Hadoop and MapReduce

Post on 17-May-2015

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Transcript of Hadoop and MapReduce

HADOOPFramework and Applications

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CONTENTS WHY HADOOP?

INTRODUCTION TO MapReduce

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WHAT?“... to create building blocks for programmers who just happen to have lots of data to store, lots of data to analyze, or lots of machines to coordinate, and who don’t have the time, the skill, or the inclination to become distributed systems experts to build the infrastructure to handle it.” -Tom White Source: Hadoop: The Definitive Guide

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WHAT? Hadoop contains many subprojects: Hadoop Common Chukwa HBase ZooKeeper Pig Zombie Hive MapReduce

We will focus on MapReduce

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WHO & WHEN? Pre-2004 : Cutting and Cafarella develop

open source projects for web-scale indexing, crawling and search.

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WHO & WHEN? 2004: Jeffrey Dean and Sanjay

Ghemawat introduce map reduce model used internally at Google.

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WHO & WHEN? 2006: Hadoop becomes official Apache

project, Cutting joins Yahoo!Yahoo adopts Hadoop.

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TRENDS

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WHO USES IT?

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Roughly how long to read 1TB from a commodity hard disk?

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Roughly how long to read 1TB from a commodity hard disk?

Around 4 hours

62 seconds…

WITH HADOOP..

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INTRODUCTION TO MapReduce

"Break large problem into smaller parts, solve in parallel, combine results."

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Typical scenario How many times is the word ‘IT’

present? You’ll probably count but in a 30k paged document, can you??

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Map Reduce Typical Illustration

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Map Reduce paradigm

Input

Map

Shuffle/SortReduce

Output

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Map Reduce paradigm Map: transforms input record to

intermediate (key, value) pair

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Map Reduce paradigm Reduce: transforms all records for given

key to final output.

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Map reduce principles

Move code to data (local

computation)

Allow programs to scale

transparently w.r.t size of input

Abstract away fault tolerance, synchronization,

etc.

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Implementation: Hardware

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Map Reduce: strengths

Batch, offline jobs

Write-once, read-many across full data set

Usually, though not always, simple computations

I/O bound by disk/network bandwidth

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What it’s not!

What it’s not:

High-performance parallel computing, e.g. MPI

Low-latency random access relational database

Always the right solution

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THANK YOU!

QUESTIONS?

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