@BeforeExperiment
  void setUp() {
    Random random = new Random(randomSeed);
    xInts = new int[SAMPLE_SIZE];
    yInts = new int[SAMPLE_SIZE];
    xLongs = new long[SAMPLE_SIZE];
    yLongs = new long[SAMPLE_SIZE];
    constant = new int[SAMPLE_SIZE];
    for (int i = 0; i < SAMPLE_SIZE; i++) {
      xInts[i] = random.nextInt(Integer.MAX_VALUE);
      yInts[i] = random.nextInt(Integer.MAX_VALUE);

for idx in {1..10}; do
	# generate random nr of blocks
	COUNT=$((RANDOM % 100 + 100))
	# generate random content
	dd if=/dev/urandom bs=50K count="${COUNT}" of="${SRC_DIR}"/file"$idx"
done

# create small object that will be inlined into xl.meta
dd if=/dev/urandom bs=50K count=1 of="${INLINED_DIR}"/inlined

  @Param({"20", "2000"})
  int size;

  @Param({"2", "20"})
  int nonAlphaRatio; // one non-alpha char per this many chars

  @Param boolean noWorkToDo;

  Random random;
  String testString;

  @BeforeExperiment
  void setUp() {
    random = new Random(0xdeadbeef); // fix the seed so results are comparable across runs

    int nonAlpha = size / nonAlphaRatio;
    int alpha = size - nonAlpha;

        }
        return root;
      }
    },
    RANDOM {
      /**
       * Generates a tree with topology selected uniformly at random from the topologies of binary
       * trees of the specified size.
       */
      @Override
      Optional<BinaryNode> createTree(int size, Random rng) {
        int[] keys = new int[size];
        for (int i = 0; i < size; i++) {

  // 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);
  }

  @Benchmark

  @Param({"20", "2000"})
  int size;

  @Param({"2", "20"})
  int nonAlphaRatio; // one non-alpha char per this many chars

  @Param boolean noWorkToDo;

  Random random;
  String testString;

  @BeforeExperiment
  void setUp() {
    random = new Random(0xdeadbeef); // fix the seed so results are comparable across runs

    int nonAlpha = size / nonAlphaRatio;
    int alpha = size - nonAlpha;

        }
        return root;
      }
    },
    RANDOM {
      /**
       * Generates a tree with topology selected uniformly at random from the topologies of binary
       * trees of the specified size.
       */
      @Override
      Optional<BinaryNode> createTree(int size, Random rng) {
        int[] keys = new int[size];
        for (int i = 0; i < size; i++) {

  int[] queries = new int[ARRAY_SIZE];

  @BeforeExperiment
  void setUp() {
    Random random = new Random();
    multisets.clear();
    for (int i = 0; i < ARRAY_SIZE; i++) {
      HashMultiset<Integer> multiset = HashMultiset.<Integer>create();
      multisets.add(multiset);
      queries[i] = random.nextInt();
      multiset.add(queries[i]);
    }
  }

  @Benchmark
  int add(int reps) {

  private final SpecialRandom random;
  private final double hitRate;
  private final int size;

  private final Set<Element> valuesInSet;
  private final Element[] queries;

  CollectionBenchmarkSampleData(int size) {
    this(true, new SpecialRandom(), 1.0, size);
  }

  CollectionBenchmarkSampleData(
      boolean isUserTypeFast, SpecialRandom random, double hitRate, int size) {

  int datasetSize;

  @Param QuantilesAlgorithm algorithm;

  private final double[][] datasets = new double[0x100][];

  @BeforeExperiment
  void setUp() {
    Random rng = new Random();
    for (int i = 0; i < 0x100; i++) {
      datasets[i] = new double[datasetSize];
      for (int j = 0; j < datasetSize; j++) {
        datasets[i][j] = rng.nextDouble();
      }
    }
  }