When you write concurrent programs in Python, you must be aware of several common pitfalls that can lead to subtle and hard-to-find bugs. The most notable issues include deadlocks, starvation, and race conditions. Understanding each of these problems and how to avoid them is crucial for building reliable and efficient concurrent systems.

Deadlocks occur when two or more threads or processes are waiting for each other to release resources, causing all of them to become stuck indefinitely. This often happens when multiple locks are acquired in different orders by different threads. To avoid deadlocks:

• Always acquire locks in a consistent order;
• Minimize the use of locks and shared resources;
• Use timeout mechanisms when acquiring locks, so threads can give up after waiting too long.

Starvation happens when a thread or process is perpetually denied access to resources, preventing it from making progress. This can result from unfair scheduling or resource allocation. To reduce the risk of starvation:

• Use fair locks or semaphores when available;
• Design your program so that all threads have a chance to access shared resources;
• Avoid holding locks for long periods.

Race conditions arise when two or more threads or processes access shared data at the same time, and the final outcome depends on the order in which the accesses happen. This can lead to unpredictable and incorrect results. To prevent race conditions:

• Always protect shared data with appropriate synchronization primitives like locks;
• Prefer immutable data structures where possible;
• Limit the scope of shared state to reduce the risk of concurrent access.

Debugging concurrent programs is inherently more challenging than debugging single-threaded code because bugs may not appear every time you run the program. Here are some practical tips to help you track down and fix concurrency bugs:

• Use logging extensively, and include thread or process names in your log messages. This makes it easier to trace which thread or process performed each action;
• Assign meaningful names to threads and processes. Python’s threading.Thread and multiprocessing.Process classes allow you to set a name, which is invaluable for debugging;
• Log the acquisition and release of locks, as well as the start and end of critical sections;
• Look for signs of deadlocks, such as threads that are stuck waiting for resources or never reach certain log statements;
• Use tools like the Python debugger (pdb) and, when possible, run your code with a single thread or process to isolate issues;
• Reproduce issues with minimal test cases to better understand the underlying problem.