Christopher M. Judd
Tutorial Christopher M. Judd
Christopher M. Judd CTO and Partner at leader Columbus
Developer User Group (CIDUG)
Marc Peabody @marcpeabody
Introduction
http://hadoop.apache.org/
Scale up
Scale up
Scale up
Scale-up
Scale-out
Hadoop Approach
• scale-out
• share nothing
• expect failure
• smart software, dumb hardware
• move processing, not data
• build applications, not infrastructure
What is Hadoop good for? Don't use Hadoop - your data isn't that big 10 gb - Add memory and use Pandas 100 gb > 1 TB - Buy big hard drive and use Postgres > 5 TB - life sucks consider Hadoop
http://www.chrisstucchio.com/blog/2013/hadoop_hatred.html
Hadoop is an evolving project
Hadoop is an evolving project
old api
org.apache.hadoop.mapred
new api
org.apache.hadoop.mapreduce
Hadoop is an evolving project
MapReduce 1 Classic MapReduce
MapReduce 2 YARN
Setup
Hadoop Tutorial user/fun4all /opt/data
Configure SSH
add a second Host-only Adaptor
Configure SSH
Configure SSH
sudo /Library/StartupItems/VirtualB
sudo /Library/StartupItems/Virtual
SSH’ing
$ ssh [email protected] The authenticity of host '192.168.56.101 (192.168.56.101)' can't be established. RSA key fingerprint is 40:60:72:ce:48:03:ac:c7:4c:23:9a:4f:1e:a5:ae:b9. Are you sure you want to continue connecting (yes/no)? yes Warning: Permanently added '192.168.56.101' (RSA) to the list of known hosts. [email protected]'s password: Welcome to Ubuntu 12.04.3 LTS (GNU/Linux 3.8.0-29-generic x86_64)
! * Documentation:
https://help.ubuntu.com/
! Last login: Tue Jan 7 08:49:28 2014 from user-virtualbox.local user@user-VirtualBox:~$
Hadoop
http://www.cloudera.com/
http://hortonworks.com/
Add Hadoop User and Group
$ sudo addgroup hadoop $ sudo adduser --ingroup hadoop hduser $ sudo adduser hduser sudo !
$ su hduser $ cd ~
Install Hadoop $ $ $ $
sudo mkdir -p /opt/hadoop sudo tar vxzf /opt/data/hadoop-2.2.0.tar.gz -C /opt/hadoop sudo chown -R hduser:hadoop /opt/hadoop/hadoop-2.2.0 vim .bashrc # other stuff # java variables export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64
!
# hadoop variables export HADOOP_HOME=/opt/hadoop/hadoop-2.2.0 export PATH=$PATH:$HADOOP_HOME/bin export PATH=$PATH:$HADOOP_HOME/sbin export HADOOP_MAPRED_HOME=$HADOOP_HOME export HADOOP_COMMON_HOME=$HADOOP_HOME export HADOOP_HDFS_HOME=$HADOOP_HOME export YARN_HOME=$HADOOP_HOME
$ source .bashrc $ hadoop version
Run Hadoop Job $ hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar pi 4 1000
$ hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar
aggregatewordcount: An Aggregate based map/reduce program that counts the words in the input files.
aggregatewordhist: An Aggregate based map/reduce program that computes the histogram of the words in the input files.
bbp: A map/reduce program that uses Bailey-Borwein-Plouffe to compute exact digits of Pi.
dbcount: An example job that count the pageview counts from a database.
distbbp: A map/reduce program that uses a BBP-type formula to compute exact bits of Pi.
grep: A map/reduce program that counts the matches of a regex in the input.
join: A job that effects a join over sorted, equally partitioned datasets
multifilewc: A job that counts words from several files.
pentomino: A map/reduce tile laying program to find solutions to pentomino problems.
pi: A map/reduce program that estimates Pi using a quasi-Monte Carlo method.
randomtextwriter: A map/reduce program that writes 10GB of random textual data per node.
randomwriter: A map/reduce program that writes 10GB of random data per node.
secondarysort: An example defining a secondary sort to the reduce.
sort: A map/reduce program that sorts the data written by the random writer.
sudoku: A sudoku solver.
teragen: Generate data for the terasort
terasort: Run the terasort
teravalidate: Checking results of terasort
wordcount: A map/reduce program that counts the words in the input files.
wordmean: A map/reduce program that counts the average length of the words in the input files.
wordmedian: A map/reduce program that counts the median length of the words in the input files.
wordstandarddeviation: A map/reduce program that counts the standard deviation of the length of the words in the input files. $ hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar pi Usage: org.apache.hadoop.examples.QuasiMonteCarlo
Lab 1
1. Create Hadoop user and group
2. Install Hadoop
3. Run example Hadoop job such as pi
• Local Standalone mode
• Pseudo-distributed mode
• Fully distributed mode
HDFS
POSIX! portable operating system interface
Reading Data
Writing Data
Configure passwordless login
$ $ $ $
ssh-keygen -t rsa -P '' cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys ssh localhost exit
Configure HDFS $ $ $ $ $ $
sudo mkdir -p /opt/hdfs/namenode sudo mkdir -p /opt/hdfs/datanode sudo chmod -R 777 /opt/hdfs sudo chown -R hduser:hadoop /opt/hdfs cd /opt/hadoop/hadoop-2.2.0 sudo vim etc/hadoop/hdfs-site.xml
!
<property> dfs.replication 1 <property> dfs.namenode.name.dir file:/opt/hdfs/namenode <property> dfs.datanode.data.dir file:/opt/hdfs/datanode
Format HDFS
$ hdfs namenode -format
Configure JAVA_HOME
$ vim etc/hadoop/hadoop-env.sh # other stuff
!
export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64
!
# more stuff
Configure Core
$ sudo vim etc/hadoop/core-site.xml <property> fs.default.name hdfs://localhost:9000
Start HDFS
$ start-dfs.sh
$ jps 6433 DataNode 6844 Jps 6206 NameNode 6714 SecondaryNameNode
Use HDFS commands $ $ $ $ $ $
• • • • • • • • • • •
hdfs hdfs hdfs hdfs hdfs hdfs
dfs dfs dfs dfs dfs dfs
-ls / -mkdir /books -ls / -ls /books -copyFromLocal /opt/data/moby_dick.txt /books -cat /books/moby_dick.txt
appendToFile
cat
chgrp
chmod
chown
copyFromLocal
copyToLocal
count
cp
du
get
• • • • • • • • • • •
ls
lsr
mkdir
moveFromLocal
moveToLocal
mv
put
rm
rmr
stat
tail
• • •
test
text
touchz
http://hadoop.apache.org/docs/r2.2.0/hadoop-project-dist/hadoop-common/FileSystemShell.html
http://192.168.56.101:50070/dfshealth.jsp
Lab 2 1. Configure passwordless login
2. Configure HDFS
3. Format HDFS
4. Configure JAVA_HOME
5. Configure Core
6. Start HDFS
7. Experiment HDFS commands
(ls, mkdir, copyFromLocal, cat)
HADOOP Pseudo-Distributed
Configure YARN $ sudo vim etc/hadoop/yarn-site.xml
!
!
<property> yarn.nodemanager.aux-services mapreduce_shuffle <property> yarn.nodemanager.aux-services.mapreduce.shuffle.class org.apache.hadoop.mapred.ShuffleHandler
Configure
Map Reduce
$ sudo mv etc/hadoop/mapred-site.xml.template etc/hadoop/mapred-site.xml $ sudo vim etc/hadoop/mapred-site.xml
<property> mapreduce.framework.name yarn
Start YARN $ start-yarn.sh
$ jps 6433 DataNode 8355 Jps 8318 NodeManager 6206 NameNode 6714 SecondaryNameNode 8090 ResourceManager
Run Hadoop Job
$ hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar pi 4 1000
http://192.168.56.101:8042/node
Lab 3
1. Configure YARN
2. Configure Map Reduce
3. Start YARN
4. Run pi job
Combine Hadoop & HDFS
Run Hadoop Job $ $ $ $
hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar wordcount /books out hdfs dfs -ls out hdfs dfs -cat out/_SUCCESS hdfs dfs -cat out/part-r-00000 young-armed young; 2 younger 2 youngest 1 youngish 1 your 251 your@login yours 5 yours? 1 yourselbs yourself 14 yourself, yourself," yourself.' yourself; yourself? yourselves yourselves! yourselves, yourselves," yourselves; youth 5 youth, 2 youth. 1 youth; 1 youthful 1
1
1 1 5 1 1 4 1 1 3 1 1 1
Lab 4
1. Run wordcount job
2. Review output
3. Run wordcount job again with same parameters
Writing Map Reduce Jobs
{K1,V1}
we write
{K1,V1}
optionally write
we write
{K2, List} {K3,V3}
MOBY DICK; OR THE WHALE
!
By Herman Melville
! !
CHAPTER 1. Loomings.
! !
Call me Ishmael. Some years ago--never mind how long precisely--having little or no money in my purse, and nothing particular to interest me on shore, I thought I would sail about a little and see the watery part of the world. It is a way I have of driving off the spleen and regulating the circulation. Whenever I find myself growing grim about the mouth; whenever it is a damp, drizzly November in my soul; whenever I find myself involuntarily pausing before coffin warehouses, and bringing up the rear of every funeral I meet; and especially whenever my hypos get such an upper hand of me, that it requires a strong moral principle to prevent me from deliberately stepping into the street, and methodically knocking people's hats off--then, I account it high time to get to sea as soon as I can. This is my substitute for pistol and ball. With a philosophical flourish Cato throws himself upon his sword; I quietly take to the ship. There is nothing surprising in this. If they but knew it, almost all men in their degree, some time or other, cherish very nearly the same feelings towards the ocean with me.
!
There now is your insular city of the Manhattoes, belted round by wharves as Indian isles by coral reefs--commerce surrounds it with her surf. Right and left, the streets take you waterward. Its extreme downtown is the battery, where that noble mole is washed by waves, and cooled by breezes, which a few hours previous were out of sight of land. Look at the crowds of water-gazers there.
K V 1 2 3 4 5
Call me Ishmael. Some years ago--never mind how long precisely--having little or no money in my purse, and nothing particular to interest me on shore, I thought I would sail about a little and see the watery part of the world. It is a way I have of driving off the spleen and regulating the circulation.
{K1,V1}
{K2, List}
{K3,V3}
Mapper package com.manifestcorp.hadoop.wc;!
!
import java.io.IOException;!
!
import org.apache.hadoop.io.IntWritable;! import org.apache.hadoop.io.Text;! import org.apache.hadoop.mapreduce.Mapper;!
!
K1
V1 K2
V2
public class WordCountMapper extends Mapper { !
!
! ! ! ! ! ! ! ! ! ! ! ! ! ! }
private static final String SPACE = " ";! ! private static final IntWritable ONE = new IntWritable(1);! private Text word = new Text();! ! public void map(Object key, Text value, Context context) ! throws IOException, InterruptedException {! ! String[] words = value.toString().split(SPACE);! ! ! ! for (String str: words) {! ! ! word.set(str);! ! ! context.write(word, ONE);! ! }! ! ! }!
K1
K2 V2
V1
K V 1 2 3 4 5
Call me Ishmael. Some years ago--never mind how long precisely--having little or no money in my purse, and nothing particular to interest me on shore, I thought I would sail about a little and see the watery part of the world. It is a way I have of driving off the spleen and regulating the circulation.
K
map
{K1,V1}
Call me Ishmael. Some years ago--never mind how of long little of or of
V 1 1 1 1 1 1 1 1 1 1 1 1 1 1
K
sort
ago--never Call how Ishmael. me little long mind of of of or Some years
V
K
V
1 1 1 1 1 1 1 1 1 1 1 1 1 1
ago--never Call how Ishmael. me little long mind of or Some years
1 1 1 1 1 1 1 1 1,1,1 1 1 1
group
{K2, List}
{K3,V3}
Reducer package com.manifestcorp.hadoop.wc;!
!
import java.io.IOException;!
!
import org.apache.hadoop.io.IntWritable;! import org.apache.hadoop.io.Text;! import org.apache.hadoop.mapreduce.Reducer;!
!
K2
V2
K3
V3
public class WordCountReducer extends Reducer {! ! ! ! public void reduce(Text key, Iterable values, Context context) ! ! ! ! ! ! ! ! ! ! ! ! ! throws IOException, InterruptedException {! ! ! int total = 0;! ! ! ! ! ! for (IntWritable value : values) {! ! ! ! total++;! ! ! }! ! ! ! ! ! context.write(key, new IntWritable(total));! ! }! }
K2
K3
V2
V3
K V 1 2 3 4 5
Call me Ishmael. Some years ago--never mind how long precisely--having little or no money in my purse, and nothing particular to interest me on shore, I thought I would sail about a little and see the watery part of the world. It is a way I have of driving off the spleen and regulating the circulation.
K
map
{K1,V1}
Call me Ishmael. Some years ago--never mind how of long little of or of
V 1 1 1 1 1 1 1 1 1 1 1 1 1 1
K
sort
ago--never Call how Ishmael. me little long mind of of of or Some years
V
K
V
1 1 1 1 1 1 1 1 1 1 1 1 1 1
ago--never Call how Ishmael. me little long mind of or Some years
1 1 1 1 1 1 1 1 1,1,1 1 1 1
group
{K2, List}
K
reduce
ago--never Call how Ishmael. me little long mind of or Some years
V 1 1 1 1 1 1 1 1 3 1 1 1
{K3,V3}
Driver package com.manifestcorp.hadoop.wc;!
!
import import import import import import
!
org.apache.hadoop.fs.Path;! org.apache.hadoop.io.IntWritable;! org.apache.hadoop.io.Text;! org.apache.hadoop.mapreduce.Job;! org.apache.hadoop.mapreduce.lib.input.FileInputFormat;! org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;!
public class MyWordCount {!
!
!
public static void main(String[] args) throws Exception {!
! ! ! ! ! ! ! ! !
! ! ! ! ! ! ! ! !
Job job = new Job();! job.setJobName("my word count");! job.setJarByClass(MyWordCount.class);! ! FileInputFormat.addInputPath(job, new Path(args[0]));! FileOutputFormat.setOutputPath(job, new Path(args[1]));! ! job.setMapperClass(WordCountMapper.class);! job.setReducerClass(WordCountReducer.class);!
! !
! !
job.setOutputKeyClass(Text.class);! job.setOutputValueClass(IntWritable.class);!
! ! }
! System.exit(job.waitForCompletion(true) ? 0 : 1);! }!
!
! !
pom.xml
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0 com.manifestcorp.hadoop <artifactId>hadoop-mywordcount 0.0.1-SNAPSHOT <packaging>jar <properties> 2.2.0 <artifactId>maven-compiler-plugin 2.3.2 <source>1.6 1.6 <dependencies> <dependency> org.apache.hadoop <artifactId>hadoop-client ${hadoop.version} <scope>provided
Run Hadoop Job
$ hadoop jar target/hadoop-mywordcount-0.0.1-SNAPSHOT.jar com.manifestcorp.hadoop.wc.MyWordCount /books out
Lab 5 1. Unzip /opt/data/hadoop-mywordcount-start.zip
2. Write Mapper class
3. Write Reducer class
4. Write Driver class
5. Build (mvn clean package)
6. Run mywordcount job
7. Review output
Hadoop in the Cloud
http://aws.amazon.com/elasticmapreduce/
Making it more Real
http://aws.amazon.com/architecture/
http://aws.amazon.com/architecture/
Resources
Christopher M. Judd CTO and Partner email: [email protected] web: www.juddsolutions.com blog: juddsolutions.blogspot.com twitter: javajudd
Christopher M. Judd CTO and Partner at leader Columbus
Developer User Group (CIDUG)
Marc Peabody @marcpeabody
Introduction
http://hadoop.apache.org/
Scale up
Scale up
Scale up
Scale-up
Scale-out
Hadoop Approach
• scale-out
• share nothing
• expect failure
• smart software, dumb hardware
• move processing, not data
• build applications, not infrastructure
What is Hadoop good for? Don't use Hadoop - your data isn't that big 10 gb - Add memory and use Pandas 100 gb > 1 TB - Buy big hard drive and use Postgres > 5 TB - life sucks consider Hadoop
http://www.chrisstucchio.com/blog/2013/hadoop_hatred.html
Hadoop is an evolving project
Hadoop is an evolving project
old api
org.apache.hadoop.mapred
new api
org.apache.hadoop.mapreduce
Hadoop is an evolving project
MapReduce 1 Classic MapReduce
MapReduce 2 YARN
Setup
Hadoop Tutorial user/fun4all /opt/data
Configure SSH
add a second Host-only Adaptor
Configure SSH
Configure SSH
sudo /Library/StartupItems/VirtualB
sudo /Library/StartupItems/Virtual
SSH’ing
$ ssh [email protected] The authenticity of host '192.168.56.101 (192.168.56.101)' can't be established. RSA key fingerprint is 40:60:72:ce:48:03:ac:c7:4c:23:9a:4f:1e:a5:ae:b9. Are you sure you want to continue connecting (yes/no)? yes Warning: Permanently added '192.168.56.101' (RSA) to the list of known hosts. [email protected]'s password: Welcome to Ubuntu 12.04.3 LTS (GNU/Linux 3.8.0-29-generic x86_64)
! * Documentation:
https://help.ubuntu.com/
! Last login: Tue Jan 7 08:49:28 2014 from user-virtualbox.local user@user-VirtualBox:~$
Hadoop
http://www.cloudera.com/
http://hortonworks.com/
Add Hadoop User and Group
$ sudo addgroup hadoop $ sudo adduser --ingroup hadoop hduser $ sudo adduser hduser sudo !
$ su hduser $ cd ~
Install Hadoop $ $ $ $
sudo mkdir -p /opt/hadoop sudo tar vxzf /opt/data/hadoop-2.2.0.tar.gz -C /opt/hadoop sudo chown -R hduser:hadoop /opt/hadoop/hadoop-2.2.0 vim .bashrc # other stuff # java variables export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64
!
# hadoop variables export HADOOP_HOME=/opt/hadoop/hadoop-2.2.0 export PATH=$PATH:$HADOOP_HOME/bin export PATH=$PATH:$HADOOP_HOME/sbin export HADOOP_MAPRED_HOME=$HADOOP_HOME export HADOOP_COMMON_HOME=$HADOOP_HOME export HADOOP_HDFS_HOME=$HADOOP_HOME export YARN_HOME=$HADOOP_HOME
$ source .bashrc $ hadoop version
Run Hadoop Job $ hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar pi 4 1000
$ hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar
aggregatewordcount: An Aggregate based map/reduce program that counts the words in the input files.
aggregatewordhist: An Aggregate based map/reduce program that computes the histogram of the words in the input files.
bbp: A map/reduce program that uses Bailey-Borwein-Plouffe to compute exact digits of Pi.
dbcount: An example job that count the pageview counts from a database.
distbbp: A map/reduce program that uses a BBP-type formula to compute exact bits of Pi.
grep: A map/reduce program that counts the matches of a regex in the input.
join: A job that effects a join over sorted, equally partitioned datasets
multifilewc: A job that counts words from several files.
pentomino: A map/reduce tile laying program to find solutions to pentomino problems.
pi: A map/reduce program that estimates Pi using a quasi-Monte Carlo method.
randomtextwriter: A map/reduce program that writes 10GB of random textual data per node.
randomwriter: A map/reduce program that writes 10GB of random data per node.
secondarysort: An example defining a secondary sort to the reduce.
sort: A map/reduce program that sorts the data written by the random writer.
sudoku: A sudoku solver.
teragen: Generate data for the terasort
terasort: Run the terasort
teravalidate: Checking results of terasort
wordcount: A map/reduce program that counts the words in the input files.
wordmean: A map/reduce program that counts the average length of the words in the input files.
wordmedian: A map/reduce program that counts the median length of the words in the input files.
wordstandarddeviation: A map/reduce program that counts the standard deviation of the length of the words in the input files. $ hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar pi Usage: org.apache.hadoop.examples.QuasiMonteCarlo
Lab 1
1. Create Hadoop user and group
2. Install Hadoop
3. Run example Hadoop job such as pi
• Local Standalone mode
• Pseudo-distributed mode
• Fully distributed mode
HDFS
POSIX! portable operating system interface
Reading Data
Writing Data
Configure passwordless login
$ $ $ $
ssh-keygen -t rsa -P '' cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys ssh localhost exit
Configure HDFS $ $ $ $ $ $
sudo mkdir -p /opt/hdfs/namenode sudo mkdir -p /opt/hdfs/datanode sudo chmod -R 777 /opt/hdfs sudo chown -R hduser:hadoop /opt/hdfs cd /opt/hadoop/hadoop-2.2.0 sudo vim etc/hadoop/hdfs-site.xml
!
<property> dfs.replication 1 <property> dfs.namenode.name.dir file:/opt/hdfs/namenode <property> dfs.datanode.data.dir file:/opt/hdfs/datanode
Format HDFS
$ hdfs namenode -format
Configure JAVA_HOME
$ vim etc/hadoop/hadoop-env.sh # other stuff
!
export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64
!
# more stuff
Configure Core
$ sudo vim etc/hadoop/core-site.xml <property> fs.default.name hdfs://localhost:9000
Start HDFS
$ start-dfs.sh
$ jps 6433 DataNode 6844 Jps 6206 NameNode 6714 SecondaryNameNode
Use HDFS commands $ $ $ $ $ $
• • • • • • • • • • •
hdfs hdfs hdfs hdfs hdfs hdfs
dfs dfs dfs dfs dfs dfs
-ls / -mkdir /books -ls / -ls /books -copyFromLocal /opt/data/moby_dick.txt /books -cat /books/moby_dick.txt
appendToFile
cat
chgrp
chmod
chown
copyFromLocal
copyToLocal
count
cp
du
get
• • • • • • • • • • •
ls
lsr
mkdir
moveFromLocal
moveToLocal
mv
put
rm
rmr
stat
tail
• • •
test
text
touchz
http://hadoop.apache.org/docs/r2.2.0/hadoop-project-dist/hadoop-common/FileSystemShell.html
http://192.168.56.101:50070/dfshealth.jsp
Lab 2 1. Configure passwordless login
2. Configure HDFS
3. Format HDFS
4. Configure JAVA_HOME
5. Configure Core
6. Start HDFS
7. Experiment HDFS commands
(ls, mkdir, copyFromLocal, cat)
HADOOP Pseudo-Distributed
Configure YARN $ sudo vim etc/hadoop/yarn-site.xml
!
!
<property> yarn.nodemanager.aux-services mapreduce_shuffle <property> yarn.nodemanager.aux-services.mapreduce.shuffle.class org.apache.hadoop.mapred.ShuffleHandler
Configure
Map Reduce
$ sudo mv etc/hadoop/mapred-site.xml.template etc/hadoop/mapred-site.xml $ sudo vim etc/hadoop/mapred-site.xml
<property> mapreduce.framework.name yarn
Start YARN $ start-yarn.sh
$ jps 6433 DataNode 8355 Jps 8318 NodeManager 6206 NameNode 6714 SecondaryNameNode 8090 ResourceManager
Run Hadoop Job
$ hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar pi 4 1000
http://192.168.56.101:8042/node
Lab 3
1. Configure YARN
2. Configure Map Reduce
3. Start YARN
4. Run pi job
Combine Hadoop & HDFS
Run Hadoop Job $ $ $ $
hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar wordcount /books out hdfs dfs -ls out hdfs dfs -cat out/_SUCCESS hdfs dfs -cat out/part-r-00000 young-armed young; 2 younger 2 youngest 1 youngish 1 your 251 your@login yours 5 yours? 1 yourselbs yourself 14 yourself, yourself," yourself.' yourself; yourself? yourselves yourselves! yourselves, yourselves," yourselves; youth 5 youth, 2 youth. 1 youth; 1 youthful 1
1
1 1 5 1 1 4 1 1 3 1 1 1
Lab 4
1. Run wordcount job
2. Review output
3. Run wordcount job again with same parameters
Writing Map Reduce Jobs
{K1,V1}
we write
{K1,V1}
optionally write
we write
{K2, List} {K3,V3}
MOBY DICK; OR THE WHALE
!
By Herman Melville
! !
CHAPTER 1. Loomings.
! !
Call me Ishmael. Some years ago--never mind how long precisely--having little or no money in my purse, and nothing particular to interest me on shore, I thought I would sail about a little and see the watery part of the world. It is a way I have of driving off the spleen and regulating the circulation. Whenever I find myself growing grim about the mouth; whenever it is a damp, drizzly November in my soul; whenever I find myself involuntarily pausing before coffin warehouses, and bringing up the rear of every funeral I meet; and especially whenever my hypos get such an upper hand of me, that it requires a strong moral principle to prevent me from deliberately stepping into the street, and methodically knocking people's hats off--then, I account it high time to get to sea as soon as I can. This is my substitute for pistol and ball. With a philosophical flourish Cato throws himself upon his sword; I quietly take to the ship. There is nothing surprising in this. If they but knew it, almost all men in their degree, some time or other, cherish very nearly the same feelings towards the ocean with me.
!
There now is your insular city of the Manhattoes, belted round by wharves as Indian isles by coral reefs--commerce surrounds it with her surf. Right and left, the streets take you waterward. Its extreme downtown is the battery, where that noble mole is washed by waves, and cooled by breezes, which a few hours previous were out of sight of land. Look at the crowds of water-gazers there.
K V 1 2 3 4 5
Call me Ishmael. Some years ago--never mind how long precisely--having little or no money in my purse, and nothing particular to interest me on shore, I thought I would sail about a little and see the watery part of the world. It is a way I have of driving off the spleen and regulating the circulation.
{K1,V1}
{K2, List}
{K3,V3}
Mapper package com.manifestcorp.hadoop.wc;!
!
import java.io.IOException;!
!
import org.apache.hadoop.io.IntWritable;! import org.apache.hadoop.io.Text;! import org.apache.hadoop.mapreduce.Mapper;!
!
K1
V1 K2
V2
public class WordCountMapper extends Mapper { !
!
! ! ! ! ! ! ! ! ! ! ! ! ! ! }
private static final String SPACE = " ";! ! private static final IntWritable ONE = new IntWritable(1);! private Text word = new Text();! ! public void map(Object key, Text value, Context context) ! throws IOException, InterruptedException {! ! String[] words = value.toString().split(SPACE);! ! ! ! for (String str: words) {! ! ! word.set(str);! ! ! context.write(word, ONE);! ! }! ! ! }!
K1
K2 V2
V1
K V 1 2 3 4 5
Call me Ishmael. Some years ago--never mind how long precisely--having little or no money in my purse, and nothing particular to interest me on shore, I thought I would sail about a little and see the watery part of the world. It is a way I have of driving off the spleen and regulating the circulation.
K
map
{K1,V1}
Call me Ishmael. Some years ago--never mind how of long little of or of
V 1 1 1 1 1 1 1 1 1 1 1 1 1 1
K
sort
ago--never Call how Ishmael. me little long mind of of of or Some years
V
K
V
1 1 1 1 1 1 1 1 1 1 1 1 1 1
ago--never Call how Ishmael. me little long mind of or Some years
1 1 1 1 1 1 1 1 1,1,1 1 1 1
group
{K2, List}
{K3,V3}
Reducer package com.manifestcorp.hadoop.wc;!
!
import java.io.IOException;!
!
import org.apache.hadoop.io.IntWritable;! import org.apache.hadoop.io.Text;! import org.apache.hadoop.mapreduce.Reducer;!
!
K2
V2
K3
V3
public class WordCountReducer extends Reducer {! ! ! ! public void reduce(Text key, Iterable values, Context context) ! ! ! ! ! ! ! ! ! ! ! ! ! throws IOException, InterruptedException {! ! ! int total = 0;! ! ! ! ! ! for (IntWritable value : values) {! ! ! ! total++;! ! ! }! ! ! ! ! ! context.write(key, new IntWritable(total));! ! }! }
K2
K3
V2
V3
K V 1 2 3 4 5
Call me Ishmael. Some years ago--never mind how long precisely--having little or no money in my purse, and nothing particular to interest me on shore, I thought I would sail about a little and see the watery part of the world. It is a way I have of driving off the spleen and regulating the circulation.
K
map
{K1,V1}
Call me Ishmael. Some years ago--never mind how of long little of or of
V 1 1 1 1 1 1 1 1 1 1 1 1 1 1
K
sort
ago--never Call how Ishmael. me little long mind of of of or Some years
V
K
V
1 1 1 1 1 1 1 1 1 1 1 1 1 1
ago--never Call how Ishmael. me little long mind of or Some years
1 1 1 1 1 1 1 1 1,1,1 1 1 1
group
{K2, List}
K
reduce
ago--never Call how Ishmael. me little long mind of or Some years
V 1 1 1 1 1 1 1 1 3 1 1 1
{K3,V3}
Driver package com.manifestcorp.hadoop.wc;!
!
import import import import import import
!
org.apache.hadoop.fs.Path;! org.apache.hadoop.io.IntWritable;! org.apache.hadoop.io.Text;! org.apache.hadoop.mapreduce.Job;! org.apache.hadoop.mapreduce.lib.input.FileInputFormat;! org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;!
public class MyWordCount {!
!
!
public static void main(String[] args) throws Exception {!
! ! ! ! ! ! ! ! !
! ! ! ! ! ! ! ! !
Job job = new Job();! job.setJobName("my word count");! job.setJarByClass(MyWordCount.class);! ! FileInputFormat.addInputPath(job, new Path(args[0]));! FileOutputFormat.setOutputPath(job, new Path(args[1]));! ! job.setMapperClass(WordCountMapper.class);! job.setReducerClass(WordCountReducer.class);!
! !
! !
job.setOutputKeyClass(Text.class);! job.setOutputValueClass(IntWritable.class);!
! ! }
! System.exit(job.waitForCompletion(true) ? 0 : 1);! }!
!
! !
pom.xml
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0 com.manifestcorp.hadoop <artifactId>hadoop-mywordcount 0.0.1-SNAPSHOT <packaging>jar <properties> 2.2.0 <artifactId>maven-compiler-plugin 2.3.2 <source>1.6 1.6 <dependencies> <dependency> org.apache.hadoop <artifactId>hadoop-client ${hadoop.version} <scope>provided
Run Hadoop Job
$ hadoop jar target/hadoop-mywordcount-0.0.1-SNAPSHOT.jar com.manifestcorp.hadoop.wc.MyWordCount /books out
Lab 5 1. Unzip /opt/data/hadoop-mywordcount-start.zip
2. Write Mapper class
3. Write Reducer class
4. Write Driver class
5. Build (mvn clean package)
6. Run mywordcount job
7. Review output
Hadoop in the Cloud
http://aws.amazon.com/elasticmapreduce/
Making it more Real
http://aws.amazon.com/architecture/
http://aws.amazon.com/architecture/
Resources
Christopher M. Judd CTO and Partner email: [email protected] web: www.juddsolutions.com blog: juddsolutions.blogspot.com twitter: javajudd
