetes that feels familiar for folks who already know how to use kubectl. 

Comparison of libraries kubernetes_asyncio kr8s 

We adopted kr8s and simplified Dask Kubernetes 

Helpful person: “Have you heard of lightkube?” *Ominous music plays 

Helpful person: “Have you heard of lightkube?” 

They had some striking similarities lightkube kr8s 

Should I stop and adopt lightkube instead? I’ve already achieved my goal of learning more about the Kubernetes API, and it meets some of my requirements. Maybe this is the library I want to use? 

Digging deeper highlighted key differences lightkube kr8s 

Goals from lightkube ● Extensive type hints to avoid common mistakes and to support autocompletion. ● Models and resources generated from the swagger specifications using standard dataclasses. ● Support for installing a specific version of the kubernetes models (1.15 to 1.27). ● Lazy instantiation of inner models. 

I felt kr8s satisfied a different niche, so I continued. But felt uncertain… 

until… 

The Hacker News Effect kr8s 

When should you build from scratch? ● When you can’t contribute to or wrap an existing project. ● When your needs are met by something fundamentally different. ● When you find a niche, don’t just rebuild everything that exists. ● When you’ve done your research and exhausted your options. ● When you want to learn how something works. 

Just don’t be scared to throw it away again. 

Final thoughts Come and see me for a sticker ● Building something from scratch will take longer than you think. It’s a commitment. ● Don’t hesitate to rebuild things simply to learn! ● Consider adding interoperability with existing solutions. ● There are many valid reasons to start from scratch. ● Check out the companion blog post to this talk for a deeper dive into everything that wouldn’t fit into this talk. http://jacobtomlinson.dev/posts/2023/when-to-rebuild-things-that-already-exist/ 

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