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

    R apply(double from, long index);
  }

  /**
   * Returns the last element of the specified stream, or {@link java.util.Optional#empty} if the
   * stream is empty.
   *
   * <p>Equivalent to {@code stream.reduce((a, b) -> b)}, but may perform significantly better. This
   * method's runtime will be between O(log n) and O(n), performing better on <a

   * storedPermits becomes 10.0 (assuming maxStoredPermits >= 10.0). At that point, a request of
   * acquire(3) arrives. We serve this request out of storedPermits, and reduce that to 7.0 (how
   * this is translated to throttling time is discussed later). Immediately after, assume that an
   * acquire(10) request arriving. We serve the request partly from storedPermits, using all the

    R apply(double from, long index);
  }

  /**
   * Returns the last element of the specified stream, or {@link java.util.Optional#empty} if the
   * stream is empty.
   *
   * <p>Equivalent to {@code stream.reduce((a, b) -> b)}, but may perform significantly better. This
   * method's runtime will be between O(log n) and O(n), performing better on <a

   * Nearly all declarations in this class are package-private,
   * accessed directly by subclasses.
   *
   * Table entries are of class Cell; a variant of AtomicLong padded
   * to reduce cache contention on most processors. Padding is
   * overkill for most Atomics because they are usually irregularly
   * scattered in memory and thus don't interfere much with each

   * following conditions:
   *
   * <ul>
   *   <li>You want to assign the same fraction of inputs to each bucket.
   *   <li>When you reduce the number of buckets, you can accept that the most recently added
   *       buckets will be removed first. More concretely, if you are dividing traffic among tasks,

 *       hand, is multiple-use, and does implement {@link Iterable}.
 *   <li>Streams offer many features not found here, including {@code min/max}, {@code distinct},
 *       {@code reduce}, {@code sorted}, the very powerful {@code collect}, and built-in support for
 *       parallelizing stream operations.
 *   <li>{@code FluentIterable} contains several features not available on {@code Stream}, which are

		// a row, then make sure we print it to call attention.
		if m[err.Error()] > 2 {
			peersLogAlwaysIf(ctx, fmt.Errorf("Following error has been printed %d times.. %w", m[err.Error()], err))
			// Reduce the count to introduce further delay in printing
			// but let it again print after the 2th attempt
			m[err.Error()]--
			m[err.Error()]--
		}
		m[err.Error()]++
	}
}()

	onlineDisks = make([]StorageAPI, len(disks))

	// List all the file commit ids from parts metadata.
	modTimes := listObjectModtimes(partsMetadata, errs)

	// Reduce list of UUIDs to a single common value.
	modTime = commonTime(modTimes, quorum)

	if modTime.IsZero() || modTime.Equal(timeSentinel) {
		etags := listObjectETags(partsMetadata, errs, quorum)

   * Nearly all declarations in this class are package-private,
   * accessed directly by subclasses.
   *
   * Table entries are of class Cell; a variant of AtomicLong padded
   * to reduce cache contention on most processors. Padding is
   * overkill for most Atomics because they are usually irregularly
   * scattered in memory and thus don't interfere much with each