Concurrency & Multithreading Interview Questions

GIL is a mutex that protects access to Python objects, preventing multiple native threads from executing Python bytecode simultaneously. Limits CPU-bound threads to run sequentially. Affects only CPython implementation. Consider multiprocessing for CPU-bound tasks.

Threading shares memory space, affected by GIL, good for I/O-bound tasks. Multiprocessing uses separate memory spaces, bypasses GIL, good for CPU-bound tasks. Consider overhead, data sharing needs, and system resources when choosing.

Use async/await syntax, create event loop using asyncio.get_event_loop(). Handle coroutine execution, task scheduling. Consider error handling, cancellation. Implement proper cleanup. Handle event loop lifecycle.

Lock, RLock (reentrant lock), Semaphore, Event, Condition, Barrier. Each serves different synchronization needs. Handle proper acquisition/release. Consider deadlock prevention. Implement proper error handling.

Use multiprocessing.Queue, Pipe, shared memory (Value, Array). Handle synchronization properly. Consider serialization overhead. Implement proper locking mechanisms. Handle process termination cleanup.

gather() returns results in order, raises first exception. wait() provides more control over completion, timeout, returns done/pending sets. Consider exception handling, cancellation behavior. Handle task dependencies.

Use concurrent.futures.ThreadPoolExecutor. Handle task submission, result collection. Implement proper shutdown. Consider pool size optimization. Handle exceptions in worker threads. Manage resource usage.

Race conditions occur when multiple threads access shared data simultaneously. Prevent using locks, atomic operations. Implement proper synchronization. Consider thread safety in design. Use thread-safe data structures.

Prevent using lock ordering, timeouts. Detect using deadlock detection algorithms. Implement recovery mechanisms. Consider lock hierarchy. Handle nested locks carefully. Implement proper error recovery.

async with handles asynchronous context managers. async for iterates over async iterables. Properly handle resource cleanup, iteration. Consider exception handling. Implement proper asynchronous patterns.

Use multiprocessing.Pool or concurrent.futures.ProcessPoolExecutor. Handle task distribution, result collection. Consider process communication overhead. Implement proper cleanup. Handle process failures.

Implement proper exception handling in worker threads/processes. Handle task failures gracefully. Consider exception propagation. Implement logging and monitoring. Handle cleanup in error cases.

Use Queue from queue module, implement proper locking. Consider atomic operations. Use thread-safe collections. Implement proper synchronization mechanisms. Handle concurrent access patterns.

CPU-bound tasks primarily use processor (calculations). I/O-bound tasks wait for external operations (network, disk). Choose appropriate concurrency model (multiprocessing vs threading). Consider resource utilization.

Implement __aenter__ and __aexit__ methods. Handle async resource acquisition/release. Consider exception handling. Implement proper cleanup. Handle async initialization.

Use Queue for thread-safe communication. Implement proper synchronization. Handle termination conditions. Consider buffer size. Implement proper error handling. Handle backpressure.

Use Task.cancel(), handle CancelledError. Implement cleanup handlers. Consider task dependencies. Handle cancellation propagation. Implement proper state cleanup.

Consider GIL implications, use thread-safe operations. Implement proper synchronization. Use atomic operations when possible. Consider thread-local storage. Handle shared resource access.

Use multiprocessing pools, handle data partitioning. Consider communication overhead. Implement proper merging strategy. Handle process synchronization. Consider memory usage.

Use timeout parameters, implement timeout handling. Consider cancellation. Handle cleanup after timeout. Implement proper error reporting. Consider partial results handling.

Use logging, debugger support for threads/processes. Implement proper monitoring. Consider race condition detection. Handle debugging synchronization issues. Implement proper error tracking.

Thread-local storage provides thread-specific data storage. Prevents data sharing between threads. Implement using threading.local(). Consider cleanup requirements. Handle initialization properly.

Use async def with yield, implement __aiter__ and __anext__. Handle async iteration properly. Consider resource management. Implement proper cleanup. Handle cancellation.

Handle pool lifecycle, implement proper shutdown. Consider resource cleanup. Handle worker process errors. Implement proper task distribution. Consider pool size optimization.

Use appropriate IPC mechanisms (Queue, Pipe). Handle synchronization properly. Consider serialization overhead. Implement proper error handling. Handle process termination.

Use event loops, implement event handlers. Consider event propagation. Handle event ordering. Implement proper error handling. Consider event queue management.

Balance thread/process count, optimize resource usage. Consider overhead costs. Implement proper task granularity. Handle load balancing. Consider system resources.

Handle resource limitations, implement proper load balancing. Consider scalability bottlenecks. Handle distributed processing. Implement proper monitoring. Consider performance metrics.

Handle termination signals, implement proper cleanup. Consider ongoing operations. Handle resource release. Implement proper state saving. Consider recovery procedures.