Know more about Hadoop, it's benefits and practical applications.

1. A new way to store and analyze data
Sandesh Deshmane

2. • What is Hadoop?
• Why, Where, When?
• Benefits of Hadoop
• How Hadoop Works?
• Hadoop Architecture
• HDFS
• Hadoop MapReduce
• Installation &
Execution
• Demo
Topics Covered

3. In pioneer days they used oxen for heavy pulling, and when
one ox couldn’t budge a log, they didn’t try to grow a
larger ox. We shouldn’t be trying for bigger computers, but
more systems of computers.
—Grace Hopper
History

4. • Size of digital Universe was estimated 0.18 zeta byte in 2006 and was 3
zeta byte in 2012
1 zeta byte =10^21
bytes=1k exa bytes=1 million petabyte=
1 billion terabytes
• The New York stock exchange generate 1TB data per day
• Facebook stores around 10 billion photos . Around 1 petabyte.
• The internet archive stores 1 peta byte data and its growing
( 20 TB per month).
Background

5. • Created by Douglas Reed Cutting,
• Open-source Apache Software Foundation.
• consists of two key services:
a. Reliable data storage using the Hadoop Distributed File System
(HDFS).
b. High-performance parallel data processing using a technique called
Map Reduce.
• Hadoop is large-scale, high-performance processing jobs — in spite
of system changes or failures.
What is Hadoop?

6. • Need to process 100TB datasets
• On 1 node:
– scanning @ 50MB/s = 23 days
• On 1000 node cluster:
– scanning @ 50MB/s = 33 min
• Need Efficient, Reliable and Usable framework
Hadoop, Why?

7. Where
• Batch data processing, not
real-time / user facing (e.g.
Document Analysis and
Indexing, Web Graphs and
Crawling)
• Highly parallel data intensive
distributed applications
• Very large production
deployments
When
• Process lots of unstructured
data
• When your processing can
easily be made parallel
• Running batch jobs is
acceptable
• When you have access to lots
of cheap hardware
Where and When Hadoop?

8. • Runs on cheap commodity hardware
• Automatically handles data replication and node
failure
• It does the hard work – you can focus on processing
data
• Cost Saving and efficient and reliable data
processing
Benefits of Hadoop

9. • Hadoop implements a computational paradigm named
Map/Reduce, where the application is divided into many small
fragments of work, each of which may be executed or re-executed
on any node in the cluster.
• In addition, it provides a distributed file system (HDFS) that
stores data on the compute nodes, providing very high aggregate
bandwidth across the cluster.
• Both Map/Reduce and the distributed file system are designed so
that node failures are automatically handled by the framework.
How Hadoop Works?

10. The Apache Hadoop project develops open-source software for reliable,
scalable, distributed computing
Hadoop Consists of:
• Hadoop Common: The common utilities that support the other
Hadoop subprojects.
• HDFS: A distributed file system that provides high throughput
access to application data.
• MapReduce: A software framework for distributed processing of
large data sets on compute clusters.
Hadoop Architecture

11. Web
Servers
Scribe
Servers
Network
Storage
Hadoop ClusterOracle DB MySQL
Hadoop Architecture

12. • Java
• Python
• Ruby
• C++ (Hadoop Pipes)
Supported Languages

13. • Know as Hadoop Distribute File System
• Primary storage system for Hadoop Apps
• Multiple replicas of data blocks distributed on compute nodes for
reliability
• Files are stored on multiple boxes for durability and high availability
HDFS

14. • Distributed File System = holds large amount of data and
provides access to this data to many clients distributed
across a network . e.g NFS
• HDFS stores large amount of Information than DFS
• HDFS stores data reliably.
• HDFS provides fast, scalable access to this information to
large number of clients in Cluster
DFS vs. HDFS

15. • Optimized for long sequential reads
• Data written once , read multiple times, no append possible
• Large file, sequential reads so no local caching of data.
• Data replication
HDFS

16. HDFS Architecture

17. • Block Structure files system
• File is divided to bocks and stored
• Each individual machine in cluster is Data Node
• Default block size is 64 MB
• Information of blocks is stored in metadata
• All this meta data is stored on machine which is Name Node
HDFS Architecture

18. Data Node and Data Name

19. <configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://your.server.name.com:9000</value>
</property>
<property>
<name>dfs.data.dir</name>
<value>/home/username/hdfs/data</value>
</property>
<property>
<name>dfs.name.dir</name>
<value>/home/username/hdfs/name</value>
</property>
</configuration>
HDFS Config File

20. public class HDFSHelloWorld {
public static final String theFilename = "hello.txt";
public static final String message = "Hello, world!n";
public static void main (String [] args) throws IOException {
Configuration conf = new Configuration();
FileSystem fs = FileSystem.get(conf);
Path filenamePath = new Path(theFilename);
try {
if (fs.exists(filenamePath)) {
// remove the file first
fs.delete(filenamePath);
}
FSDataOutputStream out = fs.create(filenamePath);
out.writeUTF(message);
out.close();
FSDataInputStream in = fs.open(filenamePath);
String messageIn = in.readUTF();
System.out.print(messageIn);
in.close();
} catch (IOException ioe) {
System.err.println("IOException during operation: " + ioe.toString());
System.exit(1);
}
}
Sample Java Code to Read/Write from HDFS

21. Map Reduce

22. Cluster Look

23. Map

24. Reduce

25. • HDFS handles the Distributed File System layer
• MapReduce is how we process the data
• MapReduce Daemons
JobTracker
TaskTracker
• Goals
Distribute the reading and processing of data
Localize the processing when possible
Share as little data as possible while processing
MapReduce

26. MapReduce

27. • One per cluster “master node”
• Takes jobs from clients
• Splits work into “tasks”
• Distributes “tasks” to TaskTrackers
• Monitors progress, deals with failures
Job Tracker

28. • Many per cluster “slave nodes”
• Does the actual work, executes the code for the job
• Talks regularly with JobTracker
• Launches child process when given a task
• Reports progress of running “task” back to JobTracker
Task Tracker

29. • Client Submits job:
I want to count the count of each word
We will assume that the data to process is already there in HDFS
• Job Tracker receives job
• Queries the NamNode for number of blocks in File
• The job is split into Tasks
• One map task per each block
• As many reduce tasks as specified in the Job
• TaskTracker checks in Regularly with JobTracker
Is there any work for me ?
• If the JobTracker has a MapTask that the TaskTracker has a local block for
the file being processed then the TaskTracker will be given the “task”
Anatomy of Map Reduce Job

30. Map Reduce Job – Big Picture

31. Client Submits to JobTracker

32. JobTracker Queries Name Node for Block Info

33. Job tracker Defines Job as Collection of Tasks

34. Task Trackers Checking in are Assigned tasks

35. Task Trackers Checking in are Assigned tasks

36. • Read text files and count how often words occur.
o The input is text files
o The output is a text file
 each line: word, tab, count
• Map: Produce pairs of (word, count)
• Reduce: For each word, sum up the counts.
Example of MapReduce - Word Count

37. public static class MapClass extends MapReduceBase implements
Mapper<LongWritable, Text, Text, IntWritable>
{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map (LongWritable key, Text value, OutputCollector<Text,
IntWritable> output, Reporter reporter) throws IOException
{
String line = value.toString();
StringTokenizer itr = new StringTokenizer(line);
while (itr.hasMoreTokens())
{
word.set(itr.nextToken());
output.collect(word, one);
}
}
}
Map Class

38. public static class ReduceClass extends MapReduceBase implements
Reducer<Text, IntWritable, Text, IntWritable>
{
public void reduce(Text key, Iterator<IntWritable> values,
OutputCollector<Text, IntWritable> output, Reporter reporter) throws
IOException
{
int sum = 0;
while (values.hasNext())
{
sum += values.next().get();
}
output.collect(key, new IntWritable(sum));
}
}
Reduce Class

39. public void run(String inputPath, String outputPath) throws Exception
{
JobConf conf = new JobConf(WordCount.class);
conf.setJobName("wordcount"); // the keys are words (strings)
conf.setOutputKeyClass(Text.class); // the values are counts (ints)
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(MapClass.class);
conf.setReducerClass(Reduce.class);
FileInputFormat.addInputPath(conf, new Path(inputPath));
FileOutputFormat.setOutputPath(conf, new Path(outputPath));
JobClient.runJob(conf);
}
Driver Class

40. import static org.mockito.Matchers.anyObject;
import static org.mockito.Mockito.*;
import java.io.IOException;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.OutputCollector;
import org.junit.*;
public class WordCountMapperTest {
@Test
public void processesValidRecord() throws IOException {
MapClass mapper = new MapClass ();
Text value = new Text(“test test”)
OutputCollector<Text, IntWritable> output = mock(OutputCollector.class);
mapper.map(null, value, output, null);
verify(output).collect(new Text(“test"), new IntWritable(2));
}
}
Junit For Mapper

41. Junit for Reducer
import static org.mockito.Matchers.anyObject;
import static org.mockito.Mockito.*;
import java.io.IOException;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.OutputCollector;
import org.junit.*;
@Test
public void returnsMaximumIntegerInValues() throws IOException {
ReduceClass reducer = new ReduceClass ();
Text key = new Text(“test");
Iterator<IntWritable> values = Arrays.asList(
new IntWritable(1), new IntWritable(1)).iterator();
OutputCollector<Text, IntWritable> output = mock(OutputCollector.class);
reducer.reduce(key, values, output, null);
verify(output).collect(key, new IntWritable(2));
}

42. Installation :
• Requirements: Linux, Java 1.6,
sshd,
• Configure SSH for password-free
authentication
• Unpack Hadoop distribution
• Edit a few configuration files
• Format the DFS on the name
node
• Start all the daemon processes
Execution:
• Compile your job into a JAR file
• Copy input data into HDFS
• Execute bin/hadoop jar with
relevant args
• Monitor tasks via Web interface
(optional)
• Examine output when job is
complete
Let’s Go…

43. Demo

44. Hadoop Users
• Adobe
• Alibaba
• Amazon
• AOL
• Facebook
• Google
• IBM
Major Contributor
• Apache
• Cloudera
• Yahoo
Hadoop Community

45. • Apache Hadoop! (http://hadoop.apache.org )
• Hadoop on Wikipedia
(http://en.wikipedia.org/wiki/Hadoop)
• Free Search by Doug Cutting
(http://cutting.wordpress.com )
• Hadoop and Distributed Computing at Yahoo!
(http://developer.yahoo.com/hadoop )
• Cloudera - Apache Hadoop for the Enterprise
(http://www.cloudera.com )
References