Multithreading with threading
Explore how to implement multithreading in Python using the threading module to execute I/O-bound operations concurrently. Understand thread creation, starting, and synchronization with join and locks to prevent race conditions. Learn about the Global Interpreter Lock's impact and when to use daemon threads for background tasks. This lesson equips you to write responsive Python programs that efficiently manage waiting periods.
In many real-world applications, programs spend a significant portion of their execution time waiting. Tasks such as downloading files, querying databases, or reading from disk are I/O-bound operations. During these periods, the CPU remains largely idle while external systems perform the required work. If such operations are executed sequentially, overall performance suffers and the application may appear unresponsive.
By introducing threads, we can utilize this idle time more effectively. While one thread waits for an I/O operation to complete, another thread can make progress on a different task. This overlapping of waiting periods improves throughput and responsiveness without requiring additional CPU cores.
In this lesson, we will explore how to structure programs using Python’s standard threading module to handle multiple I/O-bound operations concurrently.
Creating and starting threads
The core of Python’s threading model is the Thread class from the threading module. When we create a Thread instance, we associate it ...