}

  public void testAutoIncrementStep_millis() {
    FakeTicker ticker = new FakeTicker().setAutoIncrementStep(1, TimeUnit.MILLISECONDS);
    assertEquals(0, ticker.read());
    assertEquals(1000000, ticker.read());
    assertEquals(2000000, ticker.read());
  }

  public void testAutoIncrementStep_seconds() {
    FakeTicker ticker = new FakeTicker().setAutoIncrementStep(3, TimeUnit.SECONDS);
    assertEquals(0, ticker.read());

 *   <li>hashMethod: how to hash the data (using the Hashing API or the MessageDigest API).
 * </ul>
 *
 * @author Kurt Alfred Kluever
 */
public class MessageDigestAlgorithmBenchmark {
  @Param({"10", "1000", "100000", "1000000"})
  int size;

  @Param Algorithm algorithm;
  @Param HashMethod hashMethod;

  private enum HashMethod {
    MESSAGE_DIGEST_API() {
      @Override

    assertThat(bf.approximateElementCount()).isAtMost((long) (sizeGuess * 1.01));
  }

  public void testCreateAndCheckMitz32BloomFilterWithKnownFalsePositives() {
    int numInsertions = 1000000;
    BloomFilter<String> bf =
        BloomFilter.create(
            Funnels.unencodedCharsFunnel(),
            numInsertions,
            0.03,
            BloomFilterStrategies.MURMUR128_MITZ_32);

 *
 * @author Sverre Sundsdal
 */
public class MinMaxPriorityQueueBenchmark {
  @Param private ComparatorType comparator;

  // TODO(kevinb): add 1000000 back when we have the ability to throw
  // NotApplicableException in the expensive comparator case.
  @Param({"100", "10000"})
  private int size;

  @Param private HeapType heap;

  private Queue<Integer> queue;

  private final Random random = new Random();

  }

  public void testAutoIncrementStep_millis() {
    FakeTicker ticker = new FakeTicker().setAutoIncrementStep(1, TimeUnit.MILLISECONDS);
    assertEquals(0, ticker.read());
    assertEquals(1000000, ticker.read());
    assertEquals(2000000, ticker.read());
  }

  public void testAutoIncrementStep_seconds() {
    FakeTicker ticker = new FakeTicker().setAutoIncrementStep(3, TimeUnit.SECONDS);
    assertEquals(0, ticker.read());

 */
public class HashFunctionBenchmark {

  // Use a statically configured random instance for all of the benchmarks
  private static final Random random = new Random(42);

  @Param({"10", "1000", "100000", "1000000"})
  private int size;

  @Param HashFunctionEnum hashFunctionEnum;

  private byte[] testBytes;

  @BeforeExperiment
  void setUp() {
    testBytes = new byte[size];
    random.nextBytes(testBytes);

 */
public class HashFunctionBenchmark {

  // Use a statically configured random instance for all of the benchmarks
  private static final Random random = new Random(42);

  @Param({"10", "1000", "100000", "1000000"})
  private int size;

  @Param HashFunctionEnum hashFunctionEnum;

  private byte[] testBytes;

  @BeforeExperiment
  void setUp() {
    testBytes = new byte[size];
    random.nextBytes(testBytes);

  public void testKnownValues() {
    assertSip(new byte[] {}, 0x726fdb47dd0e0e31L);
    assertSip(new byte[] {0x61}, 0x2ba3e8e9a71148caL);
    assertSip(new byte[1000000], 0x28205108397aa742L);
    assertSip("12345678", 0x02130609caea37ebL);
    assertSip("abcdef", 0x2a6e77e733c7c05dL);
    assertSip("SipHash", 0x8325093242a96f60L);
  }

  fun largeRead() {
    val data = randomByteString(1000000)
    write(data)
    val operator =
      FileOperator(
        randomAccessFile!!.getChannel(),
      )
    val buffer = Buffer()
    operator.read(0, buffer, data.size.toLong())
    assertThat(buffer.readByteString()).isEqualTo(data)
  }

  @Test
  fun largeWrite() {
    val data = randomByteString(1000000)
    val operator =
      FileOperator(

	const bucket = "bucket"
	files := []usageTestFile{
		{name: "rootfile", size: 10000},
		{name: "rootfile2", size: 10000},
		{name: "dir1/d1file", size: 2000},
		{name: "dir2/d2file", size: 300},
		{name: "dir1/dira/dafile", size: 100000},
		{name: "dir1/dira/dbfile", size: 200000},
		{name: "dir1/dira/dirasub/dcfile", size: 1000000},
		{name: "dir1/dira/dirasub/sublevel3/dccccfile", size: 10},
	}