O'Reilly logo

ZeroMQ by Pieter Hintjens

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Prototyping the Local and Cloud Flows

Let’s now prototype the flow of tasks via the local and cloud sockets (Figure 3-21). This code pulls requests from clients and then distributes them to local workers and cloud peers on a random basis.

The flow of tasks

Figure 3-21. The flow of tasks

Before we jump into the code, which is getting a little complex, let’s sketch the core routing logic and break it down into a simple but robust design.

We need two queues, one for requests from local clients and one for requests from cloud clients. One option would be to pull messages off the local and cloud frontends and pump these onto their respective queues. But this is kind of pointless, because ØMQ sockets are queues already. So let’s use the ØMQ socket buffers as queues.

This was the technique we used in the load-balancing broker earlier in this chapter, and it worked nicely. We only read from the two frontends when there is somewhere to send the requests. We can always read from the backends, as they give us replies to route back. As long as the backends aren’t talking to us, there’s no point in even looking at the frontends.

So, our main loop becomes:

  • Poll the backends for activity. When we get a message, it may be “ready” from a worker or it may be a reply. If it’s a reply, we route it back via the local or cloud frontend.

  • If a worker has replied, it has become available, so we queue it and count it.

  • While there are ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required