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Concurrency

Threading Basics

Creating and managing threads — the threading module, thread lifecycle, daemon threads, and when to use threads in Python.

Interview: Core concurrency concept — interviewers test understanding of threads, the GIL, and when threading helps vs hurts.

Last Updated: June 12, 2026 • 8 min read

Threads allow concurrent execution within a single process. Python's threading module creates OS-level threads that share memory. Threads are ideal for I/O-bound tasks (network requests, file I/O) but limited for CPU-bound work due to the GIL.

Creating Threads

  • Thread(target=func, args=(...)) — create a thread
  • thread.start() — begin execution
  • thread.join() — wait for thread to finish
  • thread.is_alive() — check if still running

Thread Safety

  • Threads share memory — race conditions possible on shared data
  • Use Lock, RLock, or Semaphore for synchronization
  • Thread-safe data structures: queue.Queue
  • GIL prevents true parallelism for CPU-bound work in CPython

Interview Insight

Know when threads help (I/O-bound) vs when they don't (CPU-bound due to GIL). Be able to explain thread safety and race conditions.

Use Cases

HTTP requests — downloading multiple URLs concurrently

File I/O — reading/writing multiple files in parallel

Database queries — executing independent queries concurrently

Web scraping — fetching multiple pages at once

Background tasks — daemon threads for periodic cleanup

Common Mistakes

Using threads for CPU-bound work — GIL prevents true parallelism, use multiprocessing

Not joining threads — main program may exit before threads finish

Sharing mutable data without locks — causes race conditions

Creating too many threads — overhead exceeds benefit, use a pool

Not handling exceptions in threads — they fail silently without proper handling