Apache MapReduce

MapReduce is the key algorithm that the Hadoop data processing engine uses to distribute work around a cluster. A MapReduce job splits a large data set into independent chunks and organizes them into key, value pairs for parallel processing. This parallel processing improves the speed and reliability of the cluster, returning solutions more quickly and with greater reliability.

The Map function divides the input into ranges by the InputFormat and creates a map task for each range in the input. The JobTracker distributes those tasks to the worker nodes. The output of each map task is partitioned into a group of key-value pairs for each reduce.

·         map(key1,value) -> list<key2,value2>

The Reduce function then collects the various results and combines them to answer the larger problem that the master node needs to solve. Each reduce pulls the relevant partition from the machines where the maps executed, then writes its output back into HDFS. Thus, the reduce is able to collect the data from all of the maps for the keys and combine them to solve the problem.

·         reduce(key2, list<value2>) -> list<value3>

The current Apache Hadoop MapReduce System is composed of the JobTracker, which is the master, and the per-node slaves called TaskTrackers. The JobTracker is responsible for resource management (managing the worker nodes i.e. TaskTrackers), tracking resource consumption/availability and also job life-cycle management (scheduling individual tasks of the job, tracking progress, providing fault-tolerance for tasks etc).

The TaskTracker has simple responsibilities – launch/teardown tasks on orders from the JobTracker and provide task-status information to the JobTracker periodically.

The Apache Hadoop projects provide a series of tools designed to solve big data problems. The Hadoop cluster implements a parallel computing cluster using inexpensive commodity hardware. The cluster is partitioned across many servers to provide a near linear scalability. The philosophy of the cluster design is to bring the computing to the data. So each datanode will hold part of the overall data and be able to process the data that it holds. The overall framework for the processing software is called MapReduce.

 Mapreduce

Overview Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.

A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks. Typically the compute nodes and the storage nodes are the same, that is, the MapReduce framework and the Hadoop Distributed File System (see HDFS Architecture Guide) are running on the same set of nodes.a reliable, fault-tolerant manner.

The MapReduce framework consists of a single master JobTracker and one slave TaskTracker per cluster-node. The master is responsible for scheduling the jobs' component tasks on the slaves, monitoring them and re-executing the failed tasks. The slaves execute the tasks as directed by the master.

Inputs and Outputs:

 The MapReduce framework operates exclusively on pairs, that is, the framework views the input to the job as a set of pairs and produces a set of pairs as the output of the job, conceivably of different types. The key and value classes have to be serializable by the framework and hence need to implement the Writable interface. Additionally, the key

classes have to implement the WritableComparable interface to facilitate sorting by the framework. Input and Output types of a MapReduce job: (input) -> map -> -> combine -> -> reduce -> (output)

  Example: Before we jump into the details, lets walk through an example MapReduce application to get a flavour for how they work. WordCount is a simple application that counts the number of occurences of each word in a given input set.

Source Code

WordCount.java

 1. package org.myorg;

 2.3. import java.io.IOException;

4. import java.util.*;

5. 6. import org.apache.hadoop.fs.Path;

 7. import org.apache.hadoop.conf.*;           

8. import org.apache.hadoop.io.*;

 9. import org.apache.hadoop.mapred.*;

 10. import org.apache.hadoop.util.*;

 11.12. public class WordCount {

13.14. public static class Map extends MapReduceBase implements Mapper {

 15. private final static IntWritable one = new IntWritable(1);

16. private Text word = new Text();

17. 18. public void map(LongWritable key, Text value, OutputCollector output, Reporter reporter) throws IOException {

19. String line = value.toString();

 20. StringTokenizer tokenizer = new StringTokenizer(line);

 21. while (tokenizer.hasMoreTokens()) {

22. word.set(tokenizer.nextToken());

 23. output.collect(word, one);

 24. }

 25. }

26. }

 27. 28. public static class Reduce extends MapReduceBase implements Reducer {

29. public void reduce(Text key, Iterator values, OutputCollector output, Reporter reporter) throws IOException {

 30. int sum = 0;

 31. while (values.hasNext()) {

 32. sum += values.next().get();

 33. }

 34. output.collect(key, new IntWritable(sum));

 35. }

 36. }

37. 38. public static void main(String[] args) throws Exception {

39. JobConf conf = new JobConf(WordCount.class); 40. conf.setJobName("wordcount");

41. 42. conf.setOutputKeyClass(Text.class);

 43. conf.setOutputValueClass(IntWritable.class);

 44.45. conf.setMapperClass(Map.class);

46. conf.setCombinerClass(Reduce.class);

47. conf.setReducerClass(Reduce.class);

48. 49. conf.setInputFormat(TextInputFormat.class);

 50. conf.setOutputFormat(TextOutputFormat.class);

51. 52. FileInputFormat.setInputPaths(conf, new Path(args[0]));

 53. FileOutputFormat.setOutputPath(conf, new Path(args[1]));

 54. 55. JobClient.runJob(conf);

 57. }

 58. }

Usage

Assuming HADOOP_HOME is the root of the installation and HADOOP_VERSION is the Hadoop version installed, compile WordCount.java and create a jar:

 $ mkdir wordcount_classes

$ javac -classpath ${HADOOP_HOME}/hadoop-${HADOOP_VERSION}- core.jar -d wordcount_classes WordCount.java

$ jar -cvf /usr/joe/wordcount.jar -C wordcount_classes/ .

 Assuming that:

• /usr/joe/wordcount/input - input directory in HDFS

 • /usr/joe/wordcount/output - output directory in HDFS

Sample text-files as input:

$ bin/hadoop dfs -ls /usr/joe/wordcount/input/

 /usr/joe/wordcount/input/file01

/usr/joe/wordcount/input/file02

$ bin/hadoop dfs -cat /usr/joe/wordcount/input/file01 Hello World Bye World

$ bin/hadoop dfs -cat /usr/joe/wordcount/input/file02 Hello Hadoop Goodbye Hadoop

 Run the application:

$ bin/hadoop jar /usr/joe/wordcount.jar org.myorg.WordCount / usr/joe/wordcount/input /usr/joe/wordcount/output

 Output:

 $ bin/hadoop dfs -cat /usr/joe/wordcount/output/part-00000

Bye 1

 Goodbye 1

 Hadoop 2

 Hello 2

World 2

Walk-through:

 The WordCount application is quite straight-forward.

The Mapper implementation (lines 14-26), via the map method (lines 18-25), processes one line at a time, as provided by the specified TextInputFormat (line 49). It then splits the line into tokens separated by whitespaces, via the StringTokenizer, and emits a keyvalue pair of < , 1>.

For the given sample input the first map emits:

 < Hello, 1>

< World, 1>

 < Bye, 1>

 < World, 1>

The second map emits:

< Hello, 1>

< Hadoop, 1>

< Goodbye, 1>

< Hadoop, 1>

 WordCount also specifies a combiner (line 46). Hence, the output of each map is passed through the local combiner (which is same as the Reducer as per the job configuration) for local aggregation, after being sorted on the keys.

The output of the first map:

< Bye, 1>

 < Hello, 1>

< World, 2>

The output of the second map:

 < Goodbye, 1>

< Hadoop, 2>

 < Hello, 1>

 The Reducer implementation (lines 28-36), via the reduce method (lines 29-35) just sums up the values, which are the occurence counts for each key (i.e. words in this example).

Thus the output of the job is:

< Bye, 1>

< Goodbye, 1>

< Hadoop, 2>

< Hello, 2>

 < World, 2>

 The run method specifies various facets of the job, such as the input/output paths (passed via the command line), key/value types, input/output formats etc., in the JobConf. It then calls the JobClient.runJob (line 55) to submit the and monitor its progress.

MapReduce - User Interfaces

Applications typically implement the Mapper and Reducer interfaces to provide the map and reduce methods. These form the core of the job. 

Mapper:

 Mapper maps input key/value pairs to a set of intermediate key/value pairs. Maps are the individual tasks that transform input records into intermediate records. The transformed intermediate records do not need to be of the same type as the input records. A given input pair may map to zero or many output pairs. Overall, Mapper implementations are passed the JobConf for the job via the JobConfigurable.configure(JobConf) method and override it to initialize themselves. The framework then calls map(WritableComparable, Writable, OutputCollector, Reporter) for each key/value pair in the InputSplit for that task. Applications can then override the Closeable.close() method to perform any required cleanup.Output pairs do not need to be of the same types as input pairs. A given input pair may map to zero or many output pairs. Output pairs are collected with calls to OutputCollector.collect(WritableComparable,Writable). Applications can use the Reporter to report progress, set application-level status messages and update Counters, or just indicate that they are alive. All intermediate values associated with a given output key are subsequently grouped by the framework, and passed to the Reducer(s) to determine the final output. Users can control the grouping by specifying a Comparator via JobConf.setOutputKeyComparatorClass(Class). The Mapper outputs are sorted and then partitioned per Reducer. The total number of partitions is the same as the number of reduce tasks for the job. Users can control which keys (and hence records) go to which Reducer by implementing a custom Partitioner. Users can optionally specify a combiner, via JobConf.setCombinerClass(Class), to perform local aggregation of the intermediate outputs, which helps to cut down the amount of data transferred from the Mapper to the Reducer. The intermediate, sorted outputs are always stored in a simple (key-len, key, value-len, value) format. Applications can control if, and how, the intermediate outputs are to be compressed and the CompressionCodec to be used via the JobConf.

 How Many Maps?

 The number of maps is usually driven by the total size of the inputs, that is, the total number of blocks of the input files. The right level of parallelism for maps seems to be around 10-100 maps per-node, although it has been set up to 300 maps for very cpu-light map tasks. Task setup takes awhile, so it is best if the maps take at least a minute to execute. Thus, if you expect 10TB of input data and have a blocksize of 128MB, you'll end up with 82,000 maps, unless setNumMapTasks(int) (which only provides a hint to the framework) is used to set it even higher.

Reducer:

 Reducer reduces a set of intermediate values which share a key to a smaller set of values. The number of reduces for the job is set by the user via JobConf.setNumReduceTasks(int). Overall, Reducer implementations are passed the JobConf for the job via the JobConfigurable.configure(JobConf) method and can override it to initialize themselves. The framework then calls reduce(WritableComparable, Iterator, OutputCollector, Reporter) method for each pair in the grouped inputs. Applications can then override the Closeable.close() method to perform any required cleanup. Reducer has 3 primary phases: shuffle, sort and reduce.

Shuffle:

Input to the Reducer is the sorted output of the mappers. In this phase the framework fetches the relevant partition of the output of all the mappers, via HTTP.

Sort:

 The framework groups Reducer inputs by keys (since different mappers may have output the same key) in this stage. The shuffle and sort phases occur simultaneously; while map-outputs are being fetched they are merged.

Secondary Sort:

 If equivalence rules for grouping the intermediate keys are required to be different from those for grouping keys before reduction, then one may specify a Comparator via JobConf.setOutputValueGroupingComparator(Class). Since JobConf.setOutputKeyComparatorClass(Class) can be used to control how intermediate keys are grouped, these can be used in conjunction to simulate secondary sort on values.

 Reduce:

In this phase the reduce(WritableComparable, Iterator, OutputCollector, Reporter) method is called for each pair in the grouped inputs. The output of the reduce task is typically written to the FileSystem via OutputCollector.collect(WritableComparable, Writable). Applications can use the Reporter to report progress, set application-level status messages and update Counters, or just indicate that they are alive. The output of the Reducer is not sorted.

 How Many Reduces?

 The right number of reduces seems to be 0.95 or 1.75 multiplied by ( * mapred.tasktracker.reduce.tasks.maximum). With 0.95 all of the reduces can launch immediately and start transfering map outputs as the maps finish. With 1.75 the faster nodes will finish their first round of reduces and launch a second wave of reduces doing a much better job of load balancing. Increasing the number of reduces increases the framework overhead, but increases load balancing and lowers the cost of failures. The scaling factors above are slightly less than whole numbers to reserve a few reduce slots in the framework for speculative-tasks and failed tasks.

 Reducer NONE:

 It is legal to set the number of reduce-tasks to zero if no reduction is desired. In this case the outputs of the map-tasks go directly to the FileSystem, into the output path set by setOutputPath(Path). The framework does not sort the map-outputs before writing them out to the FileSystem.

 Partitioner :

Partitioner partitions the key space. Partitioner controls the partitioning of the keys of the intermediate map-outputs. The key (or a subset of the key) is used to derive the partition, typically by a hash function. The total number of partitions is the same as the number of reduce tasks for the job. Hence this controls which of the m reduce tasks the intermediate key (and hence the record) is sent to for reduction. HashPartitioner is the default Partitioner.

Reporter :

Reporter is a facility for MapReduce applications to report progress, set application-level status messages and update Counters. Mapper and Reducer implementations can use the Reporter to report progress or just indicate that they are alive. In scenarios where the application takes a significant amount of time to process individual key/value pairs, this is crucial since the framework might assume that the task has timed-out and kill that task. Another way to avoid this is to set the configuration parameter mapred.task.timeout to a high-enough value (or even set it to zero for no time-outs). Applications can also update Counters using the Reporter.

 OutputCollector:

 OutputCollector is a generalization of the facility provided by the MapReduce framework to collect data output by the Mapper or the Reducer (either the intermediate outputs or the output of the job). Hadoop MapReduce comes bundled with a library of generally useful mappers, reducers, and partitioners.