8/13/2023 0 Comments Python queue operationsNote, your specific output will differ given the use of random numbers. Once the producer threads finish, the main thread wakes up and then blocks on the queue, waiting for all remaining tasks to be consumed and marked done by the consumer thread.Ī truncated sample output of the program is listed below. The thread will run forever as a background daemon thread. The consumer thread will consume items from the queue as fast as it can, report their values and mark them as done. The capacity of the queue is so limited that these threads will block most of the time on their calls to put() until a position becomes available to add a new item to the queue. The main thread then blocks until the producer threads finish.Įach of the five producer threads then attempt to add ten items each to the queue as fast as they are able. Running the example first starts the daemon consumer thread, then starts all five non-daemon producer threads. The producer() function below implements this by taking the queue instance as an argument. Once the task is complete it will put the value None on the queue to signal to the consumer that there is no further work. It will then block for that fraction of a second, then put the value on the queue. Each iteration, it will generate a new random value between 0 and 1 via the random.random() function. The task will iterate ten times in a loop. The queue.Queue provides a way to allow these producer and consumer threads to communicate data with each other.įirst, we can define the function to be executed by the producer thread. We will also create a consumer thread that will get numbers from the queue and report their values. In this example, we will create a producer thread that will generate ten random numbers and put them on the queue. We can explore how to use the queue.Queue class with a worked example. This will create an unbounded queue by default, that is, a queue with no size limit.ĭownload my FREE PDF cheat sheet Example of Using a Queue The queue.Queue can be used by first creating an instance of the class. This is opposed to other queue types such as last-in, first-out and priority queues. The first items added to the queue will be the first items retrieved. The queue.Queue provides a first-in, first-out FIFO queue, which means that the items are retrieved from the queue in the order they were added. Thread-safe means that it can be used by multiple threads to put and get items concurrently without a race condition. The Queue class in this module implements all the required locking semantics. It is especially useful in threaded programming when information must be exchanged safely between multiple threads. The queue module implements multi-producer, multi-consumer queues. Python provides a thread-safe queue in the queue.Queue class.Ī queue is a data structure on which items can be added by a call to put() and from which items can be retrieved by a call to get(). Run your loops using all CPUs, download my FREE book to learn how. What is the Queue and how can we use it in Python? Python provides a number of thread-safe queues in the queue module, such as the queue.Queue class. One approach to sharing data is to use a queue data structure. In concurrent programming, we often need to share data between threads. Threading in Python: The Complete Guide.You can learn more about Python threads in the guide: Python provides the ability to create and manage new threads via the threading module and the threading.Thread class. Sometimes we may need to create additional threads in our program in order to execute code concurrently. Both processes and threads are created and managed by the underlying operating system.
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