Threading (article): https://realpython.com/intro-to-python-threading/
Tkinter (tutorial): https://www.pythontutorial.net/tkinter/
What Is a Thread?
A thread is a separate flow of execution. This means that your program will have two things happening at once. But for most Python 3 implementations the different threads do not actually execute at the same time: they merely appear to. It’s tempting to think of threading as having two (or more) different processors running on your program, each one doing an independent task at the same time. That’s almost right. The threads may be running on different processors, but they will only be running one at a time.
Getting multiple tasks running simultaneously requires a non-standard implementation of Python, writing some of your code in a different language, or using multiprocessing which comes with some extra overhead. Tasks that spend much of their time waiting for external events are generally good candidates for threading. Problems that require heavy CPU computation and spend little time waiting for external events might not run faster at all.
If you are running a standard Python implementation, writing in only Python, and have a CPU-bound problem, you should check out the multiprocessing module instead. Architecting your program to use threading can also provide gains in design clarity. Most of the examples you’ll learn about in this tutorial are not necessarily going to run faster because they use threads. Using threading in them helps to make the design cleaner and easier to reason about.
Starting a Thread
To start a separate thread, you create a Thread instance and then tell it to .start():
1import logging 2import threading 3import time 4 5def thread_function(name): 6 logging.info("Thread %s: starting", name) 7 time.sleep(2) 8 logging.info("Thread %s: finishing", name) 9 10if __name__ == "__main__": 11 format = "%(asctime)s: %(message)s" 12 logging.basicConfig(format=format, level=logging.INFO, 13 datefmt="%H:%M:%S") 14 15 logging.info("Main : before creating thread") 16 x = threading.Thread(target=thread_function, args=(1,)) 17 logging.info("Main : before running thread") 18 x.start() 19 logging.info("Main : wait for the thread to finish") 20 # x.join() 21 logging.info("Main : all done")
If you look around the logging statements, you can see that the main section is creating and starting the thread:
x = threading.Thread(target=thread_function, args=(1,)) x.start()
When you create a Thread, you pass it a function and a list containing the arguments to that function. In this case, you’re telling the Thread to run thread_function() and to pass it 1 as an argument. thread_function() itself doesn’t do much. It simply logs some messages with a time.sleep() in between them.
join() a Thread
To tell one thread to wait for another thread to finish, you call .join(). If you uncomment that line, the main thread will pause and wait for the thread x to complete running. Did you test this on the code with the daemon thread or the regular thread? It turns out that it doesn’t matter. If you .join() a thread, that statement will wait until either kind of thread is finished.
Working With Many Threads
The example code so far has only been working with two threads: the main thread and one you started with the threading.Thread object.
Frequently, you’ll want to start a number of threads and have them do interesting work. Let’s start by looking at the harder way of doing that, and then you’ll move on to an easier method. The harder way of starting multiple threads is the one you already know:
import logging import threading import time def thread_function(name): logging.info("Thread %s: starting", name) time.sleep(2) logging.info("Thread %s: finishing", name) if __name__ == "__main__": format = "%(asctime)s: %(message)s" logging.basicConfig(format=format, level=logging.INFO, datefmt="%H:%M:%S") threads = list() for index in range(3): logging.info("Main : create and start thread %d.", index) x = threading.Thread(target=thread_function, args=(index,)) threads.append(x) x.start() for index, thread in enumerate(threads): logging.info("Main : before joining thread %d.", index) thread.join() logging.info("Main : thread %d done", index)
This code uses the same mechanism you saw above to start a thread, create a Thread object, and then call .start(). The program keeps a list of Thread objects so that it can then wait for them later using .join(). The order in which threads are run is determined by the operating system and can be quite hard to predict. It may (and likely will) vary from run to run, so you need to be aware of that when you design algorithms that use threading. Fortunately, Python gives you several primitives that you’ll look at later to help coordinate threads and get them running together. Before that, let’s look at how to make managing a group of threads a bit easier.
Before you move on to some of the other features tucked away in Python threading, let’s talk a bit about one of the more difficult issues you’ll run into when writing threaded programs: race conditions. Once you’ve seen what a race condition is and looked at one happening, you’ll move on to some of the primitives provided by the standard library to prevent race conditions from happening. Race conditions can occur when two or more threads access a shared piece of data or resource. In this example, you’re going to create a large race condition that happens every time, but be aware that most race conditions are not this obvious. Frequently, they only occur rarely, and they can produce confusing results. As you can imagine, this makes them quite difficult to debug. Fortunately, this race condition will happen every time, and you’ll walk through it in detail to explain what is happening.
Your FakeDatabase will have .init() and .update() methods:
class FakeDatabase: def __init__(self): self.value = 0 def update(self, name): logging.info("Thread %s: starting update", name) local_copy = self.value local_copy += 1 time.sleep(0.1) self.value = local_copy logging.info("Thread %s: finishing update", name)
FakeDatabase is keeping track of a single number: .value. This is going to be the shared data on which you’ll see the race condition.
.init() simply initializes .value to zero. So far, so good.
.update() looks a little strange. It’s simulating reading a value from a database, doing some computation on it, and then writing a new value back to the database. In this case, reading from the database just means copying .value to a local variable. The computation is just to add one to the value and then .sleep() for a little bit. Finally, it writes the value back by copying the local value back to .value.
Here’s how you’ll use this FakeDatabase:
if __name__ == "__main__": format = "%(asctime)s: %(message)s" logging.basicConfig(format=format, level=logging.INFO, datefmt="%H:%M:%S") database = FakeDatabase() logging.info("Testing update. Starting value is %d.", database.value) with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor: for index in range(2): executor.submit(database.update, index) logging.info("Testing update. Ending value is %d.", database.value)
The program creates a ThreadPoolExecutor with two threads and then calls .submit() on each of them, telling them to run database.update().
.submit() has a signature that allows both positional and named arguments to be passed to the function running in the thread: .submit(function, *args, **kwargs)
In the usage above, index is passed as the first and only positional argument to database.update(). You’ll see later in this article where you can pass multiple arguments in a similar manner.
Before you dive into this issue with two threads, let’s step back and talk a bit about some details of how threads work.
You won’t be diving into all of the details here, as that’s not important at this level. We’ll also be simplifying a few things in a way that won’t be technically accurate but will give you the right idea of what is happening. When you tell your ThreadPoolExecutor to run each thread, you tell it which function to run and what parameters to pass to it: executor.submit(database.update, index).
The result of this is that each of the threads in the pool will call database.update(index). Note that database is a reference to the one FakeDatabase object created in main. Calling .update() on that object calls an instance method on that object. Each thread is going to have a reference to the same FakeDatabase object, database. Each thread will also have a unique value, index, to make the logging statements a bit easier to read.
When the thread starts running .update(), it has its own version of all of the data local to the function. In the case of .update(), this is local_copy. This is definitely a good thing. Otherwise, two threads running the same function would always confuse each other. It means that all variables that are scoped (or local) to a function are thread-safe. Now you can start walking through what happens if you run the program above with a single thread and a single call to .update().
When Thread 1 starts, FakeDatabase.value is zero. The first line of code in the method, local_copy = self.value, copies the value zero to the local variable. Next it increments the value of local_copy with the local_copy += 1 statement. You can see .value in Thread 1 getting set to one. Next time.sleep() is called, which makes the current thread pause and allows other threads to run. Since there is only one thread in this example, this has no effect.
When Thread 1 wakes up and continues, it copies the new value from local_copy to FakeDatabase.value, and then the thread is complete. You can see that database.value is set to one. So far, so good. You ran .update() once and FakeDatabase.value was incremented to one.
Getting back to the race condition, the two threads will be running concurrently but not at the same time. They will each have their own version of local_copy and will each point to the same database. It is this shared database object that is going to cause the problems. The program starts with Thread 1 running .update().
When Thread 1 calls time.sleep(), it allows the other thread to start running. This is where things get interesting.
Thread 2 starts up and does the same operations. It’s also copying database.value into its private local_copy, and this shared database.value has not yet been updated:
When Thread 2 finally goes to sleep, the shared database.value is still unmodified at zero, and both private versions of local_copy have the value one. Thread 1 now wakes up and saves its version of local_copy and then terminates, giving Thread 2 a final chance to run. Thread 2 has no idea that Thread 1 ran and updated database.value while it was sleeping. It stores its version of local_copy into database.value, also setting it to one: The two threads have interleaving access to a single shared object, overwriting each other’s results. Similar race conditions can arise when one thread frees memory or closes a file handle before the other thread is finished accessing it.
Basic Synchronization Using Lock
There are a number of ways to avoid or solve race conditions. You won’t look at all of them here, but there are a couple that are used frequently. Let’s start with Lock. To solve your race condition above, you need to find a way to allow only one thread at a time into the read-modify-write section of your code. The most common way to do this is called Lock in Python. In some other languages this same idea is called a mutex. Mutex comes from MUTual EXclusion, which is exactly what a Lock does. A Lock is an object that acts like a hall pass. Only one thread at a time can have the Lock. Any other thread that wants the Lock must wait until the owner of the Lock gives it up. The basic functions to do this are .acquire() and .release(). A thread will call my_lock.acquire() to get the lock. If the lock is already held, the calling thread will wait until it is released. There’s an important point here. If one thread gets the lock but never gives it back, your program will be stuck. You’ll read more about this later. Fortunately, Python’s Lock will also operate as a context manager, so you can use it in a with statement, and it gets released automatically when the with block exits for any reason. Let’s look at the FakeDatabase with a Lock added to it. The calling function stays the same:
class FakeDatabase: def __init__(self): self.value = 0 self._lock = threading.Lock() def locked_update(self, name): logging.info("Thread %s: starting update", name) logging.debug("Thread %s about to lock", name) with self._lock: logging.debug("Thread %s has lock", name) local_copy = self.value local_copy += 1 time.sleep(0.1) self.value = local_copy logging.debug("Thread %s about to release lock", name) logging.debug("Thread %s after release", name) logging.info("Thread %s: finishing update", name)
Other than adding a bunch of debug logging so you can see the locking more clearly, the big change here is to add a member called ._lock, which is a threading.Lock() object. This ._lock is initialized in the unlocked state and locked and released by the with statement.
It’s worth noting here that the thread running this function will hold on to that Lock until it is completely finished updating the database. In this case, that means it will hold the Lock while it copies, updates, sleeps, and then writes the value back to the database.
Before you move on, you should look at a common problem when using Locks. As you saw, if the Lock has already been acquired, a second call to .acquire() will wait until the thread that is holding the Lock calls .release(). What do you think happens when you run this code: import threading
l = threading.Lock() print("before first acquire") l.acquire() print("before second acquire") l.acquire() print("acquired lock twice")
When the program calls l.acquire() the second time, it hangs waiting for the Lock to be released. In this example, you can fix the deadlock by removing the second call, but deadlocks usually happen from one of two subtle things:
- An implementation bug where a Lock is not released properly
- A design issue where a utility function needs to be called by functions that might or might not already have the Lock
The first situation happens sometimes, but using a Lock as a context manager greatly reduces how often. It is recommended to write code whenever possible to make use of context managers, as they help to avoid situations where an exception skips you over the .release() call.
The design issue can be a bit trickier in some languages. Thankfully, Python threading has a second object, called RLock, that is designed for just this situation. It allows a thread to .acquire() an RLock multiple times before it calls .release(). That thread is still required to call .release() the same number of times it called .acquire(), but it should be doing that anyway.
Lock and RLock are two of the basic tools used in threaded programming to prevent race conditions. There are a few other that work in different ways. Before you look at them, let’s shift to a slightly different problem domain.
There are a few more primitives offered by the Python threading module. While you didn’t need these for the examples above, they can come in handy in different use cases, so it’s good to be familiar with them.
The first Python threading object to look at is threading.Semaphore. A Semaphore is a counter with a few special properties. The first one is that the counting is atomic. This means that there is a guarantee that the operating system will not swap out the thread in the middle of incrementing or decrementing the counter.
The internal counter is incremented when you call .release() and decremented when you call .acquire().
The next special property is that if a thread calls .acquire() when the counter is zero, that thread will block until a different thread calls .release() and increments the counter to one.
Semaphores are frequently used to protect a resource that has a limited capacity. An example would be if you have a pool of connections and want to limit the size of that pool to a specific number.
A threading.Timer is a way to schedule a function to be called after a certain amount of time has passed. You create a Timer by passing in a number of seconds to wait and a function to call:
t = threading.Timer(30.0, my_function)
You start the Timer by calling .start(). The function will be called on a new thread at some point after the specified time, but be aware that there is no promise that it will be called exactly at the time you want. If you want to stop a Timer that you’ve already started, you can cancel it by calling .cancel(). Calling .cancel() after the Timer has triggered does nothing and does not produce an exception.
A Timer can be used to prompt a user for action after a specific amount of time. If the user does the action before the Timer expires, .cancel() can be called.
A threading.Barrier can be used to keep a fixed number of threads in sync. When creating a Barrier, the caller must specify how many threads will be synchronizing on it. Each thread calls .wait() on the Barrier. They all will remain blocked until the specified number of threads are waiting, and then the are all released at the same time. Remember that threads are scheduled by the operating system so, even though all of the threads are released simultaneously, they will be scheduled to run one at a time. One use for a Barrier is to allow a pool of threads to initialize themselves. Having the threads wait on a Barrier after they are initialized will ensure that none of the threads start running before all of the threads are finished with their initialization. Conclusion You’ve now seen much of what Python threading has to offer and some examples of how to build threaded programs and the problems they solve. You’ve also seen a few instances of the problems that arise when writing and debugging threaded programs.
- Make the start function
- Make the clock function with threading
- Make reset function
If you are having any issues with this process feel free to email me any time for assitance. firstname.lastname@example.org