An Introduction to MapReduce

AN INTRODUCTION TO MAPREDUCE

David Zülke

David Zuelke

http://en.wikipedia.org/wiki/File:München_Panorama.JPG

Founder

awesome

Lead Developer

@dzuelke

BEFORE WE BEGIN... Installing Prerequisites

I brought a pre-conﬁgured VM

(I stole it from the nice folks over at Cloudera)

to save some time

PLEASE COPY FROM THE HD • /cloudera-training-0.3.2/ • VMWare for Windows, Linux (i386 or x86_64) or Mac OS from /vmware/ if you don’t have it. • For Fusion, go to vmware.com and get an evaluation key. • /php/

(but be so kind as to pretend to be still listening)

FROM 30.000 FEET Distributed And Parallel Computing

we want to process data

how much data exactly?

SOME NUMBERS • Google • Facebook • Dataprocessed per • New data per day: month: 400 PB (in 2007!) • 200 GB (March 2008) • Average job size: 180 GB • 2 TB (April 2009) • 4 TB (October 2009)

what if you have that much data?

what if you have just 1% of that amount?

“no problemo”, you say?

reading 180 GB sequentially off a disk will take ~45 minutes

but you only have 16 GB or so of RAM per computer

data can be processed much faster than it can be read

solution: parallelize your I/O

but now you need to coordinate what you’re doing

and that’s hard

what if a node dies?

is data lost? will other nodes in the grid have to re-start? how do you coordinate this?

ENTER: OUR HERO Introducing MapReduce

in the olden days, the workload was distributed across a grid

but the data was shipped around between nodes

or even stored centrally on something like an SAN

I/O bottleneck

Google made a publication in 2004

MapReduce: Simpliﬁed Data Processing on Large Clusters http://labs.google.com/papers/mapreduce.html

now the data is distributed

computing happens on the nodes where the data already is

processes are isolated and don’t communicate (share-nothing)

BASIC PRINCIPLE: MAPPER •A Mapper reads records and emits <key, value> pairs • Example: Apache access.log • Each line is a record • Extract client IP address and number of bytes transferred • Emit IP address as key, number of bytes as value • For hourly rotating logs, the job can be split across 24 nodes* * In pratice, it’s a lot smarter than that

BASIC PRINCIPLE: REDUCER •A Reducer is given a key and all values for this speciﬁc key • Even if there are many Mappers on many computers; the results are aggregated before they are handed to Reducers • Example: Apache access.log • The Reducer is called once for each client IP (that’s our key), with a list of values (transferred bytes) • We simply sum up the bytes to get the total trafﬁc per IP!

EXAMPLE OF MAPPED INPUT IP Bytes 212.122.174.13 18271 212.122.174.13 191726 212.122.174.13 198 74.119.8.111 91272 74.119.8.111 8371 212.122.174.13 43

REDUCER WILL RECEIVE THIS IP Bytes 18271 191726 212.122.174.13 198 43 91272 74.119.8.111 8371

AFTER REDUCTION IP Bytes 212.122.174.13 210238 74.119.8.111 99643

PSEUDOCODE function map($line_number, $line_text) { $parts = parse_apache_log($linetext); emit($parts['ip'], $parts['bytes']); } function reduce($key, $values) { $bytes = array_sum($values); emit($key, $bytes); } 212.122.174.13 ‐ ‐ [30/Oct/2009:18:14:32 +0100] "GET /foo HTTP/1.1" 200 18271 212.122.174.13 ‐ ‐ [30/Oct/2009:18:14:32 +0100] "GET /bar HTTP/1.1" 200 191726 212.122.174.13 ‐ ‐ [30/Oct/2009:18:14:32 +0100] "GET /baz HTTP/1.1" 200 198 74.119.8.111 ‐ ‐ [30/Oct/2009:18:14:32 +0100] "GET /egg HTTP/1.1" 200 43 74.119.8.111 ‐ ‐ [30/Oct/2009:18:14:32 +0100] "GET /moo HTTP/1.1" 200 91272 212.122.174.13 ‐ ‐ [30/Oct/2009:18:14:32 +0100] "GET /yay HTTP/1.1" 200 8371 212.122.174.13 210238 74.119.8.111 99643

FINGER EXERCISE Let’s Try PHP First

HANDS-ON Time To Write Some Code!

ANOTHER ELEPHANT Introducing Apache Hadoop

Hadoop is a MapReduce framework

it allows us to focus on writing Mappers, Reducers etc.

and it works extremely well

how well exactly?

HADOOP AT FACEBOOK • Predominantly used in combination with Hive (~95%) • 4800 cores with 12 TB of storage per node • Per day: • 4 TB of new data (compressed) • 135 TB of data scanned (compressed) • 7500+ Hive jobs per day, ~80k compute hours http://www.slideshare.net/cloudera/hw09-rethinking-the-data-warehouse-with-hadoop-and-hive

HADOOP AT YAHOO! • Over 25,000 computers with over 100,000 CPUs • Biggest Cluster: • 4000 Nodes • 2x4 CPU cores each • 16 GB RAM each • Over 40% of jobs run using Pig http://wiki.apache.org/hadoop/PoweredBy

there’s just one little problem

it’s written in Java

however, there is hope...

STREAMING Hadoop Won’t Force Us To Use Java

Hadoop Streaming can use any script as Mapper or Reducer

many conﬁguration options (parsers, formats, combining, …)

it works using STDIN and STDOUT

Mappers are streamed the records (usually by line: <byteoffset>t<line>n) and emit key/value pairs: <key>t<value>n

Reducers are streamed key/value pairs: <keyA>t<value1>n <keyA>t<value2>n <keyA>t<value3>n <keyB>t<value4>n

Caution: no separate Reducer processes per key (but keys are sorted)

HANDS-ON Let’s Say Hello To Our Hadoop VM

THE HADOOP ECOSYSTEM A Little Tour

APACHE AVRO Efﬁcient Data Serialization System With Schemas (compare: Facebook’s Thrift)

APACHE CHUKWA Distributed Data Collection System (compare: Facebook’s Scribe)

APACHE HBASE Like Google’s BigTable, Only That You Can Have It, Too!

HDFS Your Friendly Distributed File System

HIVE Data Warehousing Made Simple With An SQL Interface

PIG A High-Level Language For Modelling Data Processing Tasks

ZOOKEEPER Your Distributed Applications, Coordinated

ISABEL DROST Welcome Our Special Guest, Presenting Apache Mahout

!e End

Questions?

THANK YOU! • http://hadoop.apache.org/ is the Hadoop project website • http://www.cloudera.com/hadoop-training has useful resources • Send me an E-Mail: david.zuelke@bitextender.com • Follow @dzuelke on Twitter • Slides will be on SlideShare

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An Introduction to MapReduce

by David Zuelke on Nov 15, 2009

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