Map Reduce basics
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This document provides an overview of topics to be covered in a Big Data training. It will discuss uses of Big Data, Hadoop, HDFS architecture, MapReduce algorithm, WordCount example, tips for MapReduce, and distributing Twitter data for testing. Key concepts that will be covered include what Big Data is, how HDFS is architected, the MapReduce phases of map, sort, shuffle, and reduce, and how WordCount works as a simple MapReduce example. The goal is to introduce foundational Big Data and Hadoop concepts.
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Map Reduce basics
- 1. Abhishek Mukherjee Utkarsh Srivastava 13th,September Not everything that can be counted counts, and not everything that counts can be counted. WELCOME TO BIG DATA TRANING
- 2. What are we going to cover today? Uses of Big Data What is Hadoop? Short intro to the HDFS architecture. What is Map Reduce? The components of Map Reduce Algorithm Hello world of map reduce i.e. Word Count Algorithm Tips and Tricks of Map Reduce Distribution of twitter data to test Map Reduce jars
- 3. Big data is an evolving term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information. Lots of Data(Zetabytes or Terabytes or Petabytes) Systems / Enterprises generate huge amount of data from Terabytes to and even Petabytes of information. A airline jet collects 10 terabytes of sensor data for every 30 minutes of flying time. What is Big Data?
- 6. Map Phase Combiner Phase(Optional) Sort Phase Shuffle Phase Partition Phase(Optional) Reducer Phase Key points Map Reduce Algorithm
- 8. Hello my name is abhishek Hello my name is utsav Hello my passion is cricket Imagine this as the input file: Map Phase This file has 2 lines. Each line in the file has a byte offset of its own which serves as a key to the mapper and the value of the mapper is the data which is present In the line.
- 9. Operation on output of map phase Hello 1 my 1 name 1 is 1 abhishek 1 Hello 1 my 1 name 1 is 1 utsav 1 Hello 1 my 1 passion 1 is 1 cricket 1 Hello(1,1,1) my(1,1,1) name(1,1,1) is(1,1,1) abhishek(1) utsav(1) passion(1) cricket(1) Key(tuple of values)
- 10. The key points are as follows: Sort the key value pairs according to the key values Shuffle the mapped output to get values with same key to create a tuple of values with same key This output is fed to the reducer which in turn maps the values of the tuple by returning a single value for a list of values present in the tuple Explaination of sort and shuffle phase
- 11. Reducer phase Hello(1,1,1) my(1,1,1) name(1,1,1) is(1,1,1) abhishek(1) utsav(1) passion(1) cricket(1) Key(tuple of values) abhishek(1) cricket(1) Hello(3) is(3) my(3) name(3) passion(1) utsav(1) Key(single value)
- 12. ANY QUERIES?