All articles tagged #traffic-management

SSH is yet another example of an ancient technology that is still in wide use today. It may very well be that learning a couple of SSH tricks is more profitable in the long run than mastering a dozen Cloud Native tools destined to become deprecated next quarter.

One of my favorite parts of this technology is SSH Tunnels. With nothing but standard tools and often using just a single command, you can achieve the following:

• Access internal VPC endpoints through a public-facing EC2 instance.
• Open a port from the localhost of a development VM in the host's browser.
• Expose any local server from a home/private network to the outside world.

And more 😍

But despite the fact that I use SSH Tunnels daily, it always takes me a while to figure out the right command. Should it be a Local or a Remote tunnel? What are the flags? Is it a local_port:remote_port or the other way around? So, I decided to finally wrap my head around it, and it resulted in a series of labs and a visual cheat sheet 🙈

Full version 1.5MB

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Before jumping to any Kubernetes specifics, let's talk about the service discovery problem in general.

What is Service Discovery

In the world of web service development, it's a common practice to run multiple copies of a service at the same time. Every such copy is a separate instance of the service represented by a network endpoint (i.e. some IP and port) exposing the service API. Traditionally, virtual or physical machines have been used to host such endpoints, with the shift towards containers in more recent times. Having multiple instances of the service running simultaneously increases its availability and helps to adjust the service capacity to meet the traffic demand. On the other hand, it also complicates the overall setup - before accessing the service, a client (the term client is intentionally used loosely here; oftentimes a client of some service is another service) needs to figure out the actual IP address and the port it should use. The situation becomes even more tricky if we add the ephemeral nature of instances to the equation. New instances come and existing instances go because of the non-zero failure rate, up- and downscaling, or maintenance. That's how a so-called service discovery problem arises.

Service discovery problem.

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Traefik is The Cloud Native Edge Router yet another reverse proxy and load balancer. Omitting all the Cloud Native buzzwords, what really makes Traefik different from Nginx, HAProxy, and alike is the automatic and dynamic configurability it provides out of the box. And the most prominent part of it is probably its ability to do automatic service discovery. If you put Traefik in front of Docker, Kubernetes, or even an old-fashioned VM/bare-metal deployment and show it how to fetch the information about the running services, it'll automagically expose them to the outside world. If you follow some conventions of course...

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How services talk to each other?

Imagine you're developing a service... For certainty, let's call it A. It's going to provide some public HTTP API to its clients. However, to serve requests it needs to call another service. Let's call this upstream service - B.

Obviously, neither network nor service B is ideal. If service A wants to decrease the impact of the failing upstream requests on its public API success rate, it has to do something about errors. For instance, it could start retrying failed requests.

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