Concurrency and async / await (2023)

Details about the async def syntax for path operation functions and some background about asynchronous code, concurrency, and parallelism.

In a hurry?

TL;DR:

If you are using third party libraries that tell you to call them with await, like:

results = await some_library()

Then, declare your path operation functions with async def like:

@app.get('/')async def read_results(): results = await some_library() return results

Note

You can only use await inside of functions created with async def.

If you are using a third party library that communicates with something (a database, an API, the file system, etc.) and doesn't have support for using await, (this is currently the case for most database libraries), then declare your path operation functions as normally, with just def, like:

@app.get('/')def results(): results = some_library() return results

If your application (somehow) doesn't have to communicate with anything else and wait for it to respond, use async def.

If you just don't know, use normal def.

Note: You can mix def and async def in your path operation functions as much as you need and define each one using the best option for you. FastAPI will do the right thing with them.

Anyway, in any of the cases above, FastAPI will still work asynchronously and be extremely fast.

But by following the steps above, it will be able to do some performance optimizations.

Technical Details

Modern versions of Python have support for "asynchronous code" using something called "coroutines", with async and await syntax.

Let's see that phrase by parts in the sections below:

  • Asynchronous Code
  • async and await
  • Coroutines

Asynchronous Code

Asynchronous code just means that the language 💬 has a way to tell the computer / program 🤖 that at some point in the code, it 🤖 will have to wait for something else to finish somewhere else. Let's say that something else is called "slow-file" 📝.

So, during that time, the computer can go and do some other work, while "slow-file" 📝 finishes.

Then the computer / program 🤖 will come back every time it has a chance because it's waiting again, or whenever it 🤖 finished all the work it had at that point. And it 🤖 will see if any of the tasks it was waiting for have already finished, doing whatever it had to do.

Next, it 🤖 takes the first task to finish (let's say, our "slow-file" 📝) and continues whatever it had to do with it.

That "wait for something else" normally refers to I/O operations that are relatively "slow" (compared to the speed of the processor and the RAM memory), like waiting for:

  • the data from the client to be sent through the network
  • the data sent by your program to be received by the client through the network
  • the contents of a file in the disk to be read by the system and given to your program
  • the contents your program gave to the system to be written to disk
  • a remote API operation
  • a database operation to finish
  • a database query to return the results
  • etc.

As the execution time is consumed mostly by waiting for I/O operations, they call them "I/O bound" operations.

It's called "asynchronous" because the computer / program doesn't have to be "synchronized" with the slow task, waiting for the exact moment that the task finishes, while doing nothing, to be able to take the task result and continue the work.

Instead of that, by being an "asynchronous" system, once finished, the task can wait in line a little bit (some microseconds) for the computer / program to finish whatever it went to do, and then come back to take the results and continue working with them.

For "synchronous" (contrary to "asynchronous") they commonly also use the term "sequential", because the computer / program follows all the steps in sequence before switching to a different task, even if those steps involve waiting.

Concurrency and Burgers

This idea of asynchronous code described above is also sometimes called "concurrency". It is different from "parallelism".

Concurrency and parallelism both relate to "different things happening more or less at the same time".

But the details between concurrency and parallelism are quite different.

To see the difference, imagine the following story about burgers:

Concurrent Burgers

You go with your crush to get fast food, you stand in line while the cashier takes the orders from the people in front of you. 😍

Concurrency and async / await (1)

(Video) Learn Swift Concurrency online for FREE, Async Await, Actors, Continuations And More

Then it's your turn, you place your order of 2 very fancy burgers for your crush and you. 🍔🍔

Concurrency and async / await (2)

The cashier says something to the cook in the kitchen so they know they have to prepare your burgers (even though they are currently preparing the ones for the previous clients).

Concurrency and async / await (3)

You pay. 💸

The cashier gives you the number of your turn.

Concurrency and async / await (4)

While you are waiting, you go with your crush and pick a table, you sit and talk with your crush for a long time (as your burgers are very fancy and take some time to prepare).

As you are sitting at the table with your crush, while you wait for the burgers, you can spend that time admiring how awesome, cute and smart your crush is ✨😍✨.

Concurrency and async / await (5)

While waiting and talking to your crush, from time to time, you check the number displayed on the counter to see if it's your turn already.

Then at some point, it finally is your turn. You go to the counter, get your burgers and come back to the table.

Concurrency and async / await (6)

You and your crush eat the burgers and have a nice time. ✨

Concurrency and async / await (7)

Info

Beautiful illustrations by Ketrina Thompson. 🎨

Imagine you are the computer / program 🤖 in that story.

While you are at the line, you are just idle 😴, waiting for your turn, not doing anything very "productive". But the line is fast because the cashier is only taking the orders (not preparing them), so that's fine.

Then, when it's your turn, you do actual "productive" work, you process the menu, decide what you want, get your crush's choice, pay, check that you give the correct bill or card, check that you are charged correctly, check that the order has the correct items, etc.

But then, even though you still don't have your burgers, your work with the cashier is "on pause" ⏸, because you have to wait 🕙 for your burgers to be ready.

But as you go away from the counter and sit at the table with a number for your turn, you can switch 🔀 your attention to your crush, and "work" ⏯ 🤓 on that. Then you are again doing something very "productive" as is flirting with your crush 😍.

Then the cashier 💁 says "I'm finished with doing the burgers" by putting your number on the counter's display, but you don't jump like crazy immediately when the displayed number changes to your turn number. You know no one will steal your burgers because you have the number of your turn, and they have theirs.

So you wait for your crush to finish the story (finish the current work ⏯ / task being processed 🤓), smile gently and say that you are going for the burgers ⏸.

Then you go to the counter 🔀, to the initial task that is now finished ⏯, pick the burgers, say thanks and take them to the table. That finishes that step / task of interaction with the counter ⏹. That in turn, creates a new task, of "eating burgers" 🔀 ⏯, but the previous one of "getting burgers" is finished ⏹.

Parallel Burgers

Now let's imagine these aren't "Concurrent Burgers", but "Parallel Burgers".

You go with your crush to get parallel fast food.

You stand in line while several (let's say 8) cashiers that at the same time are cooks take the orders from the people in front of you.

Everyone before you is waiting for their burgers to be ready before leaving the counter because each of the 8 cashiers goes and prepares the burger right away before getting the next order.

Concurrency and async / await (8)

Then it's finally your turn, you place your order of 2 very fancy burgers for your crush and you.

You pay 💸.

(Video) Async/Await: Modern Concurrency In JavaScript

Concurrency and async / await (9)

The cashier goes to the kitchen.

You wait, standing in front of the counter 🕙, so that no one else takes your burgers before you do, as there are no numbers for turns.

Concurrency and async / await (10)

As you and your crush are busy not letting anyone get in front of you and take your burgers whenever they arrive, you cannot pay attention to your crush. 😞

This is "synchronous" work, you are "synchronized" with the cashier/cook 👨‍🍳. You have to wait 🕙 and be there at the exact moment that the cashier/cook 👨‍🍳 finishes the burgers and gives them to you, or otherwise, someone else might take them.

Concurrency and async / await (11)

Then your cashier/cook 👨‍🍳 finally comes back with your burgers, after a long time waiting 🕙 there in front of the counter.

Concurrency and async / await (12)

You take your burgers and go to the table with your crush.

You just eat them, and you are done. ⏹

Concurrency and async / await (13)

There was not much talk or flirting as most of the time was spent waiting 🕙 in front of the counter. 😞

In this scenario of the parallel burgers, you are a computer / program 🤖 with two processors (you and your crush), both waiting 🕙 and dedicating their attention ⏯ to be "waiting on the counter" 🕙 for a long time.

The fast food store has 8 processors (cashiers/cooks). While the concurrent burgers store might have had only 2 (one cashier and one cook).

But still, the final experience is not the best. 😞

This would be the parallel equivalent story for burgers. 🍔

For a more "real life" example of this, imagine a bank.

Up to recently, most of the banks had multiple cashiers 👨‍💼👨‍💼👨‍💼👨‍💼 and a big line 🕙🕙🕙🕙🕙🕙🕙🕙.

All of the cashiers doing all the work with one client after the other 👨‍💼⏯.

And you have to wait 🕙 in the line for a long time or you lose your turn.

You probably wouldn't want to take your crush 😍 with you to do errands at the bank 🏦.

Burger Conclusion

In this scenario of "fast food burgers with your crush", as there is a lot of waiting 🕙, it makes a lot more sense to have a concurrent system ⏸🔀⏯.

This is the case for most of the web applications.

Many, many users, but your server is waiting 🕙 for their not-so-good connection to send their requests.

And then waiting 🕙 again for the responses to come back.

This "waiting" 🕙 is measured in microseconds, but still, summing it all, it's a lot of waiting in the end.

That's why it makes a lot of sense to use asynchronous ⏸🔀⏯ code for web APIs.

This kind of asynchronicity is what made NodeJS popular (even though NodeJS is not parallel) and that's the strength of Go as a programming language.

And that's the same level of performance you get with FastAPI.

(Video) Concurrency using Actors | Swift 5.5 | Async/Await | Data Race

And as you can have parallelism and asynchronicity at the same time, you get higher performance than most of the tested NodeJS frameworks and on par with Go, which is a compiled language closer to C (all thanks to Starlette).

Is concurrency better than parallelism?

Nope! That's not the moral of the story.

Concurrency is different than parallelism. And it is better on specific scenarios that involve a lot of waiting. Because of that, it generally is a lot better than parallelism for web application development. But not for everything.

So, to balance that out, imagine the following short story:

You have to clean a big, dirty house.

Yep, that's the whole story.

There's no waiting 🕙 anywhere, just a lot of work to be done, on multiple places of the house.

You could have turns as in the burgers example, first the living room, then the kitchen, but as you are not waiting 🕙 for anything, just cleaning and cleaning, the turns wouldn't affect anything.

It would take the same amount of time to finish with or without turns (concurrency) and you would have done the same amount of work.

But in this case, if you could bring the 8 ex-cashier/cooks/now-cleaners, and each one of them (plus you) could take a zone of the house to clean it, you could do all the work in parallel, with the extra help, and finish much sooner.

In this scenario, each one of the cleaners (including you) would be a processor, doing their part of the job.

And as most of the execution time is taken by actual work (instead of waiting), and the work in a computer is done by a CPU, they call these problems "CPU bound".

Common examples of CPU bound operations are things that require complex math processing.

For example:

  • Audio or image processing.
  • Computer vision: an image is composed of millions of pixels, each pixel has 3 values / colors, processing that normally requires computing something on those pixels, all at the same time.
  • Machine Learning: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.
  • Deep Learning: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

Concurrency + Parallelism: Web + Machine Learning

With FastAPI you can take the advantage of concurrency that is very common for web development (the same main attractive of NodeJS).

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for CPU bound workloads like those in Machine Learning systems.

That, plus the simple fact that Python is the main language for Data Science, Machine Learning and especially Deep Learning, make FastAPI a very good match for Data Science / Machine Learning web APIs and applications (among many others).

To see how to achieve this parallelism in production see the section about Deployment.

async and await

Modern versions of Python have a very intuitive way to define asynchronous code. This makes it look just like normal "sequential" code and do the "awaiting" for you at the right moments.

When there is an operation that will require waiting before giving the results and has support for these new Python features, you can code it like:

burgers = await get_burgers(2)

The key here is the await. It tells Python that it has to wait ⏸ for get_burgers(2) to finish doing its thing 🕙 before storing the results in burgers. With that, Python will know that it can go and do something else 🔀 ⏯ in the meanwhile (like receiving another request).

For await to work, it has to be inside a function that supports this asynchronicity. To do that, you just declare it with async def:

...instead of def:

# This is not asynchronousdef get_sequential_burgers(number: int): # Do some sequential stuff to create the burgers return burgers

With async def, Python knows that, inside that function, it has to be aware of await expressions, and that it can "pause" ⏸ the execution of that function and go do something else 🔀 before coming back.

When you want to call an async def function, you have to "await" it. So, this won't work:

# This won't work, because get_burgers was defined with: async defburgers = get_burgers(2)

So, if you are using a library that tells you that you can call it with await, you need to create the path operation functions that uses it with async def, like in:

@app.get('/burgers')async def read_burgers(): burgers = await get_burgers(2) return burgers

More technical details

You might have noticed that await can only be used inside of functions defined with async def.

But at the same time, functions defined with async def have to be "awaited". So, functions with async def can only be called inside of functions defined with async def too.

(Video) Async Await Concurrency with Examples

So, about the egg and the chicken, how do you call the first async function?

If you are working with FastAPI you don't have to worry about that, because that "first" function will be your path operation function, and FastAPI will know how to do the right thing.

But if you want to use async / await without FastAPI, you can do it as well.

Write your own async code

Starlette (and FastAPI) are based on AnyIO, which makes it compatible with both Python's standard library asyncio and Trio.

In particular, you can directly use AnyIO for your advanced concurrency use cases that require more advanced patterns in your own code.

And even if you were not using FastAPI, you could also write your own async applications with AnyIO to be highly compatible and get its benefits (e.g. structured concurrency).

Other forms of asynchronous code

This style of using async and await is relatively new in the language.

But it makes working with asynchronous code a lot easier.

This same syntax (or almost identical) was also included recently in modern versions of JavaScript (in Browser and NodeJS).

But before that, handling asynchronous code was quite more complex and difficult.

In previous versions of Python, you could have used threads or Gevent. But the code is way more complex to understand, debug, and think about.

In previous versions of NodeJS / Browser JavaScript, you would have used "callbacks". Which leads to callback hell.

Coroutines

Coroutine is just the very fancy term for the thing returned by an async def function. Python knows that it is something like a function that it can start and that it will end at some point, but that it might be paused ⏸ internally too, whenever there is an await inside of it.

But all this functionality of using asynchronous code with async and await is many times summarized as using "coroutines". It is comparable to the main key feature of Go, the "Goroutines".

Conclusion

Let's see the same phrase from above:

Modern versions of Python have support for "asynchronous code" using something called "coroutines", with async and await syntax.

That should make more sense now. ✨

All that is what powers FastAPI (through Starlette) and what makes it have such an impressive performance.

Very Technical Details

Warning

You can probably skip this.

These are very technical details of how FastAPI works underneath.

If you have quite some technical knowledge (co-routines, threads, blocking, etc.) and are curious about how FastAPI handles async def vs normal def, go ahead.

Path operation functions

When you declare a path operation function with normal def instead of async def, it is run in an external threadpool that is then awaited, instead of being called directly (as it would block the server).

If you are coming from another async framework that does not work in the way described above and you are used to defining trivial compute-only path operation functions with plain def for a tiny performance gain (about 100 nanoseconds), please note that in FastAPI the effect would be quite opposite. In these cases, it's better to use async def unless your path operation functions use code that performs blocking I/O.

Still, in both situations, chances are that FastAPI will still be faster than (or at least comparable to) your previous framework.

Dependencies

The same applies for dependencies. If a dependency is a standard def function instead of async def, it is run in the external threadpool.

Sub-dependencies

You can have multiple dependencies and sub-dependencies requiring each other (as parameters of the function definitions), some of them might be created with async def and some with normal def. It would still work, and the ones created with normal def would be called on an external thread (from the threadpool) instead of being "awaited".

Other utility functions

Any other utility function that you call directly can be created with normal def or async def and FastAPI won't affect the way you call it.

This is in contrast to the functions that FastAPI calls for you: path operation functions and dependencies.

If your utility function is a normal function with def, it will be called directly (as you write it in your code), not in a threadpool, if the function is created with async def then you should await for that function when you call it in your code.

Again, these are very technical details that would probably be useful if you came searching for them.

Otherwise, you should be good with the guidelines from the section above: In a hurry?.

(Video) How to use async / await keywords in Swift | Swift Concurrency #3

FAQs

Is async await concurrent? ›

It is concurrent, in the sense that many outstanding asychronous operations may be in progress at any time. It may or may not be multithreaded. By default, await will schedule the continuation back to the "current execution context".

Does async await improve performance? ›

The main benefits of asynchronous programming using async / await include the following: Increase the performance and responsiveness of your application, particularly when you have long-running operations that do not require to block the execution.

Is async await better than promises? ›

Using Async/Await makes it easier to read and understand the flow of the program as compared to promise chains.

Is used to write concurrent code using the async await? ›

Writing async code may be considered the first step for writing concurrent code. Since async/await is just a pattern, it doesn't make javascript any more concurrent or asynchronous than callbacks did. But it does make it exponentially easier to read it.

Is async await concurrent or parallel? ›

Concurrency is having two tasks run in parallel on separate threads. However, asynchronous methods run in parallel but on the same 1 thread.

Is concurrency same as synchronization? ›

The word synchronization generally means sharing data between multiple processors or threads, while concurrency refers to a measure of– or the art of improving– how effectively an application allows multiple jobs required by that application (e.g. serving web page requests from a web server) to run simultaneously.

Is multithreading faster than async? ›

So why asyncio is faster than multi-threading if they both belong to asynchronous programming? It's because asyncio is more robust with task scheduling and provides the user with full control of code execution.

Why async is faster than sync? ›

Async is non-blocking, which means it will send multiple requests to a server. Sync is blocking — it will only send the server one request at a time and will wait for that request to be answered by the server. Async increases throughput because multiple operations can run at the same time.

Is async better than threading? ›

Asyncio vs threading: Async runs one block of code at a time while threading just one line of code at a time. With async, we have better control of when the execution is given to other block of code but we have to release the execution ourselves.

Can we use async await without promise? ›

This rule applies when the await operator is used on a non-Promise value. await operator pauses the execution of the current async function until the operand Promise is resolved.

Why use async await instead of then? ›

Async/await and then() are very similar. The difference is that in an async function, JavaScript will pause the function execution until the promise settles. With then() , the rest of the function will continue to execute but JavaScript won't execute the . then() callback until the promise settles.

Is async await slower? ›

I found out that running async-await can be much slower in some scenarios. But if I click on the 'both' button, the 'await' version is ~3-4 times slower than the promises version.

Is async and await multithreading? ›

The async and await keywords don't cause additional threads to be created. Async methods don't require multithreading because an async method doesn't run on its own thread. The method runs on the current synchronization context and uses time on the thread only when the method is active. You can use Task.

Will async await replace combine? ›

Before moving forward, it's important to highlight that Async/Await is not meant to replace Combine. Thus, code that operate on streams should remain using the latter. This article will be focused on code that doesn't necessarily need such capabilities.

Does async await make it synchronous? ›

An async function runs synchronously until the first await keyword. This means that within an async function body, all synchronous code before the first await keyword executes immediately.

Why is concurrency better than parallelism? ›

Parallelism leads to overlapping of central processing units and input-output tasks in one process with the central processing unit and input-output tasks of another process. Whereas in concurrency the speed is increased by overlapping the input-output activities of one process with CPU process of another process.

Is concurrency the same as multithreading? ›

Concurrency is the ability of your program to deal (not doing) with many things at once and is achieved through multithreading.

Is multitasking same as concurrency? ›

Using multiple processors (which may or may not be within the same physical package), concurrency can be achieved without multitasking. In fact, without multiple processors, concurrency is illusory - the tasks aren't really executing at the same instant.

How does concurrency improve performance? ›

If you want to increase the performance of your program one possible solution is to add concurrent programming techniques. Basically, in concurrent execution, multiple threads of the same program executes at the same time. It is similar to adding more workers to complete a job.

Why do we need concurrency? ›

It enable to run multiple applications at the same time. It enables that the resources that are unused by one application can be used for other applications. Without concurrency, each application has to be run to completion before the next one can be run. It enables the better performance by the operating system.

How can concurrency issues be prevented? ›

The main way we can avoid such concurrency issues and build reliable code is to work with immutable objects. This is because their state cannot be modified by the interference of multiple threads. However, we can't always work with immutable objects.

Does async use more memory? ›

So, for the same number of threads being, it can handle more requests. But it does that at the cost of more control data structures and more work. So, (other than saving thread pool threads) it consumes more resources (memory, CPU).

Why coroutines are better than threads? ›

Coroutines keep the system utilization at a higher rate comparatively. Coroutines can achieve preemptive scheduling and require lesser resources than threads. Threads mostly minimize the context switching time and provide concurrency in the process.

Does multithreading improve latency? ›

Multithreading is a useful mechanism for reducing latency.

Is async like multithreading? ›

Asynchronous Programming vs Multithreading

It is a general misconception that both asynchronous programming and multithreading are the same although that's not true. Asynchronous programming is about the asynchronous sequence of Tasks, while multithreading is about multiple threads running in parallel.

Is REST API synchronous or asynchronous? ›

REST clients can be implemented either synchronously or asynchronously. Both MicroProfile Rest Client and JAX-RS can enable asynchronous clients. A synchronous client constructs an HTTP structure, sends a request, and waits for a response.

Does async wait for response? ›

Asynchronous requests will wait for a timer to finish or a request to respond while the rest of the code continues to execute. Then when the time is right a callback will spring these asynchronous requests into action.

Is async always faster? ›

This is just one more reason why async does not equal "faster". It can and often will be slower. The benefit of async is that it allows the web server to use its available resources more efficiently, maximizing time that otherwise would be spent idling to do additional work instead.

Can async be single threaded? ›

Yes, a single-thread program can be asynchronous. Making a method/ function async does not mean creating threads in the background. If you execute the above code snippet, look up the running threads at lines 13 and 14.

What happens if async method is called without await? ›

The call to the async method starts an asynchronous task. However, because no Await operator is applied, the program continues without waiting for the task to complete. In most cases, that behavior isn't expected.

What can I use instead of async await? ›

Async/await is a new way to write asynchronous code. Previous alternatives for asynchronous code are callbacks and promises.

Is promise better than callback? ›

What makes promises a way to avoid callback hell is that you can chain multiple . then() on each other which avoid nested callbacks and a neater line of code. For the failure of completing a task you can pass it through a . catch().

What is the advantage of async await advantages? ›

NET framework's async and await modifiers make it simple and easy to convert your code from synchronous programming to asynchronous programming. As a result, you can call an async method independently by passing it a task object, then do some unrelated work, and then await that task to see if it has already finished.

Is async faster than defer? ›

domInteractive). Comparing the ASYNC and DEFER waterfalls, we see that using DEFER makes DOM Interactive fire sooner and allows rendering to proceed more quickly. HOWEVER: Even though ASYNC and DEFER don't block the HTML parser, they can block rendering.

Why is async so slow? ›

The problem is that the async iteration API is relatively slow because it involves Promises which only resolve at the end of an event loop. Thus there is one event loop of overhead per character.

Does await run in parallel? ›

In order to run multiple async/await calls in parallel, all we need to do is add the calls to an array, and then pass that array as an argument to Promise. all() . Promise. all() will wait for all the provided async calls to be resolved before it carries on(see Conclusion for caveat).

Does async await block main thread? ›

Because await is only valid inside async functions and modules, which themselves are asynchronous and return promises, the await expression never blocks the main thread and only defers execution of code that actually depends on the result, i.e. anything after the await expression.

Are threads actually concurrent? ›

Concurrency and Parallelism

In the same multithreaded process in a shared-memory multiprocessor environment, each thread in the process can run concurrently on a separate processor, resulting in parallel execution, which is true simultaneous execution.

What happens if you await a promise twice? ›

The implication of this is that promises can be used to memoize async computations. If you consume a promise whose result will be needed again later: consider holding on to the promise instead of its result! It's fine to await a promise twice, if you're happy to yield twice.

Is combine better than RxSwift? ›

Combine Subjects are thread safe whereas RxSwift Subjects are not. In RxSwift you rarely need Subjects anyway, in Combine you need them constantly.

› JavaScript Tutorial ›

In this tutorial, you will learn a new syntax to write asynchronous code by using JavaScript async/ await keywords.

Async/await

https://en.wikipedia.org › wiki › Async › await
https://en.wikipedia.org › wiki › Async › await
In computer programming, the async/await pattern is a syntactic feature of many programming languages that allows an asynchronous, non-blocking function to be s...

async & await

https://davidwalsh.name › async-await
https://davidwalsh.name › async-await
The JavaScript async / await API provides a more maintainable method for handling promises. Learn how to use the async and await function keywords.

Is async await multithreaded? ›

The async and await keywords don't cause additional threads to be created. Async methods don't require multithreading because an async method doesn't run on its own thread. The method runs on the current synchronization context and uses time on the thread only when the method is active.

Is asynchronous code concurrent? ›

In async, you write code as tasks which are executed concurrently. You never know which tasks will be started first — it depends on the executing context, whether you run tasks in parallel or do a bit of one task then progress to another.

Is async await really asynchronous? ›

In computer programming, the async/await pattern is a syntactic feature of many programming languages that allows an asynchronous, non-blocking function to be structured in a way similar to an ordinary synchronous function.

Is promise all parallel or concurrent? ›

As you can see, Promise. all executes code concurrently, but what is parallel execution? JavaScript is single-threaded and can only perform a single chunk of work at a time, so parallel execution is not possible with JavaScript, except for some circumstances such as web workers.

Is multithreading and concurrency same? ›

A multi-threaded program will take advantage of additional threads — and cores — to distribute the load of the program more efficiently, as opposed to have one poor core do all the work while the others simply watch. The premise of concurrency is to run two or more different programs, kind of at the same time.

Why async is better than multithreading? ›

Multi threading means that your program runs parallel on multiple threads. Nothing more, nothing less. Async programming means in layterms that instead of blocking and wait for a call (most likely IO operations) you just register a callback and you will be notified when the underlying operation has finished.

Is async faster than multithreading? ›

So why asyncio is faster than multi-threading if they both belong to asynchronous programming? It's because asyncio is more robust with task scheduling and provides the user with full control of code execution.

Why is concurrency better than parallelism? ›

Parallelism leads to overlapping of central processing units and input-output tasks in one process with the central processing unit and input-output tasks of another process. Whereas in concurrency the speed is increased by overlapping the input-output activities of one process with CPU process of another process.

Is asynchronous code multithreaded? ›

Asynchronous Programming vs Multithreading

It is a general misconception that both asynchronous programming and multithreading are the same although that's not true. Asynchronous programming is about the asynchronous sequence of Tasks, while multithreading is about multiple threads running in parallel.

What happens if we execute an asynchronous method but do not await it? ›

If you forget to use await while calling an async function, the function starts executing. This means that await is not required for executing the function. The async function will return a promise, which you can use later.

Why async is faster than sync? ›

Async is non-blocking, which means it will send multiple requests to a server. Sync is blocking — it will only send the server one request at a time and will wait for that request to be answered by the server. Async increases throughput because multiple operations can run at the same time.

Can we use async await without promise? ›

This rule applies when the await operator is used on a non-Promise value. await operator pauses the execution of the current async function until the operand Promise is resolved.

Is async await slower than promises? ›

I found out that running async-await can be much slower in some scenarios. But if I click on the 'both' button, the 'await' version is ~3-4 times slower than the promises version.

Does await run in parallel? ›

In order to run multiple async/await calls in parallel, all we need to do is add the calls to an array, and then pass that array as an argument to Promise. all() . Promise. all() will wait for all the provided async calls to be resolved before it carries on(see Conclusion for caveat).

What if one promise fails in promise all? ›

Promise.all is rejected if any of the elements are rejected. For example, if you pass in four promises that resolve after a timeout and one promise that rejects immediately, then Promise.all will reject immediately.

Are threads actually concurrent? ›

Concurrency and Parallelism

In the same multithreaded process in a shared-memory multiprocessor environment, each thread in the process can run concurrently on a separate processor, resulting in parallel execution, which is true simultaneous execution.

Videos

1. The Async Await Episode I Promised
(Fireship)
2. Swift 5.5: Async Await Operations (Xcode 13, iOS 15, 2022) - iOS for Beginners
(iOS Academy)
3. A crash course of async await (Swift Concurrency) - Shai Mishali - Swift Heroes 2022
(Swift Heroes)
4. Python Asynchronous Programming - AsyncIO & Async/Await
(Tech With Tim)
5. Async JS Crash Course - Callbacks, Promises, Async Await
(Traversy Media)
6. Swift Concurrency Lesson 4 - Async and Await
(CodeWithChris)
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