@MapFeature.Require({SUPPORTS_PUT, SUPPORTS_REMOVE})
  public void testCompute_absentToPresent() {
    assertEquals(
        "Map.compute(absent, functionReturningValue) should return value",
        v3(),
        getMap()
            .compute(
                k3(),
                (k, v) -> {
                  assertEquals(k3(), k);
                  assertNull(v);
                  return v3();

   * - median with compute taking a double-collection and with computeInPlace;
   * - quartiles with index and with indexes taking int-varargs, and with compute taking a
   *   double-collection and with computeInPlace;
   * - scale with index and with indexes taking int-varargs, and with all overloads of compute
   *   taking a double-collection and with computeInPlace;

   * - median with compute taking a double-collection and with computeInPlace;
   * - quartiles with index and with indexes taking int-varargs, and with compute taking a
   *   double-collection and with computeInPlace;
   * - scale with index and with indexes taking int-varargs, and with all overloads of compute
   *   taking a double-collection and with computeInPlace;

 *
 * <h3>Examples</h3>
 *
 * <p>To compute the median:
 *
 * {@snippet :
 * double myMedian = median().compute(myDataset);
 * }
 *
 * where {@link #median()} has been statically imported.
 *
 * <p>To compute the 99th percentile:
 *
 * {@snippet :
 * double myPercentile99 = percentiles().index(99).compute(myDataset);
 * }
 *

    // b/80241237

    Cache<String, String> c = CacheBuilder.newBuilder().maximumSize(1).build();

    assertThat(c.asMap().compute("hash-1", (k, v) -> "a")).isEqualTo("a");
    assertThat(c.asMap().compute("hash-1", (k, v) -> "b")).isEqualTo("b");
    assertThat(c.asMap().compute("hash-1", (k, v) -> "c")).isEqualTo("c");
    assertThat(c.size()).isEqualTo(1);
    assertThat(c.asMap().computeIfAbsent("hash-2", k -> "")).isEqualTo("");

    // Compute the filter.
    long filter = 0;
    int size = chars.cardinality();
    boolean containsZero = chars.get(0);
    // Compute the hash table.
    char[] table = new char[chooseTableSize(size)];
    int mask = table.length - 1;
    for (int c = chars.nextSetBit(0); c != -1; c = chars.nextSetBit(c + 1)) {
      // Compute the filter at the same time.
      filter |= 1L << c;

    this.nextOrNull = firstOrNull;
  }

  /**
   * Returns the element that follows {@code previous}, or returns {@code null} if no elements
   * remain. This method is invoked during each call to {@link #next()} in order to compute the
   * result of a <i>future</i> call to {@code next()}.
   */
  protected abstract @Nullable T computeNext(T previous);

  @Override
  public final boolean hasNext() {
    return nextOrNull != null;
  }

    assertEquals(0xcf54171253fdc198L, hasher.hash().asLong());
  }

  /** Convenience method to compute a fingerprint on a full bytes array. */
  private static long fingerprint(byte[] bytes) {
    return fingerprint(bytes, bytes.length);
  }

  /** Convenience method to compute a fingerprint on a subset of a byte array. */
  private static long fingerprint(byte[] bytes, int length) {

   *
   * @since 21.0
   */
  @CanIgnoreReturnValue
  public long updateAndGet(K key, LongUnaryOperator updaterFunction) {
    checkNotNull(updaterFunction);
    Long result =
        map.compute(
            key, (k, value) -> updaterFunction.applyAsLong(value == null ? 0L : value.longValue()));
    return requireNonNull(result);
  }

  /**

Kubernetes manages stateless Spark and Hive containers elastically on the compute nodes. Spark has native scheduler integration with Kubernetes. Hive, for legacy reasons, uses YARN scheduler on top of Kubernetes.