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Map Reduce Framework Overview and Implementation in Java
1.
© RAGINIJAIN CC
SA 4.0 Ragini Jain MSc CA 1st Year (2015 - 2017) Map Reduce
2.
© RAGINIJAIN CC
SA 4.0 Overview ● What is Map Reduce ● Map Reduce schematic ● Map Reduce in detail ● Comparison of Map Reduce models ● Demo ● References
3.
© RAGINIJAIN CC
SA 4.0 What is Map Reduce ● A software framework which supports – Parallel – Distributed computing on large data sets. ● The framework abstracts the data flow of running a parallel program on a distributed computing system by providing users with two interfaces in the form of functions: – Map – Reduce ● Users can control and manipulate the data flow of their programs by overriding the Map() and Reduce() function ● Map Reduce library is the controller.
4.
© RAGINIJAIN CC
SA 4.0 Map – reduce schematic Source: jeremykyun
5.
© RAGINIJAIN CC
SA 4.0 Map – reduce schematic (2) Source: hadoop project
6.
© RAGINIJAIN CC
SA 4.0 Map Reduce (in detail) ● The Map function is applied in parallel to every input (key, value) pair and produces new set of intermediate (key, value) pairs (key1, val1) ------(map function)---> List (key2, val2) ● Then the MapReduce library collects all the produced intermediate (key, value) pairs from all input (key, val) pairs and sorts them based on the key part ● Finally Reduce function is applied in parallel to each group producing the collection of values (key2, List(val2)) -----(reduce function) ---> List (val2)
7.
© RAGINIJAIN CC
SA 4.0 Map Reduce (as a query framework) ● SQL clauses that are the building block for Map Reduce operations on structured data and data warehouses – GROUP BY – ORDER BY ● On a very large set of demographic data SELECT age, AVG(contacts) FROM social.person GROUP BY age ORDER BY age
8.
© RAGINIJAIN CC
SA 4.0 GROUP BY (SQL vs Pig)
9.
© RAGINIJAIN CC
SA 4.0 Comparison Map Reduce models ● Google Map Reduce – Prog Model: Map Reduce – Data handling: Google file system ● Apache Hadoop – Prog Model: Map Reduce – Data Handling: HDFS (Hadoop Distributed File system) ● Microsoft Dryad – Prog Model: DAG (Directed Acyclic Graph) execution – Data Handling: Shared directories, Local disks ● Twister – Prog Model: Iterative Map Reduce – Data Handling: Local disks
10.
© RAGINIJAIN CC
SA 4.0 Demo ● Java program – Utilizes concepts from Java 8 programming language platform. ● Lambda expressions ● Streams – JDK ref ● java.util.Collection.stream() ● java.lang.Iterable.forEach( ) ● java.util.List
11.
© RAGINIJAIN CC
SA 4.0 References ● Jeffrey Dean et' al MapReduce: Simplified Data Processing on Large Clusters http://research.google.com/archive/mapreduce.html ● Michelle Stonebraker et' al MapReduce and Parallel DBMSs: Friends or Foes ? http://dl.acm.org/citation.cfm?id=1629197 ● Java Lambda expressions https://docs.oracle.com/javase/tutorial/java/javaOO/lambdaexp ● PostgreSQL GROUP BY and ORDER BY http://www.postgresql.org/docs/devel/static/sqlselect.html
12.
© RAGINIJAIN CC
SA 4.0 Thank you. ● Questions ● Clarifications ● Suggestions ● Feedback Ragini Jain 15030142023@sicsr.ac.in
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