1. Hadoop MapReduce Types: 2/2
Spring 2015,
X. Zhang
Fordham Univ.

2. Outline MapReduce: how to control number of tasks
InputFormat class decides # of splits => number of map tasks
reduce tasks # configured by client
ChainMapper: modular design
Input processing:
XML file processing
whole file as a record
Binary output & sorting

3. MapReduce Programming
InputFormat
Partition: hash key to
reducer task
Split
Shuffle
OutputFormat
Input: a set of
[key,value] pairs
[k,(v1,v2, …)]
[k’,(v1,v2,…)] …
File (text, binary)
Database
intermediate
[key,value] pairs
Output: a set of
[key,value] pairs

4. Usage of InputFormat
An InputFormat is responsible for creating input splits and dividing them into records:
public abstract class InputFormat<K, V> {
public abstract List<InputSplit> getSplits(JobContext context) throws IOException, InterruptedException;
public abstract RecordReader<K, V> createRecordReader(InputSplit split, TaskAttemptContext context) throws IOException, InterruptedException; }
Default input format is TextInputFormat, which produces one (key,value) pair for each line in the text file
We will later look at a customized InputFormat class …

5. InputSplit
Input split is a chunk of the input that is processed by a single map.
Each split is divided into records, and map processes each record—a key-value pair—in turn.
public abstract class InputSplit {
public abstract long getLength() throws IOException, InterruptedException;
public abstract String[] getLocations() throws IOException,InterruptedException; }
An InputSplit has a length in bytes and a set of storage locations, host- name strings.

6. Starting map tasks
Client running job calculates the splits for the job by calling getSplits(),
Client sends splits to jobtracker/ResourceManager, which uses their storage locations to schedule map tasks to process them
Each map task passes split to createRecordReader() method on InputFormat to obtain a RecordReader for that split.
A RecordReader is little more than an iterator over records,
Map task uses RecordReader to generate key-value pairs, and passes them to map function.
public void run(Context context) throws IOException, InterruptedException {
setup(context); while (context.nextKeyValue()) {
map(context.getCurrentKey(), context.getCurrentValue(), context); }
cleanup(context);
default implementation of Mapper class’s run function

7. Split and Record Sometimes a record span two blocks/input splits:
map task #1: located on same node as first block/split of file
needs to perform remote read to obtain record 5 (which spans two blocks)

8. InputFormat Hierarchy

9. FileInputFormat Parent class for all that read from files
Input to a job: a collection of paths:
void addInputPath(…) void addInputPaths(…)
void setInputPaths(…) void setInputPaths(…)

10. Split Size
FileInputFormat splits only large files. Here “large” means larger than an HDFS block.
by default, minimumSize < blockSize < MaximumSize
Formula for split size:
max (minimumSize, min (maxmumSize, blockSize))

11. Demo: How to?
Change MaximumSplit size so that a file smaller than BlockSize is splitter?
~/hadoop_sampleCodes/citibike/ShellScript/RunDefaultJob.sh
-dmapred.max.split.size=20000
Try ./RunDefaultJob.sh >& dd
grep csv dd | wc -l => 655 map tasks
:38:40,425 INFO [main] input.FileInputFormat (FileInputFormat.java:listStatus(245)) - Total input paths to process : 1
:38:40,496 INFO [main] mapreduce.JobSubmitter (JobSubmitter.java:submitJobInternal(371)) - number of splits: :25:49,678 INFO [Thread-2] mapred.Merger (Merger.java:merge(568)) - Merging 655 sorted segments

12. Demo: How to configure number of reducers?
Do not work in local mode
See LocalJobRunner.java code: sequentially run the map tasks, and then start one reduce tasks
in command line: -Dmapred.reduce.tasks=2
in code: job.setNumreduceTasks(2);
~/hadoop_sampleCodes/citibike/ShellScript/RunDefaultJob.sh
-dmapred.max.split.size=20000
RunDefaultJob_pseudo.sh

13. Shuffling Split Shuffle InputFormat Partition: hash key to
reducer task
Split
Shuffle
OutputFormat
Input: a set of
[key,value] pairs
[k,(v1,v2, …)]
[k’,(v1,v2,…)] …
File (text, binary)
Database
intermediate
[key,value] pairs
Output: a set of
[key,value] pairs

14. Which reduce task?
For each (K2,V2), intermediate key-value pair, which reduce task to go to?
Partition the whole domain of K2 into multiple partitions
each reduce tasks process one partition
partition function operates on intermediate key and value types (K2 and V2), and returns partition index.
In practice, partition is determined solely by key (value is ignored):
Default partitioner is HashPartioner
public class HashPartitioner<K, V> extends Partitioner<K, V> {
public int getPartition(K key, V value, int numReduceTasks) {
return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks; } }

15. Outline MapReduce: how to control number of tasks
InputFormat class decides # of splits => number of map tasks
reduce tasks # configured by client
ChainMapper: modular design
Input processing:
XML file processing
whole file as a record
Binary output & sorting

16. ChainMapper class Benefit:
ChainMapper class: use multiple Mapper classes within a single Map task.
mapper1 => mapper2 => mapper3 …=>lastmapper
output of the first becomes the input of the second, and so on until the last Mapper
output of the last Mapper will be written to the task's output.
Benefit:
Modularity (simple and reusable specialized Mappers)
Composibility (mapper can combined to perform composite operations)
reduction in disk IO: compared to multiple “chained” map reduce jobs

17. a word count job Job job = Job.getInstance();
Configuration splitMapConfig = new Configuration(false);
ChainMapper.addMapper(job, SplitMapper.class, LongWritable.class,
Text.class,
IntWritable.class, splitMapConfig);
Configuration lowerCaseMapConfig = new Configuration(false);
ChainMapper.addMapper(job, LowerCaseMapper.class, Text.class,
IntWritable.class, Text.class, IntWritable.class,
lowerCaseMapConfig);
job.setJarByClass(ChainMapperDriver.class);
job.setCombinerClass(ChainMapReducer.class);
job.setReducerClass(ChainMapReducer.class);

18. forming a chain of mapper
public static <K1,V1,K2,V2> void addMapper(JobConf job,
Class<? extends Mapper<K1,V1,K2,V2>> klass,
Class<? extends K1> inputKeyClass,
Class<? extends V1> inputValueClass,
Class<? extends K2> outputKeyClass,
Class<? extends V2> outputValueClass,
boolean byValue,
JobConf mapperConf)
Adds a Mapper class to the chain job's JobConf.
byValue - indicates if key/values should be passed by value to the next Mapper in the chain (if any) or by reference. If a Mapper leverages the assumed semantics that the key and values are not modified by the collector 'by value' must be used. If the Mapper does not expect this semantics, as an optimization to avoid serialization and deserialization 'by reference' can be used.
MPORTANT: There is no need to specify the output key/value classes for the ChainMapper, this is done by the addMapper for the last mapper in the chain

19. Outline MapReduce: how to control number of tasks
InputFormat class decides # of splits => number of map tasks
reduce tasks # configured by client
ChainMapper: modular design
Input processing:
XML file processing
whole file as a record
Binary output & sorting

20. SequenceFile
SequenceFile: provides a persistent data structure for binary key-value pairs
keys and values stored in a SequenceFile do not necessarily need to be Writable. Any types that can be serialized and deserialized by a Serialization may be used.
convert an object/value to/from a byte stream,
In contrast, default TextOutputFormat
writes key, value by calling toString() method on them
convert an object/value to/from a stream of text (e.g., ASCII)

21. MapFile
A file-based map from keys to values. A map is a directory containing two files
data file, containing all keys and values in the map
index file, containing a fraction of the keys. The fraction is determined by MapFile.Writer.getIndexInterval().
Index file is read entirely into memory. Thus key implementations should try to keep themselves small.
Allowing for quick lookup of record
Exercise: run the MapReduce job that uses MapFileOutputFormat class, examine the output directory…

22. Sorting in MapReduce Framework

23. All about sorting Partial sort “comes free”, see previous slides
output from map => partitioned, sorted, and merged (sorted) => reduce
each partition is sorted, i.e., each reduce task output is sorted
To sort globally (total sort)
one partition, i.e., one reduce task
or, use customized partition class

24. All about sorting
Total sort: outputs of reduce tasks can be concatenated together to get a sorted output
Ideas:
Use TotalOrderPartition class,
if k1<k2, partition(k1)<partition(k2)
How to make sure the partitioning is balanced, to minimize running time?
use InputSampler to sample output to get an estimated distribution of keys

25. Secondary sort
Goal: sort output by year, and then within the year, sort by temperature …
You can do this by writing a reduce class
intermediate key-value pairs are grouped: reduce (k, <v1,v2,…>), but v1,v2, … are not sorted …
Or we can again take advantage of MapReduce’s framework (how it already partitions, sorts, groups data for us)

26. How?
Goal: sort output by year, and then within the year, sort by temperature
Plan:
Use year and temperature as key (a composite key)
Partition and group based on year only
so that records of same year are sent to same reduce tasks, and grouped together in a list
Sort based on the composite key (year and temperature
so that the ordering of records (within same group) are ordered by temperature…

27. Details
PartitionerClass: FirstPartitioner class, uses only first part (e.g., year) of composite key

28. Details setGroupingComparatorClass
Define the comparator that controls which keys are grouped together for a single call to Reducer.reduce function
Use GroupComparator which just comparing first part of key (e.g., year)

29. Details setSortComparatorClass:
Define the comparator that controls how the keys are sorted before they are passed to the Reducer.
We use KeyCompartor class, sort by first part (e.g., year), and then by second part of composite key (e.g., temperature)
SortComparator …

30. Outline MapReduce: how to control number of tasks
InputFormat class decides # of splits => number of map tasks
reduce tasks # configured by client
ChainMapper: modular design
Input processing:
XML file processing
whole file as a record
Binary output & sorting
Join

31. Join
Combine two datasets
together using a key
Here, use StationID…

32. An example of reduce-side joins
Multiple inputs, of different formats
e.g., one station record, another weather data
mapper class: tag record with composite key, e.g., station_id-0 for station record, station_id-1 for weather record
Secondary sort:
use first part of composite key to partition and group

33. Commonly Used Mapper/Reducer

34. Debugging
MapReduce job and task logs

35. User-level logs