MapReduce is a programming framework that allows for distributed and parallel processing of large datasets. It consists of a map step that processes key-value pairs in parallel, and a reduce step that aggregates the outputs of the map step. As an example, a word counting problem is presented where words are counted by mapping each word to a key-value pair of the word and 1, and then reducing by summing the counts of each unique word. MapReduce jobs are executed on a cluster in a reliable way using YARN to schedule tasks across nodes, restarting failed tasks when needed.

1. MapReduce
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Submitted By
GAURAV BISWAS

2. Contents
Traditional Big Data Processing Approach
MapReduce
Word count Problem
Reduce Operation
Data Flow
Scope of Map Reduce
Summary
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3. Traditional Big Data Processing Approach
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4. Map Reduce is a programming framework that
allows us to perform distributed and parallel
processing on large data sets in a distributed
environment.
MapReduce
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5. MapReduce
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6. Word Counter Problem
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7. Reduce
Reduce
Reduce
Reduce Operation
MAP: Input data  <key, value> pair
REDUCE: <key, value> pair  <result>
Data
Collection: split1 Split the data to
Supply multiple
processors
Data
Collection: split 2
Data
Collection: split n Map
Map
……
Map
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…

8. A Map Reduce job is a unit of work that the
client wants to be performed
It consists of input data, the map reduce
program and the configuration information
The tasks are scheduled using YARN which
run on nodes in the clusters
If a task fails, it will be automatically
reschedule and run on different node
Data flow
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9. Contd...
A good split size is the size of an HDFS block i.e. 128 MB by default
If the number of splits are more then the overhead of managing the
splits and the map task creation begins to dominate the total job
execution time
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10. Scope of MapReduce
Pipelined Instruction level
Concurrent Thread level
Service Object level
Indexed File level
Mega Block level
Virtual System Level
Data size: small
Data size: large
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11. Summary
We introduced MapReduce programming model for
processing large scale data
We discussed the supporting Hadoop Distributed
File System
The concepts were illustrated using a simple
example
We reviewed some important parts of the source
code for the example.
Relationship to Cloud Computing
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12. References
1. Apache Hadoop Tutorial: http://hadoop.apache.org
http://hadoop.apache.org/core/docs/current/mapred_tu
torial.html
2. Dean, J. and Ghemawat, S. 2008. MapReduce:
simplified data processing on large clusters.
Communication of ACM 51, 1 (Jan. 2008), 107-113.
3. Cloudera Videos by Aaron Kimball:
http://www.cloudera.com/hadoop-training-basic
4. http://www.cse.buffalo.edu/faculty/bina/mapreduce.html
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13. Thank you