Big Data Analytics Using Hadoop Cluster On Amazon EMR

Danairat T., 2013, danairat@gmail.comBig Data Hadoop – Hands On Workshop 1
Big Data Analytics
Using Hadoop Cluster
On Amazon EMR
February 2015
Dr.Thanachart Numnonda
IMC Institute
thanachart@imcinstitute.com
Modifiy from Original Version by Danairat T.
Certified Java Programmer, TOGAF – Silver
danairat@gmail.com

hanachart Numnonda, thanachart@imcinstitute.com Feb 2015Big Data Hadoop on Amazon EMR – Hands On Workshop
Running this lab using Amazon EMR

Hands-On: Create an EMR cluster

Architecture Overview of Amazon EMR

Amazon EMR Cluster

Creating an AWS account

Signing up for the necessary services
●
Simple Storage Service (S3)
●
Elastic Compute Cloud (EC2)
●
Elastic MapReduce (EMR)
Caution! This costs real money!

Creating Amazon S3 bucket

Create access key using Security Credentials
in the AWS Management Console

Select EMR

Creating a cluster in EMR

Creating a cluster in EMR (cont.)
Name the cluster and also specify Log folder

Leave the Software Configuration as default

Leave the Hardware Configuration as default
Choose an exisitng EC2 key pair

Leave the others as default
Select Create Cluster

EMR Cluster Details
Note on the Master public DNS:
To see the details on how to connect to the Master Node using SSH click at SSH

SSH Instruction

Connect to the Master Node

Web Interface Host on EMR Cluster

Launch the Hue Web Interface
●
Set Up an SSH Tunnel to the Master Node
– See instruction at
– http://docs.aws.amazon.com/ElasticMapReduce/latest/Developer
Guide/emr-ssh-tunnel.html
●
Configure Proxy Settings to View Websites
– See instruction at
– http://docs.aws.amazon.com/ElasticMapReduce/latest/Developer
Guide/emr-connect-master-node-proxy.html

Launch the Hue Web Interface (Cont.)
●
http://master-public-dns-name:8888/
●
Create your own Hue account

Launch the Hue Web Interface (Cont.)

Hands-On: Importing/Exporting
Data to HDFS

Importing Data to Hadoop
Download War and Peace Full Text
www.gutenberg.org/ebooks/2600

Review file in Hadoop HDFS using
File Browse

Create new directory
Create two new directory name: input and output

Upload Files
Upload file: pg2600.txt into input directory

Lecture: Understanding Map Reduce
Processing
Client
Name Node Job Tracker
Data Node
Task Tracker
Data Node
Task Tracker
Data Node
Task Tracker
Map Reduce

High Level Architecture of MapReduce

Hands-On: Writing you own Map
Reduce Program

Wordcount (HelloWord in Hadoop)
1. package org.myorg;
2.
3. import java.io.IOException;
4. import java.util.*;
5.
6. import org.apache.hadoop.fs.Path;
7. import org.apache.hadoop.conf.*;
8. import org.apache.hadoop.io.*;
9. import org.apache.hadoop.mapred.*;
10. import org.apache.hadoop.util.*;
11.
12. public class WordCount {
13.
14.
public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text,
IntWritable> {
15. private final static IntWritable one = new IntWritable(1);
16. private Text word = new Text();
17.
18.
public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output,
Reporter reporter) throws IOException {
19. String line = value.toString();
20. StringTokenizer tokenizer = new StringTokenizer(line);
21. while (tokenizer.hasMoreTokens()) {
22. word.set(tokenizer.nextToken());
23. output.collect(word, one);
24. }
25. }
26. }

27.
28. public static class Reduce extends MapReduceBase implements Reducer<Text, IntWritable, Text,
IntWritable> {
29.
public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable>
output, Reporter reporter) throws IOException {
30. int sum = 0;
31. while (values.hasNext()) {
32. sum += values.next().get();
33. }
34. output.collect(key, new IntWritable(sum));
35. }
36. }
37.

38. public static void main(String[] args) throws Exception {
39. JobConf conf = new JobConf(WordCount.class);
40. conf.setJobName("wordcount");
41.
42. conf.setOutputKeyClass(Text.class);
43. conf.setOutputValueClass(IntWritable.class);
44.
45. conf.setMapperClass(Map.class);
46.
47. conf.setReducerClass(Reduce.class);
48.
49. conf.setInputFormat(TextInputFormat.class);
50. conf.setOutputFormat(TextOutputFormat.class);
51.
52. FileInputFormat.setInputPaths(conf, new Path(args[1]));
53. FileOutputFormat.setOutputPath(conf, new Path(args[2]));
54.
55. JobClient.runJob(conf);
57. }
58. }
59.

Hands-On: Writing Map/Reduce
Program on Eclipse

Starting Eclipse in a local machine

Create a Java Project
Let's name it HadoopWordCount

Add dependencies to the project
●
Note you may need to download Hadoop-core-jar.zip
●
Add the following two JARs to your build path
●
hadoop-common.jar and hadoop-mapreduce-client-core.jar.
●
By perform the following steps
– Add a folder named lib to the project
– Copy the mentioned JARs in this folder
– Right-click on the project name >> select Build Path >> then
Configure Build Path
– Click on Add Jars, select these two JARs from the lib folder

Add dependencies to the project

Writing a source code
●
Right click the project, the select New >> Package
●
Name the package as org.myorg
●
Right click at org.myorg, the select New >> Class
●
Name the package as WordCount
●
Writing a source code as shown in previoud slides

Building a Jar file
●
Right click the project, the select Export
●
Select Java and then JAR file
●
Provide the JAR name, as wordcount.jar
●
Leave the JAR package options as default
●
In the JAR Manifest Specification section, in the botton, specify the Main
class
●
In this case, select WordCount
●
Click on Finish
●
The JAR file will be build and will be located at cloudera/workspace
Note: you may need to re-size the dialog font size by select
Windows >> Preferences >> Appearance >> Colors and Fonts

Hands-On: Running Map Reduce and
Deploying to Hadoop Runtime
Environment

Running a Jar file
●
Create a folder applications on Amazon S3
●
Upload wordcount.jar to s3://imcbucket/apps

Running a Jar file (cont)
●
Open the Master node using SSH command
– ssh hadoop@ec2-54-213-220-37.us-west-2.compute.amazonaws.com
-i imckey.pem
●
Run the following commands
– $ mkdir apps
– $ hadoop fs -get s3://imcbucket/applications/wordcount.jar apps
– $ hadoop jar apps/wordcount.jar org.myorg.WordCount
s3://imcbucket/input/* s3://imcbucket/output/wordcount_result

Reviewing MapReduce Output Result
Browse to the s3://imcbucket/output/wordcount_result
Open part-xxxx files

Reviewing MapReduce Output Result

Lecture
Understanding Hive

Introduction
A Petabyte Scale Data Warehouse Using Hadoop
Hive is developed by Facebook, designed to enable easy data
summarization, ad-hoc querying and analysis of large
volumes of data. It provides a simple query language called
Hive QL, which is based on SQL

Hands-On: Creating Table and
Retrieving Data using Hive

Running Hive from the Master node
Starting Hive
hive> quit;
Quit from Hive

Starting Hive Editor from Hue

Creating Hive Table
hive (default)> CREATE TABLE test_tbl(id INT, country STRING) ROW
FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TEXTFILE;
OK
Time taken: 4.069 seconds
hive (default)> show tables;
OK
test_tbl
hive (default)> describe test_tbl;
OK
id int
country string
hive (default)>
See also: https://cwiki.apache.org/Hive/languagemanual-ddl.html

Using Hue Query Editor
See also: https://cwiki.apache.org/Hive/languagemanual-ddl.html

Reviewing Hive Table in HDFS

Alter and Drop Hive Table
hive (default)> alter table test_tbl add columns (remarks STRING);
hive (default)> describe test_tbl;
OK
id int
country string
remarks string
hive (default)> drop table test_tbl;
OK
See also: https://cwiki.apache.org/Hive/adminmanual-metastoreadmin.html

Loading Data to Hive Table
$ hive
hive (default)> CREATE TABLE test_tbl(id INT, country STRING) ROW
FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TEXTFILE;
Creating Hive table
hive (default)> LOAD DATA LOCAL INPATH '/tmp/country.csv' INTO TABLE test_tbl;
Copying data from file:/tmp/test_tbl_data.csv
Copying file: file:/tmp/test_tbl_data.csv
Loading data to table default.test_tbl
OK
hive (default)>
Loading data to Hive table

Querying Data from Hive Table
hive (default)> select * from test_tbl;
OK
1 USA
62 Indonesia
63 Philippines
65 Singapore
66 Thailand
hive (default)>

Insert Overwriting the Hive Table
hive (default)> LOAD DATA LOCAL INPATH
'/home/cloudera/Downloads/test_tbl_data_updated.csv' overwrite INTO
TABLE test_tbl;
Copying data from file:/tmp/test_tbl_data_updated.csv
Copying file: file:/tmp/test_tbl_data_updated.csv
Loading data to table default.test_tbl
Deleted hdfs://localhost:54310/user/hive/warehouse/test_tbl
OK
hive (default)>

MovieLens
http://grouplens.org/datasets/movielens/

Create the Hive Table for movielen
hive (default)> CREATE TABLE u_data (
userid INT,
movieid INT,
rating INT,
unixtime STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY 't'
STORED AS TEXTFILE;
hive (default)> LOAD DATA LOCAL INPATH
'/home/cloudera/Downloads/u.data' overwrite INTO TABLE u_data;

Create the Hive Table for Apache LOf
hive (default)> CREATE TABLE apachelog (
host STRING,
identity STRING,
user STRING,
time STRING,
request STRING,
status STRING,
size STRING,
referer STRING,
agent STRING)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.RegexSerDe'
WITH SERDEPROPERTIES (
"input.regex" = "([^]*) ([^]*) ([^]*) (-|[^]*])
([^ "]*|"[^"]*") (-|[0-9]*) (-|[0-9]*)(?: ([^ "]*|".*")
([^ "]*|".*"))?"
)
STORED AS TEXTFILE;

Lecture
Understanding Pig

Introduction
A high-level platform for creating MapReduce programs Using Hadoop
Pig is 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.
The salient property of Pig programs is that their structure is
amenable to substantial parallelization, which in turns enables
them to handle very large data sets.

Hands-On: Running a Pig script

Starting Pig Command Line

Starting Pig from Hue

countryFilter.pig
A = load 'hdi-data.csv' using PigStorage(',') AS (id:int, country:chararray, hdi:float,
lifeex:int, mysch:i
nt, eysch:int, gni:int);
B = FILTER A BY gni > 2000;
C = ORDER B BY gni;
dump C;
Writing a Pig Script

[hdadmin@localhost ~]$ cd Downloads
[hdadmin@localhost ~]$ pig -x local
grunt > run countryFilter.pig
....
(150,Cameroon,0.482,51,5,10,2031)
(126,Kyrgyzstan,0.615,67,9,12,2036)
(156,Nigeria,0.459,51,5,8,2069)
(154,Yemen,0.462,65,2,8,2213)
(138,Lao People's Democratic Republic,0.524,67,4,9,2242)
(153,Papua New Guinea,0.466,62,4,5,2271)
(165,Djibouti,0.43,57,3,5,2335)
(129,Nicaragua,0.589,74,5,10,2430)
(145,Pakistan,0.504,65,4,6,2550)
Running a Pig Script

Thank you
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Big Data Analytics Using Hadoop Cluster On Amazon EMR

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