Stopwatch s = Stopwatch.createStarted();
      // here is where you would do something
      total += s.elapsed(NANOSECONDS);
    }
    return total;
  }

  @Benchmark
  long manual(int reps) {
    long total = 0;
    for (int i = 0; i < reps; i++) {
      long start = System.nanoTime();
      // here is where you would do something
      total += System.nanoTime() - start;
    }
    return total;
  }

      Stopwatch s = Stopwatch.createStarted();
      // here is where you would do something
      total += s.elapsed(NANOSECONDS);
    }
    return total;
  }

  @Benchmark
  long manual(int reps) {
    long total = 0;
    for (int i = 0; i < reps; i++) {
      long start = System.nanoTime();
      // here is where you would do something
      total += System.nanoTime() - start;
    }
    return total;
  }

If you check, the function is decorated with an `@asynccontextmanager`.

That converts the function into something called an "**async context manager**".

{* ../../docs_src/events/tutorial003.py hl[1,13] *}

A **context manager** in Python is something that you can use in a `with` statement, for example, `open()` can be used as a context manager:

```Python
with open("file.txt") as file:

At that point, by noticing that the server took some microseconds longer to send the "Incorrect username or password" response, the attackers will know that they got _something_ right, some of the initial letters were right.

And then they can try again knowing that it's probably something more similar to `stanleyjobsox` than to `johndoe`.

#### A "professional" attack { #a-professional-attack }

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.

/// info | `@decorator` Info

That `@something` syntax in Python is called a "decorator".

You put it on top of a function. Like a pretty decorative hat (I guess that's where the term came from).

A "decorator" takes the function below and does something with it.

# JSON Compatible Encoder { #json-compatible-encoder }

There are some cases where you might need to convert a data type (like a Pydantic model) to something compatible with JSON (like a `dict`, `list`, etc).

For example, if you need to store it in a database.

For that, **FastAPI** provides a `jsonable_encoder()` function.

## Using the `jsonable_encoder` { #using-the-jsonable-encoder }

Most of these ideas would be more or less **independent**, and in most cases you should only need to study them if they apply directly to **your project**.

If something seems interesting and useful to your project, go ahead and check it, but otherwise, you might probably just skip them.

/// tip

## Return Type and Data Filtering { #return-type-and-data-filtering }

Let's continue from the previous example. We wanted to **annotate the function with one type**, but we wanted to be able to return from the function something that actually includes **more data**.

* It takes each **request** that comes to your application.
* It can then do something to that **request** or run any needed code.
* Then it passes the **request** to be processed by the rest of the application (by some *path operation*).
* It then takes the **response** generated by the application (by some *path operation*).
* It can do something to that **response** or run any needed code.
* Then it returns the **response**.