Mastering MicroPython Thread Classes

Table of Contents#

1. Fundamental Concepts of MicroPython Thread Classes
2. Usage Methods
3. Common Practices
4. Best Practices
5. Conclusion
6. References

1. Fundamental Concepts of MicroPython Thread Classes#

What are Threads?#

A thread is an independent sequence of instructions that can be scheduled to run by the operating system. In the context of MicroPython, threads allow different parts of your code to execute concurrently. This means that you can have multiple tasks running seemingly at the same time, which can be useful for handling multiple sensors, communicating with different devices, or performing background calculations.

MicroPython's _thread Module#

MicroPython uses the _thread module to implement thread functionality. This module provides a basic set of functions and classes to create and manage threads. The main function for creating a new thread is _thread.start_new_thread(), which takes two arguments: a function to run in the new thread and a tuple of arguments to pass to that function.

Thread States#

Threads in MicroPython can be in different states:

• Running: The thread is currently executing instructions.
• Ready: The thread is waiting to be scheduled by the operating system to run.
• Blocked: The thread is waiting for an event, such as I/O completion or a semaphore.

2. Usage Methods#

Creating a Simple Thread#

The following example demonstrates how to create a simple thread using the _thread module in MicroPython:

import _thread
import time
 
# Function to be run in the new thread
def thread_function():
    for i in range(5):
        print(f"Thread: {i}")
        time.sleep(1)
 
# Start a new thread
_thread.start_new_thread(thread_function, ())
 
# Main thread continues to execute
for i in range(3):
    print(f"Main: {i}")
    time.sleep(1)
 
# Give some time for the new thread to finish
time.sleep(5)

In this example, we define a function thread_function that will run in a new thread. We then use _thread.start_new_thread() to start the new thread and pass the function and an empty tuple of arguments. The main thread continues to execute its own loop while the new thread runs concurrently.

Using Thread Locks#

When multiple threads access shared resources, there is a risk of race conditions. A race condition occurs when two or more threads access a shared resource simultaneously, leading to unpredictable results. To prevent race conditions, we can use thread locks.

import _thread
import time
 
# Create a lock object
lock = _thread.allocate_lock()
 
# Shared resource
shared_variable = 0
 
# Function to be run in the new thread
def thread_function():
    global shared_variable
    for i in range(5):
        # Acquire the lock
        lock.acquire()
        shared_variable += 1
        print(f"Thread: Shared variable = {shared_variable}")
        # Release the lock
        lock.release()
        time.sleep(1)
 
# Start a new thread
_thread.start_new_thread(thread_function, ())
 
# Main thread also accesses the shared resource
for i in range(3):
    # Acquire the lock
    lock.acquire()
    shared_variable += 1
    print(f"Main: Shared variable = {shared_variable}")
    # Release the lock
    lock.release()
    time.sleep(1)
 
# Give some time for the new thread to finish
time.sleep(5)

In this example, we create a lock object using _thread.allocate_lock(). Before accessing the shared variable, a thread must acquire the lock using lock.acquire(), and after it is done, it must release the lock using lock.release().

3. Common Practices#

Thread Pooling#

In some cases, creating a new thread for every task can be resource - intensive. A common practice is to use a thread pool, which is a collection of pre - created threads that can be reused to execute tasks. While MicroPython does not have a built - in thread pool implementation, you can implement a simple one using queues and threads.

Error Handling in Threads#

When a thread encounters an unhandled exception, it can cause the entire program to crash. It is important to implement proper error handling in each thread to prevent this. You can use try - except blocks inside the thread function to catch and handle exceptions.

import _thread
import time
 
def thread_function():
    try:
        for i in range(5):
            print(f"Thread: {i}")
            time.sleep(1)
    except Exception as e:
        print(f"Thread error: {e}")
 
_thread.start_new_thread(thread_function, ())
 
# Main thread
for i in range(3):
    print(f"Main: {i}")
    time.sleep(1)
 
time.sleep(5)

4. Best Practices#

Minimise Thread Creation#

Creating and destroying threads is a relatively expensive operation in terms of system resources. Try to reuse threads as much as possible, for example, by using a thread pool.

Use Non - Blocking I/O#

When performing I/O operations in threads, use non - blocking I/O whenever possible. Blocking I/O can cause a thread to pause execution, which can lead to performance issues and unresponsiveness.

Keep Threads Short - Lived#

Long - running threads can consume system resources and make it difficult to manage the program. Try to keep threads short - lived and terminate them when they are no longer needed.

5. Conclusion#

MicroPython thread classes provide a powerful way to implement concurrent programming in embedded systems. By understanding the fundamental concepts, usage methods, common practices, and best practices, you can write more efficient and responsive MicroPython applications. However, it is important to be aware of the potential pitfalls, such as race conditions and resource management, and take appropriate measures to avoid them.

6. References#

• MicroPython official documentation: https://docs.micropython.org/
• Python threading tutorial: https://docs.python.org/3/library/threading.html (Although for standard Python, the concepts are similar)

Remember to test your code thoroughly on your target MicroPython device, as the behavior may vary depending on the hardware and the specific MicroPython port you are using.