But in many cases, you will want to run several worker processes at the same time.

### Multiple Processes - Workers

If you have more clients than what a single process can handle (for example if the virtual machine is not too big) and you have **multiple cores** in the server's CPU, then you could have **multiple processes** running with the same application at the same time, and distribute all the requests among them.

    // value will then be replaced by null, signaling that later entries with the same key should
    // be deleted.
    Map<K, @Nullable V> duplicates = null;
    int dupCount = 0;
    for (int i = 0; i < n; i++) {
      // requireNonNull is safe because the first `n` elements have been filled in.
      entryArray[i] = makeImmutable(requireNonNull(entryArray[i]));
      K key = entryArray[i].getKey();

* Azure: Container permissions for provisioned volumes have changed to private. If you have existing Azure volumes that were created by Kubernetes v1.6.0-v1.6.5, you should change the permissions on them manually. ([#47605](https://github.com/kubernetes/kubernetes/pull/47605), [@brendandburns](https://github.com/brendandburns))

        return new IllegalArgumentException(
            "Multiple entries with same key: " + key + "=" + value1 + " and " + key + "=" + value2);
      }
    }
  }

  /**
   * Returns an immutable map containing the same entries as {@code map}. The returned map iterates
   * over entries in the same order as the {@code entrySet} of the original map. If {@code map}

    }

    boolean empty;
    if (effectiveRange.isEmpty()) {
      empty = true;
    } else {
      /*
       * requireNonNull is safe because the effectiveRange operations above would have thrown or
       * effectiveRange.isEmpty() would have returned true.
       */
      C afterLower = requireNonNull(range.lowerBound.leastValueAbove(domain));

 * Ordering#leastOf(Iterable, int)}, which provides the same implementation with an interface
 * tailored to that use case.
 *
 * <p>This uses the same efficient implementation as {@link Ordering#leastOf(Iterable, int)},
 * offering expected O(n + k log k) performance (worst case O(n log k)) for n calls to {@link
 * #offer} and a call to {@link #topK}, with O(k) memory. In comparison, quickselect has the same

In fact, in some cases, it will even have **two JSON Schemas** in OpenAPI for the same Pydantic model, for input and output, depending on if they have **default values**.

Let's see how that works and how to change it if you need to do that.

## Pydantic Models for Input and Output

Let's say you have a Pydantic model with default values, like this one:

<p>
A Go <i>program execution</i> is modeled as a set of goroutine executions,
together with a mapping <i>W</i> that specifies the write-like operation that each read-like operation reads from.
(Multiple executions of the same program can have different program executions.)
</p>

<p>
<b>Requirement 2</b>:
For a given program execution, the mapping <i>W</i>, when limited to synchronizing operations,

import com.google.common.primitives.Doubles;

/**
 * A mutable object which accumulates paired double values (e.g. points on a plane) and tracks some
 * basic statistics over all the values added so far. This class is not thread safe.
 *
 * @author Pete Gillin
 * @since 20.0
 */
@J2ktIncompatible
@GwtIncompatible
@ElementTypesAreNonnullByDefault
public final class PairedStatsAccumulator {

* **Uvicorn**:
    * Will have the best performance, as it doesn't have much extra code apart from the server itself.