Hadoop Programming - MapReduce, Input, Output, Serialization, Job

Overview
• MapReduce Types
• Input Formats
• Output Formats
• Serialization
• Job
• http://hadoop.apache.org/docs/r2.2.0/api/or
g/apache/hadoop/mapreduce/package-
summary.html

Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>
• Maps input key/value pairs to a set of intermediate key/value pairs.
• Maps are the individual tasks which transform input records into a intermediate
records. The transformed intermediate records need not be of the same type as the
input records. A given input pair may map to zero or many output pairs.
• The Hadoop Map-Reduce framework spawns one map task for each InputSplit
generated by the InputFormat for the job.
• The framework first calls setup(org.apache.hadoop.mapreduce.Mapper.Context),
followed by map(Object, Object, Context) for each key/value pair in the InputSplit.
Finally cleanup(Context) is called.
http://hadoop.apache.org/docs/r2.2.0/api/org/apache/hadoop/mapreduce/Mapper.ht
ml

public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}

What is Writable?
• Hadoop defines its own “box” classes for
strings (Text), integers (IntWritable), etc.
• All values are instances of Writable
• All keys are instances of WritableComparable

Writable
• A serializable object which implements a simple,
efficient, serialization protocol, based on DataInput
and DataOutput.
• Any key or value type in the Hadoop Map-Reduce
framework implements this interface.
• Implementations typically implement a static
read(DataInput) method which constructs a new
instance, calls readFields(DataInput) and returns the
instance.
g/apache/hadoop/io/Writable.html

Hadoop Programming - MapReduce, Input, Output, Serialization, Job

public class MyWritable implements Writable {
// Some data
private int counter;
private long timestamp;
public void write(DataOutput out) throws IOException {
out.writeInt(counter);
out.writeLong(timestamp);
}
public void readFields(DataInput in) throws IOException {
counter = in.readInt();
timestamp = in.readLong();
}
public static MyWritable read(DataInput in) throws IOException {
MyWritable w = new MyWritable();
w.readFields(in);
return w;
}
}

public class MyWritableComparable implements WritableComparable {
// Some data
private int counter;
private long timestamp;
public void write(DataOutput out) throws IOException {
out.writeInt(counter);
out.writeLong(timestamp);
}
public void readFields(DataInput in) throws IOException {
counter = in.readInt();
timestamp = in.readLong();
}
public int compareTo(MyWritableComparable w) {
int thisValue = this.value;
int thatValue = ((IntWritable)o).value;
return (thisValue < thatValue ? -1 : (thisValue==thatValue ? 0 : 1));
}
}

Getting Data To The Mapper
Input file
InputSplit InputSplit InputSplit InputSplit
Input file
RecordReader RecordReader RecordReader RecordReader
Mapper
(intermediates)
Mapper
(intermediates)
Mapper
(intermediates)
Mapper
(intermediates)
InputFormat

public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount <in> <out>");
System.exit(2);
}
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}

Reading Data
• Data sets are specified by InputFormats
– Defines input data (e.g., a directory)
– Identifies partitions of the data that form an
InputSplit
– Factory for RecordReader objects to extract (k, v)
records from the input source

Input Format
• InputFormat describes the input-specification for a Map-
Reduce job
• The Map-Reduce framework relies on the InputFormat of the
job to:
– Validate the input-specification of the job.
– Split-up the input file(s) into logical InputSplits, each of which is then
assigned to an individual Mapper.
– Provide the RecordReader implementation to be used to glean input
records from the logical InputSplit for processing by the Mapper.
http://hadoop.apache.org/docs/r2.2.0/api/org/apache/hadoop/mapreduce/Inp
utFormat.html

FileInputFormat and Friends
• TextInputFormat
– Treats each ‘n’-terminated line of a file as a value
• KeyValueTextInputFormat
– Maps ‘n’- terminated text lines of “k SEP v”
• SequenceFileInputFormat
– Binary file of (k, v) pairs (passing data between the output
of one MapReduce job to the input of some other
MapReduce job)
• SequenceFileAsTextInputFormat
– Same, but maps (k.toString(), v.toString())

Filtering File Inputs
• FileInputFormat will read all files out of a
specified directory and send them to the
mapper
• Delegates filtering this file list to a method
subclasses may override
– e.g., Create your own “xyzFileInputFormat” to
read *.xyz from directory list

Record Readers
• Each InputFormat provides its own
RecordReader implementation
– Provides (unused?) capability multiplexing
• LineRecordReader
– Reads a line from a text file
• KeyValueRecordReader
– Used by KeyValueTextInputFormat

Input Split Size
• FileInputFormat will divide large files into
chunks
– Exact size controlled by mapred.min.split.size
• RecordReaders receive file, offset, and length
of chunk
• Custom InputFormat implementations may
override split size
– e.g., “NeverChunkFile”

public class ObjectPositionInputFormat extends
FileInputFormat<Text, Point3D> {
public RecordReader<Text, Point3D> getRecordReader(
InputSplit input, JobConf job, Reporter reporter)
throws IOException {
reporter.setStatus(input.toString());
return new ObjPosRecordReader(job, (FileSplit)input);
}
InputSplit[] getSplits(JobConf job, int numSplits) throuw IOException;
}

class ObjPosRecordReader implements RecordReader<Text, Point3D> {
public ObjPosRecordReader(JobConf job, FileSplit split) throws IOException
{}
public boolean next(Text key, Point3D value) throws IOException {
// get the next line}
public Text createKey() {
}
public Point3D createValue() {
}
public long getPos() throws IOException {
}
public void close() throws IOException {
}
public float getProgress() throws IOException {}
}

Sending Data To Reducers
• Map function produces Map.Context object
– Map.context() takes (k, v) elements
• Any (WritableComparable, Writable) can be
used

WritableComparator
• Compares WritableComparable data
– Will call WritableComparable.compare()
– Can provide fast path for serialized data

Partition And Shuffle
Mapper
(intermediates)
Mapper
(intermediates)
Mapper
(intermediates)
Mapper
(intermediates)
Reducer Reducer Reducer
(intermediates) (intermediates) (intermediates)
Partitioner Partitioner Partitioner Partitioner
shuffling

Partitioner
• int getPartition(key, val, numPartitions)
– Outputs the partition number for a given key
– One partition == values sent to one Reduce task
• HashPartitioner used by default
– Uses key.hashCode() to return partition num
• Job sets Partitioner implementation

public class MyPartitioner implements Partitioner<IntWritable,Text> {
@Override
public int getPartition(IntWritable key, Text value, int numPartitions) {
/* Pretty ugly hard coded partitioning function. Don't do that in practice,
it is just for the sake of understanding. */
int nbOccurences = key.get();
if( nbOccurences < 3 )
return 0;
else
return 1;
}
@Override
public void configure(JobConf arg0) {
}
}
job.setPartitionerClass(MyPartitioner.class);

Reducer<KEYIN,VALUEIN,KEYOUT,VALUEOUT>
• Reduces a set of intermediate values which
share a key to a smaller set of values.
• Reducer has 3 primary phases:
– Shuffle
– Sort
– Reduce
g/apache/hadoop/mapreduce/Reducer.html

public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}

Finally: Writing The Output
Reducer Reducer Reducer
RecordWriter RecordWriter RecordWriter
output file output file output file
OutputFormat

OutputFormat
• Analogous to InputFormat
• TextOutputFormat
– Writes “key valn” strings to output file
• SequenceFileOutputFormat
– Uses a binary format to pack (k, v) pairs
• NullOutputFormat
– Discards output

Job
• The job submitter's view of the Job.
• It allows the user to configure the job, submit it,
control its execution, and query the state. The set
methods only work until the job is submitted,
afterwards they will throw an IllegalStateException.
• Normally the user creates the application, describes
various facets of the job via Job and then submits the
job and monitor its progress.
http://hadoop.apache.org/docs/r2.2.0/api/org/apache/hadoop/mapreduce/Job.html

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