Those worker processes would be the ones running your application, they would perform the main computations to receive a **request** and return a **response**, and they would load anything you put in variables in RAM.

<img src="/img/deployment/concepts/process-ram.drawio.svg">

    int numActions = 100;
    // hashcodes from non-overlapping hash computations
    HashCode expected1 = randomHash(hashFunction, new Random(1L), numActions);
    HashCode expected2 = randomHash(hashFunction, new Random(2L), numActions);

    // equivalent, but overlapping, computations (should produce the same results as above)
    Random random1 = new Random(1L);
    Random random2 = new Random(2L);

    computationLatch.countDown();
    // let some computations complete
    while (computedCount.get() < nThreads) {
      Thread.yield();
    }
    cache.invalidateAll();
    tasksFinished.await();

    // Check all of the removal notifications we received: they should have had correctly-associated
    // keys and values. (An earlier bug saw removal notifications for in-progress computations,
    // which had real keys with null values.)

    computationLatch.countDown();
    // let some computations complete
    while (computedCount.get() < nThreads) {
      Thread.yield();
    }
    cache.invalidateAll();
    tasksFinished.await();

    // Check all of the removal notifications we received: they should have had correctly-associated
    // keys and values. (An earlier bug saw removal notifications for in-progress computations,
    // which had real keys with null values.)

   * usually have much noticeable impact. A value of one permits only one thread to modify the cache
   * at a time, but since read operations and cache loading computations can proceed concurrently,
   * this still yields higher concurrency than full synchronization.
   *
   * <p>Defaults to 4. <b>Note:</b>The default may change in the future. If you care about this

      }
    }.start();

    getStartedSignal.await();

    // computation is in progress; asMap shouldn't have changed
    assertThat(map).hasSize(1);
    assertThat(map.containsKey(getKey)).isFalse();
    assertThat(map.get(refreshKey)).isSameInstanceAs(refreshKey);

    // let computation complete
    letGetFinishSignal.countDown();
    getFinishedSignal.await();
    checkNothingLogged();

      }
    }.start();

    getStartedSignal.await();

    // computation is in progress; asMap shouldn't have changed
    assertThat(map).hasSize(1);
    assertThat(map.containsKey(getKey)).isFalse();
    assertThat(map.get(refreshKey)).isSameInstanceAs(refreshKey);

    // let computation complete
    letGetFinishSignal.countDown();
    getFinishedSignal.await();
    checkNothingLogged();

   * usually have much noticeable impact. A value of one permits only one thread to modify the cache
   * at a time, but since read operations and cache loading computations can proceed concurrently,
   * this still yields higher concurrency than full synchronization.
   *
   * <p>Defaults to 4. <b>Note:</b>The default may change in the future. If you care about this

  * Note that NumPy's type promotion rules have been changed(See [NEP 50](https://numpy.org/neps/nep-0050-scalar-promotion.html#nep50)for details). This may change the precision at which computations happen, leading either to type errors or to numerical changes to results.

       * to a valid final state ({@link #COMPLETED}, {@link #CANCELLED}, or {@link #INTERRUPTED}).
       *
       * @param v the value to set as the result of the computation.
       * @param t the exception to set as the result of the computation.
       * @param finalState the state to transition to.
       */
      private boolean complete(@Nullable V v, @Nullable Throwable t, int finalState) {