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  1. 8
      docs/en/mkdocs.yml
  2. 444
      docs/mn/docs/async.md
  3. 500
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toggle:
icon: material/lightbulb-auto
name: Switch to light mode
- media: '(prefers-color-scheme: light)'
- media: "(prefers-color-scheme: light)"
scheme: default
primary: teal
accent: amber
toggle:
icon: material/lightbulb
name: Switch to dark mode
- media: '(prefers-color-scheme: dark)'
- media: "(prefers-color-scheme: dark)"
scheme: slate
primary: teal
accent: amber
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separate_signature: true
unwrap_annotated: true
filters:
- '!^_'
- "!^_"
merge_init_into_class: true
docstring_section_style: spacy
signature_crossrefs: true
@ -366,6 +366,8 @@ extra:
name: ja - 日本語
- link: /ko/
name: ko - 한국어
- link: /mn/
name: mn - Монгол хэл
- link: /nl/
name: nl - Nederlands
- link: /pl/

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# Concurrency and async / await
Details about the `async def` syntax for *path operation functions* and some background about asynchronous code, concurrency, and parallelism.
## In a hurry?
<abbr title="too long; didn't read"><strong>TL;DR:</strong></abbr>
If you are using third party libraries that tell you to call them with `await`, like:
```Python
results = await some_library()
```
Then, declare your *path operation functions* with `async def` like:
```Python hl_lines="2"
@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:
```Python hl_lines="2"
@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 <abbr title="Input and Output">I/O</abbr> 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 <abbr title="Input and Output">I/O</abbr> 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. 😍
<img src="/img/async/concurrent-burgers/concurrent-burgers-01.png" class="illustration">
Then it's your turn, you place your order of 2 very fancy burgers for your crush and you. 🍔🍔
<img src="/img/async/concurrent-burgers/concurrent-burgers-02.png" class="illustration">
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).
<img src="/img/async/concurrent-burgers/concurrent-burgers-03.png" class="illustration">
You pay. 💸
The cashier gives you the number of your turn.
<img src="/img/async/concurrent-burgers/concurrent-burgers-04.png" class="illustration">
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 ✨😍✨.
<img src="/img/async/concurrent-burgers/concurrent-burgers-05.png" class="illustration">
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.
<img src="/img/async/concurrent-burgers/concurrent-burgers-06.png" class="illustration">
You and your crush eat the burgers and have a nice time. ✨
<img src="/img/async/concurrent-burgers/concurrent-burgers-07.png" class="illustration">
/// info
Beautiful illustrations by <a href="https://www.instagram.com/ketrinadrawsalot" class="external-link" target="_blank">Ketrina Thompson</a>. 🎨
///
---
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.
<img src="/img/async/parallel-burgers/parallel-burgers-01.png" class="illustration">
Then it's finally your turn, you place your order of 2 very fancy burgers for your crush and you.
You pay 💸.
<img src="/img/async/parallel-burgers/parallel-burgers-02.png" class="illustration">
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.
<img src="/img/async/parallel-burgers/parallel-burgers-03.png" class="illustration">
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.
<img src="/img/async/parallel-burgers/parallel-burgers-04.png" class="illustration">
Then your cashier/cook 👨‍🍳 finally comes back with your burgers, after a long time waiting 🕙 there in front of the counter.
<img src="/img/async/parallel-burgers/parallel-burgers-05.png" class="illustration">
You take your burgers and go to the table with your crush.
You just eat them, and you are done. ⏹
<img src="/img/async/parallel-burgers/parallel-burgers-06.png" class="illustration">
There was not much talk or flirting as most of the time was spent waiting 🕙 in front of the counter. 😞
/// info
Beautiful illustrations by <a href="https://www.instagram.com/ketrinadrawsalot" class="external-link" target="_blank">Ketrina Thompson</a>. 🎨
///
---
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 run 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**.
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 <a href="https://www.techempower.com/benchmarks/#section=data-r17&hw=ph&test=query&l=zijmkf-1" class="external-link" target="_blank">(all thanks to Starlette)</a>.
### 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 <abbr title="Central Processing Unit">CPU</abbr>, 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 advantage of concurrency that is very common for web development (the same main attraction 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](deployment/index.md){.internal-link target=_blank}.
## `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:
```Python
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`:
```Python hl_lines="1"
async def get_burgers(number: int):
# Do some asynchronous stuff to create the burgers
return burgers
```
...instead of `def`:
```Python hl_lines="2"
# This is not asynchronous
def 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:
```Python
# This won't work, because get_burgers was defined with: async def
burgers = 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:
```Python hl_lines="2-3"
@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.
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 <a href="https://anyio.readthedocs.io/en/stable/" class="external-link" target="_blank">AnyIO</a>, which makes it compatible with both Python's standard library <a href="https://docs.python.org/3/library/asyncio-task.html" class="external-link" target="_blank">asyncio</a> and <a href="https://trio.readthedocs.io/en/stable/" class="external-link" target="_blank">Trio</a>.
In particular, you can directly use <a href="https://anyio.readthedocs.io/en/stable/" class="external-link" target="_blank">AnyIO</a> 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 <a href="https://anyio.readthedocs.io/en/stable/" class="external-link" target="_blank">AnyIO</a> to be highly compatible and get its benefits (e.g. *structured concurrency*).
I created another library on top of AnyIO, as a thin layer on top, to improve a bit the type annotations and get better **autocompletion**, **inline errors**, etc. It also has a friendly introduction and tutorial to help you **understand** and write **your own async code**: <a href="https://asyncer.tiangolo.com/" class="external-link" target="_blank">Asyncer</a>. It would be particularly useful if you need to **combine async code with regular** (blocking/synchronous) code.
### 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 <a href="https://www.gevent.org/" class="external-link" target="_blank">Gevent</a>. 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 <a href="http://callbackhell.com/" class="external-link" target="_blank">callback hell</a>.
## 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 (coroutines, 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 <abbr title="Input/Output: disk reading or writing, network communications.">I/O</abbr>.
Still, in both situations, chances are that **FastAPI** will [still be faster](index.md#_11){.internal-link target=_blank} than (or at least comparable to) your previous framework.
### Dependencies
The same applies for [dependencies](tutorial/dependencies/index.md){.internal-link target=_blank}. 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](tutorial/dependencies/sub-dependencies.md){.internal-link target=_blank} 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: <a href="#in-a-hurry">In a hurry?</a>.

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{!../../docs/missing-translation.md!}
# FastAPI
<style>
.md-content .md-typeset h1 { display: none; }
</style>
<p align="center">
<a href="https://fastapi.tiangolo.com"><img src="https://fastapi.tiangolo.com/img/logo-margin/logo-teal.png" alt="FastAPI"></a>
</p>
<p align="center">
<em>FastAPI фреймворк, өндөр гүйцэтгэлтэй, сурахад хялбар, хурдан кодлох боломжтой, ашиглалтанд оруулахад бэлэн</em>
</p>
<p align="center">
<a href="https://github.com/fastapi/fastapi/actions?query=workflow%3ATest+event%3Apush+branch%3Amaster" target="_blank">
<img src="https://github.com/fastapi/fastapi/actions/workflows/test.yml/badge.svg?event=push&branch=master" alt="Test">
</a>
<a href="https://coverage-badge.samuelcolvin.workers.dev/redirect/fastapi/fastapi" target="_blank">
<img src="https://coverage-badge.samuelcolvin.workers.dev/fastapi/fastapi.svg" alt="Coverage">
</a>
<a href="https://pypi.org/project/fastapi" target="_blank">
<img src="https://img.shields.io/pypi/v/fastapi?color=%2334D058&label=pypi%20package" alt="Package version">
</a>
<a href="https://pypi.org/project/fastapi" target="_blank">
<img src="https://img.shields.io/pypi/pyversions/fastapi.svg?color=%2334D058" alt="Supported Python versions">
</a>
</p>
---
**Документ**: <a href="https://fastapi.tiangolo.com" target="_blank">https://fastapi.tiangolo.com</a>
**Эх код**: <a href="https://github.com/fastapi/fastapi" target="_blank">https://github.com/fastapi/fastapi</a>
---
FastAPI нь Python type hint стандартад суурилсан, API хөгжүүлэх зориулалттай, орчин үеийн, хурдан (өндөр гүйцэтгэлтэй) вэб фреймворк юм.
{ .annotate }
1. `тайп хинт` - Тохиромжтой Монгол орчуулга байгаагүй тул Англи галигаар нь үлдээв.
Гол онцлогууд нь:
* **Хурд**: **NodeJS** болон **Go**-тэй адил түвшний маш хурдан гүйцэтгэлтэй (Starlette ба Pydantic-ийн ачаар). [Хамгийн хурдан Python фреймворкийн нэг](#_11).
* **Кодлоход хурдан**: Ямар нэгэн шинэ зүйл хөгжүүлэх хурдыг 200-гаас 300% нэмэгдүүлнэ. *
* **Алдааг багасгана**: Хүнээс үүдэлтэй (инженерээс) алдааг 40% орчим бууруулна. *
* **Ойлгомжтой**: Код эдитэрийн дэмжлэг сайтай. Код эдитэрийн <abbr title="Өөрөөр: auto-complete, autocompletion, IntelliSense">зөвлөгөө</abbr> маш их тул алдааг засахад зарцуулах хугацаа бага.
* **Хялбар**: Сурч, хэрэглэхэд амархан. Документ унших шаардлага бага.
* **Богинохон**: Кодын давхардал багатай. Параметр ашиглан функцуудыг удирдах боломжтой. Алдаа багатай.
* **Бат бөх**: Интерактив документтэй, борлуулалтанд бэлэн код хөгжүүлэхэд нэн тохиромжтой.
* **Стандартад нийцсэн**: Дараах API-ийн стандартад нийцсэн, бас түүн дээр суурилсан: <a href="https://github.com/OAI/OpenAPI-Specification" class="external-link" target="_blank">OpenAPI</a> (Өмнө нь Swagger гэж нэрлэгддэг байсан), <a href="https://json-schema.org/" class="external-link" target="_blank">JSON Schema</a>.
<small>* Шууд ашиглалтанд оруулахад зориулагдсан програмууд хөгжүүлэх явцад хийгдсэн туршилтуудад үндэслэв.</small>
## Спонсор
<!-- sponsors -->
{% if sponsors %}
{% for sponsor in sponsors.gold -%}
<a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
{% endfor -%}
{%- for sponsor in sponsors.silver -%}
<a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
{% endfor %}
{% endif %}
<!-- /sponsors -->
<a href="https://fastapi.tiangolo.com/fastapi-people/#sponsors" class="external-link" target="_blank">бусад спонсорууд</a>
## Коммент
"_[...] **FastAPI**-г сүүлийн үед маш их ашиглаж байна. [...] Энэ чигээрээ **Microsoft**-ийн багийнхаа бүх **ML**-д, зарим нь **Windows**-ийн үндсэн бүтээгдэхүүн, зарим нь **Office**-ийн бүтээгдэхүүнүүдэд **FastAPI**-ийг ашиглахаар төлөвлөж байна._"
<div style="text-align: right; margin-right: 10%;">Kabir Khan - <strong>Microsoft</strong> <a href="https://github.com/fastapi/fastapi/pull/26" target="_blank"><small>(ref)</small></a></div>
---
"_Бид **FastAPI**-г ашиглан **REST** серверийг хөгжүүлж, түүгээр дамжуулан **прогноз/таавар** авч байна. [Ludwig]_"
<div style="text-align: right; margin-right: 10%;">Piero Molino, Yaroslav Dudin, and Sai Sumanth Miryala - <strong>Uber</strong> <a href="https://eng.uber.com/ludwig-v0-2/" target="_blank"><small>(ref)</small></a></div>
---
"_**Netflix** нь **уналтын менежмент** фремворк буюу **Dispatch**-ийг нээлттэй эх сурвалжтайгаар гаргаснаа зарлаж байгаадаа баяртай байна! [**FastAPI**-ээр бүтээсэн]_"
<div style="text-align: right; margin-right: 10%;">Kevin Glisson, Marc Vilanova, Forest Monsen - <strong>Netflix</strong> <a href="https://netflixtechblog.com/introducing-dispatch-da4b8a2a8072" target="_blank"><small>(ref)</small></a></div>
---
"_Би **FastAPI**-д маш их сэтгэл хангалуун, баяртай байна!_"
<div style="text-align: right; margin-right: 10%;">Brian Okken - <strong><a href="https://pythonbytes.fm/episodes/show/123/time-to-right-the-py-wrongs?time_in_sec=855" target="_blank">Python Bytes</a> podcast host</strong> <a href="https://twitter.com/brianokken/status/1112220079972728832" target="_blank"><small>(ref)</small></a></div>
---
"_Яг үнэнийг хэлэхэд та бүхний бүтээсэн зүйл маш бат бөх, төгс харагдаж байна. **Hug**-г яг ийм байгаасай гэж төсөөлж байсан. Хэн нэгэн нь үүнийг бүтээж байгааг харахад урам орж байна._"
<div style="text-align: right; margin-right: 10%;">Timothy Crosley - <strong><a href="https://github.com/hugapi/hug" target="_blank">Hug</a> үүсгэн байгуулагч</strong> <a href="https://news.ycombinator.com/item?id=19455465" target="_blank"><small>(ref)</small></a></div>
---
"_Хэрэв та REST API-г хөгжүүлэхэд зориулагдсан **орчин үеийн фремворк**-г сурахыг хүсэж байгаа бол, **FastAPI**-г шалгаарай [...] Хурдан, хэрэглэхэд хялбар, сурахад амар [...]_"
"_Бид **API**-аа **FastAPI**-гаар хийхээр болсон. [...] Та бүхэн ч бас тэгнэ гэж найдаж байна. [...]_"
<div style="text-align: right; margin-right: 10%;">Ines Montani - Matthew Honnibal - <strong><a href="https://explosion.ai" target="_blank">Explosion AI</a> үүсгэн байгуулагч - <a href="https://spacy.io" target="_blank">spaCy</a> үүсгэн байгуулагч</strong> <a href="https://twitter.com/_inesmontani/status/1144173225322143744" target="_blank"><small>(ref)</small></a> - <a href="https://twitter.com/honnibal/status/1144031421859655680" target="_blank"><small>(ref)</small></a></div>
---
"_Хэрэв хэн нэгэн нь борлуулалтанд бэлэн Python API хөгжүүлэхийг хүсч байвал, би **FastAPI**-г 100% санал болгоно. **FastAPI** нь **төгс бүтээгдсэн**, **хэрэглэхэд хялбар** бөгөөд **томсгох, жижигсгэхэд түүртээд байхгүй**. Манай API-н хөгжүүлэлтийн стратегийн **түлхүүр бүрэлдэхүүн** болж, олон автоматаци болон үйлчилгээнд, тухайлбал манай Virtual TAC Engineer-д ашиглагдаж байна._"
<div style="text-align: right; margin-right: 10%;">Deon Pillsbury - <strong>Cisco</strong> <a href="https://www.linkedin.com/posts/deonpillsbury_cisco-cx-python-activity-6963242628536487936-trAp/" target="_blank"><small>(ref)</small></a></div>
---
## **Typer**, CLI дахь FastAPI
<a href="https://typer.tiangolo.com" target="_blank"><img src="https://typer.tiangolo.com/img/logo-margin/logo-margin-vector.svg" style="width: 20%;"></a>
Хэрэв та веб интерфейс биш, <abbr title="Command Line Interface">CLI</abbr> интерфейс апп хөгжүүлж байвал <a href="https://typer.tiangolo.com/" class="external-link" target="_blank">**Typer**</a>-ийг шалгаарай!.
**Typer** нь FastAPI-ийн төрсөн дүү нь юм. **CLI дахь FastAPI** байхад зориулагдсан. ⌨️ 🚀
## Өмнөтгөл
FastAPI-ийн үндсэн тулгуур:
* Bеб-тэй холбоотой зүйлс: <a href="https://www.starlette.io/" class="external-link" target="_blank">Starlette</a>.
* Дата болон өгөгдөлтэй холбоотой зүйлс: <a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a>.
## FastAPI-г суулгах
<a href="https://fastapi.tiangolo.com/virtual-environments/" class="external-link" target="_blank">Виртуал орчинг</a> үүсгэн идэвхижүүлээд FastAPI-г суулгана:
<div class="termy">
```console
$ pip install "fastapi[standard]"
---> 100%
```
</div>
**Тэмдэглэл**: Бүх терминал дээр асуудалгүй ажиллуулахын тулд `"fastapi[standard]"`-ийг хашилтад хийж бичихээ мартуузай.
## Жишээ
### Файл үүсгэцгээе
* Доорх код-оор `main.py` файлийг үүсгэнэ үү:
```Python
from typing import Union
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
def read_root():
return {"Сайн уу?": "Эх дэлхий минь!"}
@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
return {"item_id": item_id, "q": q}
```
<details markdown="1">
<summary>Эсвэл <code>async def</code>-ийг...</summary>
Хэрэв код тань `async` / `await`-ийг ашигладаг бол, `async def`ийг ашиглана уу:
```Python hl_lines="9 14"
from typing import Union
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
async def read_root():
return {"Сайн уу?": "Эх дэлхий минь!"}
@app.get("/items/{item_id}")
async def read_item(item_id: int, q: Union[str, None] = None):
return {"item_id": item_id, "q": q}
```
**Тэмдэглэл**:
Хэрэв та мэдэхгүй, эргэлзээд байвал, _"Яаралтай хэрэгтэй байна уу?"_ хэсгийг шалгана уу. <a href="https://fastapi.tiangolo.com/async/#in-a-hurry" target="_blank">`async` ба `await`-ийн тухай</a>.
</details>
### Хэрэгжүүлцгээе
Доорх коммандаар серверээ асаана уу:
<div class="termy">
```console
$ fastapi dev main.py
╭────────── FastAPI CLI - Development mode ───────────╮
│ │
│ Serving at: http://127.0.0.1:8000 │
│ │
│ API docs: http://127.0.0.1:8000/docs │
│ │
│ Running in development mode, for production use: │
│ │
│ fastapi run │
│ │
╰─────────────────────────────────────────────────────╯
INFO: Will watch for changes in these directories: ['/home/user/code/awesomeapp']
INFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO: Started reloader process [2248755] using WatchFiles
INFO: Started server process [2248757]
INFO: Waiting for application startup.
INFO: Application startup complete.
```
</div>
<details markdown="1">
<summary><code>fastapi dev main.py</code> коммандын тухай...</summary>
`fastapi dev` комманд нь `main.py` файлыг уншснаар доторх **FastAPI**-г олон, <a href="https://www.uvicorn.org" class="external-link" target="_blank">Uvicorn</a>-ийг ашиглан серверийг асаана.
`fastapi dev` комманд нь нэмэлт тохиргоогүйгээр, локал хөгжүүлэлтийн үе дэх кодны өөрчлөлтийг автоматаар мэдэрч ре-старт хийнэ..
Энэ талаар дэлгэрэнгүйг <a href="https://fastapi.tiangolo.com/fastapi-cli/" target="_blank">FastAPI CLI документ</a>-ээс уншина уу.
</details>
### Шалгацгаая
Энэ линк-ээр <a href="http://127.0.0.1:8000/items/5?q=somequery" class="external-link" target="_blank">http://127.0.0.1:8000/items/5?q=somequery</a> браузер-аа нээнэ үү.
Доорхтой адил JSON хариу харагдах болно:
```JSON
{"item_id": 5, "q": "somequery"}
```
Ингэснээр та аль хэдийн доорх API-г үүсгэсэн:
* HTTP хүсэлтийг `/` ба `/items/{item_id}`_хаяг_ дээр хүлээн авдаг API.
* 2 _хаяг_ хоёулаа `GET` гэсэн <em>үйл ажиллагаа</em> (HTTP протоколын `GET` _арга_)-тай API.
* `/items/{item_id}` _хаяг_ нь `item_id` гэж нэрлэгдсэн, `int` тайптай, зайлшгүй хэрэгтэй _**хаягийн параметртэй**_ API.
* Мөн `/items/{item_id}` _хаяг_ нь `q` гэж нэрлэгдсэн, `str` тайптай, зайлшгүй бус _**хайлтын параметртай**_ API.
### Интерактив API документ
<a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>-г нээснээр та:
<a href="https://github.com/swagger-api/swagger-ui" class="external-link" target="_blank">Swagger UI</a>-аар бүтээгдсэн, aвтомат интерактив API документ-ийг харах болно:
![Swagger UI](https://fastapi.tiangolo.com/img/index/index-01-swagger-ui-simple.png)
### Хоёр дахь интерактив документ
Мөн <a href="http://127.0.0.1:8000/redoc" class="external-link" target="_blank">http://127.0.0.1:8000/redoc</a>-г нээснээр:
<a href="https://github.com/Rebilly/ReDoc" class="external-link" target="_blank">ReDoc</a>-ooр бүтээгдсэн, xоёр дахь автомат документ-ийг олж харах болно:
![ReDoc](https://fastapi.tiangolo.com/img/index/index-02-redoc-simple.png)
## Дээрх жишээг үргэлжлүүлцгээе
`main.py` файлыг, `PUT` хүсэлтийг хүлээн авах зориулалттай болгон өөрчлье.
Pydantic-ийн ачаар body-г нь Python-ий стандарт тайп-аар зарлах боломжтой.
```Python hl_lines="4 9-12 25-27"
from typing import Union
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
price: float
is_offer: Union[bool, None] = None
@app.get("/")
def read_root():
return {"Сайн уу?": "Эх дэлхий минь!"}
@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
return {"item_id": item_id, "q": q}
@app.put("/items/{item_id}")
def update_item(item_id: int, item: Item):
return {"item_name": item.name, "item_id": item_id}
```
`fastapi dev` сервер нь автоматаар ре-старт хийх ёстой.
### Интерактив API документийн шинчлэлт
Дахин <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>-г нээнэ үү.
* Интерактив API документ маань автоматаар шинэ body-той болж шинэчлэгдсэн байх ёстой:
![Swagger UI](https://fastapi.tiangolo.com/img/index/index-03-swagger-02.png)
* "Try it out" гэсэн товчлуурыг дарснаар та API-тай интерактив харилцаа (харилцан нөлөөлөх харилцаа) хийх боломжтой болно:
![Swagger UI interaction](https://fastapi.tiangolo.com/img/index/index-04-swagger-03.png)
* Дараа нь "Execute" товч дээр дарснаар таны харч буй интерфэйс тань API-тай холбогдож, параметрүүдийг илгээн, хариуг авч, дэлгэцэн дээр харуулна:
![Swagger UI interaction](https://fastapi.tiangolo.com/img/index/index-05-swagger-04.png)
### Хоёр дахь интерактив API документийн шинчлэлт
Мөн <a href="http://127.0.0.1:8000/redoc" class="external-link" target="_blank">http://127.0.0.1:8000/redoc</a>-ийг дахин нээнэ үү.
* Хоёр дахь документ маань ч бас шинэ хайлтын параметр болон шинэ body-г харуулах болно:
![ReDoc](https://fastapi.tiangolo.com/img/index/index-06-redoc-02.png)
### Товч дүгнэлт
Товчхондоо, та параметрүүд болон body гэх мэт зүйлсийн тайпыг, функцийн параметр болгон ганц л удаа зарлана.
Үүнийг орчин үеийн стандарт Python тайпаар л хийчихнэ.
Шинэ синтакс, тодорхой нэгэн library-ний аргачлал, класс зэргийг сураад байх шаардлага байхгүй.
Зүгээр л илүү ч үгүй дутуу ч үгүй, ориг Python.
Жишээлбэл, тоо `int` байхад:
```Python
item_id: int
```
Эсвэл комплекс `Item` модел байхад:
```Python
item: Item
```
Дээрх ганц удаагийн зарлалтаар та:
* Kод эдитэрийн тусламж:
* Код гүйцэтгэлт.
* Тайп шалгалт.
* Аргументийн шалгалт буюу баталгаажуулалт:
* Аргумент нь алдаатай өөр үед, автоматaap тодорхой бөгөөд ойлгомжтой алдаат хариуг авна.
* Аргумент нь маш гүн салаалсан комплекс JSON байсан ч хамаагүй ягш баталгаажуулалт хийгдэнэ.
* Ирэх өгөгдлийн <abbr title="Өөрөөр: сериалчлалт, парсчлах, маршаллах">хөрвүүлэлт</abbr>.<br> Дараах эх сурвалжаас ирсэн өгөгдлийг Python-ий дата ба тайп рүү хөрвүүлнэ:
* JSON (JSON)
* Хаягийн параметрүүд (Path parameters)
* Хайлтын параметрүүд (Query parameters)
* Күүки (Cookies)
* Хүсэлтийн хедер (Headers)
* Форм (Forms)
* Файл (Files)
* Гарах өгөгдлийн <abbr title="Өөрөөр: сериалчлалт, парсчлах, маршаллах">хөрвүүлэлт</abbr>.<br> Дараах Python-ий дата ба тайпаас гарах өгөдлийг, JSON гэх мэт тайп руу хөрвүүлнэ:
* Python тайп (`str`, `int`, `float`, `bool`, `list` гэх мэт)
* `datetime` объект
* `UUID` объект
* Database модел
* ...гэх мэтчилэн
* Дараах 2 төрлийн өөрийн гэсэн интерфэйстэй автомат интерактив API документтой:
* Swagger UI.
* ReDoc.
---
Дээрх кодын жишээнээс дурдахад, **FastAPI** нь:
* `GET` болон `PUT` хүсэлтийн хаягнд `item_id` байгаа эсэхийг шалгана.
* `GET` болон `PUT` хүсэлтийн `item_id` нь `int` тайп байхыг шалгана.
* Хэрэв `int` тайп буюу тоо биш бол ойлгомжтой бөгөөд тодорхой алдаат хариуг илгээнэ.
* `GET` хүсэлтэнд `q` гэж нэрлэгдсэн зайлшгүй бус хайлтын параметр байгаа эсэхийг шалгана. (`http://127.0.0.1:8000/items/foo?q=somequery` гэх мэт)
* `q` параметр нь `= None` гэж зарлагдсан учраас байсан ч, байхгүй байсан ч болно. (3айлшгүй бус)
* `None` биш бол, параметр нь залшгүй хэрэгтэй параметр болно. (Дээрх жишээний `PUT` хүсэлтэнд дурдагдсан body мэт).
* `/items/{item_id}` хаяг дээрх `PUT` хүсэлтийн body-г JSON гэж уншин:
* `name` гэсэн `str` тайп байх ёстой, зайлшгүй хэрэгтэй атрибут байгаа эсэхийг шалгана.
* `price` гэсэн `float` тайп байх ёстой, зайлшгүй хэрэгтэй атрибут байгаа эсэхийг шалгана.
* `is_offer` гэсэн `bool` тайп байх ёстой, зайлшгүй бус атрибут байгаа эсэхийг шалгана.
* Маш гүн салаалсан, комплекс JSON объект байсан ч үүн шиг шалгалт болон баталгаажуулалт явагдана.
* Автоматаар JSON-г хөрвүүлнэ.
* OpenAPI-аар бүх зүйлийг документчүүлэн:
* Интерактив документ систэмд,
* Автоматаар олон төрлийн хэлд зориулсан, клаянт код үүсгэх системд тус тус ашиглана.
* 2 төрлийн веб интерфэйс-тэй интерактив документийг санал болгоно.
---
Ингэснээр бид ердөө л мөсөн уулын оройг л хөндлөө, гэвч та аль хэдий нь яг яаж ажлаад байгааг тодорхой хэмжээгээр ойлгосон байх.
```Python
return {"item_name": item.name, "item_id": item_id}
```
Дээрх эгнээг:
```Python
... "item_name": item.name ...
```
...aaс:
```Python
... "item_price": item.price ...
```
...луу солиод үзнэ үү. Таны код эдитэр атрибутын тайп зэргийг аль хэдийн мэдээд, кодыг тань автоматаар гүйцэтгээд өгч байгааг ажиглаарай:
![editor support](https://fastapi.tiangolo.com/img/vscode-completion.png)
Илүү өргөн хүрээтэй, олон боломжийг харуулсан жишээг үзэхийг хүсвэл <a href="https://fastapi.tiangolo.com/tutorial/">Сургалт – Хэрэглэгчийн гарын авлага</a>-аас хараарай.
**Спойлер**: Сургалт – Хэрэглэгчийн гарын авлага нь:
* **Параметрүүдийг** хэрхэн **хедер**, **күүки**, **форм**, **файл** зэрэг олон төрлийн эх үүсвэрээс хялбархан авч ашиглах талаар дурдана.
* `maximum_length` эсвэл `regex` зэрэг **аргументийн шалгуурыг** хэрхэн тохируулах талаар дурдана.
* Хэрэглэхэд хялбар атлаа маш хүчирхэг **<abbr title="өөрөөр components, resources, providers, services, injectables">Dependency Injection</abbr>** системийн тухай дурдана.
* **JWT токен**-той **OAuth2**-ийн тухай, мөн **HTTP Basic** нотолгоо зэргийг хавсаргасан аюулгүй байдал болон хамгаалалтын тухай дурдана.
* **Гүн салаалсан JSON модел**-ийг хэрхэн зарлах тухай гүнзгий түвшинд авч хэлэлцэнэ. (Гэхдээ нэг их хэцүү биш. Pydantic-ийн ачаар!)
* **GraphQL**-ийг <a href="https://strawberry.rocks" class="external-link" target="_blank">Strawberry</a> болон бусад library-тай интеграци хийх талаар дурдана.
* Starlette-ийн ачаар дараахи oлон нэмэлт онцлогийн талаар дурдана:
* **WebSockets**
* HTTPX and `pytest` дээр суурилсан тест
* **CORS**
* **Cookie Sessions**
* гэх мэтчилэн...
## Үзүүлэлт
TechEmpower-ийн бие даасан бенчмарк статистикт **FastAPI** нь Starlette болон Uvicorn-ийн дараагаар ордог, <a href="https://www.techempower.com/benchmarks/#section=test&runid=7464e520-0dc2-473d-bd34-dbdfd7e85911&hw=ph&test=query&l=zijzen-7" class="external-link" target="_blank">хамгийн хурдан Python фреймворкуудын нэг</a> гэдгийг харуулсан байна. (Uvicorn, Starlette нь FastAPI-ийн дотор ашиглагддаг фремворк)
Үүний тухай илүү ихийг мэдэхийг хүсвэл <a href="https://fastapi.tiangolo.com/benchmarks/" class="internal-link" target="_blank">Бенчмарк</a>хэсгийг үзнэ үү.
## Хамаарлууд
FastAPI нь Pydantic ба Starlette-ээс хараат фремворк юм.
### `standard` Хамаарлууд
FastAPI-г `pip install "fastapi[standard]"` коммандаар суулгахад, доорх `standard` хамаарлуудтай хамт суулгагдана:
Pydantic-т ашиглагддаг:
* <a href="https://github.com/JoshData/python-email-validator" target="_blank"><code>email-validator</code></a> - aргумент нь э-мэйл эсэхийг шалгагч бөгөөд хэрэв хэрэгтэй бол шаардлагатай.
Starlette-т ашиглагддаг:
* <a href="https://www.python-httpx.org" target="_blank"><code>httpx</code></a> - Хэрэв `TestClient`-ийг ашиглах хэрэгтэй бол шаардлагатай.
* <a href="https://jinja.palletsprojects.com" target="_blank"><code>jinja2</code></a> - Хэрэв дефолт загварын тохиргоог ашиглах хэрэгтэй бол шаардлагатай.
* <a href="https://github.com/Kludex/python-multipart" target="_blank"><code>python-multipart</code></a> - Хэрэв `request.form()`-ийг ашиглаж <abbr title="HTTP хүсэлтээс ирсэн өгөдлийг Python дата болгон хувиргах">"задлан шинжлэгээ"</abbr> хийх хэрэгтэй бол шаардлагатай.
FastAPI / Starlette-т ашиглагддаг:
* <a href="https://www.uvicorn.org" target="_blank"><code>uvicorn</code></a> - веб серверийн төлөөх хамаарал бөгөөд энэ нь `uvicorn[standard]` , улмаар `uvloop` зэрэг өндөр үзүүлэлттэй веб серверт хэрэгтэй хамаарлыг агуулсан.
* `fastapi-cli` - `fastapi` коммандын төлөөх хамаарал.
### `standard` хамаарал хэрэггүй бол:
Хэрэв таны апп-нд `standard` хамаарал хэрэггүй бол `pip install "fastapi[standard]"` биш `pip install fastapi` коммандаар FastAPI-г суулгана уу.
### Нэмэлт, зайлшгүй биш хамаарлууд
Таньд хэрэгтэй нэмэлт хамаарлууд байж магадгүй юм.
Жишээлбэл, нэмэлт Pydantic хамаарлуудаас дурдахад:
* <a href="https://docs.pydantic.dev/latest/usage/pydantic_settings/" target="_blank"><code>pydantic-settings</code></a> - тохиргооны зохицуулалтанд зориулагдсан.
* <a href="https://docs.pydantic.dev/latest/usage/types/extra_types/extra_types/" target="_blank"><code>pydantic-extra-types</code></a> - Pydantic-т ашиглагдах нэмэлт тайп-нд зориулагдсан.
Hэмэлт FastAPI хамаарлуудаас дурдахад:
* <a href="https://github.com/ijl/orjson" target="_blank"><code>orjson</code></a> - `ORJSONResponse`-г ашиглах хэрэгтэй бол шаардлагатай.
* <a href="https://github.com/esnme/ultrajson" target="_blank"><code>ujson</code></a> - `UJSONResponse`-г ашиглах хэрэгтэй бол шаардлагатай.
## Лиценз
Энэхүү төсөл нь MIT лицензийн нөхцлөөр лицензжигдсэн билээ.

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