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See:
Description
Interface Summary | |
---|---|
InputFormat<K,V> | Deprecated. Use InputFormat instead. |
InputSplit | Deprecated. Use InputSplit instead. |
JobConfigurable | Deprecated. |
JobHistory.Listener | Callback interface for reading back log events from JobHistory. |
Mapper<K1,V1,K2,V2> | Deprecated. Use Mapper instead. |
MapRunnable<K1,V1,K2,V2> | Deprecated. Use Mapper instead. |
OutputCollector<K,V> | Collects the <key, value> pairs output by Mapper s
and Reducer s. |
OutputFormat<K,V> | Deprecated. Use OutputFormat instead. |
Partitioner<K2,V2> | Deprecated. Use Partitioner instead. |
RawKeyValueIterator | RawKeyValueIterator is an iterator used to iterate over
the raw keys and values during sort/merge of intermediate data. |
RecordReader<K,V> | RecordReader reads <key, value> pairs from an
InputSplit . |
RecordWriter<K,V> | RecordWriter writes the output <key, value> pairs
to an output file. |
Reducer<K2,V2,K3,V3> | Deprecated. Use Reducer instead. |
Reporter | A facility for Map-Reduce applications to report progress and update counters, status information etc. |
RunningJob | RunningJob is the user-interface to query for details on a
running Map-Reduce job. |
SequenceFileInputFilter.Filter | filter interface |
Class Summary | |
---|---|
ClusterStatus | Status information on the current state of the Map-Reduce cluster. |
Counters | Deprecated. Use Counters instead. |
Counters.Counter | A counter record, comprising its name and value. |
Counters.Group | Group of counters, comprising of counters from a particular
counter Enum class. |
DefaultJobHistoryParser | Default parser for job history files. |
FileInputFormat<K,V> | Deprecated. Use FileInputFormat
instead. |
FileOutputCommitter | An OutputCommitter that commits files specified
in job output directory i.e. |
FileOutputFormat<K,V> | A base class for OutputFormat . |
FileSplit | Deprecated. Use FileSplit
instead. |
ID | Deprecated. |
IsolationRunner | |
JobClient | JobClient is the primary interface for the user-job to interact
with the JobTracker . |
JobConf | Deprecated. Use Configuration instead |
JobContext | Deprecated. Use JobContext instead. |
JobEndNotifier | |
JobHistory | Provides methods for writing to and reading from job history. |
JobHistory.HistoryCleaner | Delete history files older than one month. |
JobHistory.JobInfo | Helper class for logging or reading back events related to job start, finish or failure. |
JobHistory.MapAttempt | Helper class for logging or reading back events related to start, finish or failure of a Map Attempt on a node. |
JobHistory.ReduceAttempt | Helper class for logging or reading back events related to start, finish or failure of a Map Attempt on a node. |
JobHistory.Task | Helper class for logging or reading back events related to Task's start, finish or failure. |
JobHistory.TaskAttempt | Base class for Map and Reduce TaskAttempts. |
JobID | Deprecated. |
JobProfile | A JobProfile is a MapReduce primitive. |
JobQueueInfo | Class that contains the information regarding the Job Queues which are maintained by the Hadoop Map/Reduce framework. |
JobStatus | Describes the current status of a job. |
JobTracker | JobTracker is the central location for submitting and tracking MR jobs in a network environment. |
KeyValueLineRecordReader | This class treats a line in the input as a key/value pair separated by a separator character. |
KeyValueTextInputFormat | An InputFormat for plain text files. |
LineRecordReader | Deprecated. Use
LineRecordReader instead. |
LineRecordReader.LineReader | Deprecated. Use LineReader instead. |
MapFileOutputFormat | An OutputFormat that writes MapFile s. |
MapReduceBase | Deprecated. |
MapReducePolicyProvider | PolicyProvider for Map-Reduce protocols. |
MapRunner<K1,V1,K2,V2> | Default MapRunnable implementation. |
MultiFileInputFormat<K,V> | Deprecated. Use CombineFileInputFormat instead |
MultiFileSplit | Deprecated. Use CombineFileSplit instead |
OutputCommitter | Deprecated. Use OutputCommitter instead. |
OutputLogFilter | This class filters log files from directory given It doesnt accept paths having _logs. |
SequenceFileAsBinaryInputFormat | InputFormat reading keys, values from SequenceFiles in binary (raw) format. |
SequenceFileAsBinaryInputFormat.SequenceFileAsBinaryRecordReader | Read records from a SequenceFile as binary (raw) bytes. |
SequenceFileAsBinaryOutputFormat | An OutputFormat that writes keys, values to
SequenceFile s in binary(raw) format |
SequenceFileAsBinaryOutputFormat.WritableValueBytes | Inner class used for appendRaw |
SequenceFileAsTextInputFormat | This class is similar to SequenceFileInputFormat, except it generates SequenceFileAsTextRecordReader which converts the input keys and values to their String forms by calling toString() method. |
SequenceFileAsTextRecordReader | This class converts the input keys and values to their String forms by calling toString() method. |
SequenceFileInputFilter<K,V> | A class that allows a map/red job to work on a sample of sequence files. |
SequenceFileInputFilter.FilterBase | base class for Filters |
SequenceFileInputFilter.MD5Filter | This class returns a set of records by examing the MD5 digest of its key against a filtering frequency f. |
SequenceFileInputFilter.PercentFilter | This class returns a percentage of records The percentage is determined by a filtering frequency f using the criteria record# % f == 0. |
SequenceFileInputFilter.RegexFilter | Records filter by matching key to regex |
SequenceFileInputFormat<K,V> | Deprecated. Use
SequenceFileInputFormat
instead. |
SequenceFileOutputFormat<K,V> | Deprecated. Use
SequenceFileOutputFormat
instead. |
SequenceFileRecordReader<K,V> | An RecordReader for SequenceFile s. |
SkipBadRecords | Utility class for skip bad records functionality. |
TaskAttemptContext | Deprecated. Use TaskAttemptContext
instead. |
TaskAttemptID | Deprecated. |
TaskCompletionEvent | This is used to track task completion events on job tracker. |
TaskGraphServlet | The servlet that outputs svg graphics for map / reduce task statuses |
TaskID | Deprecated. |
TaskLog | A simple logger to handle the task-specific user logs. |
TaskLogAppender | A simple log4j-appender for the task child's map-reduce system logs. |
TaskLogServlet | A servlet that is run by the TaskTrackers to provide the task logs via http. |
TaskReport | A report on the state of a task. |
TaskTracker | TaskTracker is a process that starts and tracks MR Tasks in a networked environment. |
TaskTracker.MapOutputServlet | This class is used in TaskTracker's Jetty to serve the map outputs to other nodes. |
TextInputFormat | Deprecated. Use TextInputFormat
instead. |
TextOutputFormat<K,V> | Deprecated. Use
TextOutputFormat instead. |
TextOutputFormat.LineRecordWriter<K,V> |
Enum Summary | |
---|---|
JobClient.TaskStatusFilter | |
JobHistory.Keys | Job history files contain key="value" pairs, where keys belong to this enum. |
JobHistory.RecordTypes | Record types are identifiers for each line of log in history files. |
JobHistory.Values | This enum contains some of the values commonly used by history log events. |
JobPriority | Used to describe the priority of the running job. |
JobTracker.State | |
TaskCompletionEvent.Status | |
TaskLog.LogName | The filter for userlogs. |
TIPStatus | The states of a TaskInProgress as seen by the JobTracker. |
Exception Summary | |
---|---|
FileAlreadyExistsException | Used when target file already exists for any operation and is not configured to be overwritten. |
InvalidFileTypeException | Used when file type differs from the desired file type. |
InvalidInputException | This class wraps a list of problems with the input, so that the user can get a list of problems together instead of finding and fixing them one by one. |
InvalidJobConfException | This exception is thrown when jobconf misses some mendatory attributes or value of some attributes is invalid. |
JobTracker.IllegalStateException | A client tried to submit a job before the Job Tracker was ready. |
A software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) parallelly on large clusters (thousands of nodes) built of commodity hardware in a reliable, fault-tolerant manner.
A Map-Reduce job usually splits the input data-set into independent
chunks which processed by map tasks in completely parallel manner,
followed by reduce tasks which aggregating their output. Typically both
the input and the output of the job are stored in a
FileSystem
. The framework takes care of monitoring
tasks and re-executing failed ones. Since, usually, the compute nodes and the
storage nodes are the same i.e. Hadoop's Map-Reduce framework and Distributed
FileSystem are running on the same set of nodes, tasks are effectively scheduled
on the nodes where data is already present, resulting in very high aggregate
bandwidth across the cluster.
The Map-Reduce framework operates exclusively on <key, value>
pairs i.e. the input to the job is viewed as a set of <key, value>
pairs and the output as another, possibly different, set of
<key, value> pairs. The keys and values have to
be serializable as Writable
s and additionally the
keys have to be WritableComparable
s in
order to facilitate grouping by the framework.
Data flow:
(input) <k1, v1> | V map | V <k2, v2> | V combine | V <k2, v2> | V reduce | V <k3, v3> (output)
Applications typically implement
Mapper.map(Object, Object, OutputCollector, Reporter)
and
Reducer.reduce(Object, Iterator, OutputCollector, Reporter)
methods. The application-writer also specifies various facets of the job such
as input and output locations, the Partitioner, InputFormat
& OutputFormat implementations to be used etc. as
a JobConf
. The client program,
JobClient
, then submits the job to the framework
and optionally monitors it.
The framework spawns one map task per
InputSplit
generated by the
InputFormat
of the job and calls
Mapper.map(Object, Object, OutputCollector, Reporter)
with each <key, value> pair read by the
RecordReader
from the InputSplit for
the task. The intermediate outputs of the maps are then grouped by keys
and optionally aggregated by combiner. The key space of intermediate
outputs are paritioned by the Partitioner
, where
the number of partitions is exactly the number of reduce tasks for the job.
The reduce tasks fetch the sorted intermediate outputs of the maps, via http,
merge the <key, value> pairs and call
Reducer.reduce(Object, Iterator, OutputCollector, Reporter)
for each <key, list of values> pair. The output of the reduce tasks' is
stored on the FileSystem by the
RecordWriter
provided by the
OutputFormat
of the job.
Map-Reduce application to perform a distributed grep:
public class Grep extends Configured implements Tool { // map: Search for the pattern specified by 'grep.mapper.regex' & // 'grep.mapper.regex.group' class GrepMapper<K, Text> extends MapReduceBase implements Mapper<K, Text, Text, LongWritable> { private Pattern pattern; private int group; public void configure(JobConf job) { pattern = Pattern.compile(job.get("grep.mapper.regex")); group = job.getInt("grep.mapper.regex.group", 0); } public void map(K key, Text value, OutputCollector<Text, LongWritable> output, Reporter reporter) throws IOException { String text = value.toString(); Matcher matcher = pattern.matcher(text); while (matcher.find()) { output.collect(new Text(matcher.group(group)), new LongWritable(1)); } } } // reduce: Count the number of occurrences of the pattern class GrepReducer<K> extends MapReduceBase implements Reducer<K, LongWritable, K, LongWritable> { public void reduce(K key, Iterator<LongWritable> values, OutputCollector<K, LongWritable> output, Reporter reporter) throws IOException { // sum all values for this key long sum = 0; while (values.hasNext()) { sum += values.next().get(); } // output sum output.collect(key, new LongWritable(sum)); } } public int run(String[] args) throws Exception { if (args.length < 3) { System.out.println("Grep <inDir> <outDir> <regex> [<group>]"); ToolRunner.printGenericCommandUsage(System.out); return -1; } JobConf grepJob = new JobConf(getConf(), Grep.class); grepJob.setJobName("grep"); FileInputFormat.setInputPaths(grepJob, new Path(args[0])); FileOutputFormat.setOutputPath(grepJob, args[1]); grepJob.setMapperClass(GrepMapper.class); grepJob.setCombinerClass(GrepReducer.class); grepJob.setReducerClass(GrepReducer.class); grepJob.set("mapred.mapper.regex", args[2]); if (args.length == 4) grepJob.set("mapred.mapper.regex.group", args[3]); grepJob.setOutputFormat(SequenceFileOutputFormat.class); grepJob.setOutputKeyClass(Text.class); grepJob.setOutputValueClass(LongWritable.class); JobClient.runJob(grepJob); return 0; } public static void main(String[] args) throws Exception { int res = ToolRunner.run(new Configuration(), new Grep(), args); System.exit(res); } }
Notice how the data-flow of the above grep job is very similar to doing the same via the unix pipeline:
cat input/* | grep | sort | uniq -c > out
input | map | shuffle | reduce > out
Hadoop Map-Reduce applications need not be written in JavaTM only. Hadoop Streaming is a utility which allows users to create and run jobs with any executables (e.g. shell utilities) as the mapper and/or the reducer. Hadoop Pipes is a SWIG-compatible C++ API to implement Map-Reduce applications (non JNITM based).
See Google's original Map/Reduce paper for background information.
Java and JNI are trademarks or registered trademarks of Sun Microsystems, Inc. in the United States and other countries.
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