本文整理匯總了Java中org.apache.hadoop.mapreduce.Job.getMapOutputKeyClass方法的典型用法代碼示例。如果您正苦於以下問題：Java Job.getMapOutputKeyClass方法的具體用法？Java Job.getMapOutputKeyClass怎麽用？Java Job.getMapOutputKeyClass使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類org.apache.hadoop.mapreduce.Job的用法示例。
在下文中一共展示了Job.getMapOutputKeyClass方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。
示例1: setConf
import org.apache.hadoop.mapreduce.Job; //導入方法依賴的package包/類
/**
* Read in the partition file and build indexing data structures.
* If the keytype is {@link org.apache.hadoop.io.BinaryComparable} and
* <tt>total.order.partitioner.natural.order</tt> is not false, a trie
* of the first <tt>total.order.partitioner.max.trie.depth</tt>(2) + 1 bytes
* will be built. Otherwise, keys will be located using a binary search of
* the partition keyset using the {@link org.apache.hadoop.io.RawComparator}
* defined for this job. The input file must be sorted with the same
* comparator and contain {@link Job#getNumReduceTasks()} - 1 keys.
*/
@SuppressWarnings("unchecked") // keytype from conf not static
public void setConf(Configuration conf) {
try {
this.conf = conf;
String parts = getPartitionFile(conf);
final Path partFile = new Path(parts);
final FileSystem fs = (DEFAULT_PATH.equals(parts))
? FileSystem.getLocal(conf) // assume in DistributedCache
: partFile.getFileSystem(conf);
Job job = Job.getInstance(conf);
Class<K> keyClass = (Class<K>)job.getMapOutputKeyClass();
K[] splitPoints = readPartitions(fs, partFile, keyClass, conf);
if (splitPoints.length != job.getNumReduceTasks() - 1) {
throw new IOException("Wrong number of partitions in keyset");
}
RawComparator<K> comparator =
(RawComparator<K>) job.getSortComparator();
for (int i = 0; i < splitPoints.length - 1; ++i) {
if (comparator.compare(splitPoints[i], splitPoints[i+1]) >= 0) {
throw new IOException("Split points are out of order");
}
}
boolean natOrder =
conf.getBoolean(NATURAL_ORDER, true);
if (natOrder && BinaryComparable.class.isAssignableFrom(keyClass)) {
partitions = buildTrie((BinaryComparable[])splitPoints, 0,
splitPoints.length, new byte[0],
// Now that blocks of identical splitless trie nodes are
// represented reentrantly, and we develop a leaf for any trie
// node with only one split point, the only reason for a depth
// limit is to refute stack overflow or bloat in the pathological
// case where the split points are long and mostly look like bytes
// iii...iixii...iii . Therefore, we make the default depth
// limit large but not huge.
conf.getInt(MAX_TRIE_DEPTH, 200));
} else {
partitions = new BinarySearchNode(splitPoints, comparator);
}
} catch (IOException e) {
throw new IllegalArgumentException("Can't read partitions file", e);
}
}