Introduction to map reduce

Introduction to MapReduceBhupesh Chawda

bhupesh@apache.org

DataTorrent

Why Hadoop?● Data Growth is mind boggling. Forecast for 2020: 40 Trillion GB

● Cost effective

● Scalable

● Fast

● Open source

Source: https://rapidminer.com/rapidminer-acquires-radoop/Image: http://seikun.kambashi.com/images/blog/interning_at_placeiq/2.jpg

What is Mapreduce● It is a powerful paradigm for parallel computation

● Hadoop uses MapReduce to execute jobs on files in HDFS

● Hadoop will intelligently distribute computation over cluster

● Take computation to data

Analogy: Counting Fans● Given a cricket stadium, count the number of fans for each player / team

● Traditional way

● Smart way

● Smarter way?

Origin: Functional Programming● Map - Returns a list constructed by applying a function (the first argument) to all

items in a list passed as the second argument

○ map f [a, b, c] = [f(a), f(b), f(c)]

○ map sq [1, 2, 3] = [sq(1), sq(2), sq(3)] = [1,4,9]

● Reduce - Returns a list constructed by applying a function (the first argument) on

the list passed as the second argument. Can be identity (do nothing).

○ reduce f [a, b, c] = f(a, b, c)

○ reduce sum [1, 4, 9] = sum(1, sum(4,sum(9,sum(NULL)))) = 14

Sum of squares example

Sum of squares of even and odd numbers

Programming model - Key Value Pairs● Format of input- output

(key, value)

● Map: (k1 , v1 ) → list (k2 , v2 )● Reduce: (k2 , list v2 ) → list (k3 , v3 )

Sum of squares of odd, even and prime

Map reduce overview

Map reduce with combiner

The Big Picture

Image Source: http://blog.csdn.net/bingduanlbd/article/details/51933914

The Bigger Picture

Image Source: http://blog.csdn.net/bingduanlbd/article/details/51933914

MapReduce Code Example - Word Count

Image Source: http://arnon.me/2014/06/mapreduce/

MapReduce - The Mapper

Source: https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html

MapReduce - The Reducer

Source: https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html

MapReduce - The Driver

Image Source: https://memegenerator.net/instance/56997204

Hadoop Distributions

Who is using Hadoop?

References● https://hadoop.apache.org/

● www.slideshare.net/SandeepDeshmukh5/hadoopintroduction-46841859

● Hadoop - The Definitive Guide - 4th Edition

● Images shamelessly stolen from the internet - Have credited though!

Acknowledgements● Sandeep Deshmukh, DataTorrent - For some of the slides

Thank You!!

Please send your questions at:bhupesh@apache.org

Extra Slides

Anatomy of a Map reduce run● In Map reduce context

○ The client which submits the job

○ Job tracker which coordinates the run

○ Task trackers which run the map and

reduce tasks

○ HDFS

● In YARN context - Will see later

○ The client which submits the job

○ YARN resource manager

○ YARN node managers

○ Map Reduce App Master

○ HDFS

Map reduce in YARN - Will see later

The Map Side - Details● Map task writes to a circular buffer which it writes the output to

● Once it reaches a threshold, it starts to spill the contents to local disk

● Before writing to disk, the data is partitioned corresponding to the reducers that

the data will be sent to

● Each partition is sorted by key and combiner is run on the sorted output

● Multiple spill files may be created by the time map finishes. These spill files are

merged into a single partitioned, sorted output file

● The output file partitions are made available to reducers over HTTP

The Reduce Side - Details● The map outputs are sitting on local disks. Reduce tasks will need this data in

order to proceed with the reduce task

● Reduce task needs the map output for its particular partition from several maps

across the cluster

● The reduce task starts copying the map outputs as soon as each map completes.

This is the copy phase. The map outputs are fetched in parallel by multiple

threads.

● Map outputs are copied to jvm’s memory if small enough, else copied to disk. As

copies accumulate, they are merged into larger sorted files. When all are copied,

they are merged maintaining their sort order

● Reduce function is invoked for each key in sorted output and output is written

directly to HDFS

Map reduce as unix commandsProblem:

● Input

○ 1 TB file containing color

names - Red, Blue, Green,

Yellow, Purple, Maroon

● Output

○ Number of occurrences of

colors Blue and Green