


How to ensure concurrency is safe and efficient when writing multi-process logs?
Efficiently solve the concurrency security problem of multi-process log writing
In a multi-process environment, multiple processes write the same log file at the same time. How to take into account concurrency security and efficiency? This is a tricky problem, especially when the log sizes vary, from small bytes to giant files, the challenge is even more prominent. Although using file locks directly ensures security, their performance overhead is huge, which is contrary to the efficiency pursued by multiple processes.
This article discusses solutions to efficiently and gracefully solve the concurrency security problem of multi-process log writing. There are two main methods involved: file lock and message queue.
File lock is the most direct solution, but it is inefficient, especially in high log volume and large log file scenarios. Even though some log libraries (such as concurrent-log-handler) use file locks, their performance is still limited, and the file lock is a "consultative lock", which cannot completely avoid interference from external processes.
In contrast, message queueing schemes (such as loguru log library) have more advantages. Its core idea is asynchronous log writing: each process writes log messages to the message queue of inter-process communication (IPC), and a separate process is responsible for reading messages from the queue and writing to the log file. This decoupling method effectively avoids frequent file lock competition and significantly improves efficiency. Although the queue itself also requires locking, the overhead is much smaller than the file lock. Loguru utilizes the queue mechanism provided by the multiprocessing module, which is much lighter than direct operation of file locks.
It should be noted that although the asynchronous writing method based on message queues is efficient, there is a potential risk of data loss and needs to be weighed according to actual situations.
In addition, some other optimization strategies, such as using SSD to improve disk I/O performance, or in extreme cases, allowing each process to write independent log files, can also effectively alleviate concurrent conflicts. Some programming languages and frameworks (such as Golang's log module and Java's Log4j) also provide an asynchronous disk drop mechanism, which is essentially reducing file locking overhead through asynchronous and queues.
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