It also queues a “Status” event message to the Event Queue. “Status Subprocess” also runs every 10 seconds, and collects data about the IoT system, including such things as uptime, network settings and status, and storage usage.In this example, it is assumed that “Observation Subprocess” is CPU intensive as it performs a significant amount of data processing on the observation before sending it to the server. “Observation Subprocess” runs every 10 seconds and collects data about the HVAC systems being monitored, queuing an “Observation” event message to the Event Queue.The application consists of a “Main Process” - which manages initialization, shutdown and event loop handling - and four subprocesses. This example is based on an implementation of an HVAC system that I worked on in 2018. Since Python multiprocessing is best for complex problems, we’ll discuss these tips using a sketched out example that emulates an IoT monitoring device. Using these features introduces a number of complicated issues that now need to be managed, especially with regard to cleanly starting and stopping subprocesses, as well as coordinating between them. For these kinds of problems, one needs to make use of somewhat more complex features of the multiprocessing module, such as Process, Queue and Event. ![]() But Pool and map() aren’t suited to situations that need to maintain state over time or, especially, situations where there are two or more different operations that need to run and interact with each other in some way. This is extremely simple and efficient for doing conversion, analysis, or other situations where the same operation will be applied to each data. One interface the module provides is the Pool and map() workflow, allowing one to take a large set of data that can be broken into chunks that are then mapped to a single function. Python ships with the multiprocessing module which provides a number of useful functions and classes to manage subprocesses and the communications between them. If your application is I/O bound and doesn’t require large blocks of CPU time, then, as of Python version 3.4, the asyncio system is the preferred approach. This lock constrains all Python code to run on only one processor at a time so that Python multi- threaded applications are largely only useful when there is a lot of waiting for IO. This also gets around one of the notorious Achilles Heels in Python: the Global Interpreter Lock (aka theGIL). If multithreading is so problematic, though, how do we take advantage of systems with 8, 16, 32, and even thousands, of separate CPUs? When presented with large Data Science and HPC data sets, how to you use all of that lovely CPU power without getting in your own way? How do you tightly coordinate the use of resources and processing power needed by servers, monitors, and Internet of Things applications - where there can be a lot of waiting for I/O, many distinct but interrelated operations, and non-sharable resources - and you still have to crank through the preprocessing of data before you send it somewhere?Īn excellent solution is to use multiprocessing, rather than multithreading, where work is split across separate processes, allowing the operating system to manage access to shared resources. Nothhw tpe yawrve o oblems.” (Eiríkr Åsheim, 2012) “Some people, when confronted with a problem, think ‘I know, I’ll use multithreading’.
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