Python process `apply_async` vs `apply`: Understanding the Differences and When to Use Each
In Python, multiprocessing provides the `multiprocessing` module, which allows us to create processes and execute functions in parallel. Among the various functions available in this module, `apply_async` and `apply` are two commonly used methods for running functions in separate processes. This article aims to discuss the differences between these two methods and when to use each one.
1. Introduction to `apply` and `apply_async`
The `apply` method is a straightforward way to execute a function in a separate process. It takes a function and its arguments as input and returns a `AsyncResult` object. The function is executed in a new process, and the result is stored in the `AsyncResult` object. Here’s an example:
“`python
from multiprocessing import Process
def my_function(x):
return x x
if __name__ == ‘__main__’:
p = Process(target=my_function, args=(4,))
p.start()
p.join()
print(p.exitcode)
“`
In this example, the `my_function` is executed in a separate process, and the result is printed after the process has finished executing.
On the other hand, the `apply_async` method also executes a function in a separate process but provides more flexibility. It returns a `AsyncResult` object, similar to `apply`, but also allows you to specify a callback function that will be called with the result of the function once it has completed execution. Here’s an example:
“`python
from multiprocessing import Process
def my_function(x):
return x x
if __name__ == ‘__main__’:
result = Process(target=my_function, args=(4,)).apply_async()
result.add_done_callback(lambda res: print(res.get()))
“`
In this example, the `my_function` is executed in a separate process, and the result is printed once the function has completed execution.
2. Differences between `apply` and `apply_async`
Now that we have an introduction to both methods, let’s discuss the differences between them:
– Flexibility: `apply_async` is more flexible than `apply` because it allows you to specify a callback function. This makes it easier to handle the result of the function once it has completed execution.
– Blocking: The `apply` method is blocking, meaning that the calling process will wait for the function to complete before proceeding. In contrast, `apply_async` is non-blocking, allowing the calling process to continue executing other tasks while waiting for the function to complete.
– Concurrency: `apply_async` supports concurrency better than `apply`. Since it is non-blocking, you can create multiple `apply_async` calls in a loop, which can lead to better performance in some cases.
3. When to use `apply` and `apply_async`
Now that we understand the differences between `apply` and `apply_async`, let’s discuss when to use each method:
– Use `apply` when you need a simple and straightforward way to execute a function in a separate process and don’t require any additional functionality, such as a callback function.
– Use `apply_async` when you need more flexibility, such as handling the result of the function in a non-blocking manner or specifying a callback function to process the result once it’s available.
In conclusion, both `apply` and `apply_async` are useful methods for executing functions in separate processes in Python. Understanding their differences and when to use each one can help you write more efficient and flexible multiprocessing code.
