This could be achieved with a mutual exclusion lock (mutex) and a boolean variable, but provides no way for threads to wait for the variable to be set True. Thread 1 then starts and attempts to acquire the same lock. The operating system can swap which thread is running at any time. 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.

Upon completion you will receive a score so you can track your learning progress over time: A thread is a separate flow of execution. Heres an example output from my machine: If you walk through the output carefully, youll see all three threads getting started in the order you might expect, but in this case they finish in the opposite order!

The easiest way to create it is as a context manager, using the with statement to manage the creation and destruction of the pool.

The program creates a ThreadPoolExecutor with two threads and then calls .submit() on each of them, telling them to run database.update(). Lets look at the FakeDatabase with a Lock added to it. 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. Calling .update() on that object calls an instance method on that object. If youd like to explore other options for concurrency in Python, check out Speed Up Your Python Program With Concurrency. Lock and Queue are handy classes to solve concurrency issues, but there are others provided by the standard library. Notice that the first message was 43, and that is exactly what the consumer read, even though the producer had already generated the 45 message. Leave a comment below and let us know.

Before you move on, you should look at a common problem when using Locks. Now lets take a look at the Pipeline that passes messages from the producer to the consumer: Woah! We're using multiple threads to spin separate operations off to run concurrently, however, there are times when it is important to be able to synchronize two or more threads' operations. Technically, this example wont have a race condition because x is local to inc(). For this example, youre going to write a class that updates a database. Testing unlocked update. You might be wondering where all of the locking code that prevents the threads from causing race conditions went. If you dont specify maxsize, then the queue will grow to the limits of your computers memory. Right at the top, you can see the producer got to create five messages and place four of them on the queue. The details of how this happens are quite interesting, but not needed for the rest of this article, so feel free to skip over this hidden section. Testing locked update. They all will remain blocked until the specified number of threads are waiting, and then the are all released at the same time. thread_function() did not get a chance to complete. Now you can start walking through what happens if you run the program above with a single thread and a single call to .update(). Fortunately, Pythons 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. The first line of code in the method, local_copy = self.value, copies the value zero to the local variable. You now know how to use a threading.Event Object in Python. Running your corrected example code will produce output that looks like this: Again, notice how Thread 1 finished before Thread 0. Before you wrap up this tutorial, lets do a quick survey of some of them. A threading.Event object wraps a boolean variable that can either be set (True) or not set (False). When you run this program as it is (with line twenty commented out), the output will look like this: Youll notice that the Thread finished after the Main section of your code did. Other threads can wait() for the flag to be set(). Few people know about it (or how to use it well). Each of these steps is a separate instruction to the processor. The triggering of the event can be many things. This happens if the event gets triggered after the producer has checked the .is_set() condition but before it calls pipeline.set_message(). Threads sharing the event instance can check if the event is set, set the event, clear the event (make it not set), or wait for the event to be set. This is where things get interesting. Deep Learning II : Image Recognition (Image classification), 10 - Deep Learning III : Deep Learning III : Theano, TensorFlow, and Keras. You can see that database.value is set to one. While you didnt need these for the examples above, they can come in handy in different use cases, so its good to be familiar with them. When Thread 1 starts, FakeDatabase.value is zero. For this example, youre going to imagine a program that needs to read messages from a network and write them to disk. Note that the wait() method blocks until the flag is true.

A Lock is an object that acts like a hall pass. Thats why the producer usually runs until it blocks in the second call to .set_message(). If the internal flag is true on entry, return immediately. This is a little awkward, but dont worry, youll see ways to get rid of this SENTINEL value after you work through this example. Heres what the final code looks like using queue.Queue directly: Thats easier to read and shows how using Pythons built-in primitives can simplify a complex problem. best-practices Queue has an optional parameter when initializing to specify a maximum size of the queue. On the other side, once you have a message, you need to write it to a database. Sponsor Open Source development activities and free contents for everyone. What if you could use all of the CPU cores in your system right now, with just a very small change to your code? It begins when Thread 1 is created and ends when it is terminated. Finally, the main thread will block for a moment, then trigger the processing in all of the threads via the event object. The isSet() method can be used separately on the event, and it's a non-blocking call. Before you look at them, lets shift to a slightly different problem domain. Heres the answer. Introducing: "Python Multiprocessing Pool Jump-Start". The basic functions to do this are .acquire() and .release(). Thankfully, Python threading has a second object, called RLock, that is designed for just this situation. The most common way to do this is called Lock in Python. They basically wrap .get() and .put() on the Queue. The Python standard library provides threading, which contains most of the primitives youll see in this article. If one thread gets the lock but never gives it back, your program will be stuck. Race conditions can occur when two or more threads access a shared piece of data or resource. Even slight changes to these elements of the program will make large differences in your results. Try playing with different queue sizes and calls to time.sleep() in the producer or the consumer to simulate longer network or disk access times respectively. You ran .update() once and FakeDatabase.value was incremented to one. The first Python threading object to look at is threading.Semaphore. This is going to be the shared data on which youll see the race condition.

Jim has been programming for a long time in a variety of languages. Once a thread is blocked, however, the operating system will always swap it out and find a different thread to run. An Event manages an internal flag that callers can either set() or clear(). In between the producer and the consumer, you will create a Pipeline that will be the part that changes as you learn about different synchronization objects. Many of the examples in the rest of this article will have WARNING and DEBUG level logging. Watch Now This tutorial has a related video course created by the Real Python team. The wait(timeout=None) blocks until the internal flag is true by the set() method. Starting value is 0. .__init__() simply initializes .value to zero. The Pipeline really isnt needed for this problem. Any threads waiting on the event to be set will be notified. Next, we can start five new threads specifying the target task() function with the event object and a unique integer as arguments. The program keeps a list of Thread objects so that it can then wait for them later using .join(). .update() looks a little strange.

Python threading has a more specific meaning for daemon. Frequently, this behavior is what you want, but there are other options available to us. When you run the program, youll notice that there is a pause (of about 2 seconds) after __main__ has printed its all done message and before the thread is finished. Lets run the code that has logging set to WARNING and see what it looks like: At first, you might find it odd that the producer gets two messages before the consumer even runs. They will each have their own version of local_copy and will each point to the same database. It is entirely possible that, every once in while, the operating system would switch threads at that exact point even without sleep(), but the call to sleep() makes it happen every time. The threading.Event provides an easy way to share a boolean variable between threads that can act as a trigger for an action. Testing unlocked update. It got swapped out by the operating system before it could place the fifth one. The queue is down to size three after a single message was removed. Testing locked update. There is threading.get_ident(), which returns a unique name for each thread, but these are usually neither short nor easily readable. The same LOAD, MODIFY, STORE set of operations also happens on global and shared values. If youre not sure if you want to use Python threading, asyncio, or multiprocessing, then you can check out Speed Up Your Python Program With Concurrency. Join us and get access to hundreds of tutorials, hands-on video courses, and a community of expertPythonistas: Master Real-World Python SkillsWith Unlimited Access to RealPython. March 1, 2022 by Jason Brownlee No Comments. Did you test this on the code with the daemon thread or the regular thread? Lets start by looking at the harder way of doing that, and then youll move on to an easier method. 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. The internal counter is incremented when you call .release() and decremented when you call .acquire(). Youve now seen much of what Python threading has to offer and some examples of how to build threaded programs and the problems they solve. When the timeout argument is present and not None, it should be a floating point number specifying a timeout for the operation in seconds (or fractions thereof).

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(). Fortunately, Python gives you several primitives that youll look at later to help coordinate threads and get them running together. The wait() method takes an argument representing the number of seconds to wait for the event before timing out. Thread 2 has no idea that Thread 1 ran and updated database.value while it was sleeping. Youll come back to why that is and talk about the mysterious line twenty in the next section. another thread calling the set() function). Thread, in this module, nicely encapsulates threads, providing a clean interface to work with them. A Timer can be used to prompt a user for action after a specific amount of time. contactus@bogotobogo.com, Copyright 2020, bogotobogo It will acquire the .producer_lock, set the .message, and the call .release() on then consumer_lock, which will allow the consumer to read that value. intermediate, Recommended Video Course: Threading in Python, Recommended Video CourseThreading in Python. Lets look at this in detail. The computation is just to add one to the value and then .sleep() for a little bit. Okay, youre not really going to have a database: youre just going to fake it, because thats not the point of this article. The image below steps through the execution of .update() if only a single thread is run. Threads that are daemons, however, are just killed wherever they are when the program is exiting. .get_message() calls .acquire() on the consumer_lock. Ending value is 2. Lock and RLock are two of the basic tools used in threaded programming to prevent race conditions. The example above is contrived to make sure that the race condition happens every time you run your program. A timeout argument can be passed to the wait() function which will limit how long a thread is willing to wait in seconds for the event to be marked as set. If you want to be able to handle more than one value in the pipeline at a time, youll need a data structure for the pipeline that allows the number to grow and shrink as data backs up from the producer. Now back to your regularly scheduled tutorial! Curated by the Real Python team. You do that by changing how you construct the Thread, adding the daemon=True flag: When you run the program now, you should see this output: The difference here is that the final line of the output is missing. While it works for this limited test, it is not a great solution to the producer-consumer problem in general because it only allows a single value in the pipeline at a time. Note, your specific results will differ given the use of random numbers. Running this code multiple times will likely produce some interesting results.

wait() returns true if and only if the internal flag has been set to true, either before the wait call or after the wait starts, so it will always return True except if a timeout is given and the operation times out. 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. On the other side of the pipeline is the consumer: The consumer reads a message from the pipeline and writes it to a fake database, which in this case is just printing it to the display. Once you take away the logging, it just becomes a queue.Queue. Deep Learning I : Image Recognition (Image uploading), 9. In the example, we're not setting it on entry but we're doing it much later. Lets look at a solution using Lock. Since this is an article about Python threading, and since you just read about the Lock primitive, lets try to solve this problem with two threads using a Lock or two. If you are running on a different Python implementation, check with the documentation too see how it handles threads.

Since there is only one thread in this example, this has no effect. Lets start with the Event. There are a few other that work in different ways. The consumer still has a bunch of work do to, so it keeps running until it has cleaned out the pipeline. If you need a refresher, you can start with the Python Learning Paths and get up to speed.

It allows a thread to .acquire() an RLock multiple times before it calls .release(). Were stopping here for a specific reason. Any other thread that wants the Lock must wait until the owner of the Lock gives it up. Before you move on to some of the other features tucked away in Python threading, lets talk a bit about one of the more difficult issues youll run into when writing threaded programs: race conditions. The Pipeline has changed dramatically, however: You can see that Pipeline is a subclass of queue.Queue. In the case of .update(), this is local_copy. That could happen before .release() returns! You can use an Event Object in Python via the threading.Event class. This means that your program will have two things happening at once. The main thread will set the event and trigger the processing in all threads. An example would be if you have a pool of connections and want to limit the size of that pool to a specific number. The messages will not come in at a regular pace, but will be coming in bursts. It turns out that it doesnt matter. It was a daemon thread, so when __main__ reached the end of its code and the program wanted to finish, the daemon was killed. Daemon threads are handy, but what about when you want to wait for a thread to stop? Because of the way CPython implementation of Python works, threading may not speed up all tasks. Commenting Tips: The most useful comments are those written with the goal of learning from or helping out other students. Before you go on to .set_message(), theres something subtle going on in .get_message() thats pretty easy to miss. It simply logs some messages with a time.sleep() in between them. Now that youve got an idea of what a thread is, lets learn how to make one. The consumer calls .get_message(), which reads the message and calls .release() on the .producer_lock, thus allowing the producer to run again the next time threads are swapped. In this case, the only other thread with anything to do is the consumer. It calls .set_message() on the pipeline to send it to the consumer. "Main : wait for the thread to finish". Queue is thread-safe. So, lets stop talking about threading and start using it!

Thread 1 now wakes up and saves its version of local_copy and then terminates, giving Thread 2 a final chance to run.

This triggers all five threads that perform their processing and report a message. To start a separate thread, you create a Thread instance and then tell it to .start(): If you look around the logging statements, you can see that the main section is creating and starting the thread: When you create a Thread, you pass it a function and a list containing the arguments to that function. No spam ever. The end of the with block causes the ThreadPoolExecutor to do a .join() on each of the threads in the pool. Releasing this lock is what allows the producer to insert the next message into the pipeline. LinkedIn | Twitter | Facebook | RSS, # wait for the event to be set with a timeout, Event Objects, threading Thread-based parallelism, Calculate Fibonacci Numbers Concurrently in Python, Configure the Multiprocessing Pool Context, Analyze HackerNews Posts Concurrently with Threads in Python. It does illustrate how a thread can be interrupted during a single Python operation, however. The program starts with Thread 1 running .update(): When Thread 1 calls time.sleep(), it allows the other thread to start running. Remember that you can turn on DEBUG logging to see all of the logging messages by uncommenting this line: It can be worthwhile to walk through the DEBUG logging messages to see exactly where each thread acquires and releases the locks.

Try out the programs with the logging turned up and see what they do. Instead, this can be achieved using an event object. .get_message() and .set_message() got much smaller. Each tutorial at Real Python is created by a team of developers so that it meets our high quality standards. The main thread blocks for a moment, allowing all threads to get started and start waiting on the event. Using Event objects is the simple way to communicate between threads.

The main thread will first create the shared threading.Event instance, which will be in the not set state by default.

Pythons standard library has a queue module which, in turn, has a Queue class. Most of the examples youll learn about in this tutorial are not necessarily going to run faster because they use threads. Finally, threads can wait for the event to set via the wait() function.

Thats almost right. Using threading in them helps to make the design cleaner and easier to reason about. A daemon thread will shut down immediately when the program exits. If you uncomment that line, the main thread will pause and wait for the thread x to complete running. It returns a boolean indicating whether or not the event is set, so the caller knows why wait() returned.

This is definitely a good thing. """, # logging.getLogger().setLevel(logging.DEBUG). Almost there! Calling this function will block until the event is marked as set (e.g.

Heres the __main__ from the last example rewritten to use a ThreadPoolExecutor: The code creates a ThreadPoolExecutor as a context manager, telling it how many worker threads it wants in the pool. Architecting your program to use threading can also provide gains in design clarity. Now lets go back to your original program and look at that commented out line twenty: To tell one thread to wait for another thread to finish, you call .join(). In this tutorial you will discover how to use an event object in Python. Once created we can check if the event has been set via the is_set() function which will return True if the event is set, or False otherwise. If you .join() a thread, that statement will wait until either kind of thread is finished. So far, so good. Special thanks to reader JL Diaz for helping to clean up the introduction. The code above isnt quite as out there as you might originally have thought.

The Pipeline in this version of your code has three members: __init__() initializes these three members and then calls .acquire() on the .consumer_lock. Otherwise, two threads running the same function would always confuse each other. It also no longer puts the SENTINEL value into the pipeline. In some other languages this same idea is called a mutex. It was designed to force a race condition every time you run it, but that makes it much easier to solve than most race conditions. From reviewing the source code for threading.Event, waiting threads are only notified when the set() function is called, not when the clear() function is called. The design issue can be a bit trickier in some languages. The Producer-Consumer Problem is a standard computer science problem used to look at threading or process synchronization issues. Ph.D. / Golden Gate Ave, San Francisco / Seoul National Univ / Carnegie Mellon / UC Berkeley / DevOps / Deep Learning / Visualization. The multiprocessing.Pool class provides easy-to-use process-based concurrency. Note: Your output will be different. Making sure the queue is empty before the consumer finishes prevents another fun issue. Since each thread runs .update(), and .update() adds one to .value, you might expect database.value to be 2 when its printed out at the end.

The scheduling of threads is done by the operating system and does not follow a plan thats easy to figure out. Not only does it loop until the event is set, but it also needs to keep looping until the pipeline has been emptied. Every Python program has at least one thread of execution called the main thread. He has worked on embedded systems, built distributed build systems, done off-shore vendor management, and sat in many, many meetings. It does a LOAD_FAST of the data value x, it does a LOAD_CONST 1, and then it uses the INPLACE_ADD to add those values together.

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. When the producer attempts to send this second message, it will call .set_message() the second time and it will block. You can see .value in Thread 1 getting set to one. This is the call that will make the consumer wait until a message is ready. When the producer gets a burst of messages, it will have nowhere to put them. The event can be set via the set() function. Youve also seen a few instances of the problems that arise when writing and debugging threaded programs. If youve got some experience in Python and want to speed up your program using threads, then this tutorial is for you!

It means that all variables that are scoped (or local) to a function are thread-safe. 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. As soon as the consumer calls .producer_lock.release(), it can be swapped out, and the producer can start running. If you look at the source for Python threading, youll see that threading._shutdown() walks through all of the running threads and calls .join() on every one that does not have the daemon flag set.

If you run this version with logging set to warning level, youll see this: Look at that. It was swapped out by the OS. """, Consumer storing message: 32 (queue size=3), Consumer storing message: 51 (queue size=3), Consumer storing message: 25 (queue size=3), Consumer storing message: 94 (queue size=6), Consumer storing message: 20 (queue size=6), Consumer storing message: 31 (queue size=6), Consumer storing message: 98 (queue size=6), Consumer storing message: 59 (queue size=6), Consumer storing message: 75 (queue size=6), Consumer storing message: 97 (queue size=5), Consumer storing message: 80 (queue size=4), Consumer storing message: 33 (queue size=3), Consumer storing message: 48 (queue size=2), Consumer storing message: 52 (queue size=1), Consumer storing message: 13 (queue size=0), Speed Up Your Python Program With Concurrency, Async IO in Python: A Complete Walkthrough, get answers to common questions in our support portal, How to create threads and wait for them to finish, A design issue where a utility function needs to be called by functions that might or might not already have the. Its 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. Your FakeDatabase will have .__init__() and .update() methods: FakeDatabase is keeping track of a single number: .value. Note that database is a reference to the one FakeDatabase object created in __main__. How are you going to put your newfound skills to use? The REPL below shows a function that takes a parameter and increments it: The REPL example uses dis from the Python standard library to show the smaller steps that the processor does to implement your function. The database access is slow, but fast enough to keep up to the average pace of messages. Related Tutorial Categories: Python provides the ability to create and manage new threads via the threading module and the threading.Thread class. This means that there is a slight possibility that when the function returns self.message, that could actually be the next message generated, so you would lose the first message. The producer also did not have to change too much: It now will loop until it sees that the event was set on line 3. You wont look at all of them here, but there are a couple that are used frequently. The first one is that the counting is atomic. The consumer has already exited, so this will not happen and the producer will not exit. Once triggered, the thread will generate a random number, block for a moment and report a message.