Example of Python's Implementation of Multiprocess Data Sharing

I. Introduction

In Python, is a class for sharing data between processes. This class enables multi-process data sharing by allowing multiple processes to access the same data object at the same time. Specifically, a built-in atomic types, such as int, float, etc., are provided, and the values of these atomic types can be shared among multiple processes. When manipulating the values of these atomic types, each operation is guaranteed to be atomic, meaning that each operation is complete for that value and will not be interrupted by other processes. There are two things to keep in mind when using this: 1. When multiple processes share the same object, this object must have been created by the () method. This method returns a new object. 2. Make sure it is thread-safe when using it. If more than one process operates on the same object at the same time, it may result in a contention condition, which can cause the program to become abnormal. The sample code is as follows:

from multiprocessing import Value, Process
import time
def worker(val):
    for i in range(10):
        (1) # sleep 1 second
        with val.get_lock():
             += 1 # increase val
        print('Process:', )
if __name__ == '__main__':
    v = Value('i', 0)
    p = Process(target=worker, args=(v,))
    ()
    ()

In this example, we created a shared variable v and ran the worker method in a new process. In the worker method, we use the with statement to get a lock on variable v and then perform a +1 operation on variable v. The use of get_lock() is to avoid multiple processes modifying the value of variable v at the same time, which could trigger a contention condition. Running the above code will output the following:

Process: 1
Process: 2
Process: 3
Process: 4
Process: 5
Process: 6
Process: 7
Process: 8
Process: 9
Process: 10

II. Introduction to application scenarios

Use, we can achieve data sharing between multiple processes, so as to realize some complex parallel computing or distributed computing tasks. The following is an introduction to some application scenarios: 1. Inter-process communication In parallel computing, it is often necessary to pass some shared data between multiple processes. Using can easily achieve this function, after modifying the variable value in one process, the variable value will be automatically synchronized to other processes. 2. counter In some application scenarios, it may be necessary to use a counter to count the number of occurrences of an event. Using , you can easily implement the function of sharing this counter among multiple processes. The sample code is as follows:

from multiprocessing import Value, Process
def worker(val):
    with val.get_lock():
         += 1 # increase counter
if __name__ == '__main__':
    v = Value('i', 0)
    ps = [Process(target=worker, args=(v,)) for _ in range(10)]
    for p in ps:
        ()
    for p in ps:
        ()
    print('Counter:', )

In this example, we created a counter v and started 10 processes to perform a +1 operation on this counter. In each process, we use a with statement to acquire a lock on counter v and then perform a +1 operation on the counter. Finally, we join all the processes and output the value of the counter. Running the above code will output the following:

Counter: 10

3. Data caching In some application scenarios where a large amount of data needs to be processed efficiently, it may be necessary to split the data into multiple processes for processing due to the large amount of data. Data sharing between multiple processes can be implemented using. Data is transferred between the buffer and the processes through assignment and reading. This can greatly improve the efficiency of data access.

III. Considerations for multi-process data sharing

There are some things to be aware of in multi-process data sharing. Here are some suggestions: 1. Variable locking When sharing variables between multiple processes, we need to use variable locking to ensure data synchronization. If there are multiple processes operating on the same variable at the same time, it may lead to problems such as data anomalies or thread fluctuations. All operations on shared variables must be performed with locks. 2. Data synchronization between multiple processes When sharing data between multiple processes, we need to initialize the object with (typecode, value). Any changes in this object will be synchronized to other processes. 3. must be the same type of data read and written between processes Multiple processes, if you want to share data, you must ensure that the same type of data read and written. For example, if a process is writing a string type of data, another process in the reading of the numerical type of reading, there will be a program error.

IV. Summary

This article introduces the Python, through the sample code shows how to use this class to achieve multi-process data sharing, as well as the use of scenarios and precautions and other content. I believe we have a certain understanding of the actual development can be flexible in the application of the function.

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