mapred.maxthreads.generate.mapoutput=2 # Num threads to write map outputs
mapred.maxthreads.partition.closer=0 # Asynchronous map flushers
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

 * subtype of {@code ValueGraph} that provides methods for adding and removing nodes and edges. If
 * you do not need to mutate a graph (e.g. if you write a method than runs a read-only algorithm on
 * the graph), you should use the non-mutating {@link ValueGraph} interface, or an {@link
 * ImmutableValueGraph}.
 *
 * <p>You can create an immutable copy of an existing {@code ValueGraph} using {@link

    public static final String UNSAFE_URL = "unsafe-url";
  }

  /**
   * The HTTP <a href="https://www.w3.org/TR/service-workers/#update-algorithm">{@code
   * Service-Worker}</a> header field name.
   *
   * @since 20.0
   */
  public static final String SERVICE_WORKER = "Service-Worker";
  /** The HTTP {@code TE} header field name. */

 * of {@code Graph} that provides methods for adding and removing nodes and edges. If you do not
 * need to mutate a graph (e.g. if you write a method than runs a read-only algorithm on the graph),
 * you should use the non-mutating {@link Graph} interface, or an {@link ImmutableGraph}.
 *
 * <p>You can create an immutable copy of an existing {@code Graph} using {@link
 * ImmutableGraph#copyOf(Graph)}:

 * of {@code Graph} that provides methods for adding and removing nodes and edges. If you do not
 * need to mutate a graph (e.g. if you write a method than runs a read-only algorithm on the graph),
 * you should use the non-mutating {@link Graph} interface, or an {@link ImmutableGraph}.
 *
 * <p>You can create an immutable copy of an existing {@code Graph} using {@link
 * ImmutableGraph#copyOf(Graph)}:

)

// newFileInfo - initializes new FileInfo, allocates a fresh erasure info.
func newFileInfo(object string, dataBlocks, parityBlocks int) (fi FileInfo) {
	fi.Erasure = ErasureInfo{
		Algorithm:    erasureAlgorithm,
		DataBlocks:   dataBlocks,
		ParityBlocks: parityBlocks,
		BlockSize:    blockSizeV2,
		Distribution: hashOrder(object, dataBlocks+parityBlocks),
	}
	return fi
}

	values.Set(storageRESTFilePath, path)
	values.Set(storageRESTOffset, strconv.Itoa(int(offset)))
	values.Set(storageRESTLength, strconv.Itoa(len(buf)))
	if verifier != nil {
		values.Set(storageRESTBitrotAlgo, verifier.algorithm.String())
		values.Set(storageRESTBitrotHash, hex.EncodeToString(verifier.sum))
	} else {
		values.Set(storageRESTBitrotAlgo, "")
		values.Set(storageRESTBitrotHash, "")
	}

 * traditional exclusive locks. Although this increases the false positives that the locks detect
 * (i.e. cycles that will not actually result in deadlock), it simplifies the algorithm and
 * implementation considerably. The assumption is that a user of this factory wishes to eliminate
 * any cyclic acquisition ordering.
 *
 * <p><strong>Explicit Lock Acquisition Ordering</strong>
 *

    of the contribution must be compared against the cost of maintaining the
    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in
    [tensorflow/addons](https://github.com/tensorflow/addons) to get some
    airtime before a decision is made regarding whether they are to be migrated
    to the core.

    //     fgh[b]c|[a]de -> fgh[a]c|[b]de. Finally we need to swap [c] with [b]:
    //     fgha[c]|[b]de -> fgha[b]|[c]de. Because these two blocks are the same size, we are done.
    // The Dolphin algorithm is attractive because it does the fewest array reads and writes: each
    // array slot is read and written exactly once. However, it can have very poor memory locality: