How to solve Python's subprocess communication error?

Python multi-process programming can effectively improve program performance. However, various errors often occur during sub-process communication, such as deadlocks, blocking and other issues. This article will introduce how to solve Python's sub-process communication errors and help readers make better use of Python's multi-process programming.

1. Use process pool instead of separate process
   Most Python programmers use separate processes to handle tasks, which can bring some benefits, such as improving performance in scenarios with simple implementation logic, but this This method will also introduce some problems. When the number of individual processes exceeds a certain range, the processes will affect the system's resource usage efficiency (such as memory, I/O, etc.), and the throughput will also decrease. In order to solve this problem, a process pool can be used instead of a single process, which can control the number of processes within a reasonable range and improve the throughput of multi-process communication.
2. Avoid using global variables
   In Python multi-process programming, since all processes use the same global variable, it is easy to cause variable inconsistency. Therefore, it is best to avoid using global variables and instead use process queues for inter-process data transfer.
3. Use locks to avoid deadlock problems
   Deadlock is a common error in multi-process programming. It will cause multi-process blocking and affect system performance. To prevent deadlocks, locking mechanisms can be used between processes. Locks can ensure that only one process can access shared resources at the same time. When a process obtains a lock, other processes cannot access shared resources before the process releases the lock.
4. Use non-blocking methods to avoid blocking problems
   Due to the large number of child processes, waiting for the running results of each child process in the main process can easily cause the main process to be blocked. In order to avoid blocking problems, you can use non-blocking mode to run the child process. In Python, it can be implemented using functions such as select, poll, and epoll.
5. Use process queue for data transfer
   Process queue (multiprocessing.Queue) is an important tool in Python multi-process programming, which can realize data transfer between processes. In the process queue, you can use the put and get methods to send and receive data. Compared with using global variables, using process queues has the following advantages: it can avoid process synchronization problems, can safely transfer data between processes, and the queue will be automatically closed when the process ends.
6. Using inter-process shared memory
   Inter-process shared memory (multiprocessing.shared_memory) is another inter-process communication method in Python multi-process programming. Shared memory can be used to share large amounts of data between multiple processes. Common scenarios include reading large image files, reading and writing audio/video files, etc. The biggest benefit of shared memory is that it is fast, but data consistency and security need to be ensured.

Conclusion
Python's multi-process programming is an efficient method that can bring great performance improvements. However, in multi-process communication, various errors often occur, such as deadlock, blocking, variable inconsistency and other problems. This article describes how to solve Python's sub-process communication errors and help readers make better use of Python's multi-process programming. In order to achieve more efficient multi-process communication, it is necessary to carefully design the inter-process communication method, and use locks, non-blocking methods, shared memory and other methods to achieve inter-process data transfer in the implementation.

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