Topic 6: MapReduce Applications

6: MapReduce Applications

Zubair Nabi

[email protected]

April 18, 2013

Zubair Nabi 6: MapReduce Applications April 18, 2013 1 / 27

Outline

1 The Anatomy of a MapReduce Application

2 MapReduce Design Patterns

3 Common MapReduce Application Types


Outline





MapReduce job phases

A MapReduce job can be divided into 4 phases:

1 Input split: The input dataset is sliced into M splits, one per map task

2 Map logic: The user-supplied map function is invokedI In tandem a sort phase is also applied that ensures that map output is

locally sorted by keyI In addition, the key space is also partitioned amongst the reducers

3 Shuffle: Map output is relayed to all reduce tasks4 Reduce logic: The user-provided reduce function is invoked

I Before the application of the reduce function, the input keys are mergedto get globally sorted key/value pairs




1 Input split: The input dataset is sliced into M splits, one per map task2 Map logic: The user-supplied map function is invoked

I In tandem a sort phase is also applied that ensures that map output islocally sorted by key

I In addition, the key space is also partitioned amongst the reducers

























3 Shuffle: Map output is relayed to all reduce tasks

4 Reduce logic: The user-provided reduce function is invokedI Before the application of the reduce function, the input keys are merged

to get globally sorted key/value pairs


















Of mappers and reducers

In the common case, programmers only need to write a map and areduce function

The user-provided map function is invoked for every line (can bemodified) in the input file and is passed the line number as key and linecontents as value

The user-provided reduce function is invoked for each key output bythe map phase and is passed the set of associated values as iterablevalues












Wordcount: High-level view

Input: A text corpus such as Wikipedia dump, books from Gutenberg,etc.

The map function is invoked once for each text line

Map output: Words as keys and 1 as values

Reduce input: Key/value pairs of words and values (1)

The reduce function is invoked once for each word with a list of 1s

Reduce output: Words and their final counts










































Wordcount: Low-level view

A new process is created for each map, called MapRunner

MapRunner has a RecordReader instance that is used to read theinput file

RecordReader reads the input file in chunks and parses the chunksinto lines

MapRunner also has a Mapper instance with a map function,WordCountMapper in this case

For each line parse by RecordReader, MapRunner callsWordCountMapper.map() and passes it the line






























Wordcount: Low-level view (2)

WordCountMapper has an OutputCollector instance which maintainsan in-memory buffer for each output partition (one partition per reduce)

Each time WordCountMapper.map() is invoked it, it tokenizes the lineinto words

For each word, it writes the word as key and 1 as value toOutputCollector

OutputCollector uses the Partitioner instance to select a partitionbuffer for each key

Whenever the size of a partition buffer exceeds a configurablethreshold, its contents are first sorted by key and then flushed to disk

This process is repeated till the map logic has been applied to all lineswithin the input file











































Once all maps have completed their execution, the reduce phase isstarted

For each reduce task, a ReduceRunner process is created

Each reduce task fetches its input partitions from machines on whichmap tasks were run

All input partitions are then merged to get a globally sorted partition ofkey/value pairs





















ReduceRunner contains a Reducer instance with a reduce function,WordCountReducer in this case

For each word, ReduceRunner invokes WordCountReducer.reduce()and passes it the word and a list of its values (1s)

WordCountReducer also has an OutputCollector instance with anin-memory buffer

WordCountReducer.reduce() sums the list of values it is passed andwrites the word and its final count to the OutputCollector

This process is repeated till the reduce logic has been appliedkey/value pairs

At the end of the entire job, each reduce produces an output file withwords and their number of occurrences










































Wordcount map in Java

1 public void map(Object key, Text value, Context context) {

2 StringTokenizer itr = new StringTokenizer(value.toString());

3 while (itr.hasMoreTokens()) {

4 word.set(itr.nextToken());

5 context.write(word, one);

6 }

7 }


Wordcount reduce in Java

1 public void reduce(Text key, Iterable<IntWritable > values,

2 Context context) {

3 int sum = 0;

4 for (IntWritable val : values) {

5 sum += val.get();

6 }

7 result.set(sum);

8 context.write(key, result);

9 }


Wordcount map in Python

1 def map(self, key, value):

2 [self._output_collector.collect(word, 1) for word in value.split(’ ’)]


Wordcount reduce in Python

1 def reduce(self, key, values):

2 sum__ = 0

3 for value in values:

4 sum__ += value

5 self._output_collector.collect(key, sum__)


Outline





Bird’s-eye view

The MapReduce paradigm is amenable to divide-and-conqueralgorithms

One way to look at MapReduce is that it is just a large-scale sortingplatform

User-logic is only involved at specific hook pointsAlgorithms must be expressed in terms of a small number of specificcomponents that fit together in preset ways

I Like putting together a jigsaw puzzle in which all the other pieces havealready been assembled and you only need to add two pieces: The mapand the reduce pieces

Fortunately a large number of algorithms easily fit this rigid pattern


Bird’s-eye view







Bird’s-eye view



User-logic is only involved at specific hook points

Algorithms must be expressed in terms of a small number of specificcomponents that fit together in preset ways




Bird’s-eye view







Bird’s-eye view







Bird’s-eye view







Programmer control

The programmer has no control over

1 The location of a map or reduce task in terms of nodes in the cluster

2 The start and end time of a map or a reduce task

3 The input key/value pairs processed by a specific map task

4 The intermediate key/value pairs processed by a specific reduce task


Programmer control







Programmer control







Programmer control







Programmer control (2)

The programmer does have control over

1 The data structures to be used as keys and values

2 Initialization code at the beginning of map/reduce tasks andtermination code at the end

3 Preservation of state across multiple invocations of map/reduce tasks

4 The sort order of intermediate keys and in turn, the order in which areducer encounters keys

5 Partitioning of key space and in turn, the set of keys that a particularreducer encounters


































Multi-job algorithms

Many algorithms cannot be easily expressed as a single MapReducejob

Complex algorithms need to be decomposed into a sequence of jobsI The output of one job becomes the input to the next

Most interactive algorithms need to be run by an external driverprogram that performs the convergence check



Many algorithms cannot be easily expressed as a single MapReducejobComplex algorithms need to be decomposed into a sequence of jobs

I The output of one job becomes the input to the next













Local aggregation

Network and disk latencies are expensive compared to otheroperations

Decreasing the amount of data transferred over the network during theshuffle phase results in efficiency

Aggressive user of combiners for commutative and associativealgorithms can greatly reduce intermediate data

Another strategy, dubbed “in-mapper combining” can not only decreasethe amount of intermediate data but also the number of key/valur pairsemitted by the map tasks


Local aggregation






Local aggregation






Local aggregation






Outline





Counting and Summing

1 ProblemI A number of documents with a set of terms

I Need to calculate the number of occurrences of each term (word count)or some arbitrary function over the terms (average response time in logfiles)

2 SolutionI Map: For each term, emit the term and “1”I Reduce: Take the sum (or any other operation) of each term values



1 ProblemI A number of documents with a set of termsI Need to calculate the number of occurrences of each term (word count)

or some arbitrary function over the terms (average response time in logfiles)






2 SolutionI Map: For each term, emit the term and “1”

I Reduce: Take the sum (or any other operation) of each term values







Collating

1 ProblemI A number of documents with a set of terms and some function of one

item

I Need to group all items that have the same value of function to eitherstore items together or perform some computation over them

2 SolutionI Map: For each item, compute given function and emit function value as

key and item as valueI Reduce: Either save all grouped items or perform further computationI Example: Inverted Index: Items are words and function is document ID


Collating


itemI Need to group all items that have the same value of function to either

store items together or perform some computation over them




Collating





key and item as value

I Reduce: Either save all grouped items or perform further computationI Example: Inverted Index: Items are words and function is document ID


Collating





key and item as valueI Reduce: Either save all grouped items or perform further computation

I Example: Inverted Index: Items are words and function is document ID


Collating







Filtering, Parsing, and Validation

1 ProblemI A set of records

I Need to collect all records that meet some condition or transform eachrecord into another representation

2 SolutionI Map: For each record, emit it if passes the condition or emit its

transformed versionI Reduce: IdentityI Example: Text parsing or transformation such as word capitalization



1 ProblemI A set of recordsI Need to collect all records that meet some condition or transform each

record into another representation








transformed version

I Reduce: IdentityI Example: Text parsing or transformation such as word capitalization






transformed versionI Reduce: Identity

I Example: Text parsing or transformation such as word capitalization








Distributed Task Execution

1 ProblemI Large computational problem

I Need to divide it into multiple parts and combine results from all parts toobtain a final result

2 SolutionI Map: Perform corresponding computationI Reduce: Combine all emitted results into a final oneI Example: RGB histogram calculation of bitmap images



1 ProblemI Large computational problemI Need to divide it into multiple parts and combine results from all parts to

obtain a final result






2 SolutionI Map: Perform corresponding computation

I Reduce: Combine all emitted results into a final oneI Example: RGB histogram calculation of bitmap images





2 SolutionI Map: Perform corresponding computationI Reduce: Combine all emitted results into a final one

I Example: RGB histogram calculation of bitmap images







Sorting

1 ProblemI A set of records

I Need to sort records in some order

2 SolutionI Map: IdentityI Reduce: IdentityI Also possible to sort by value, either perform a secondary sort or

perform a key-to-value conversion


Sorting

1 ProblemI A set of recordsI Need to sort records in some order




Sorting


2 SolutionI Map: Identity

I Reduce: IdentityI Also possible to sort by value, either perform a secondary sort or



Sorting


2 SolutionI Map: IdentityI Reduce: Identity

I Also possible to sort by value, either perform a secondary sort orperform a key-to-value conversion


Sorting





References

1 Jimmy Lin and Chris Dyer. 2010. Data-Intensive Text Processing withMapReduce. Morgan and Claypool Publishers.

2 MapReduce Patterns, Algorithms, and Use Cases:http://highlyscalable.wordpress.com/2012/02/01/mapreduce-patterns/


http://highlyscalable.wordpress.com/2012/02/01/mapreduce-patterns/

http://highlyscalable.wordpress.com/2012/02/01/mapreduce-patterns/

Topic 6: MapReduce Applications

Technology

mapreduce job

map logic

mapreduce applicationsapril

mapreduce applicationszubair

map taskzubair nabi

mapreduce applications

usersupplied map function

input dataset