Hadoop Master Class : A concise overview

Big Data Hadoop Master Class

By Abhishek Roy

About Me

Abhishek Roy

• Around 10 years of tech experience with services, product and startups

• Was leading the data team at Qyuki Digital Media

• Lately involved in Big data with emphasis on recommendation systems and machine learning approaches

• Currently working on building an employee wellness platform, www.feetapart.com

• Linkedin : http://www.linkedin.com/in/abhishekroy8

Agenda

• What is Big Data

• History and Background

• Why Data

• Intro to Hadoop

• Intro to HDFS

• Setup & some hands on

• HDFS architecture

• Intro to Map Reduce

Agenda

• Pig

• Hive

• Apache Mahout

– Running an example MR Job

• Sqoop, Flume, Hue, Zookeeper

• Impala

• Some Real World Use cases

What Is Big Data?

Like the term Cloud, it is a bit ill-defined/hazy

Big Data – A Misnomer

What’s the “BIG” hype about Big Data?

There may be hype, but problems are real and of big value. How?

• We are in the age of advanced analytics (that’s where all the problem is ,we want to analyze the data) where valuable business insight is mined out of historical data.

• We live in the age of crazy data where every individual , enterprise and machine leaves so much data behind summing up to many terabytes & petabytes , and it is only expected to grow.

What’s the “BIG” hype about Big Data?

• Good news. Blessing in disguise. More data means more precision.

• More data usually beats better algorithms.

• But how are we going to analyze?

• Traditional database or warehouse systems crawl or crack at these volumes.

• Inflexible to handle most of these formats.

• This is the very characteristic of Big Data.

Key Hadoop/Big Data Data Sources

• Sentiment • Clickstream • Sensor/Machine • Geographic • Text • Chatter from social networks • Traffic flow sensors • Satellite imagery • Broadcast audio streams

Sources of Big Data

• Banking transactions

• MP3s of rock music

• Scans of government documents

• GPS trails

• Telemetry from automobiles

• Financial market data

• ….

Key Drivers

Spread of cloud computing, mobile computing and social media

technologies, financial transactions

Introduction cont.

• Nature of Big Data

– Huge volumes of data that cannot be handled by traditional database or warehouse systems.

– It is mostly machine produced.

– Mostly unstructured and grows at high velocity.

– Big data doesn’t always mean huge data, it means “difficult” data.

The 4 Vs

Data Explosion

Inflection Points

• Is divide the data and rule a solution here? – Have a multiple disk drive , split your data file into

small enough pieces across the drives and do parallel reads and processing.

– Hardware Reliability (Failure of any drive) is a challenge.

– Resolving data interdependency between drives is a notorious challenge.

– Number of disk drives that can be added to a server is limited

Inflection Points

• Analysis – Much of big data is unstructured. Traditional

RDBMS/EDW cannot handle it.

– Lots of Big Data analysis is adhoc in nature, involves whole data scan, referencing itself, joining, combing etc.

– Traditional RDBMS/EDW cannot handle these with their limited scalability options and architectural limitations.

– You can incorporate betters servers, processors and throw in more RAM but there is a limit to it.

Inflection Points

• We need a Drastically different approach

– A distributed file system with high capacity and high reliability.

– A process engine that can handle structure /unstructured data.

– A computation model that can operate on distributed data and abstracts data dispersion.

What is Apache Hadoop

What is Hadoop?

• “framework for running [distributed] applications on large cluster built of commodity hardware” –from Hadoop Wiki

• Originally created by Doug Cutting

– Named the project after his son’s toy

• The name “Hadoop” has now evolved to cover a family of products, but at its core, it’s essentially just the MapReduce programming paradigm + a distributed file system

History

Core Hadoop : HDFS

Core Hadoop: MapReduce

Pig

A platform for analyzing large data sets that consists of a high-level language for

expressing data analysis programs, coupled with infrastructure for evaluating these

programs.

Mahout

A machine learning library with algorithms for clustering, classification and batch based collaborative filtering that are

implemented on top of Apache Hadoop.

Hive

Data warehouse software built on top of Apache Hadoop that facilitates querying and managing large datasets residing in

distributed storage.

Sqoop

A tool designed for efficiently transferring bulk data between Apache Hadoop and structured data stores such as relational

databases.

Apache Flume

A distributed service for collecting, aggregating, and moving large log data

amounts to HDFS.

Twitter Storm

Storm can be used to process a stream of new data and update

databases in real time.

Funding & IPO

• Cloudera, (Commerical Hadoop) more than $75 million

• MapR (Cloudera competitor) has raised more than $25 million

• 10Gen (Maker of the MongoDB) $32 million

• DataStax (Products based on Apache Cassandra) $11 million

• Splunk raised about $230 million through IPO

Big Data Application Domains

• Healthcare

• The public sector

• Retail

• Manufacturing

• Personal-location data

• Finance

Relational Databases:

Hadoop:

Use The Right Tool For The Right Job

When to use?

• Affordable Storage/Compute

• Structured or Not (Agility)

• Resilient Auto Scalability

When to use?

• Interactive Reporting (<1sec)

• Multistep Transactions

• Lots of Inserts/Updates/Deletes

Ship the Function to the Data

SAN/NAS

data data data

data data data

data data data

data data data

data data data

function

RDBMS

Traditional Architecture

data

function

data

function

data

function

data

function

data

function

data

function

data

function

data

function

data

function

data

function

data

function

data

function

Distributed Computing

Economics of Hadoop Storage • Typical Hardware:

– Two Quad Core Nehalems – 24GB RAM – 12 * 1TB SATA disks (JBOD mode, no need for RAID) – 1 Gigabit Ethernet card

• Cost/node: $5K/node • Effective HDFS Space:

– ¼ reserved for temp shuffle space, which leaves 9TB/node – 3 way replication leads to 3TB effective HDFS space/node – But assuming 7x compression that becomes ~ 20TB/node

Effective Cost per user TB: $250/TB Other solutions cost in the range of $5K to $100K per user

TB

Market and Market Segments

Research Data and Predictions

http://wikibon.org/wiki/v/Big_Data_Market_Size_and_Vendor_Revenues


Market for big data tools will rise from $9 billion to $86 billion in 2020

Future of Big Data

• More Powerful and Expressive Tools for Analysis • Streaming Data Processing (Storm from Twitter and S4 from

Yahoo) • Rise of Data Market Places (InfoChimps, Azure

Marketplace) • Development of Data Science Workflows and Tools (Chorus,

The Guardian, New York Times) • Increased Understanding of Analysis and Visualization

http://www.evolven.com/blog/big-data-predictions.html






Opportunities

Skills Gap

• Statistics

• Operations Research

• Math

• Programming

• So-called "Data Hacking"



Market for big data tools will rise from $9 billion to $86 billion in 2020

Typical Hadoop Architecture

Hadoop: Storage and Batch Processing

Data Collection

OLAP Data Mart

Business Intelligence

OLTP Data Store

Interactive Application

Business Users End Customers

Engineers

HDFS Introduction

• Written in Java

• Optimized for larger files

– Focus on streaming data (high-throughput > low-latency)

• Rack-aware

• Only *nix for production env.

• Web consoles for stats

HDFS Architecture

Lets us see what MapReduce is

MapReduce basics

• Take a large problem and divide it into sub-problems

• Perform the same function on all sub-problems

• Combine the output from all sub-problems

MapReduce(M/R) facts

• M/R is excellent for problems where the “sub-problems” are not interdependent

• For example, the output of one “mapper” should not depend on the output or communication with another “mapper”

• The reduce phase does not begin execution until all mappers have finished

• Failed map and reduce tasks get auto-restarted

• Rack/HDFS-aware

What is MapReduce Model

What is the MapReduce Model

• MapReduce is a computation model that supports parallel processing on distributed data using clusters of computers.

• The MapReduce model expects the input data to be split and distributed to the machines on the clusters so that each splits cab be processed independently and in parallel.

• There are two stages of processing in MapReduce model to achieve the final result: Map and Reduce. Every machine in the cluster can run independent map and reduce processes.

What is MapReduce Model

• Map phase processes the input splits. The output of the Map phase is distributed again to reduce processes to combine the map output to give final expected result.

• The model treats data at every stage as a key and value pair, transforming one set of key/value pairs into different set of key/value pairs to arrive at the end result.

• Map process tranforms input key/values pairs to a set of intermediate key/vaue pairs.

• MapReduce framework passes this output to reduce processes which will transform this to get final result which again will be in the form of key/value pairs.

Design of MapReduce -Daemons

The MapReduce system is managed by two daemons • JobTracker & TaskTracker • JobTracker TaskTracker function in master / slave fashion

– JobTracker coordinates the entire job execution – TaskTracker runs the individual tasks of map and

reduce – JobTracker does the bookkeeping of all the tasks run on

the cluster – One map task is created for each input split – Number of reduce tasks is configurable

(mapred.reduce.tasks)

Conceptual Overview

Who Loves it

• Yahoo !! Runs 20,000 servers running Hadoop

• Largest Hadoop clusters is 4000 servers,16PB raw storage

• Facebook runs 2000 Hadoop servers

• 24 PB raw storage and 100 TB raw log/day

• eBay and LinkedIn has production use of Hadoop.

• Sears retails uses Hadoop.

Hadoop and it’s ecosystem

Hadoop Requirements

• Supported Platforms • GNU/Linux is supported as development and

production

• Required Software

• java 1.6.x +

• ssh to be installed ,sshd must be running (for machined in the cluster to interact with master machines)

• Development Environment • Eclipse 3.6 or above

Lab Requirements

• Window 7-64 bit OS , Min 4 GB Ram • VMWare Player5.0.0 • Linux VM-Ubuntu 12.04 LTS

– User: hadoop, password :hadoop123 • Java 6 installed on Linux VM • Open SSH installed on Linux VM • Putty-For opening Telnet sessions to the Linux VM • WinSCP- For transferring files between windows and

Linux VM • Other Linux machines will do as well • Eclipse 3.6

Extract VM

• Folder:

Hadoop_MasterClass\Ubuntu_VM

• Copy:

ubuntu-12.04.1-server-amd64_with_hadoop.zip

• Extract to local using 7-zip

Starting VM

Starting VM

• Enter userID/password

• Type ifconfig

– Note down the ip address

– Connect to the VM using Putty

Install and Configure ssh(non-VM users)

• Install ssh sudo apt –get install ssh – Check ssh installation: which ssh

which sshd which ssh-keygen

– Generate ssh Key ssh-keygen –t rsa –P ” –f ~/.ssh/id_rsa

– Copy public key as an authorized key(equivalent to slave node)

cp ~/.ssh/id_rsa.pub ~/.ssh/authorized_keys chmod 700 ~/.ssh chmod 600 ~/.ssh/authorized_keys

Install and Configure ssh

• Verify SSHby logging into target(localhost here)

– Command:

ssh localhost

– (this command will log you into the machine localhost)

Accessing VM Putty and WinSCP

• Get IP address of the VM by using command in Linus VM

• Use Putty to telnet to VM

• Use WinSCP to FTP files to VM

Accesing VM using Putty & WinSCP

Putty WinSCP

Lab-VM Directory Structure(non-VM users)

• User Home directory for user “hadoop”(created default by OS) – /home/hadoop

• Create working directory for the lab session – /home/hadoop/lab

• Create directory for storing all downloads(installables) – /home/hadoop/lab/downloads

• Create directory for storing data for analysis – /home/hadoop/lab/data

• Create directory for installing tools – /home/hadoop/lab/install

Install and Configure Java(non-VM users)

• Install Open JDK – Command :

– sudo apt-get install openjdk-6-jdk

– Check installation :java -version

• Configure java home in environment – Add a line to .bash_profile to set java Home

• export JAVA_HOME =/usr/lib/jvm/java-6-openjdk-amd64

– Hadoop will use during runtime

Install Hadoop(non-VM users)

• Download hadoop jar with wget

– http://archive.apache.org/dist/hadoop/core/hadoop-1.0.3/hadoop-1.0.3.tar.gz

• Untar

– cd ~/lab/install

– tar xzf ~/lab/downloads/hadoop-1.03.tar.gz

– Check extracted directory “hadoop-1.0.3”

• Configure environment in .bash_profile ( or .bashrc)

– Add below two lines and execute bash profile

• export HADOOP_INSTALL=~/lab/install/hadoop-1.0.3

• export PATH=$PATH:$HADOOP_INSTALL/bin

• . .bash_profile (Execute bashrc)

• Check Hadoop installation

– hadoop version

http://archive.apache.org/dist/hadoop/core/hadoop-1.0.3/hadoop-1.0.3.tar.gz





Setting up Hadoop

• Open $HADOOP_HOME/conf/hadoop-env.sh

• set the JAVA_HOME environment variable to the $JAVA_HOME directory.

export JAVA_HOME=/usr/lib/jvm/java-6-openjdk-amd64

Component of core Hadoop

Component of core Hadoop

• At a high-level Hadoop architecture components can be classified into two categories

– Distributed File management system-HDFS

• This has central and distributed sub components • NameNode: Centrally Monitors and controls the whole file system • DataNode: Take care of the local file segments and constantly communicates with NameNode • Secondary NameNode:Do not confuse. This is not a NameNode Backup. This just backs up the file

system status from the NameNode periodically.

• Distributed computing system-MapReduce Framework • This again has central and distributed sub components

• Job tracker: Centrally monitors the submitted Job and controls all processes running on the nodes(computers) of the clusters.This communicated with Name Node for file system access

• Task Tracker: Take care of the local job execution on the local file segments. This talks to DataNode for file information. This constantly communicates with job Tracker daemon to report the task progress.

• When the Hadoop system is running in a distributed mode all the daemons would be running in the respective computer.

Hadoop Operational Modes

Hadoop can be run in one of the three modes • Standalone(Local) Mode

– No daemons launched – Everything runs in single JVM – Suitable for development

• Pseudo Distributed Mode – All daemons are launched – Runs on a single machine thus simulating a cluster environment – Suitable for testing and debugging

• Fully Distributed Mode

– The Hadoop daemons runs in a cluster environment – Each daemons run on machine respectively assigned to them – Suitable for integration Testing/Production

Hadoop Configuration Files

Filename Format Description

hadoop-env.sh Bash script Environment variables that are used in the scripts to run Hadoop

core-site.xml

Hadoop configuration.XML

Configuration settings for Hadoop Core, such as I/O settings that are common to HDFS and MapReduce

hdfs-site.xml


Configuration settings for HDFS daemons: the namenode, the secondary namenode, and the datanodes

mapred-site.xml


Configuration settings for MapReduce daemons: the jobtracker, and the tasktrackers

masters Plain text A list of machines (one per line) that each run a secondary namenode

slaves Plain text A list of machines (one per line) that each run a datanode and a tasktracker

hadoop-metrics.properties

Java Properties Properties for controlling how metrics are published in Hadoop

log4j.properties Java Properties

Properties for system logfiles, the namenode audit log, and the task log for the tasktracker child process

Key Configuration Properties

Property Name Conf File Standalone Pseudo Distributed Fully Distributed

Fs.default.name core -site.xml File:///(default) hdfs://localhost/ hdfs://namenode/

dfs.replication hdfs -site.xml NA 1 3(default)

mapred.job.tracker mapes-site.xml local(default) localhost:8021 jobtracket:8021

/(default)

HDFS

Design of HDFS

• HDFS is a Hadoop's Distributed File System

• Designed for storing very large files(Petabytes)

• Single file can be stored across several disk

• Not suitable for low latency data access

• Designed to be highly fault tolerant hence can run on commodity hardware

HDFS Concepts

• Like any file system HDFS stores files by breaking it into smallest units called Blocks.

• The default HDFS block size is 64 MB

• The large block size helps in maintaining high throughput

• Each block is replicated across multiple machine in the cluster for redundancy.

Design of HDFS -Daemons

• The HDFS file system is managed by two daemons

• NameNode and DataNode

• NameNode and DataNode function in master/slave fashion

– NameNode Manages File system namespace

– Maintains file system and metadata of all the files and directories

• Namespace image

• Edit log

Design of HDFS –Daemons (cont.)

• Datanodes store and retrieve the blocks for the files when they are told by NameNode.

• NameNode maintains the information on which DataNodes all the blocks for a given files are located.

• DataNodes report to NameNode periodically with the list of blocks they are storing.

• With NameNode off ,the HDFS is inaccessible. • Secondary NameNode

– Not a backup for NameNode – Just helps in merging namespace image with edit log to avoid

edit log becoming too large

HDFS Architecture

Fault Tolerance

Fault Tolerance

Live Horizontal Scaling + Rebalancing

Live Horizontal Scaling + Rebalancing

Configuring conf/*-site.xml files

• Need to set the hadoop.tmp.dir parameter

to a directory of your choice.

• We will use /app/hadoop/tmp

• sudo mkdir -p /app/hadoop/tmp

• sudo chmod 777 /app/hadoop/tmp

Configuring HDFS: core-site.xml(Pseudo Distributed Mode)

<?xml version ="1.0"?>



<configuration>

<property>

<name>hadoop.tmp.dir</name>

<value>/app/hadoop/tmp</value>

</property>

<property>

<name>fs.default.name</name>

<value>hdfs://localhost/</value>

</property>

</configuration>

Note: Add "fs.default.name" property under configuration tag to specify NameNode location.

"localhost" for pseudo distributed mode. Name node runs at port 8020 by default if no port specified.

Configuring mapred-site.xml(Pseudo Distributed Mode)

<?xml version="1.0"?> <!— mapred-site.xml —> <configuration> <property> <name>mapred.job.tracker</name> <value>localhost:8021</value> </property> </configuration> Note:Add "mapred.job.tracker" property under configuration tag to specify JobTracker location. "localhost:8021" for Pseudo distributed mode. Lastly set JAVA_HOME in conf/hadoop-env.sh export JAVA_HOME=/usr/lib/jvm/java-6-openjdk-amd64

Configuring HDFS: hdfs-site.xml(Pseudo Distributed Mode)

• <?xml version ="1.0"?>

• 

<configuration>

<property>

<name>dfs.replication</name>

<value>1</value>

</property>

</configuration>

Starting HDFS

• Format NameNode

hadoop namenode -format

– creates empty file system with storage directories and persistent data structure

– Data nodes are not involved

• Start dfs service

– start-dfs.sh

– Verify daemons : jps If you get the namespace exception, copy the namespace id of the namenode Paste it in the : /app/hadoop/tmp/dfs/data/current/VERSION file

– Stop : stop-dfs.sh

• List/check HDFS

hadoop fsck / -files -blocks

hadoop fs -ls

hadoop fs -mkdir testdir

hadoop fs -ls


Verify HDFS

• Stop dfs services

stop-dfs.sh

Verify Daemons :jps

No java processes should be running

Configuration HDFS -hdfs-site .xml(Pseudo Distributed Mode)

Property Name Description Default Value

dfs.name.dir Directories for NameNode to store it's persistent data(Comma seperated directory name).A copy of metadata is stored in each of listed directory ${hadoop.tmp.dir}/dfs/name

dfs.data.dir Directories where DataNode stores blocks.Each block is stored in only one of these directories ${hadoop.tmp.dir}/dfs/data

fs.checkpoint.dir Directories where secondary namenode stores checkpoints.A copy of the checkpoint is stored in each of the listed directory ${hadoop.tmp.dir}/dfs/namesecondary

Basic HDFS Commands • Creating Directory

hadoop fs -mkdir <dirname>

• Removing Directory

hadoop fs -rm <dirname>

• Copying files to HDFS from local file system

hadoop fs -put <local dir>/<filename> <hdfs dir Name>/

• Copying files from HDFS to local file system

hadoop fs -get <hdfs dir Name>/<hdfs file Name> <local dir>/

• List files and directories

hadoop fs -ls <dir name>

• List the blocks that make up each files in HDFS


HDFS Web UI

• Hadoop provides a web UI for viewing HDFS

– Available at http://namenode-host-ip:50070/

– Browse file system

– Log files

MapReduce

• A distributed parallel processing engine of Hadoop • Processes the data in sequential parallel steps called • Map • Reduce • Best run with a DFS supported by Hadoop to exploit it's parallel processing abilities • Has the ability to run on a cluster of computers • Each computer called as a node • Input and output data at every stage is handled in terms of key / value pairs • Key / Value can be choose by programmer • Mapper output is always sorted by key • Mapper output with the same key are sent to the same reducer • Number of mappers and reducers per node can be configured

Start the Map reduce daemons

• start-mapred.sh

Input Splitting Mapping Shuffling Reducing Final Result

The Overall MapReduce Word count Process

Design of MapReduce -Daemons

The MapReduce system is managed by two daemons • JobTracker & TaskTracker • JobTracker TaskTracker function in master / slave fashion

– JobTracker coordinates the entire job execution – TaskTracker runs the individual tasks of map and

reduce – JobTracker does the bookkeeping of all the tasks run on

the cluster – One map task is created for each input split – Number of reduce tasks is configurable

(mapred.reduce.tasks)

Conceptual Overview

Job Sumission – Map phase

Job Submission – Reduce Phase

Anatomy of a MapReduce program

Hadoop - Reading Files

Rack1 Rack2 Rack3 RackN

read file (fsimage/edit) Hadoop Client

NameNode SNameNode

return DNs, block ids, etc.

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

checkpoint

heartbeat/ block report read blocks

Hadoop - Writing Files


request write (fsimage/edit) Hadoop Client

NameNode SNameNode

return DNs, etc.

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

checkpoint

block report write blocks

replication pipelining

Hadoop - Running Jobs


Hadoop Client

JobTracker

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

DN | TT

submit job

deploy job

part 0

map

reduce

shuffle

mapred-site. xml - Pseudo Distributed Mode

<?xml version="1.0"?> <!— mapred-site.xml —> <configuration> <property> <name>mapred.job.tracker</name> <value>localhost:8021</value> </property> </configuration> Note:Add "mapred.job.tracker" property under configuration tag to specify JobTracker location. "localhost:8021" for Pseudo distributed mode. Lastly set JAVA_HOME in conf/hadoop-env.sh export JAVA_HOME=/usr/lib/jvm/java-6-openjdk-amd64

Starting Hadoop -MapReduce Daemons

• Start MapReduce Services

start-mapred.sh

– Verify Daemons:jps

MapReduce Programming

• Having seen the architecture of MapReduce, to perform a job in hadoop a programmer need to create

• A MAP function

• A REDUCE function

• A Driver to communicate with the framework, configure and launch the job

Map Function

• The Map function is represented by Mapper class, which declares an abstract method map()

• Mapper class is generic type with four type parameters for the input and output key/ value pairs

• Mapper <k1, v1, k2, v2> • kl, vi are the types of the input key / value pair • k2, v2 are the types of the output key/value pair • Hadoop provides it's own types that are optimized for network serialization • Text Corresponds to Java String • LongWritable Corresponds to Java Long • IntWritable Corresponds to Java Int • The map() method must be implemented to achieve the input key / value transformation • map method is called by MapReduce framework passing the input key values from the input file • map method is provided with a context object to which the transformed key values can be written to

Mapper — Word Count

• public static class TokenizerMapper • extends Mapper<LongWritable, Text, Text, IntWritable> {

• private final static IntWritable one = new IntWritable(1); • private Text word = new Text(); • • @Override • public void map(LongWritable key, Text value, Context context) • throws 1OException, InterruptedException { • • StringTokenizer itr = new StringTokenizer(value,toString0, " \t\n\r\f,.;:?[]"); • • while (itr.hasMoreTokens()) { • word.set(itr.nextToken0.toLowerCase(); • context.write(word, one); • } • }

Reduce Function

• The Reduce function is represented by Reducer class, which declares an abstract method reduce() • Reducer class is generic type with four type parameters for the input and output

key/ value pairs • Reducer <k2, v2, k3, v3> • k2, v2 are the types of the input key / value pair, this type of this pair must match the output types of Mapper • k3, v3 are the types of the output key / value pair • The reduce() method must be implemented to achieve the desired transformation

of input key / value • Reduce method is called by MapReduce framework passing the input key values

from out of map phase • MapReduce framework guarantees that the records with the same keys from all

the map tasks will reach a single reduce task • Similar to the map, reduce method is provided with a context object to which the

transformed key values can be written to

Reducer — Word Count

• public static class IntSumReducer • extends Reducer< Text, IntWritable, Text, IntWritable> {

• @Override • public void reduce(Text key, Iterable<IntWritable> values, Context context) • throws I0Exception, InterruptedException • { • int sum = 0; • for (IntWritable value values) { • sum += value.get(); • } • context.write(key, new IntWritable(sum)); • } • }

MapReduce Job Driver— Word Count Public class WordCount (

public static void main(String args[])) throws Exception {

if (args.length 1=2) {

System.errprintln("Usage: WordCount <input Path> <output Path>");

System.exit(-1); }

Configuration conf- new Configuration();

Job job = new Job(conf, "Word Count");

job.setJarByClass(WordCount.class);

job.setMapperClass(TokenizerMapperclass);

job.setReducerClass(IntSumReducer.class);

job.setMapOutputKeyClass(Textclass);

job.setMapOutputValueClass(IntWritable.class);

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

FllelnputFormat.addlnputPath(job, new Path(args[0]);

FlleOutputFormatsetOutputPathCob, new Path(args[1]);

System.exit(job.waitForCompletion(true) ? 0: 1);

}

}

MapReduce Job — Word Count • Copy wordcount.jar from the Hard drive folder to the

Ubuntu VM

hadoop jar <path_to_jar>/wordcount.jar com.hadoop.WordCount <hdfs_input_dir>/pg5000.txt <hdfs_output_dir>

• The <hdfs_output_dir> must not exist

• To view the output directly:

hadoop fs –cat ../../part-r-00000

• To copy the result to local:

hadoop fs -get <part-r-*****>

The MapReduce Web UI

• Hadoop provides a web UI for viewing job information

• Available at http://jobtracker-host:50030/

• follow job's progress while it is running

• find job statistics

• View job logs

• Task Details

Combiner

• Combiner function helps to aggregate the map output before passing on to reduce function

• Reduces intermediate data to be written to disk

• Reduces data to be transferred over network

• Combiner for a job is specified as

job.setCombinerClass(<combinerclassname>.class);

• Combiner is represented by same interface as Reducer

Combiner—local reduce

Combiner

Word Count — With Combiner Public class WordCount {

public static void main(String args[]) throws Exception{

if (args.length!=2) {

System. err printIn("Usage: WordCount <input Path> <output Path>");

System.exit(-1);

}

Configuration conf = new Configuration();

Job job = new Job(conf, "Word Count");

job.setJarByClass(WordCount.class);

job.setMapperClass(TokenizerMapperclass);

job.setComblnerClass(IntSumReducer.class);

job.setReducerClass(IntSurriReducer class);

job.setMapOutputKeyClass(Text.class);

job.setMapOutputValueClass(IntWritable.class);

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

FilelnputFormataddlnputPath(job, new Path(args[0]) );

FileOutputFormat.setOutputPath(job, new Path(args[1]) );

System.exit(job.wait-ForCompletion(true) ? 0 : 1);

}

}

Note: In case of cumulative & associative functions the reducer can work as combiner.

Otherwise a separate combiner needs to be created

• hadoop jar lineindexer.jar com.hadoop.LineIndexer <hdfs_input_dir> <hdfs_output_dir>

• hadoop fs –cat ../../part-r-*****

• hadoop fs -get <part-r-*****>

Partitioning

• Map tasks partition their output keys by the number of reducers • There can be many keys in a partition • All records for a given key will be in a single partition • A Partitioner class controls partitioning based on the Key • Hadoop uses hash partition by default (HashPartitioner) • The default behavior can be changed by implementing the getPartition() method in the Partitioner (abstract) class public abstract class Partitioner<KEY, VALUE> { public abstract int getPartition(KEY key, VALUE value, int numPartitions); } • A custom partitioner for a job can be set as Job.setPartitionerClass(<customPartitionerClass>.class);

Partitioner— redirecting output from Mapper

Partitioner Example

public class WordPartitioner extends Partitioner <Text, IntWritable>{ @Override public int getPartition(Text key, Int-Writable value,int numPartitions){ String ch = key.toStringasubstring(0,1); /*if (ch.matches("[abcdefghijklm]")) return 0; } else if (ch.matches("[Inopqrstuvwxyzn]")){ return 1; } return 2; */ //return (ch.charAt(0) % numPartitions); //round robin based on ASCI value return 0; return 0;// default behavior } }

Reducer progress during Mapper

• MapReduce job shows something like Map(50%) Reduce(10%)

• Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The progress calculation also takes in account the processing of data transfer which is done by reduce process, therefore the reduce progress starts showing up as soon as any intermediate key-value pair for a mapper is available to be transferred to reducer.

• The programmer defined reduce method is called only after all the mappers have finished.

http://www.fromdev.com/2010/12/interview-questions-hadoop-mapreduce.html





Hadoop Streaming

• Hadoop Streaming is a utility which allows users to create and run jobs with any executables (e.g. shell utilities) as the mapper and/or the reducer.

• Using the streaming system you can develop working hadoop jobs with extremely limited knowldge of Java

• Hadoop actually opens a process and writes and reads from stdin and stdout

Hadoop Streaming

• Hadoop Streaming uses Unix standard streams as the interface between Hadoop and your program, so you can use any language that

• Can read standard input and write to standard output to write your MapReduce program.

• Streaming is naturally suited for text processing.

Hands on

• Folder :

– examples/Hadoop_streaming_python

– Files “url1” & “url2’ are the input

– multifetch.py is the mapper (open it)

– reducer.py is the reducer(open this as well)

Hands on

• Run : hadoop fs -mkdir urls hadoop fs -put url1 urls/ hadoop fs -put url2 urls/ hadoop jar contrib/streaming/hadoop-streaming-1.0.3.jar -mapper <path>/multifetch.py -reducer <path>reducer.py -input urls/* -output titles

Decomposing problems into M/R jobs

• Small map-reduce jobs are usually better

– Easier to implement, test & maintain

– Easier to scale & reuse

• Problem : Find the word/letter that has the maximum occurrences in a set of documents

Decomposing

• Count of each word/letter M/R job (Job 1)

• Find max word/letter count M/R job (Job 2)

Choices can depend on complexity of jobs

Job Chaining

• Multiple jobs can be run in a linear or complex dependent fashion Simple Dependency /Linear Chain Directed Acyclic Graph(DAG) • Simple way is to call the job drivers one after the other with respective configurations JobClient.runJob(conf1); JobClient.runJob(conf2); • If a job fails, the runJob() method will throw an IOException, so later jobs

in the pipeline don’t get executed.

Job Chaining

For complex dependencies you can use JobControI, and ControlledJob classes

Controlledjob cjob1 = new ControlledJob(conf1);

ControlledJob cjob2 = new ControlledJob(conf2);

cjob2.addDependingjob(cjob1);

JobControl jc = new JobControl("Chained Job");

jc.addjob(cjob1);

jc.addjob(cjob2);

jc.run();

Apache Oozie

• Work flow scheduler for Hadoop

• Manages Hadoop Jobs

• Integrated with many Hadoop apps i.e. Pig

• Scalable

• Schedule jobs

• A work flow is a collection of actions i.e.

– map/reduce, pig

• A work flow is

– Arranged as a DAG ( direct acyclic graph )

– Graph stored as hPDL ( XML process definition )

Oozie

• Engine to build complex DAG workflows

• Runs in it’s own daemon

• Describe workflows in set of XML & configuration files

• Has coordinator engine that schedules workflows based on time & incoming data

• Provides ability to re-run failed portions of the workflow

Need for High-Level Languages

• Hadoop is great for large-data processing!

– But writing Java programs for everything is verbose and slow

– Not everyone wants to (or can) write Java code

• Solution: develop higher-level data processing languages

– Hive: HQL is like SQL

– Pig: Pig Latin is a bit like Perl

Hive and Pig

• Hive: data warehousing application in Hadoop – Query language is HQL, variant of SQL – Tables stored on HDFS as flat files – Developed by Facebook, now open source

• Pig: large-scale data processing system – Scripts are written in Pig Latin, a dataflow language – Developed by Yahoo!, now open source – Roughly 1/3 of all Yahoo! internal jobs

• Common idea: – Provide higher-level language to facilitate large-data processing – Higher-level language “compiles down” to Hadoop jobs

Hive: Background

• Started at Facebook

• Data was collected by nightly cron jobs into Oracle DB

• “ETL” via hand-coded python

• Grew from 10s of GBs (2006) to 1 TB/day new data (2007), now 10x that

Source: cc-licensed slide by Cloudera

Hive Components

• Shell: allows interactive queries

• Driver: session handles, fetch, execute

• Compiler: parse, plan, optimize

• Execution engine: DAG of stages (MR, HDFS, metadata)

• Metastore: schema, location in HDFS, SerDe


Data Model

• Tables

– Typed columns (int, float, string, boolean)

– Also, list: map (for JSON-like data)

• Partitions

– For example, range-partition tables by date

• Buckets

– Hash partitions within ranges (useful for sampling, join optimization)


Metastore

• Database: namespace containing a set of tables

• Holds table definitions (column types, physical layout)

• Holds partitioning information

• Can be stored in Derby, MySQL, and many other relational databases


Physical Layout

• Warehouse directory in HDFS

– E.g., /user/hive/warehouse

• Tables stored in subdirectories of warehouse

– Partitions form subdirectories of tables

• Actual data stored in flat files

– Control char-delimited text, or SequenceFiles

– With custom SerDe, can use arbitrary format


Hive: Example • Hive looks similar to an SQL database

• Relational join on two tables:

– Table of word counts from Shakespeare collection

– Table of word counts from the bible

SELECT s.word, s.freq, k.freq FROM shakespeare s JOIN bible k ON (s.word = k.word) WHERE s.freq >= 1 AND k.freq >= 1 ORDER BY s.freq DESC LIMIT 10; the 25848 62394 I 23031 8854 and 19671 38985 to 18038 13526 of 16700 34654 a 14170 8057 you 12702 2720 my 11297 4135 in 10797 12445 is 8882 6884

Hive: Behind the Scenes SELECT s.word, s.freq, k.freq FROM shakespeare s JOIN bible k ON (s.word = k.word) WHERE s.freq >= 1 AND k.freq >= 1 ORDER BY s.freq DESC LIMIT 10;

(TOK_QUERY (TOK_FROM (TOK_JOIN (TOK_TABREF shakespeare s) (TOK_TABREF bible k) (= (. (TOK_TABLE_OR_COL s) word) (. (TOK_TABLE_OR_COL k) word)))) (TOK_INSERT (TOK_DESTINATION (TOK_DIR TOK_TMP_FILE)) (TOK_SELECT (TOK_SELEXPR (. (TOK_TABLE_OR_COL s) word)) (TOK_SELEXPR (. (TOK_TABLE_OR_COL s) freq)) (TOK_SELEXPR (. (TOK_TABLE_OR_COL k) freq))) (TOK_WHERE (AND (>= (. (TOK_TABLE_OR_COL s) freq) 1) (>= (. (TOK_TABLE_OR_COL k) freq) 1))) (TOK_ORDERBY (TOK_TABSORTCOLNAMEDESC (. (TOK_TABLE_OR_COL s) freq))) (TOK_LIMIT 10)))

(one or more of MapReduce jobs)

(Abstract Syntax Tree)

Hive: Behind the Scenes STAGE DEPENDENCIES: Stage-1 is a root stage Stage-2 depends on stages: Stage-1 Stage-0 is a root stage STAGE PLANS: Stage: Stage-1 Map Reduce Alias -> Map Operator Tree: s TableScan alias: s Filter Operator predicate: expr: (freq >= 1) type: boolean Reduce Output Operator key expressions: expr: word type: string sort order: + Map-reduce partition columns: expr: word type: string tag: 0 value expressions: expr: freq type: int expr: word type: string k TableScan alias: k Filter Operator predicate: expr: (freq >= 1) type: boolean Reduce Output Operator key expressions: expr: word type: string sort order: + Map-reduce partition columns: expr: word type: string tag: 1 value expressions: expr: freq type: int

Reduce Operator Tree: Join Operator condition map: Inner Join 0 to 1 condition expressions: 0 {VALUE._col0} {VALUE._col1} 1 {VALUE._col0} outputColumnNames: _col0, _col1, _col2 Filter Operator predicate: expr: ((_col0 >= 1) and (_col2 >= 1)) type: boolean Select Operator expressions: expr: _col1 type: string expr: _col0 type: int expr: _col2 type: int outputColumnNames: _col0, _col1, _col2 File Output Operator compressed: false GlobalTableId: 0 table: input format: org.apache.hadoop.mapred.SequenceFileInputFormat output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat

Stage: Stage-2 Map Reduce Alias -> Map Operator Tree: hdfs://localhost:8022/tmp/hive-training/364214370/10002 Reduce Output Operator key expressions: expr: _col1 type: int sort order: - tag: -1 value expressions: expr: _col0 type: string expr: _col1 type: int expr: _col2 type: int Reduce Operator Tree: Extract Limit File Output Operator compressed: false GlobalTableId: 0 table: input format: org.apache.hadoop.mapred.TextInputFormat output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat Stage: Stage-0 Fetch Operator limit: 10

Example Data Analysis Task

user url time

Amy www.cnn.com 8:00

Amy www.crap.com 8:05

Amy www.myblog.com 10:00

Amy www.flickr.com 10:05

Fred cnn.com/index.htm 12:00

url pagerank

www.cnn.com 0.9

www.flickr.com 0.9

www.myblog.com 0.7

www.crap.com 0.2

Find users who tend to visit “good” pages.

Pages Visits

. . .

. . .

Conceptual Workflow

System-Level Dataflow

. . . . . .

Visits Pages . . .

. . .

join by url

the answer

load load

canonicalize

compute average pagerank

filter

group by user

Pig Slides adapted from Olston et al.

MapReduce Code i m p o r t j a v a . i o . I O E x c e p t i o n ;

i m p o r t j a v a . u t i l . A r r a y L i s t ;

i m p o r t j a v a . u t i l . I t e r a t o r ;

i m p o r t j a v a . u t i l . L i s t ;

i m p o r t o r g . a p a c h e . h a d o o p . f s . P a t h ;

i m p o r t o r g . a p a c h e . h a d o o p . i o . L o n g W r i t a b l e ;

i m p o r t o r g . a p a c h e . h a d o o p . i o . T e x t ;

i m p o r t o r g . a p a c h e . h a d o o p . i o . W r i t a b l e ;

im p o r t o r g . a p a c h e . h a d o o p . i o . W r i t a b l e C o m p a r a b l e ;

i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . F i l e I n p u t F o r m a t ;

i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . F i l e O u t p u t F o r m a t ;

i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . J o b C o n f ;

i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . K e y V a l u e T e x t I n p u t F o r m a t ;

i m p o r t o r g . ap a c h e . h a d o o p . m a p r e d . M a p p e r ;

i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . M a p R e d u c e B a s e ;

i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . O u t p u t C o l l e c t o r ;

i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . R e c o r d R e a d e r ;

i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . R e d u c e r ;

i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . R e p o r t e r ;

i m po r t o r g . a p a c h e . h a d o o p . m a p r e d . S e q u e n c e F i l e I n p u t F o r m a t ;

i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . S e q u e n c e F i l e O u t p u t F o r m a t ;

i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . T e x t I n p u t F o r m a t ;

i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . j o b c o n t r o l . J o b ;

i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . j o b c o n t r o l . J o b Co n t r o l ;

i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . l i b . I d e n t i t y M a p p e r ;

p u b l i c c l a s s M R E x a m p l e {

p u b l i c s t a t i c c l a s s L o a d P a g e s e x t e n d s M a p R e d u c e B a s e

i m p l e m e n t s M a p p e r < L o n g W r i t a b l e , T e x t , T e x t , T e x t > {

p u b l i c v o i d m a p ( L o n g W r i t a b l e k , T e x t v a l ,

O u t p u t C o l l e c t o r < T e x t , T e x t > o c ,

R e p o r t e r r e p o r t e r ) t h r o w s I O E x c e p t i o n {

/ / P u l l t h e k e y o u t

S t r i n g l i n e = v a l . t o S t r i n g ( ) ;

i n t f i r s t C o m m a = l i n e . i n d e x O f ( ' , ' ) ;

S t r i n g k e y = l i n e . s u bs t r i n g ( 0 , f i r s t C o m m a ) ;

S t r i n g v a l u e = l i n e . s u b s t r i n g ( f i r s t C o m m a + 1 ) ;

T e x t o u t K e y = n e w T e x t ( k e y ) ;

/ / P r e p e n d a n i n d e x t o t h e v a l u e s o w e k n o w w h i c h f i l e

/ / i t c a m e f r o m .

T e x t o u t V a l = n e w T e x t ( " 1" + v a l u e ) ;

o c . c o l l e c t ( o u t K e y , o u t V a l ) ;

}

}

p u b l i c s t a t i c c l a s s L o a d A n d F i l t e r U s e r s e x t e n d s M a p R e d u c e B a s e

i m p l e m e n t s M a p p e r < L o n g W r i t a b l e , T e x t , T e x t , T e x t > {

p u b l i c v o i d m a p ( L o n g W r i t a b l e k , T e x t v a l ,



/ / P u l l t h e k e y o u t



S t r i n g v a l u e = l i n e . s u b s t r i n g (f i r s t C o m m a + 1 ) ;

i n t a g e = I n t e g e r . p a r s e I n t ( v a l u e ) ;

i f ( a g e < 1 8 | | a g e > 2 5 ) r e t u r n ;

S t r i n g k e y = l i n e . s u b s t r i n g ( 0 , f i r s t C o m m a ) ;


/ / P r e p e n d a n i n d e x t o t h e v a l u e s o we k n o w w h i c h f i l e

/ / i t c a m e f r o m .

T e x t o u t V a l = n e w T e x t ( " 2 " + v a l u e ) ;

o c . c o l l e c t ( o u t K e y , o u t V a l ) ;

}

}

p u b l i c s t a t i c c l a s s J o i n e x t e n d s M a p R e d u c e B a s e

i m p l e m e n t s R e d u c e r < T e x t , T e x t , T e x t , T e x t > {

p u b l i c v o i d r e d u c e ( T e x t k e y ,

I t e r a t o r < T e x t > i t e r ,



/ / F o r e a c h v a l u e , f i g u r e o u t w h i c h f i l e i t ' s f r o m a n d

s t o r e i t

/ / a c c o r d i n g l y .

L i s t < S t r i n g > f i r s t = n e w A r r a y L i s t < S t r i n g > ( ) ;

L i s t < S t r i n g > s e c o n d = n e w A r r a y L i s t < S t r i n g > ( ) ;

w h i l e ( i t e r . h a s N e x t ( ) ) {

T e x t t = i t e r . n e x t ( ) ;

S t r i n g v a l u e = t . t oS t r i n g ( ) ;

i f ( v a l u e . c h a r A t ( 0 ) = = ' 1 ' )

f i r s t . a d d ( v a l u e . s u b s t r i n g ( 1 ) ) ;

e l s e s e c o n d . a d d ( v a l u e . s u b s t r i n g ( 1 ) ) ;

r e p o r t e r . s e t S t a t u s ( " O K " ) ;

}

/ / D o t h e c r o s s p r o d u c t a n d c o l l e c t t h e v a l u e s

f o r ( S t r i n g s 1 : f i r s t ) {

f o r ( S t r i n g s 2 : s e c o n d ) {

S t r i n g o u t v a l = k e y + " , " + s 1 + " , " + s 2 ;

o c . c o l l e c t ( n u l l , n e w T e x t ( o u t v a l ) ) ;


}

}

}

}

p u b l i c s t a t i c c l a s s L o a d J o i n e d e x t e n d s M a p R e d u c e B a s e

i m p l e m e n t s M a p p e r < T e x t , T e x t , T e x t , L o n g W r i t a b l e > {

p u b l i c v o i d m a p (

T e x t k ,

T e x t v a l ,

O u t p u t C o l l ec t o r < T e x t , L o n g W r i t a b l e > o c ,


/ / F i n d t h e u r l



i n t s e c o n d C o m m a = l i n e . i n d e x O f ( ' , ' , f i r s tC o m m a ) ;

S t r i n g k e y = l i n e . s u b s t r i n g ( f i r s t C o m m a , s e c o n d C o m m a ) ;

/ / d r o p t h e r e s t o f t h e r e c o r d , I d o n ' t n e e d i t a n y m o r e ,

/ / j u s t p a s s a 1 f o r t h e c o m b i n e r / r e d u c e r t o s u m i n s t e a d .


o c . c o l l e c t ( o u t K e y , n e w L o n g W r i t a b l e ( 1 L ) ) ;

}

}

p u b l i c s t a t i c c l a s s R e d u c e U r l s e x t e n d s M a p R e d u c e B a s e

i m p l e m e n t s R e d u c e r < T e x t , L o n g W r i t a b l e , W r i t a b l e C o m p a r a b l e ,

W r i t a b l e > {

p u b l i c v o i d r e d u c e (

T e x t k ey ,

I t e r a t o r < L o n g W r i t a b l e > i t e r ,

O u t p u t C o l l e c t o r < W r i t a b l e C o m p a r a b l e , W r i t a b l e > o c ,


/ / A d d u p a l l t h e v a l u e s w e s e e

l o n g s u m = 0 ;

w hi l e ( i t e r . h a s N e x t ( ) ) {

s u m + = i t e r . n e x t ( ) . g e t ( ) ;


}

o c . c o l l e c t ( k e y , n e w L o n g W r i t a b l e ( s u m ) ) ;

}

}

p u b l i c s t a t i c c l a s s L o a d C l i c k s e x t e n d s M a p R e d u c e B a s e

im p l e m e n t s M a p p e r < W r i t a b l e C o m p a r a b l e , W r i t a b l e , L o n g W r i t a b l e ,

T e x t > {

p u b l i c v o i d m a p (

W r i t a b l e C o m p a r a b l e k e y ,

W r i t a b l e v a l ,

O u t p u t C o l l e c t o r < L o n g W r i t a b l e , T e x t > o c ,


o c . c o l l e c t ( ( L o n g W r i t a b l e ) v a l , ( T e x t ) k e y ) ;

}

}

p u b l i c s t a t i c c l a s s L i m i t C l i c k s e x t e n d s M a p R e d u c e B a s e

i m p l e m e n t s R e d u c e r < L o n g W r i t a b l e , T e x t , L o n g W r i t a b l e , T e x t > {

i n t c o u n t = 0 ;

p u b l i c v o i d r e d u c e (

L o n g W r i t a b l e k e y ,

I t e r a t o r < T e x t > i t e r ,

O u t p u t C o l l e c t o r < L o n g W r i t a b l e , T e x t > o c ,


/ / O n l y o u t p u t t h e f i r s t 1 0 0 r e c o r d s

w h i l e ( c o u n t < 1 0 0 & & i t e r . h a s N e x t ( ) ) {

o c . c o l l e c t ( k e y , i t e r . n e x t ( ) ) ;

c o u n t + + ;

}

}

}

p u b l i c s t a t i c v o i d m a i n ( S t r i n g [ ] a r g s ) t h r o w s I O E x c e p t i o n {

J o b C o n f l p = n e w J o b C o n f ( M R E x a m p l e . c l a s s ) ;

l p . s et J o b N a m e ( " L o a d P a g e s " ) ;

l p . s e t I n p u t F o r m a t ( T e x t I n p u t F o r m a t . c l a s s ) ;

l p . s e t O u t p u t K e y C l a s s ( T e x t . c l a s s ) ;

l p . s e t O u t p u t V a l u e C l a s s ( T e x t . c l a s s ) ;

l p . s e t M a p p e r C l a s s ( L o a d P a g e s . c l a s s ) ;

F i l e I n p u t F o r m a t . a d d I n p u t P a t h ( l p , n e w

P a t h ( " /u s e r / g a t e s / p a g e s " ) ) ;

F i l e O u t p u t F o r m a t . s e t O u t p u t P a t h ( l p ,

n e w P a t h ( " / u s e r / g a t e s / t m p / i n d e x e d _ p a g e s " ) ) ;

l p . s e t N u m R e d u c e T a s k s ( 0 ) ;

J o b l o a d P a g e s = n e w J o b ( l p ) ;

J o b C o n f l f u = n e w J o b C o n f ( M R E x a m p l e . c l a s s ) ;

l f u . se t J o b N a m e ( " L o a d a n d F i l t e r U s e r s " ) ;

l f u . s e t I n p u t F o r m a t ( T e x t I n p u t F o r m a t . c l a s s ) ;

l f u . s e t O u t p u t K e y C l a s s ( T e x t . c l a s s ) ;

l f u . s e t O u t p u t V a l u e C l a s s ( T e x t . c l a s s ) ;

l f u . s e t M a p p e r C l a s s ( L o a d A n d F i l t e r U s e r s . c l a s s ) ;

F i l e I n p u t F o r m a t . a d dI n p u t P a t h ( l f u , n e w

P a t h ( " / u s e r / g a t e s / u s e r s " ) ) ;

F i l e O u t p u t F o r m a t . s e t O u t p u t P a t h ( l f u ,

n e w P a t h ( " / u s e r / g a t e s / t m p / f i l t e r e d _ u s e r s " ) ) ;

l f u . s e t N u m R e d u c e T a s k s ( 0 ) ;

J o b l o a d U s e r s = n e w J o b ( l f u ) ;

J o b C o n f j o i n = n e w J o b C o n f (M R E x a m p l e . c l a s s ) ;

j o i n . s e t J o b N a m e ( " J o i n U s e r s a n d P a g e s " ) ;

j o i n . s e t I n p u t F o r m a t ( K e y V a l u e T e x t I n p u t F o r m a t . c l a s s ) ;

j o i n . s e t O u t p u t K e y C l a s s ( T e x t . c l a s s ) ;

j o i n . s e t O u t p u t V a l u e C l a s s ( T e x t . c l a s s ) ;

j o i n . s e t M a p p e r C l a s s ( I d e n t i t y M a pp e r . c l a s s ) ;

j o i n . s e t R e d u c e r C l a s s ( J o i n . c l a s s ) ;

F i l e I n p u t F o r m a t . a d d I n p u t P a t h ( j o i n , n e w

P a t h ( " / u s e r / g a t e s / t m p / i n d e x e d _ p a g e s " ) ) ;

F i l e I n p u t F o r m a t . a d d I n p u t P a t h ( j o i n , n e w

P a t h ( " / u s e r / g a t e s / t m p / f i l t e r e d _ u s e r s " ) ) ;

F i l e O u t p u t F o r m a t . s et O u t p u t P a t h ( j o i n , n e w

P a t h ( " / u s e r / g a t e s / t m p / j o i n e d " ) ) ;

j o i n . s e t N u m R e d u c e T a s k s ( 5 0 ) ;

J o b j o i n J o b = n e w J o b ( j o i n ) ;

j o i n J o b . a d d D e p e n d i n g J o b ( l o a d P a g e s ) ;

j o i n J o b . a d d D e p e n d i n g J o b ( l o a d U s e r s ) ;

J o b C o n f g r o u p = n e w J o b C o n f ( M R Ex a m p l e . c l a s s ) ;

g r o u p . s e t J o b N a m e ( " G r o u p U R L s " ) ;

g r o u p . s e t I n p u t F o r m a t ( K e y V a l u e T e x t I n p u t F o r m a t . c l a s s ) ;

g r o u p . s e t O u t p u t K e y C l a s s ( T e x t . c l a s s ) ;

g r o u p . s e t O u t p u t V a l u e C l a s s ( L o n g W r i t a b l e . c l a s s ) ;

g r o u p . s e t O u t p u t F o r m a t ( S e q u e n c e F il e O u t p u t F o r m a t . c l a s s ) ;

g r o u p . s e t M a p p e r C l a s s ( L o a d J o i n e d . c l a s s ) ;

g r o u p . s e t C o m b i n e r C l a s s ( R e d u c e U r l s . c l a s s ) ;

g r o u p . s e t R e d u c e r C l a s s ( R e d u c e U r l s . c l a s s ) ;

F i l e I n p u t F o r m a t . a d d I n p u t P a t h ( g r o u p , n e w

P a t h ( " / u s e r / g a t e s / t m p / j o i n e d " ) ) ;

F i l e O u t p u t F o r m a t . s e t O u t p u t P a t h ( g r o u p , n e w

P a t h ( " / u s e r / g a t e s / t m p / g r o u p e d " ) ) ;

g r o u p . s e t N u m R e d u c e T a s k s ( 5 0 ) ;

J o b g r o u p J o b = n e w J o b ( g r o u p ) ;

g r o u p J o b . a d d D e p e n d i n g J o b ( j o i n J o b ) ;

J o b C o n f t o p 1 0 0 = n e w J o b C o n f ( M R E x a m p l e . c l a s s ) ;

t o p 1 0 0 . s e t J o b N a m e ( " T o p 1 0 0 s i t e s " ) ;

t o p 1 0 0 . s e t I n p u t F o r m a t ( S e q u e n c e F i l e I n p u t F o r m a t . c l a s s ) ;

t o p 1 0 0 . s e t O u t p u t K e y C l a s s ( L o n g W r i t a b l e . c l a s s ) ;

t o p 1 0 0 . s e t O u t p u t V a l u e C l a s s ( T e x t . c l a s s ) ;

t o p 1 0 0 . s e t O u t p u t F o r m a t ( S e q u e n c e F i l e O u t p u t Fo r m a t . c l a s s ) ;

t o p 1 0 0 . s e t M a p p e r C l a s s ( L o a d C l i c k s . c l a s s ) ;

t o p 1 0 0 . s e t C o m b i n e r C l a s s ( L i m i t C l i c k s . c l a s s ) ;

t o p 1 0 0 . s e t R e d u c e r C l a s s ( L i m i t C l i c k s . c l a s s ) ;

F i l e I n p u t F o r m a t . a d d I n p u t P a t h ( t o p 1 0 0 , n e w

P a t h ( " / u s e r / g a t e s / t m p / g r o u p e d " ) ) ;

F i l e O u t p u t F o r m a t . s e t O u t p u t P a t h ( t o p 1 0 0 , n e w

P a t h ( " / u s e r / g a t e s / t o p 1 0 0 s i t e s f o r u s e r s 1 8 t o 2 5 " ) ) ;

t o p 1 0 0 . s e t N u m R e d u c e T a s k s ( 1 ) ;

J o b l i m i t = n e w J o b ( t o p 1 0 0 ) ;

l i m i t . a d d D e p e n d i n g J o b ( g r o u p J o b ) ;

J o b C o n t r o l j c = n e w J o b C o n t r o l ( " F i n d t o p 1 0 0 s i t e s f o r u s e r s

1 8 t o 2 5 " ) ;

j c . a d d J o b ( l o a d P a g e s ) ;

j c . a d d J o b ( l o a d U s e r s ) ;

j c . a d d J o b ( j o i n J o b ) ;

j c . a d d J o b ( g r o u p J o b ) ;

j c . a d d J o b ( l i m i t ) ;

j c . r u n ( ) ;

}

}

Pig Slides adapted from Olston et al.

Pig Latin Script

Visits = load ‘/data/visits’ as (user, url, time);

Visits = foreach Visits generate user, Canonicalize(url), time;

Pages = load ‘/data/pages’ as (url, pagerank);

VP = join Visits by url, Pages by url;

UserVisits = group VP by user;

UserPageranks = foreach UserVisits generate user, AVG(VP.pagerank) as avgpr;

GoodUsers = filter UserPageranks by avgpr > ‘0.5’;

store GoodUsers into '/data/good_users';

HIVE • A datawarehousing framework built on top of Hadoop

• Started at Facebook in 2006

• Targets users are data analysts comfortable with SQL

• Allows to query the data using a SQL-like language called HiveQL

• Queries compiled into MR jobs that are executed on Hadoop

• Meant for structured data

Hive Architecture

Hive Architecture cont. • Can interact with Hive using :

• CLI(Command Line Interface)

• JDBC

• Web GUI

• Metastore – Stores the system catalog and metadata about tables, columns, partitions etc.

• Driver – Manages the lifecycle of a HiveQL statement as it moves through Hive. Query Compiler – Compiles HiveQL into a directed acyclic graph of map/reduce tasks.

• Execution Engine – Executes the tasks produced by the compiler interacting with the underlying Hadoop instance.

• HiveServer – Provides a thrift interface and a JDBC/ODBC server.

Hive Architecture cont.

• Physical Layout • Warehouse directory in HDFS

• e.g., /user/hive/warehouse

• Table row data stored in subdirectories of warehouse

• Partitions form subdirectories of table directories

• Actual data stored in flat files • Control char-delimited text, or SequenceFiles

Hive Vs RDBMS • Latency for Hive queries is generally very high (minutes) even

when data sets involved are very small

• On RDBMSs, the analyses proceed much more iteratively with the response times between iterations being less than a few minutes.

• Hive aims to provide acceptable (but not optimal) latency for interactive data browsing, queries over small data sets or test queries.

• Hive is not designed for online transaction processing and does not offer real-time queries and row level updates.

It is best used for batch jobs over large sets of immutable data (like web logs).

Supported Data Types • Integers

• BIGINT(8 bytes), INT(4 bytes), SMALLINT(2 bytes), TINYINT(1 byte).

• All integer types are signed.

• Floating point numbers • FLOAT(single precision), DOUBLE(double precision)

• STRING : sequence of characters

• BOOLEAN : True/False

• Hive also natively supports the following complex types: • Associative arrays – map<key-type, value-type>

• Lists – list<element-type>

• Structs – struct<field-name: field-type, ... >

Hive : Install & Configure • Download a HIVE release compatible with your Hadoop

installation from :

• http://hive.apache.org/releases.html

• Untar into a directory. This is the HIVE’s home directory

• tar xvzf hive.x.y.z.tar.gz

• Configure

• Environment variables – add in .bash_profile

• export HIVE_INSTALL=/<parent_dir_path>/hive-x.y.z

• export PATH=$PATH:$HIVE_INSTALL/bin

• Verify Installation

• Type : hive –help (Displays commands usage)

• Type : hive (Enter the hive shell)

hive>

http://hive.apache.org/releases.html

http://hive.apache.org/releases.html

Hive : Install & Configure cont. • Start Hadoop daemons (Hadoop needs to be running)

• Configure to Hadoop

• Create hive-site.xml in $HIVE_INSTALL/conf directory

• Specify the filesystem and jobtracker using the properties fs.default.name & mapred.job.tracker

• If not set, these default to the local filesystem and the local(in-process) job-runner

• Create following directories under HDFS

• /tmp (execute: hadoop fs –mkdir /tmp)

• user/hive/warehouse (execute: hadoop fs –mkdir /user/hive/warehouse)

• chmod g+w for both (execute : hadoop fs –chmod g+w <dir_path>)

Hive : Install & Configure cont. • Data Store

• Hive stores data under /user/hive/warehouse by default

• Metastore

• Hive by default comes with a light wight SQL database Derby to store the metastore metadata.

• But this can be configured to other databases like MySQL as well.

• Logging

• Hive uses Log4j

• You can find Hive’s error log on the local file system at /tmp/$USER/hive.log

Hive Data Models • Databases

• Tables

• Partitions

• Each Table can have one or more partition Keys which determines how the data is stored.

• Partitions - apart from being storage units - also allow the user to efficiently identify the rows that satisfy a certain criteria.

• For example, a date_partition of type STRING and country_partition of type STRING.

• Each unique value of the partition keys defines a partition of the Table. For example all “India" data from "2013-05-21" is a partition of the page_views table.

• Therefore, if you run analysis on only the “India" data for 2013-05-21, you can run that query only on the relevant partition of the table thereby speeding up the analysis significantly.

Partition example

CREATE TABLE logs (ts BIGINT, line STRING)

PARTITIONED BY (dt STRING, country STRING);

• When we load data into a partitioned table, the partition values are specified explicitly:

LOAD DATA LOCAL INPATH 'input/hive/partitions/file1'

INTO TABLE logs

PARTITION (dt='2001-01-01', country='GB');

Hive Data Model cont.

• Buckets

• Data in each partition may in turn be divided into Buckets based on the value of a hash function of some column of the Table.

• For example the page_views table may be bucketed by userid, which is one of the columns, other than the partitions columns, of the page_views table. These can be used to efficiently sample the data.

A Practical session Starting the Hive CLI

• Start a terminal and run :

• $hive

• Will take you to the hive shell/prompt

hive>

• Set a Hive or Hadoop conf prop:

• hive> set propkey=propvalue;

• List all properties and their values:

• hive> set –v;

A Practical Session

Hive CLI Commands

• List tables:

– hive> show tables;

• Describe a table:

– hive> describe <tablename>;

• More information:

– hive> describe extended <tablename>;

A Practical Session Hive CLI Commands • Create tables:

– hive> CREATE TABLE cite (citing INT, cited INT)

>ROW FORMAT DELIMITED

>FIELDS TERMINATED BY ',' STORED AS TEXTFILE;

• The 2nd and the 3rd lines tell Hive how the data is stored (as a text file)and how it should be parsed (fields are separated by commas).

• Loading data into tables • Let’s load the patent data into table cite

hive> LOAD DATA LOCAL INPATH ‘<path_to_file>/cite75_99.txt’ > OVERWRITE INTO TABLE cite;

• Browse data hive> SELECT * FROM cite LIMIT 10;

A Practical Session Hive CLI Commands • Count

hive>SELECT COUNT(*) FROM cite;

Some more playing around • Create table to store citation frequency of each patent

hive> CREATE TABLE cite_count (cited INT, count INT);

• Execute the query on the previous table and store the results : hive> INSERT OVERWRITE TABLE cite_count

• > SELECT cited, COUNT(citing) • > FROM cite • > GROUP BY cited;

• Query the count table hive> SELECT * FROM cite_count WHERE count > 10 LIMIT 10;

• Drop Table hive> DROP TABLE cite_count;

Data Model Partitioning Data • One or more partition columns may be specified:

hive>CREATE TABLE tbl1 (id INT, msg STRING)

PARTITIONED BY (dt STRING);

• Creates a subdirectory for each value of the partition column, e.g.:

/user/hive/warehouse/tbl1/dt=2009-03-20/

• Queries with partition columns in WHERE clause will scan through only a subset of the data.

Managing Hive Tables

• Managed table

• Default table created (without EXTERNAL keyword)

• Hive manages the data

CREATE TABLE managed_table (dummy STRING);

LOAD DATA INPATH '/user/tom/data.txt' INTO table managed_table;

• Moves the data into the warehouse directory for the table.

DROP TABLE managed_table;

• Deletes table data & metadata.

Managing Hive Tables

• External table

• You control the creation and deletion of the data.

• The location of the external data is specified at table creation time:

• CREATE EXTERNAL TABLE external_table (dummy STRING)

• LOCATION '/user/tom/external_table'; LOAD DATA INPATH '/user/tom/data.txt' INTO TABLE external_table;

• DROP TABLE external_table Hive will leave the data untouched and only delete the metadata.

Conclusion

• Supports rapid iteration of adhoc queries

• High-level Interface (HiveQL) to low-level infrastructure

(Hadoop).

• Scales to handle much more data than many similar systems

Hive Resources/References

Documentation

• cwiki.apache.org/Hive/home.html

Mailing Lists

• [email protected]

Books

• Hadoop, The Definitive Guide, 3rd edition by Tom White, (O’Reilly)

mailto:[email protected]



Hive Resources/References

Documentation

• cwiki.apache.org/Hive/home.html

Mailing Lists

• [email protected]

Books

• Hadoop, The Definitive Guide, 3rd edition by Tom White, (O’Reilly)




PIG

PIG

• PIG is an abstraction layer on top of MapReduce that frees analysts from the complexity of MapReduce programming • Architected towards handling unstructured and semi structured data • It's a dataflow language, which means the data is processed in a sequence of steps transforming the data • The transformations support relational-style operations such as filter, union, group, and join. • Designed to be extensible and reusable • Programmers can develop own functions and use (UDFs) • Programmer friendly • Allows to introspect data structures • Can do sample run on a representative subset of your input • PIG internally converts each transformation into a MapReduce job and submits to hadoop cluster • 40 percent of Yahoo's Hadoop jobs are run with PIG

Pig : What for ?

• An ad-hoc way of creating and executing map-reduce jobs on very large data sets

• Rapid development

• No Java is required

• Developed by Yahoo

PIG Vs MapReduce(MR)

Pig Use cases

Processing of Web Logs.

Data processing for search platforms.

Support for Ad Hoc queries across large datasets.

Quick Prototyping of algorithms for processing large datasets.

Problem Statement: De-identify personal health information. Challenges: Huge amount of data flows into the systems daily and there are multiple data

sources that we need to aggregate data from.

Crunching this huge data and deidentifying it in a traditional way had problems.

Use Case in Healthcare

Use Case in Healthcare

When to Not Use PIG

• Really nasty data formats or completely unstructured data (video, audio, raw human-readable text).

• Pig is definitely slow compared to Map Reduce jobs.

• When you would like more power to optimize your code.

PIG Architecture

• Pig runs as a client side application ,there is no need to install anything on the cluster.

Install and Configure PIG

• Download a version of PIG compatible with your hadoop installation • http://pig.apache.org/releases.html • Untar into a designated folder. This will be Pig's home directory • tar xzf pig-x.y.z.tar.gz • Configure • Environment Variables add in .bash_profile • export PIG_INSTALL=/<parent directory path>/pig-x.y.z • export PATH=$PATH:$PIG_INSTALL/bin • Verify Installation • Try pig -help • Displays command usage • Try pig • Takes you into Grunt shell grunt>

PIG Execution Modes

• Local Mode • Runs in a single JVM • Operates on local file system • Suitable for small datasets and for development • To run PIG in local mode • pig -x local • MapReduce Mode • In this mode the queries are translated into MapReduce jobs and run on hadoop cluster • PIG version must be compatible with hadoop version • Set HADOOP HOME environment variable to indicate pig which hadoop client to use • export HADOOP_HOME=$HADOOP_INSTALL • If not set it will use a bundled version of hadoop

Pig Latin

Data Analysis Task Example

Conceptual Workflow

Pig Latin Relational Operators

Ways of Executing PIG programs

Grunt • An interactive shell for running Pig commands • Grunt is started when the pig command is run without any options • Script • Pig commands can be executed directly from a script file pig pigscript.pig • It is also possible to run Pig scripts from Grunt shell using run and exec. • Embedded • You can run Pig programs from Java using the PigServer class, much like you can use JDBC • For programmatic access to Grunt, use PigRunner

An Example

Create a file sample.txt with(tab delimited) : 1932 23 2 1905 12 1 And so on. grunt> records = LOAD ‘<your_input_dir>/sample.txt' AS (year:chararray, temperature:int, quality:int); DUMP records; grunt>filtered_records = FILTER records BY temperature !=9999 AND (quality ==0 OR quality ==1 OR quality ==4);

Example cont.

grunt>grouped_records = GROUP filtered_records BY year;

grunt>max_temp = FOREACH grouped_records GENERATE group,

MAX (filtered_records.temperature);

grunt>DUMP max_temp;

Compilation

Data Types

• Simple Type

Category Type Description

Numeric

int 32 - bit signed integer

long 64 - bit signed integer

float 32-bit floating-point number

double 64-bit floating-point number

Text chararray Character array in UTF-16 format

Binary bytearray Byte array

Data Types

• Complex Types

Type Description Example

Tuple Sequence of fields of any type (1 ,'pomegranate' )

Bag An unordered collection of tuples, possibly with

duplicates {(1 ,'pomegranate' ) ,(2)}

map A set of key-value pairs; keys must be character

arrays but values may be any type ['a'#'pomegranate' ]

LOAD Operator

<relation name> = LOAD '<input file with path>' [USING UDF()]

[AS (<field namel>:dataType, <field nome2>:dataType„<field name3>:dataType)]

• Loads data from a file into a relation

• Uses the PigStorage load function as default unless specified otherwise with the USING option

• The data can be given a schema using the AS option.

• The default data type is bytearray if not specified

records=LOAD 'sales.txt';

records=LOAD 'sales.txt' AS (fl:chararray, f2:int, f3:f1oat.);

records=LOAD isales.txt' USING PigStorage('\t');

records= LOAD (sales.txt' USING PigStorage('\t') AS (fl:chararray, f2:int,f3:float);

Diagnostic Operators

• DESCRIBE • Describes the schema of a relation • EXPLAIN • Display the execution plan used to compute a relation • ILLUSTRATE • Illustrate step-by-step how data is transformed • Uses sample of the input data to simulate the execution.

Data Write Operators

• LIMIT

• Limits the number of tuples from a relation

• DUMP

• Display the tuples from a relation

• STORE

• Store the data from a relation into a directory.

• The directory must not exist

Relational Operators

• FILTER • Selects tuples based on Boolean expression teenagers = FILTER cust BY age <20; • ORDER • Sort a relation based on one or more fields • Further processing (FILTER, DISTINCT, etc.) may destroy the ordering ordered list = ORDER cust BY name DESC; • DISTINCT • Removes duplicate tuples unique_custlist = DISTINCT cust;


• GROUP BY • Within a relation, group tuples with the same group key • GROUP ALL will group all tuples into one group groupByProfession=GROUP cust BY profession groupEverything=GROUP cust ALL • FOR EACH • Loop through each tuple in nested_alias and generate new tuple(s) . countByProfession=FOREACH groupByProfession GENERATE group, count(cust); • Built in aggregate functions AVG, COUNT, MAX, MIN, SUM


• GROUP BY • Within a relation, group tuples with the same group key • GROUP ALL will group all tuples into one group groupByProfession=GROUP cust BY profession groupEverything=GROUP cust ALL • FOR EACH • Loop through each tuple in nested alias and generate new tuple(s). • At least one of the fields of nested alias should be a bag • DISTINCT, FILTER, LIMIT, ORDER, and SAMPLE are allowed operations in nested op to operate on the inner bag(s). • countByProfession=FOREACH groupByProfession GENERATE group, count(cust); • Built in aggregate functions AVG, COUNT, MAX, MIN, SUM

Operating on Multiple datasets

• Join Compute inner join of two or more relations based on common fields values DUMP A; DUMP B; (1,2,3) (2,4) (4,2,1) (8,9) (8,3,4) (1,3) (4,3,3) (2,7) (7,2,5) (7,9) X=JOIN A BY a1,B BY b1 DUMP X; (1,2,3,1,3) (8,3,4,8,9) (7,2,5,7,9)


• COGROUP Group tuples from two or more relations,based on common group values DUMP A; DUMP B; (1,2,3) (2,4) (4,2,1) (8,9) (8,3,4) (1,3) (4,3,3) (2,7) (7,2,5) (7,9) X=COGROUP A BY a1,B BY b1 DUMP X; (1,{(1,2,3)},{(1,3)}) (8,{(8,3,4)},{(8,9)}) (7,{(7,2,5)},{(7,9)}) (2,{},{(2,4),(2,7)}) (4,{(4,2,1),(4,3,3)},{})

Joins & Cogroups

• JOIN and COGROUP operators perform similar functions.

• JOIN creates a flat set of output records while COGROUP creates a nested set of output records

File – student

Name Age GPA

Joe 18 2.5

Sam 3.0

Angel 21 7.9

John 17 9.0

Joe 19 2.9

File – studentRoll

Name RollNo

Joe 45

Sam 24

Angel 1

John 12

Joe 19

Data

A = load 'student' as (name:chararray, age:int, gpa:float); dump A; (joe,18,2.5) (sam,,3.0) (angel,21,7.9) (john,17,9.0) (joe,19,2.9)

X = group A by name; dump X; (joe,{(joe,18,2.5),(joe,19,2.9)}) (sam,{(sam,,3.0)}) (john,{(john,17,9.0)}) (angel,{(angel,21,7.9)})

Example of GROUP Operator:

Pig Latin - GROUP Operator

A = load 'student' as (name:chararray, age:int,gpa:float); B = load 'studentRoll' as (name:chararray, rollno:int); X = cogroup A by name, B by name; dump X; (joe,{(joe,18,2.5),(joe,19,2.9)},{(joe,45),(joe,19)}) (sam,{(sam,,3.0)},{(sam,24)}) (john,{(john,17,9.0)},{(john,12)}) (angel,{(angel,21,7.9)},{(angel,1)})

Example of COGROUP Operator:

Pig Latin – COGROUP Operator


UNION Creates the union of two or more relations DUMP A; DUMP B; (1,2,3) (2,4) (4,2,1) (8,9) (8,3,4) X=UNION A , B; DUMP X; (1,2,3) (4,2,1) (8,3,4) (2,4) (8,9)


SPLITS Splits a relation into two or more relations, based on a Boolean expression Y=SPLIT X INTO C IF a1<5 , D IF a1>5; DUMP C; DUMP D; (1,2,3) (4,2,1) (8,3,4) (2,4) (8,9)

User Defined Functions (UDFs)

• PIG lets users define their own functions and lets them be used in the statements

• The UDFs can be developed in Java, Python or Javascript – Filter UDF

• To be subclassof FilterFunc which is a subclass ofEvalFunc

– Eval UDF To be subclassed of EvalFunc

public abstract class EvalFunc<T> {

public abstract T exec(Tuple input) throws 1OException;

}

– Load / Store UDF • To be subclassed of LoadFunc /StoreFunc

Creating UDF : Eval function example public class UPPER extends EvalFunc<String> {

@Override

public String exec(Tuple input) throws IOException {

if (input == null || input.size() == 0)

return null;

try {

String str = (String) input.get(0);

return str.toUpperCase(); // java upper case

function

} catch (Exception e) {

throw new IOException("Caught exception processing

input...", e);

}

}

}

Define and use an UDF

• Package the UDF class into a jar

• Define and use a UDF

• REGISTER yourUDFjar_name.jar;

• cust = LOAD some_cust_data;

• filtered = FOREACH cust GENERATE

com.pkg.UPPER(title) ;

• DUMP filtered;

Piggy Bank

• Piggy Bank is a place for Pig users to share the Java UDFs they have written for use with Pig. The functions are contributed "as-is.“

• Piggy Bank currently supports Java UDFs.

• No binary version to download, download source, build and use.

Pig Vs. Hive

• Hive was invented at Facebook.

• Pig was invented at Yahoo.

• If you know SQL, then Hive will be very familiar to you. Since Hive uses SQL, you will feel at home with all the familiar select, where, group by, and order by clauses similar to SQL for relational databases.

Pig Vs. Hive

• Pig needs some mental adjustment for SQL users to learn.

• Pig Latin has many of the usual data processing concepts that SQL has, such as filtering, selecting, grouping, and ordering, but the syntax is a little different from SQL (particularly the group by and flatten statements!).

Pig Vs. Hive

• Pig gives you more control and optimization over the flow of the data than Hive does.

• If you are a data engineer, then you’ll likely feel like you’ll have better control over the dataflow (ETL) processes when you use Pig Latin, if you come from a procedural language background.

• If you are a data analyst, however, you will likely find that you can work on Hadoop faster by using Hive, if your previous experience was more with SQL.

Pig Vs. Hive

• Pig Latin allows users to store data at any point in the pipeline without disrupting the pipeline execution.

• Pig is not meant to be an ad-hoc query tool.

Pig Vs. Hive

• Pig needs some mental adjustment for SQL users to learn.

• Pig Latin has many of the usual data processing concepts that SQL has, such as filtering, selecting, grouping, and ordering, but the syntax is a little different from SQL (particularly the group by and flatten statements!).

Apache Mahout

What is Apache Mahout

• Mahout is an open source machine learning library from Apache.

• Scalable, can run on Hadoop

• Written in Java

• Started as a Lucene sub-project, became an Apache top-level-project in 2010.

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Machine Learning : Definition

• “Machine Learning is programming computers to optimize a performance criterion using example data or past experience”

– Intro. To Machine Learning by E. Alpaydin

• Subset of Artificial Intelligence

• Lots of related fields:

– Information Retrieval

– Stats

– Biology

– Linear algebra

– Many more

Machine Learning

• Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data.

• Supervised learning is tasked with learning a function from labeled training data in order to predict the value of any valid input.

– Common examples of supervised learning include classifying e-mail messages as spam.

• Unsupervised learning is tasked with making sense of data without any examples of what is correct or incorrect. It is most commonly used for clustering similar input into logical groups.

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http://en.wikipedia.org/wiki/Artificial_intelligence

http://en.wikipedia.org/wiki/Learning

Common Use Cases

• Recommend friends/dates/products

• Classify content into predefined groups

• Find similar content

• Find associations/patterns in actions/behaviors

• Identify key topics/summarize text – Documents and Corpora

• Detect anomalies/fraud

• Ranking search results

• Others?

Apache Mahout

• An Apache Software Foundation project to create scalable machine learning libraries under the Apache Software License – http://mahout.apache.org

• Why Mahout?

– Many Open Source ML libraries either: • Lack Community • Lack Documentation and Examples • Lack Scalability • Lack the Apache License • Or are research-oriented

http://mahout.apache.org

Sampling of Who uses Mahout?

Apache Mahout components

• The Three C’s:

– Collaborative Filtering (recommenders)

– Clustering

– Classification

• Others:

– Frequent Item Mining

– Math stuff

Content Discovery & Personalization

Key points

• Automatic discovery of the most relevant content without searching for it • Automatic discovery and recommendation of the most appropriate connection between people and interests • Personalization and presentation of the most relevant content (content, inspirations, marketplace, ads) at every page/touch point

Business objectives

• More revenue from incoming traffic

• Improved consumer interaction and loyalty, as each page has more interesting and relevant content

• Drives better utilization of assets within the platform by linking “similar” products

Basic approaches to Recommendations on social sites

• Collaborative Filtering (CF): Collaborative filtering first computes similarity between two users based on their preference towards items, and recommends items which are highly rated(preferred) by similar users.

• Content based Recommendation(CBR) : Content based system provides recommendation directly based on similarity of items and the user history . Similarity is computed based on item attributes using appropriate distance measures.

Collaborative Filtering (CF) – Provide recommendations solely based on preferences

expressed between users and items – “People who watched this also watched that”

• Content-based Recommendations (CBR) – Provide recommendations based on the attributes of the

items (and user profile) – ‘Chak de India’ is a sports movie, Mohan likes sports

movies • => Suggest Chak de India to Mohan

• Mahout geared towards CF, can be extended to do CBR

• Aside: search engines can also solve these problems

Collaborative Filtering approaches

• User based

• Item based

User Similarity

Item 1 Item 2 Item 3 Item 4

User 1

User 2 User

3 User 4

What should we recommend for User 1?

Item 1 Item 2 Item 3 Item 4

User 1

User 2 User

3 User 4

Item Similarity

Component diagram

Recommender App

DataModel Extractor / User Preference Builder

Core platform DB QDSS (Cassandra)

Personalization DB

Feeder

REST API

Key architecture blocks

• Data Extraction – This involves extracting relevant data from :

• Core Platform DB (MySQL) • Qyuki custom Data store (Cassandra)

• Data Mining : – This involves a series of activities to calculate

distances/similarities between items, preferences of users and applying Machine Learning for further use cases.

• Personalization Service: – The application of relevant data contextually for the users

and creations on Qyuki, exposed as a REST api.

Entities to Recommend : Creations

For every creation, there are 2 parts to recommendations :

1) Creations similar to the current creation(Content-Based)

2) Recommendations based on the studied preferences of the user.

Features cont..

Entities to Recommend : Users

• New users/creators to follow based on his social graph & other activities

• New users/creators to collaborate with. – The engine would recommend artists to artists to

collaborate and create. – QInfluence

Personalized Creations

• Track user’s activities – MySQL + Custom Analytics(Cassandra)

• Explicit & Implicit Ratings

• Explicit : None in our case

• Implicit : Emotes, comments, views, engagement with creator

Personalized Creations cont..

• Mahout expects data input in the form : User Item Preference U253 I306 3.5 U279 I492 4.0

• Preference Compute Engine

– Consider all user activities, infer preferences

• Feed preference data to Mahout, you are done(to some extent)

• Filtering and rescoring

Some Mahout recommendations logic/code

• DataModel : preference data model – DataModel dataModel = new FileDataModel(new File(preferenceFile));

• Notion of similarity(Distance measure)

– UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);

• Neighbourhood(for user based)

– UserNeighborhood neighborhood = NearestNUserNeighborhood(10, similarity, dataModel);

• Recommender instance – GenericUserBasedRecommender userBasedRecommender = new

GenericUserBasedRecommender(dataModel, neighborhood, similarity);

• Recommendations for a user

– topUserBasedRecommendeditems = userBasedRecommender.recommend(userId, numOfItemsToBeCached, null);

Similarity algorithms

• Pearson correlation–based similarity

– Pearson correlation is a number between –1 and 1

– It measures the tendency of two users’ preference values to move together—to be relatively high, or relatively low, on the same items.

– doesn’t take into account the number of items in which two users’ preferences overlap

Similarity algorithms

• Cosine Similarity

– Ignores 0-0 matches

– A measure of alignment/direction

– Since Cos 0 = 1 (1 means 100% similar)

• Euclidean Distance Similarity

– Based on Euclidean distance between two points (sq root(sum of squares of diff in coordinates)

Which Similarity to use • If your data is dense (almost all attributes have

non-zero values) and the magnitude of the attribute values is important, use distance measures such as Euclidean.

• If the data is subject to grade-inflation (different users may be using different scales) use Pearson.

• If the data is sparse consider using Cosine Similarity.

Hybrid approaches

• Weighted hybrid – Combines scores from each component using linear formula.

• Switching hybrid – Select one recommender among candidates.

• Mixed hybrid – Based on the merging and presentation of multiple ranked lists into one. – Core algorithm of mixed hybrid merges them into a

single ranked list.

Evaluating Recommenders

• Mahout

• Training Set, Test Set

• Iterative preference weight tuning + evaluator

• RMSE

• A/B testing

• Offline

Distributed Recommendations with Mahout & Hadoop

Movie recommendations Case

• Movie rating data (17 million ratings from around 6000 users available as input)

• Goal : Compute recommendations for users based on the rating data

• Use Mahout over Hadoop

• Mahout has support to run Map Reduce Jobs

• Runs a series of Map Reduce Jobs to compute recommendations (Movies a user would like to watch)

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Run Mahout job on

• hadoop jar ~/mahout-core-0.7-job.jar org.apache.mahout.cf.taste.hadoop.item.RecommenderJob --input /user/hadoop/input/ratings.csv --output recooutput -s SIMILARITY_COOCCURRENCE

--usersFile /user/hadoop/input/users.txt

Other similarity options

• You can pass one of these as well to the

• “-s” flag : • SIMILARITY_COOCCURRENCE,

SIMILARITY_EUCLIDEAN_DISTANCE, SIMILARITY_LOGLIKELIHOOD, SIMILARITY_PEARSON_CORRELATION, SIMILARITY_TANIMOTO_COEFFICIENT, SIMILARITY_UNCENTERED_COSINE, SIMILARITY_UNCENTERED_ZERO_ASSUMING_COSINE

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Learning

• Reach out to experienced folks early

• Recommending follows the 90/10 rule

• Assign the right task to the right person

• Use Analytics metrics to evaluate recommenders periodically

• Importance to human evaluation

• Test thoroughly

• Mahout has some bugs(e.g. filtering part)

• Experiment with hybrid approaches

• You won’t need Hadoop generally

• Performance test your application

• Have a fallback option, hence take charge of your popularity algorithm

What is recommendation?

Recommendation involves the prediction of what new items a user would like or dislike based on preferences of or associations to previous items

(Made-up) Example: A user, John Doe, likes the following books (items):

A Tale of Two Cities The Great Gatsby For Whom the Bell Tolls

Recommendations will predict which new books (items), John Doe, will like: Jane Eyre The Adventures of Tom Sawyer

282

How does Mahout’s Recommendation Engine Work?

5 3 4 4 2 2 1

3 3 3 2 1 1 0

4 3 4 3 1 2 0

4 2 3 4 2 2 1

2 1 1 2 2 1 1

2 1 2 2 1 2 0

1 0 0 1 1 0 1

2

0

0

4

4.5

0

5

40

18.5

24.5

40

26

16.5

15.5

X =

S U R S is the similarity matrix between items

U is the user’s preferences for items

R is the predicted recommendations

What is the similarity matrix, S?

S is a n x n (square) matrix Each element, e, in S are indexed

by row (j) and column (k), ejk

Each ejk in S holds a value that describes how similar are its corresponding j-th and k-th items

In this example, the similarity of the j-th and k-th items are determined by frequency of their co-occurrence (when the j-th item is seen, the k-th item is seen as well)

In general, any similarity measure may be used to produce these values

We see in this example that Items 1 and 2 co-occur 3 times, Items 1 and 3 co-occur 4 times, and so on…

5 3 4 4 2 2 1

3 3 3 2 2 1 0

4 3 4 3 1 2 0

4 2 3 4 2 2 1

2 2 1 2 2 1 1

2 1 2 2 1 2 0

1 0 0 1 1 0 1

S

Item 1

Item

1

Item 2

Item 3

Item 4

Item 5

Item 6

Item 7

Item

2

Item

3

Item

4

Item

5

Item

6

Item

7

284

What is the user’s preferences, U?

The user’s preference is represented as a column vector Each value in the vector

represents the user’s preference for j-th item

In general, this column vector is sparse

Values of zero, 0, represent no recorded preferences for the j-th item

2

0

0

4

4.5

0

5

U

Item 1

Item 2

Item 3

Item 4

Item 5

Item 6

Item 7

285

What is the recommendation, R?

R is a column vector representing the prediction of recommendation of the j-th item for the user

R is computed from the multiplication of S and U S x U = R

In this running example, the user already has expressed positive preferences for Items 1, 4, 5 and 7, so we look at only Items 2, 3, and 6

We would recommend to the user Items 3, 2, and 6, in this order, to the user

40

18.5

24.5

40

26

16.5

15.5

R

Item 1

Item 2

Item 3

Item 4

Item 5

Item 6

Item 7

286

What data format does Mahout’s recommendation engine expects?

• For Mahout v0.7, look at RecommenderJob

(org.apache.mahout.cf.taste.hadoop.item.RecommenderJob)

• Each line of the input file should have the following format – userID,itemID[,preferencevalue

]

• userID is parsed as a long

• itemID is parsed as a long

• preferencevalue is parsed as a double and is optional

Format 1

123,345 123,456 123,789 … 789,458

Format 2

123,345,1.0 123,456,2.2 123,789,3.4 … 789,458,1.2

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How do you run Mahout’s recommendation engine?

Requirements Hadoop cluster on GNU/Linux Java 1.6.x SSH

Assuming you have a Hadoop cluster installed and configured correctly with the data loaded into HDFS, hadoop jar ~/mahout-core-0.7-job.jar

org.apache.mahout.cf.taste.hadoop.item.RecommenderJob --input /user/hadoop/input/ratings.csv --output recooutput -s SIMILARITY_COOCCURRENCE --usersFile /user/hadoop/input/users.txt

$HADOOP_INSTALL$ is the location where you installed Hadoop $TARGET$ is the directory where you have the Mahout jar file $INPUT$ is the input file name $OUTPUT$ is the output file name

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Running Mahout RecommenderJob options

• There are plenty of runtime options (check javadocs)

• --userFile (path) : optional; a file containing userIDs; only preferences of these userIDs will be computed

• --itemsFile (path) : optional; a file containing itemIDs; only these items will be used in the recommendation predictions

• --numRecommendations (integer) : number of recommendations to compute per user; default 10

• --booleanData (boolean) : treat input data as having no preference values; default false

• --maxPrefsPerUser (integer) : maximum number of preferences considered per user in final recommendation phase; default 10

• --similarityClassname (classname): similarity measure (cooccurence, euclidean, log-likelihood, pearson, tanimoto coefficient, uncentered cosine, cosine)

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Coordination in a distributed system • Coordination: An act that multiple nodes must

perform together.

• Examples: – Group membership

– Locking

– Publisher/Subscriber

– Leader Election

– Synchronization

• Getting node coordination correct is very hard!

ZooKeeper allows distributed processes to

coordinate with each other through a shared

hierarchical name space of data registers.

Introducing ZooKeeper

- ZooKeeper Wiki

ZooKeeper is much more than a distributed lock server!

What is ZooKeeper?

• An open source, high-performance coordination service for distributed applications.

• Exposes common services in simple interface: – naming

– configuration management

– locks & synchronization

– group services … developers don't have to write them from scratch

• Build your own on it for specific needs.

ZooKeeper Use Cases

• Configuration Management – Cluster member nodes bootstrapping configuration from a

centralized source in unattended way – Easier, simpler deployment/provisioning

• Distributed Cluster Management – Node join / leave – Node statuses in real time

• Naming service – e.g. DNS • Distributed synchronization - locks, barriers, queues • Leader election in a distributed system. • Centralized and highly reliable (simple) data registry

The ZooKeeper Service

• ZooKeeper Service is replicated over a set of machines

• All machines store a copy of the data (in memory)‏

• A leader is elected on service startup

• Clients only connect to a single ZooKeeper server & maintains a TCP connection.

• Client can read from any Zookeeper server, writes go through the leader & needs majority consensus.

Image: https://cwiki.apache.org/confluence/display/ZOOKEEPER/ProjectDescription

The ZooKeeper Data Model

• ZooKeeper has a hierarchal name space. • Each node in the namespace is called as a ZNode. • Every ZNode has data (given as byte[]) and can optionally

have children. parent : "foo" |-- child1 : "bar" |-- child2 : "spam" `-- child3 : "eggs" `-- grandchild1 : "42"

• ZNode paths: – canonical, absolute, slash-separated – no relative references. – names can have Unicode characters

Consistency Guarantees • Sequential Consistency: Updates are applied in order

• Atomicity: Updates either succeed or fail

• Single System Image: A client sees the same view of the service regardless of the ZK server it connects to.

• Reliability: Updates persists once applied, till overwritten by some clients.

• Timeliness: The clients’ view of the system is guaranteed to be up-to-date within a certain time bound. (Eventual Consistency)

Who uses ZooKeeper? Companies:

• Yahoo!

• Zynga

• Rackspace

• LinkedIn

• Netflix

• and many more…

Projects: • Apache Map/Reduce (Yarn)

• Apache HBase

• Apache Solr

• Neo4j

• Katta

• and many more…

Reference: https://cwiki.apache.org/confluence/display/ZOOKEEPER/PoweredBy

Apache Flume

My log data is not near my Hadoop

cluster Application

Servers

Customer Logs

Oracle

Big Data Appliance

?

Moving Data with Flume NG

Oracle

Big Data Appliance

Application

Servers

Flume NG

Agent

Flume NG

Agent

Flume NG

Agent

Logs

Logs

Logs

HDFS Write

HDFS Write

HDFS Write

Flume NG

Agent

Flume NG

Agent

Flume NG

Agent

Avro

Avro

Avro

Building a Basic Flume Agent

• Flume is flexible – Durable Transactions

– In-Flight Data Modification

– Compresses Data

• Flume simpler than it used to be – No Zookeeper requirement

– No Master-Slave architecture

• 3 basic pieces – Source, Channel, Sink

• One configuration file

Flume Configuration

hdfs-agent.sources= netcat-collect

hdfs-agent.sinks = hdfs-write

hdfs-agent.channels= memoryChannel

hdfs-agent.sources.netcat-collect.type = netcat

hdfs-agent.sources.netcat-collect.bind = 127.0.0.1

hdfs-agent.sources.netcat-collect.port = 11111

hdfs-agent.sinks.hdfs-write.type = hdfs

hdfs-agent.sinks.hdfs-write.hdfs.path = hdfs://localhost:8020/user/oracle/sabre_example

hdfs-agent.sinks.hdfs-write.rollInterval = 30

hdfs-agent.sinks.hdfs-write.hdfs.writeFormat=Text

hdfs-agent.sinks.hdfs-write.hdfs.fileType=DataStream

hdfs-agent.channels.memoryChannel.type = memory

hdfs-agent.channels.memoryChannel.capacity=10000

hdfs-agent.sources.netcat-collect.channels=memoryChannel

hdfs-agent.sinks.hdfs-write.channel=memoryChannel

Invoke this with: flume-ng agent –f this_file –n hdfs-agent

Sending Data to the Agent

• Connect netcat to the host

• Pipe input to it

• Records are transmitted on newline

• head example.xml | nc localhost 11111

Alternatives to Flume

• Scribe – Thrift-based

– Lightweight, but no support

– Not designed around Hadoop

• Kafka – Designed to resemble a publish-subscribe system

– Explicitly distributed

– Apache Incubator Project

• And Their Trade-Offs

Apache HCatalog

● What is it ?

● How does it work ?

● Interfaces

● Architecture

● Example

HCatalog – What is it ?

● A Hive metastore interface set

● Shared schema and data types for Hadoop tools

● Rest interface for external data access

● Assists inter operability between

– Pig, Hive and Map Reduce

● Table abstraction of data storage

● Will provide data availability notifications

HCatalog – How does it work ?

● Pig

– HCatLoader + HCatStorer interface

● Map Reduce

– HCatInputFormat + HCatOutputFormat interface

● Hive

– No interface necessary

– Direct access to meta data

● Notifications when data available

HCatalog – Interfaces

● Interface via

– Pig

– Map Reduce

– Hive

– Streaming

● Access data via

– Orc file

– RC file

– Text file

– Sequence file

– Custom format

HCatalog – Interfaces

HCatalog – Architecture

HCatalog – Example

A data flow example from hive.apache.org

First Joe in data acquisition uses distcp to get data onto the grid. hadoop distcp file:///file.dat hdfs://data/rawevents/20100819/data hcat "alter table rawevents add partition (ds='20100819') location 'hdfs://data/rawevents/20100819/data'" Second Sally in data processing uses Pig to cleanse and prepare the data. Without HCatalog, Sally must be manually informed by Joe when data is available, or poll on HDFS. A = load '/data/rawevents/20100819/data' as (alpha:int, beta:chararray, …); B = filter A by bot_finder(zeta) = 0; … store Z into 'data/processedevents/20100819/data'; With HCatalog, HCatalog will send a JMS message that data is available. The Pig job can then be started. A = load 'rawevents' using HCatLoader(); B = filter A by date = '20100819' and by bot_finder(zeta) = 0; … store Z into 'processedevents' using HcatStorer("date=20100819"); Note that the pig job refers to the data by name rawevents rather than a location Now access the data via Hive QL select advertiser_id, count(clicks) from processedevents where date = ‘20100819’ group by advertiser_id;

SQOOP

312

• sqoop allows users to extract data from a structured data store into Hadoop for analysis

• Sqoop can also export the data back the structured stores

• Installing & Configuring SQOOP • Download a version of SQOOP compatible with your hadoop

installation • Untar into a designated folder. This will be SQOOP’s home

directory tar xzf sqoop-x.y.z.tar.gz

• Configure • Environment Variables – add in .bash_profile

• export SQOOP_HOME=/<parent directory path>/sqoop-x.y.z • export PATH=$PATH:$SQOOP_HOME /bin

• Verify Installation sqoop sqoop help 313

Sqoop Connectors

• Sqoop ships with connectors for working with a range of popular relational databases, including • MySQL, PostgreSQL, Oracle, SQL Server, and DB2.

• There is also a generic JDBC connector for connecting

to any database that supports Java’s JDBC protocol.

• Third-party connectors are available for : • EDWs such as Netezza, Teradata, and Oracle • NoSQL stores (such as Couchbase).

314

315

Sqoop Client

RDBMS

Hadoop Cluster

1. Examine the schema

2. Generate Code

MyClass.java

Map Map Map 3. Launch Multiple maps on the cluster

4. Use Generate Code

316

• Install MySQL • sudo apt-get install mysql-client mysql-server

• Create a new MySQL schema • % mysql -u root –p

mysql> CREATE DATABASE hadoopguide; mysql> GRANT ALL PRIVILEGES ON hadoopguide.* TO '%'@'localhost'; mysql> GRANT ALL PRIVILEGES ON hadoopguide.* TO ''@'localhost';

317

• Create table widgets CREATE TABLE widgets(id INT NOT NULL PRIMARY KEY AUTO_INCREMENT, widget_name VARCHAR(64) NOT NULL, price DECIMAL(10,2), design_date DATE, version INT, design_comment VARCHAR(100)); Insert some records into widgets table INSERT INTO widgets VALUES (NULL, 'sprocket', 0.25, '2010-02-10',1, 'Connects two gizmos');

318

• Copy mysql jdbc driver to sqoop’s lib directory • Sqoop does not come with the jdbc driver

Sqoop import % sqoop import –connect jdbc:mysql://localhost/hadoopguide --table widgets -m 1

• Verify import to HDFS

• % hadoop fs -cat widgets/part-m-00000

Importing Data

319

• Import • The Import tool will run a MapReduce job that

connects to the database and reads the table • By default, this will use four map tasks. They write

the output to separate files but to a common directory

• Generates comma-delimited text files by default • In addition to downloading data, the import tool will

also generate a java class as per the table schema (widgets.java) on local.

Hue

Hue

Hue : Data Analysis

Impala

• SQL on HDFS • For analytical & transacational workloads • High performance • Skips the MapReduce flow • Directly queries data in HDFS & HBase • Easy for Hive users to migrate • Open source • C++ • HiveQL

Impala

• Uses Hive’s metastore

• Query is distributed to nodes with relevant data

• No fault tolerance

• Upto 100X faster than queries in Hive

• Supports common HDFS file formats

Impala

• Impala

Next Generation Testing Conference (c)

Impala Architecture

• Impalad

– Runs on every node

– Handles client requests

– Handles query planning & execution

• Statestored

– Provides name service

– Metadata distribution

– Used for finding data

Current Limitations

• No UDFs

• Joins are done in memory space no larger than that of the smallest node

Some Real world Use Cases

PayPal Tracking Architecture

• Customer Purchase History

• Merchant Designations

• Merchant Special Offers

Data Sources

Credit Card Issuer

Techniques

Purchase History

Merchant Information

Merchant Offers

Recommendation Engine Results

(Mahout)

Presentation Data Store

(DB2)

App

App

App

App

App

Hadoop Export (4 hrs)

Import (4 hrs)

Credit Card Issuer

Techniques

Purchase History

Merchant Information

Merchant Offers

Recommendation Engine Results

(Mahout)

Recommendation Search Index

(Solr)

App

App

App

App

App

Hadoop

Index Update (2 min)

Credit Card Issuer

Fraud Detection Data Lake

• Anti-Money Laundering

• Consumer Transactions

Data Sources

Techniques Anti-Money Laundering

System Consumer Transactions

System

Techniques

AML

Consumer Transactions

Data Lake (Hadoop)

Suspicious Events

Latent Dirichlet Allocation, Bayesian Learning Neural Network,

Peer Group Analysis

Analyst

Why Shopping cart analysis is useful to amazon.com

LinkedIn & Google Reader

Hadoop Master Class : A concise overview

Technology

distributed data

data intro

data file

huge data

data team

bulk data

data interdependency

historical data