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

  // +optional
  optional int32 queues = 1;

  // `handSize` is a small positive number that configures the
  // shuffle sharding of requests into queues.  When enqueuing a request
  // at this priority level the request's flow identifier (a string
  // pair) is hashed and the hash value is used to shuffle the list
  // of queues and deal a hand of the size specified here.  The

  // +optional
  optional int32 queues = 1;

  // `handSize` is a small positive number that configures the
  // shuffle sharding of requests into queues.  When enqueuing a request
  // at this priority level the request's flow identifier (a string
  // pair) is hashed and the hash value is used to shuffle the list
  // of queues and deal a hand of the size specified here.  The

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

		}
		buf = xlMetaV2TrimData(buf)

		vers = append(vers, metaCacheEntry{
			name:     "a",
			metadata: buf,
		})
	}

	// Shuffle...
	for i := 0; i < 100; i++ {
		rng := rand.New(rand.NewSource(int64(i)))
		rng.Shuffle(len(vers), func(i, j int) {
			vers[i], vers[j] = vers[j], vers[i]
		})
		var entries []chan metaCacheEntry
		for _, v := range vers {
			v.cached = nil

  // +optional
  optional int32 queues = 1;

  // `handSize` is a small positive number that configures the
  // shuffle sharding of requests into queues.  When enqueuing a request
  // at this priority level the request's flow identifier (a string
  // pair) is hashed and the hash value is used to shuffle the list
  // of queues and deal a hand of the size specified here.  The

  // +optional
  optional int32 queues = 1;

  // `handSize` is a small positive number that configures the
  // shuffle sharding of requests into queues.  When enqueuing a request
  // at this priority level the request's flow identifier (a string
  // pair) is hashed and the hash value is used to shuffle the list
  // of queues and deal a hand of the size specified here.  The

    for (int attempts = 0; attempts < reduceIterationsIfGwt(1000); attempts++) {
      ArrayList<Integer> elements = createOrderedList(10);
      Collections.shuffle(elements, random);
      MinMaxPriorityQueue<Integer> queue = MinMaxPriorityQueue.create(elements);
      Collections.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());