for (int i = 0; i < alpha; i++) {
      chars.add(randomAlpha());
    }
    for (int i = 0; i < nonAlpha; i++) {
      chars.add(randomNonAlpha());
    }
    Collections.shuffle(chars, random);
    char[] array = Chars.toArray(chars);
    this.testString = new String(array);
  }

  private char randomAlpha() {
    return ALPHA.charAt(random.nextInt(ALPHA.length()));
  }

    this.striped = impl.get(numStripes);
    stripes = new int[numStripes];
    for (int i = 0; i < numStripes; i++) {
      stripes[i] = i;
    }
    List<Integer> asList = Ints.asList(stripes);
    Collections.shuffle(asList, new Random(0xdeadbeef));

    // do bulk gets with exactly 10 keys (possibly <10 stripes) (or less if numStripes is smaller)
    bulkGetSet = ImmutableList.copyOf(limit(cycle(asList), 10));
  }

  @Footprint

	var err error
	allModels := []interface{}{&User{}, &Account{}, &Pet{}, &Company{}, &Toy{}, &Language{}, &Coupon{}, &CouponProduct{}, &Order{}, &Parent{}, &Child{}, &Tools{}}
	rand.Seed(time.Now().UnixNano())
	rand.Shuffle(len(allModels), func(i, j int) { allModels[i], allModels[j] = allModels[j], allModels[i] })

	DB.Migrator().DropTable("user_friends", "user_speaks")

	if err = DB.Migrator().DropTable(allModels...); err != nil {

mapreduce.fileoutputcommitter.algorithm.version=2 # Use the latest committer version
mapreduce.job.reduce.slowstart.completedmaps=0.99 # 99% map, then reduce
mapreduce.reduce.shuffle.input.buffer.percent=0.9 # Min % buffer in RAM
mapreduce.reduce.shuffle.merge.percent=0.9 # Minimum % merges in RAM
mapreduce.reduce.speculative=false # Disable speculation for reducing
mapreduce.task.io.sort.factor=999 # Threshold before writing to disk

    for (int i = 0; i < 10; i++) {
      hashCodes.add(HashCode.fromLong(random.nextLong()));
    }
    HashCode hashCode1 = Hashing.combineOrdered(hashCodes);
    Collections.shuffle(hashCodes, random);
    HashCode hashCode2 = Hashing.combineOrdered(hashCodes);

    assertFalse(hashCode1.equals(hashCode2));
  }

  public void testCombineUnordered_empty() {
    assertThrows(

            return null;
          });
    }
    assertEquals(allTasks.size() + 1, barrier.getParties());
    for (int i = 0; i < 1000; i++) {
      Collections.shuffle(allTasks);
      AbstractFuture<String> future = new AbstractFuture<String>() {};
      currentFuture.set(future);
      for (Callable<?> task : allTasks) {

    for (int attempts = 0; attempts < reduceIterationsIfGwt(1000); attempts++) {
      ArrayList<Integer> elements = createOrderedList(10);
      shuffle(elements, random);
      MinMaxPriorityQueue<Integer> queue = MinMaxPriorityQueue.create(elements);
      shuffle(elements, random);
      for (Integer element : elements) {
        assertThat(queue.remove(element)).isTrue();
        assertIntact(queue);

    for (int attempts = 0; attempts < reduceIterationsIfGwt(1000); attempts++) {
      ArrayList<Integer> elements = createOrderedList(10);
      shuffle(elements, random);
      MinMaxPriorityQueue<Integer> queue = MinMaxPriorityQueue.create(elements);
      shuffle(elements, random);
      for (Integer element : elements) {
        assertThat(queue.remove(element)).isTrue();
        assertIntact(queue);

      indexes.add(index);
      expectedBuilder.put(index, expectedLargeDatasetPercentile(index));
    }
    Random random = new Random(770683168895677741L);
    Collections.shuffle(indexes, random);
    assertThat(percentiles().indexes(Ints.toArray(indexes)).compute(PSEUDORANDOM_DATASET))
        .comparingValuesUsing(QUANTILE_CORRESPONDENCE)
        .containsExactlyEntriesIn(expectedBuilder.buildOrThrow());

      indexes.add(index);
      expectedBuilder.put(index, expectedLargeDatasetPercentile(index));
    }
    Random random = new Random(770683168895677741L);
    Collections.shuffle(indexes, random);
    assertThat(percentiles().indexes(Ints.toArray(indexes)).compute(PSEUDORANDOM_DATASET))
        .comparingValuesUsing(QUANTILE_CORRESPONDENCE)
        .containsExactlyEntriesIn(expectedBuilder.buildOrThrow());