/ˈθrɛdɪŋ/
noun … “Parallel paths of execution within a program.”
Threading is a programming technique that allows a single process to manage multiple independent sequences of execution, called threads, concurrently. Each thread represents a flow of control that shares the same memory space, file descriptors, and resources of the parent process while maintaining its own program counter, stack, and local variables. Threading enables programs to perform multiple operations simultaneously, improving responsiveness and throughput, particularly in I/O-bound applications.
Threads are often managed either by the operating system, in which case they are called kernel threads, or by a runtime library, known as user-level threads. In languages like Python, the Global Interpreter Lock (GIL) restricts execution of Python bytecode to one thread at a time within a single process, meaning CPU-bound tasks cannot achieve true parallelism using Threading. For I/O-bound tasks, such as network requests or file operations, Threading remains highly effective because the interpreter releases the GIL during blocking calls.
Key characteristics of Threading include:
- Shared memory: threads operate within the same address space of the process, enabling fast communication but requiring synchronization mechanisms.
- Concurrency: multiple threads can appear to run simultaneously, especially on multi-core systems.
- Lightweight execution units: threads are less resource-intensive than separate processes.
- Synchronization challenges: race conditions, deadlocks, and data corruption can occur if shared resources are not properly managed.
Workflow example: A Python web server can spawn a thread for each incoming client connection. While one thread waits for network I/O, other threads handle additional requests, maximizing resource utilization and responsiveness. If a CPU-intensive task is needed, the server may offload the computation to separate processes to bypass the GIL and achieve parallel execution.
Conceptually, Threading is like having multiple couriers delivering packages from the same warehouse. They share the same stock (memory) and infrastructure but each follows its own route (execution path). Without proper coordination, couriers could interfere with each other, but with synchronization, deliveries proceed efficiently in parallel.
See Global Interpreter Lock, Multiprocessing, Python, Concurrency.