With this approach, it doesn’t matter what you start before, neither of them will continue until the other is ready. Remember that since we are communicating between two different processes, you will need to start python in two different command lines. Since we are sending a numpy array, you need first to send a string with the topic and add the zmq. Also, you should check the status of your RAM memory for processes that run for too long or that generate a lot of data very fast. The official ZeroMQ documentation has very nice pictures to show how this pattern works.


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After sending, the client waits for an answer.

pyzmq | Read the Docs

If you run the worker and the ventilator, you will see that the ventilator actually goes through and finishes. It is similar to the Push Pull but has some differences that would make it ideal for specific applications, in pyamq the same information needs to be shared between different processes. This is how most of the web works. They can do it in the same fashion, they push a message while another process, called a sink will pull them.

After the workers are done with the task they were assigned to phzmq, they will need to pass the results downstream. I suggest you to give it a quick read if you are not familiar with open CV.

Source Package: pyzmq (16.0.2-2)

We are going to combine Threads pyzmqq Multiprocessing pymq, together with socket communication. Then the script enters into an infinite loop. Messages get answered in turns, just that it takes longer. For example, I have recently released PyNTAa program to acquire images from a camera with the option to analyze them in real time or to store them to the hard drive or both. You can also use the syntax topic.


If you want to use ZMQ with Python programs, there is a library where with all the bindings: The first few lines are the same as always. The structure of the code is very similar to what we have done before.


The examples pyzma going to be the base of the next part of this tutorial, in which we are going to focus on how to implement the same patterns using the multi-threading and multi-processing libraries of Python. The work is going to be split among different workers, and we will pyxmq the difference in time depending on the number of workers we are spinning up.

This allows filtering the messages very efficiently. One of the obvious problems is that after the first request, the server quits and we will not be able to make a second one. What you can do now is start a different amount of workers and check if the time it takes to complete the task is different or not. We have already used a camera when we discussed building a GUI for it.

Let’s add the following to the ventilator:. Adding the b in front of the string is for encoding. This applies to both the server and the client. There are better ways of doing it, but with these examples, you have a very solid starting point. For instance, you could have the server echo the messages that it receives. The core idea is that a central process broadcasts the images and other processes listen to the broadcast and acts on the information.


Now that we have explored one of the ZMQ patterns, we can see how it can be useful when dealing with a device. We are going to develop a program to acquire images from a webcam continuously and share the data between different terminals. There is also another pattern, which is to exchange information between processes running on the same computer.

Don’t close the worker, we are going to develop the sink now and see what happens. Now you see that we have changed the socket type to pull in the first highlighted case. As you see, the publisher can run even if there is nothing listening pyxmq the messages.

You see that first, we wait for receiving a message.


But we also need to define the connection to the sink, that we called sender.