An interesting detail is that the percentage of the **CPU used** by each process can **vary** a lot over time, but the **memory (RAM)** normally stays more or less **stable**.

If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).

    return file(new File(first), more);
  }

  /** Returns a {@code File} object for the given path parts. */
  private static File file(File first, String... more) {
    // not very efficient, but should definitely be correct
    File file = first;
    for (String name : more) {
      file = new File(file, name);
    }
    return file;
  }

import org.jspecify.annotations.NullUnmarked;

/**
 * @author Kevin Bourrillion
 */
@GwtCompatible
@NullUnmarked
public class ImmutableDoubleArrayTest extends TestCase {
  // Test all creation paths very lazily: by assuming asList() works

  public void testOf0() {
    assertThat(ImmutableDoubleArray.of().asList()).isEmpty();
  }

  public void testOf1() {

   * by two things.
   *
   * <ul>
   *   <li>This is only called when a waiting thread times out or is interrupted. Both of which
   *       should be rare.
   *   <li>The waiters list should be very short.
   * </ul>
   */
  private void removeWaiter(Waiter node) {
    node.thread = null; // mark as 'deleted'
    restart:
    while (true) {
      Waiter pred = null;
      Waiter curr = waitersField;

        void testNullAndEmptyKeys(String key) {
            referralData.setKey(key);
            assertEquals(key, referralData.getKey());
        }

        @Test
        @DisplayName("Should handle very long paths")
        void testVeryLongPaths() {
            StringBuilder longPath = new StringBuilder("\\server\\share");
            for (int i = 0; i < 100; i++) {
                longPath.append("\\subfolder").append(i);

  private void assertEntrySetNotContainsString(Set<Entry<K, V>> entrySet) {
    // Very unlikely that a buggy collection would ever return true. It might accidentally throw.
    assertFalse(entrySet.contains("foo"));
  }

  /**
   * Override this to check invariants which should hold true for a particular implementation, but
   * which are not generally applicable to every instance of Map.
   *

            if (failureRate > 0.5 && avgResponseTime < 5000) {
                newThreshold = Math.min(currentThreshold + 2, 15);
            }
            // If failure rate is moderate but response time is very high, be stricter
            else if (failureRate > 0.2 && avgResponseTime > 10000) {
                newThreshold = Math.max(currentThreshold - 1, 2);
            }

    // The Dolphin algorithm is attractive because it does the fewest array reads and writes: each
    // array slot is read and written exactly once. However, it can have very poor memory locality:
    // benchmarking shows it can take 7 times longer than the other two in some cases. The other two
    // do n swaps, minus a delta (0 or 2 for Reversal, gcd(d, n) for Successive), so that's about

   *       simply switch over to use the JDK implementation wholesale if probable hash flooding is
   *       detected, sacrificing the compactness guarantee in very rare cases in exchange for much
   *       more reliable worst-case behavior.
   *   <li>null, if no entries have yet been added to the map
   * </ul>
   */
  private transient @Nullable Object table;

  /**

   * by two things.
   *
   * <ul>
   *   <li>This is only called when a waiting thread times out or is interrupted. Both of which
   *       should be rare.
   *   <li>The waiters list should be very short.
   * </ul>
   */
  private void removeWaiter(Waiter node) {
    node.thread = null; // mark as 'deleted'
    restart:
    while (true) {
      Waiter pred = null;
      Waiter curr = waitersField;