assertEquals("appendOsStats should take StringBuilder parameter", 1, method.getParameterCount());
                assertEquals("appendOsStats should take StringBuilder", StringBuilder.class, method.getParameterTypes()[0]);
                break;
            case "appendProcessStats":
                hasAppendProcessStats = true;
                assertEquals("appendProcessStats should take StringBuilder parameter", 1, method.getParameterCount());

      // Fail fast if there's no response queued up.
      return failFastResponse!!
    }

    val result = responseQueue.take()

    // If take() returned because we're shutting down, then enqueue another dead letter so that any
    // other threads waiting on take() will also return.
    if (result == DEAD_LETTER) responseQueue.add(DEAD_LETTER)

    return result
  }

### Create a dependency, or "dependable" { #create-a-dependency-or-dependable }

Let's first focus on the dependency.

It is just a function that can take all the same parameters that a *path operation function* can take:

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[8:9] *}

That's it.

**2 lines**.

And it has the same shape and structure that all your *path operation functions* have.

For this, use the Python standard module <a href="https://docs.python.org/3/library/secrets.html" class="external-link" target="_blank">`secrets`</a> to check the username and password.

`secrets.compare_digest()` needs to take `bytes` or a `str` that only contains ASCII characters (the ones in English), this means it wouldn't work with characters like `á`, as in `Sebastián`.

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.

### FastAPI Data Filtering { #fastapi-data-filtering }

Now, for FastAPI, it will see the return type and make sure that what you return includes **only** the fields that are declared in the type.

Here you saw how to use multiple **workers** to **parallelize** the execution of the application, take advantage of **multiple cores** in the CPU, and be able to serve **more requests**.

From the list of deployment concepts from above, using workers would mainly help with the **replication** part, and a little bit with the **restarts**, but you still need to take care of the others:

* **Security - HTTPS**
* **Running on startup**
* ***Restarts***

      // we allow one second worth of work to go in a burst (i.e. take less than a second)
      assertTrue(burst <= 1000);
      long afterBurst = measureTotalTimeMillis(limiter, oneSecWorthOfWork, new Random());
      // but work beyond that must take at least one second
      assertTrue(afterBurst >= 1000);
    }
  }

  /**

      // we allow one second worth of work to go in a burst (i.e. take less than a second)
      assertTrue(burst <= 1000);
      long afterBurst = measureTotalTimeMillis(limiter, oneSecWorthOfWork, new Random());
      // but work beyond that must take at least one second
      assertTrue(afterBurst >= 1000);
    }
  }

  /**

redundancy or better drive space utilization.

To get an idea of how various combinations of data and parity drives affect the storage usage, let’s take an example of a 100 MiB file stored
on 16 drive MinIO deployment. If you use eight data and eight parity drives, the file space usage will be approximately twice, i.e. 100 MiB
file will take 200 MiB space. But, if you use ten data and six parity drives, same 100 MiB file takes around 160 MiB. If you use 14 data and