@Override
  public int remainingCapacity() {
    return delegate().remainingCapacity();
  }

  @CanIgnoreReturnValue // TODO(kak): consider removing this
  @Override
  public E take() throws InterruptedException {
    return delegate().take();
  }

    assertEquals("XheXXuickXXrownXXoxX", builder.build().escape("The Quick Brown Fox!"));
    // Explicit replacements take priority over unsafe characters.
    builder.addEscape(' ', "_");
    builder.addEscape('!', "_");
    assertEquals("Xhe_Xuick_Xrown_Xox_", builder.build().escape("The Quick Brown Fox!"));
    // Explicit replacements take priority over safe characters.
    builder.setSafeRange(' ', '~');

        
        gradle["Gradle CLI client"]
    
        gradlew["Gradlew CLI client"]
    
        subgraph IDE    
            tapi["Tooling API client"]
        end
        
        daemon["Gradle daemon"]
        gradle --> daemon
        gradlew --> daemon
        tapi --> daemon
        
        worker["Worker process"]
        daemon --> worker

        worker2["Worker process"]
        daemon --> worker2

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.

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***

  @Override
  public boolean offer(E e, long timeout, TimeUnit unit) throws InterruptedException {
    return delegate().offer(e, timeout, unit);
  }

  @Override
  public E take() throws InterruptedException {
    return delegate().take();
  }

  @Override
  @CheckForNull
  public E poll(long timeout, TimeUnit unit) throws InterruptedException {
    return delegate().poll(timeout, unit);
  }

Then, when you type that username and password, the browser sends them in the header automatically.

## Simple HTTP Basic Auth

* Import `HTTPBasic` and `HTTPBasicCredentials`.
* Create a "`security` scheme" using `HTTPBasic`.
* Use that `security` with a dependency in your *path operation*.
* It returns an object of type `HTTPBasicCredentials`:

the current user is joined to a slow domain, which is the
usual case for many corporate users. The new implementation
performance is now in the order of milliseconds, compared to
the previous implementation which could take several seconds,

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

protection bits added automatically to provide the regular safety for these objects up to 50% of the number of drives.
This will allow normal write operations to take place on systems that exceed the write tolerance.