Last week, I described the basics on how to develop and deploy a Rust plugin for Apache APISIX. The plugin just logged a message when it received the request. Today, I want to leverage what we learned to create something more valuable: write part of the response-rewrite plugin with Rust. Adding a hard-coded header Let’s start small and add a hard-coded response header. Last week, we used the on_http_request_headers() function. The proxy_wasm specification defines several function hooks
Apache APISIX is built upon the shoulders of two giants: NGINX, a widespread Open Source reverse-proxyOpenResty, a platform that allows scripting NGINX with the Lua programming language via LuaJIT This approach allows APISIX to provide out-of-the-box Lua plugins that should fit most business requirements. But it always comes a time when generic plugins don’t fit your requirements. In this case, you can write your own Lua plugin. However, if Lua is not part of your tech stack, diving int
Kubernetes offers a lot of benefits: an enormous ecosystem with plenty of actors, self-healing capabilities, etc. There’s no free lunch, though. It also comes with downsides, chief among them its complexity and operating costs. However, the more I work with Kubernetes, the more I think its most significant asset is extensibility. If you need something that the platform doesn’t provide by default, there’s an option to develop it yourself and integrate it. In this post, I’
For over 20 years, I’ve developed code for the JVM, first in Java, then in Kotlin. However, the JVM is not a silver bullet, e.g., in scripts: Virtual machines incur additional memory requirementsIn many cases, the script doesn’t run long enough to gain any benefit performance-wise. The bytecode is interpreted and never compiles to native code. For these reasons, I now write my scripts in Python. One of them collects social media metrics from different sources and stores them in BigQ
In one of my recent blog posts, I described several ways to access Kubernetes pods. One can access a pod through its IP, but pods are naturally transient. The nominal way is to configure a Service: its IP is stable, and Kubernetes' job is to keep the mapping between a Service and its underlying pods up-to-date. Different kinds of services are available: internal only, NodePort to finally allow access from outside the cluster, and LoadBalancer that relies on a third-party component - in general, a
Whether you implement or not microservices (and you probably shouldn’t), your system is most probably composed of multiple components. The most straightforward system is probably made of a reverse proxy, an app, and a database. In this case, monitoring is not only a good idea; it’s a requirement. The higher the number of components through which a request may flow, the strongest the requirement. However, monitoring is only the beginning of the journey. When requests start to fail en
I won’t introduce Dependabot. Lots and lots of developers use it daily on GitHub. I do use it as well. However, it suffers from two drawbacks: While it’s perfectly integrated with GitHub, integrations with other platforms are less seamless.It’s limited in the list of ecosystems it supports For example, I generally use Docker Compose files for my demos. When necessary, I use Kubernetes. Dependabot supports none. Worse, Dependabot doesn’t accept contributions to add new eco
In one of my earlier posts, I described the Backend-for-Frontend pattern. In short, it offers a single facade over multiple backend parts. Moreover, it provides each client type, e.g. desktop, mobile, exactly the data that it needs and not more in the format required by this client type. The use-case Imagine the following use-case. In a e-commerce shop, the home page should display multiple unrelated data at once. Products: The business could configure which items are shown on the home page.