Getting Started With Async Features in Python (original) (raw)

Have you heard of asynchronous programming in Python? Are you curious to know more about Python async features and how you can use them in your work? Perhaps you’ve even tried to write threaded programs and run into some issues. If you’re looking to understand how to use Python async features, then you’ve come to the right place.

In this article, you’ll learn:

All of the example code in this article have been tested with Python 3.7.2. You can grab a copy to follow along by clicking the link below:

Take the Quiz: Test your knowledge with our interactive “Getting Started With Async Features in Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Getting Started With Async Features in Python

Interactive Quiz

Getting Started With Async Features in Python

In this quiz, you'll test your understanding of asynchronous programming in Python. You'll revisit the concepts of synchronous and asynchronous programs, and why you might want to write an asynchronous program. You'll also test your knowledge on how to use Python async features.

Understanding Asynchronous Programming

A synchronous program is executed one step at a time. Even with conditional branching, loops and function calls, you can still think about the code in terms of taking one execution step at a time. When each step is complete, the program moves on to the next one.

Here are two examples of programs that work this way:

An asynchronous program behaves differently. It still takes one execution step at a time. The difference is that the system may not wait for an execution step to be completed before moving on to the next one.

This means that the program will move on to future execution steps even though a previous step hasn’t yet finished and is still running elsewhere. This also means that the program knows what to do when a previous step does finish running.

Why would you want to write a program in this manner? The rest of this article will help you answer that question and give you the tools you need to elegantly solve interesting asynchronous problems.

Building a Synchronous Web Server

A web server’s basic unit of work is, more or less, the same as batch processing. The server will get some input, process it, and create the output. Written as a synchronous program, this would create a working web server.

It would also be an absolutely terrible web server.

Why? In this case, one unit of work (input, process, output) is not the only purpose. The real purpose is to handle hundreds or even thousands of units of work as quickly as possible. This can happen over long periods of time, and several work units may even arrive all at once.

Can a synchronous web server be made better? Sure, you could optimize the execution steps so that all the work coming in is handled as quickly as possible. Unfortunately, there are limitations to this approach. The result could be a web server that doesn’t respond fast enough, can’t handle enough work, or even one that times out when work gets stacked up.

In a synchronous program, if an execution step starts a database query, then the CPU is essentially idle until the database query is returned. For batch-oriented programs, this isn’t a priority most of the time. Processing the results of that IO operation is the goal. Often, this can take longer than the IO operation itself. Any optimization efforts would be focused on the processing work, not the IO.

Asynchronous programming techniques allow your programs to take advantage of relatively slow IO processes by freeing the CPU to do other work.

Thinking Differently About Programming

When you start trying to understand asynchronous programming, you might see a lot of discussion about the importance of blocking, or writing non-blocking code. (Personally, I struggled to get a good grasp of these concepts from the people I asked and the documentation I read.)

What is non-blocking code? What’s blocking code, for that matter? Would the answers to these questions help you write a better web server? If so, how could you do it? Let’s find out!

Writing asynchronous programs requires that you think differently about programming. While this new way of thinking can be hard to wrap your head around, it’s also an interesting exercise. That’s because the real world is almost entirely asynchronous, and so is how you interact with it.

Imagine this: you’re a parent trying to do several things at once. You have to balance the checkbook, do the laundry, and keep an eye on the kids. Somehow, you’re able to do all of these things at the same time without even thinking about it! Let’s break it down:

These examples can help to illustrate the concepts of blocking and non-blocking code. Let’s think about this in programming terms. In this example, you’re like the CPU. While you’re moving the laundry around, you (the CPU) are busy and blocked from doing other work, like balancing the checkbook. But that’s okay because the task is relatively quick.

On the other hand, starting the washer and dryer does not block you from performing other tasks. It’s an asynchronous function because you don’t have to wait for it to finish. Once it’s started, you can go back to something else. This is called a context switch: the context of what you’re doing has changed, and the machine’s buzzer will notify you sometime in the future when the laundry task is complete.

As a human, this is how you work all the time. You naturally juggle multiple things at once, often without thinking about it. As a developer, the trick is how to translate this kind of behavior into code that does the same kind of thing.

Programming Parents: Not as Easy as It Looks

If you recognize yourself (or your parents) in the example above, then that’s great! You’ve got a leg up in understanding asynchronous programming. Again, you’re able to switch contexts between competing tasks fairly easily, picking up some tasks and resuming others. Now you’re going to try and program this behavior into virtual parents!

Thought Experiment #1: The Synchronous Parent

How would you create a parent program to do the above tasks in a completely synchronous manner? Since watching the kids is a high-priority task, perhaps your program would do just that. The parent watches over the kids while waiting for something to happen that might need their attention. However, nothing else (like the checkbook or laundry) would get done in this scenario.

Now, you can re-prioritize the tasks any way you want, but only one of them would happen at any given time. This is the result of a synchronous, step-by-step approach. Like the synchronous web server described above, this would work, but it might not be the best way to live. The parent wouldn’t be able to complete any other tasks until the kids fell asleep. All other tasks would happen afterward, well into the night. (A couple of weeks of this and many real parents might jump out the window!)

Thought Experiment #2: The Polling Parent

If you used polling, then you could change things up so that multiple tasks are completed. In this approach, the parent would periodically break away from the current task and check to see if any other tasks need attention.

Let’s make the polling interval something like fifteen minutes. Now, every fifteen minutes your parent checks to see if the washer, dryer or kids need any attention. If not, then the parent can go back to work on the checkbook. However, if any of those tasks do need attention, then the parent will take care of it before going back to the checkbook. This cycle continues on until the next timeout out of the polling loop.

This approach works as well since multiple tasks are getting attention. However, there are a couple of problems:

  1. The parent may spend a lot of time checking on things that don’t need attention: The washer and dryer haven’t yet finished, and the kids don’t need any attention unless something unexpected happens.
  2. The parent may miss completed tasks that do need attention: For instance, if the washer finished its cycle at the beginning of the polling interval, then it wouldn’t get any attention for up to fifteen minutes! What’s more, watching the kids is supposedly the highest priority task. They couldn’t tolerate fifteen minutes with no attention when something might be going drastically wrong.

You could address these issues by shortening the polling interval, but now your parent (the CPU) would be spending more time context switching between tasks. This is when you start to hit a point of diminishing returns. (Once again, a couple of weeks living like this and, well… See the previous comment about windows and jumping.)

Thought Experiment #3: The Threading Parent

“If I could only clone myself…” If you’re a parent, then you’ve probably had similar thoughts! Since you’re programming virtual parents, you can essentially do this by using threading. This is a mechanism that allows multiple sections of one program to run at the same time. Each section of code that runs independently is known as a thread, and all threads share the same memory space.

If you think of each task as a part of one program, then you can separate them and run them as threads. In other words, you can “clone” the parent, creating one instance for each task: watching the kids, monitoring the washer, monitoring the dryer, and balancing the checkbook. All of these “clones” are running independently.

This sounds like a pretty nice solution, but there are some issues here as well. One is that you’ll have to explicitly tell each parent instance what to do in your program. This can lead to some problems since all instances share everything in the program space.

For example, say that Parent A is monitoring the dryer. Parent A sees that the clothes are dry, so they take control of the dryer and begin unloading the clothes. At the same time, Parent B sees that the washer is done, so they take control of the washer and begin removing clothes. However, Parent B also needs to take control of the dryer so they can put the wet clothes inside. This can’t happen, because Parent A currently has control of the dryer.

After a short while, Parent A has finished unloading clothes. Now they want to take control of the washer and start moving clothes into the empty dryer. This can’t happen, either, because Parent B currently has control of the washer!

These two parents are now deadlocked. Both have control of their own resource and want control of the other resource. They’ll wait forever for the other parent instance to release control. As the programmer, you’d have to write code to work this situation out.

Here’s another issue that might arise from threading. Suppose that a child gets hurt and needs to be taken to urgent care. Parent C has been assigned the task of watching over the kids, so they take the child right away. At the urgent care, Parent C needs to write a fairly large check to cover the cost of seeing the doctor.

Meanwhile, Parent D is at home working on the checkbook. They’re unaware of this large check being written, so they’re very surprised when the family checking account is suddenly overdrawn!

Remember, these two parent instances are working within the same program. The family checking account is a shared resource, so you’d have to work out a way for the child-watching parent to inform the checkbook-balancing parent. Otherwise, you’d need to provide some kind of locking mechanism so that the checkbook resource can only be used by one parent at a time, with updates.

Using Python Async Features in Practice

Now you’re going to take some of the approaches outlined in the thought experiments above and turn them into functioning Python programs.

All of the examples in this article have been tested with Python 3.7.2. The requirements.txt file indicates which modules you’ll need to install to run all the examples. If you haven’t yet downloaded the file, you can do so now:

You also might want to set up a Python virtual environment to run the code so you don’t interfere with your system Python.

Synchronous Programming

This first example shows a somewhat contrived way of having a task retrieve work from a queue and process that work. A queue in Python is a nice FIFO (first in first out) data structure. It provides methods to put things in a queue and take them out again in the order they were inserted.

In this case, the work is to get a number from the queue and have a loop count up to that number. It prints to the console when the loop begins, and again to output the total. This program demonstrates one way for multiple synchronous tasks to process the work in a queue.

The program named example_1.py in the repository is listed in full below:

Let’s take a look at what each line does:

The task in this program is just a function accepting a string and a queue as parameters. When executed, it looks for anything in the queue to process. If there is work to do, then it pulls values off the queue, starts a for loop to count up to that value, and outputs the total at the end. It continues getting work off the queue until there is nothing left and it exits.

When this program is run, it produces the output you see below:

This shows that Task One does all the work. The while loop that Task One hits within task() consumes all the work on the queue and processes it. When that loop exits, Task Two gets a chance to run. However, it finds that the queue is empty, so Task Two prints a statement that says it has nothing to do and then exits. There’s nothing in the code to allow both Task One and Task Two to switch contexts and work together.

Simple Cooperative Concurrency

The next version of the program allows the two tasks to work together. Adding a yield statement means the loop will yield control at the specified point while still maintaining its context. This way, the yielding task can be restarted later.

The yield statement turns task() into a generator. A generator function is called just like any other function in Python, but when the yield statement is executed, control is returned to the caller of the function. This is essentially a context switch, as control moves from the generator function to the caller.

The interesting part is that control can be given back to the generator function by calling next() on the generator. This is a context switch back to the generator function, which picks up execution with all function variables that were defined before the yield still intact.

The while loop in main() takes advantage of this when it calls next(t). This statement restarts the task at the point where it previously yielded. All of this means that you’re in control when the context switch happens: when the yield statement is executed in task().

This is a form of cooperative multitasking. The program is yielding control of its current context so that something else can run. In this case, it allows the while loop in main() to run two instances of task() as a generator function. Each instance consumes work from the same queue. This is sort of clever, but it’s also a lot of work to get the same results as the first program. The program example_2.py demonstrates this simple concurrency and is listed below:

Here’s what’s happening in the code above:

This is the output produced when you run this program:

You can see that both Task One and Task Two are running and consuming work from the queue. This is what’s intended, as both tasks are processing work, and each is responsible for two items in the queue. This is interesting, but again, it takes quite a bit of work to achieve these results.

The trick here is using the yield statement, which turns task() into a generator and performs a context switch. The program uses this context switch to give control to the while loop in main(), allowing two instances of a task to run cooperatively.

Notice how Task Two outputs its total first. This might lead you to think that the tasks are running asynchronously. However, this is still a synchronous program. It’s structured so the two tasks can trade contexts back and forth. The reason why Task Two outputs its total first is that it’s only counting to 10, while Task One is counting to 15. Task Two simply arrives at its total first, so it gets to print its output to the console before Task One.

Cooperative Concurrency With Blocking Calls

The next version of the program is the same as the last, except for the addition of a time.sleep(delay) in the body of your task loop. This adds a delay based on the value retrieved from the work queue to every iteration of the task loop. The delay simulates the effect of a blocking call occurring in your task.

A blocking call is code that stops the CPU from doing anything else for some period of time. In the thought experiments above, if a parent wasn’t able to break away from balancing the checkbook until it was complete, that would be a blocking call.

time.sleep(delay) does the same thing in this example, because the CPU can’t do anything else but wait for the delay to expire.

Here’s what’s different in the code above:

When you run this program, you’ll see the following output:

As before, both Task One and Task Two are running, consuming work from the queue and processing it. However, even with the addition of the delay, you can see that cooperative concurrency hasn’t gotten you anything. The delay stops the processing of the entire program, and the CPU just waits for the IO delay to be over.

This is exactly what’s meant by blocking code in Python async documentation. You’ll notice that the time it takes to run the entire program is just the cumulative time of all the delays. Running tasks this way is not a win.

Cooperative Concurrency With Non-Blocking Calls

The next version of the program has been modified quite a bit. It makes use of Python async features using asyncio/await provided in Python 3.

The time and queue modules have been replaced with the asyncio package. This gives your program access to asynchronous friendly (non-blocking) sleep and queue functionality. The change to task() defines it as asynchronous with the addition of the async prefix on line 4. This indicates to Python that the function will be asynchronous.

The other big change is removing the time.sleep(delay) and yield statements, and replacing them with await asyncio.sleep(delay). This creates a non-blocking delay that will perform a context switch back to the caller main().

The while loop inside main() no longer exists. Instead of task_array, there’s a call to await asyncio.gather(...). This tells asyncio two things:

  1. Create two tasks based on task() and start running them.
  2. Wait for both of these to be completed before moving forward.

The last line of the program asyncio.run(main()) runs main(). This creates what’s known as an event loop). It’s this loop that will run main(), which in turn will run the two instances of task().

The event loop is at the heart of the Python async system. It runs all the code, including main(). When task code is executing, the CPU is busy doing work. When the await keyword is reached, a context switch occurs, and control passes back to the event loop. The event loop looks at all the tasks waiting for an event (in this case, an asyncio.sleep(delay) timeout) and passes control to a task with an event that’s ready.

await asyncio.sleep(delay) is non-blocking in regards to the CPU. Instead of waiting for the delay to timeout, the CPU registers a sleep event on the event loop task queue and performs a context switch by passing control to the event loop. The event loop continuously looks for completed events and passes control back to the task waiting for that event. In this way, the CPU can stay busy if work is available, while the event loop monitors the events that will happen in the future.

The example_4.py code is listed below:

Here’s what’s different between this program and example_3.py:

When you look at the output of this program, notice how both Task One and Task Two start at the same time, then wait at the mock IO call:

This indicates that await asyncio.sleep(delay) is non-blocking, and that other work is being done.

At the end of the program, you’ll notice the total elapsed time is essentially half the time it took for example_3.py to run. That’s the advantage of a program that uses Python async features! Each task was able to run await asyncio.sleep(delay) at the same time. The total execution time of the program is now less than the sum of its parts. You’ve broken away from the synchronous model!

Synchronous (Blocking) HTTP Calls

The next version of the program is kind of a step forward as well as a step back. The program is doing some actual work with real IO by making HTTP requests to a list of URLs and getting the page contents. However, it’s doing so in a blocking (synchronous) manner.

The program has been modified to import the wonderful requests module to make the actual HTTP requests. Also, the queue now contains a list of URLs, rather than numbers. In addition, task() no longer increments a counter. Instead, requests gets the contents of a URL retrieved from the queue, and prints how long it took to do so.

The example_5.py code is listed below:

Here’s what’s happening in this program:

When you run this program, you’ll see the following output:

Just like in earlier versions of the program, yield turns task() into a generator. It also performs a context switch that lets the other task instance run.

Each task gets a URL from the work queue, retrieves the contents of the page, and reports how long it took to get that content.

As before, yield allows both your tasks to run cooperatively. However, since this program is running synchronously, each session.get() call blocks the CPU until the page is retrieved. Note the total time it took to run the entire program at the end. This will be meaningful for the next example.

Asynchronous (Non-Blocking) HTTP Calls

This version of the program modifies the previous one to use Python async features. It also imports the aiohttp module, which is a library to make HTTP requests in an asynchronous fashion using asyncio.

The tasks here have been modified to remove the yield call since the code to make the HTTP GET call is no longer blocking. It also performs a context switch back to the event loop.

The example_6.py program is listed below:

Here’s what’s happening in this program:

When you run this program, you’ll see the following output:

Take a look at the total elapsed time, as well as the individual times to get the contents of each URL. You’ll see that the duration is about half the cumulative time of all the HTTP GET calls. This is because the HTTP GET calls are running asynchronously. In other words, you’re effectively taking better advantage of the CPU by allowing it to make multiple requests at once.

Because the CPU is so fast, this example could likely create as many tasks as there are URLs. In this case, the program’s run time would be that of the single slowest URL retrieval.

Conclusion

This article has given you the tools you need to start making asynchronous programming techniques a part of your repertoire. Using Python async features gives you programmatic control of when context switches take place. This means that many of the tougher issues you might see in threaded programming are easier to deal with.

Asynchronous programming is a powerful tool, but it isn’t useful for every kind of program. If you’re writing a program that calculates pi to the millionth decimal place, for instance, then asynchronous code won’t help you. That kind of program is CPU bound, without much IO. However, if you’re trying to implement a server or a program that performs IO (like file or network access), then using Python async features could make a huge difference.

To sum it up, you’ve learned:

You can get the code for all of the example programs used in this tutorial:

Now that you’re equipped with these powerful skills, you can take your programs to the next level!

Take the Quiz: Test your knowledge with our interactive “Getting Started With Async Features in Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Getting Started With Async Features in Python

Interactive Quiz

Getting Started With Async Features in Python

In this quiz, you'll test your understanding of asynchronous programming in Python. You'll revisit the concepts of synchronous and asynchronous programs, and why you might want to write an asynchronous program. You'll also test your knowledge on how to use Python async features.