Introduction to Hadoop

Dr. Sandeep G. Deshmukh
DataTorrent
1
Introduction to

Contents
 Motivation
 Scale of Cloud Computing
 Hadoop
 Hadoop Distributed File System (HDFS)
 MapReduce
 Sample Code Walkthrough
 Hadoop EcoSystem
2

Motivation - Traditional Distributed systems
 Processor Bound
 Using multiple machines
 Developer is burdened with
managing too many things
 Synchronization
 Failures
 Data moves from shared disk to
compute node
 Cost of maintaining clusters
 Scalability as and when required
not present
3

What is the scale we are talking about?
100s of CPUs?
Couple of CPUs?
10s of CPUs?
4

What we need
 Handling failure
 One computer = fails once in 1000 days
 1000 computers = 1 per day
 Petabytes of data to be processed in parallel
 1 HDD= 100 MB/sec
 1000 HDD= 100 GB/sec
 Easy scalability
 Relative increase/decrease of performance depending on
increase/decrease of nodes
8

What we’ve got : Hadoop!
 Created by Doug Cutting
 Started as a module in nutch and then matured as an
apache project
 Named it after his son's stuffed
elephant
9

What we’ve got : Hadoop!
 Fault-tolerant file system
 Hadoop Distributed File System (HDFS)
 Modeled on Google File system
 Takes computation to data
 Data Locality
 Scalability:
 Program remains same for 10, 100, 1000,… nodes
 Corresponding performance improvement
 Parallel computation using MapReduce
 Other components – Pig, Hbase, HIVE, ZooKeeper
10

HDFS
Hadoop distributed File System
11

How HDFS works
NameNode -
Master
DataNodes
- Slave
Secondary
NameNode
12

Storing file on HDFS
Motivation
 Reliability,
 Availability,
 Network Bandwidth
 The input file (say 1 TB) is split into smaller chunks/blocks of 64 MB (or multiples
of 64MB)
 The chunks are stored on multiple nodes as independent files on slave nodes
 To ensure that data is not lost, replicas are stored in the following way:
 One on local node
 One on remote rack (incase local rack fails)
 One on local rack (incase local node fails)
 Other randomly placed
 Default replication factor is 3
13

B1
B1
B1
B2
B2
B2
B3 Bn
Hub 1 Hub 2
Datanodes
File
Master
Node
8 gigabit
1 gigabit
Blocks
14

NameNode -
Master
The master node: NameNode
Functions:
 Manages File System- mapping files to blocks and blocks
to data nodes
 Maintaining status of data nodes
 Heartbeat
 Datanode sends heartbeat at regular intervals
 If heartbeat is not received, datanode is declared dead
 Blockreport
 DataNode sends list of blocks on it
 Used to check health of HDFS
15

NameNode Functions
 Replication
 On Datanode failure
 On Disk failure
 On Block corruption
 Data integrity
 Checksum for each block
 Stored in hidden file
 Rebalancing- balancer tool
 Addition of new nodes
 Decommissioning
 Deletion of some files
NameNode -
Master
16

HDFS Robustness
 Safemode
 At startup: No replication possible
 Receives Heartbeats and Blockreports from Datanodes
 Only a percentage of blocks are checked for defined replication
factor
17
All is well   Exit Safemode
 Replicate blocks wherever necessary

HDFS Summary
 Fault tolerant
 Scalable
 Reliable
 File are distributed in large blocks for
 Efficient reads
 Parallel access
18

What is MapReduce?
 It is a powerful paradigm for parallel computation
 Hadoop uses MapReduce to execute jobs on files in
HDFS
 Hadoop will intelligently distribute computation over
cluster
 Take computation to data
21

Origin: Functional Programming
map f [a, b, c] = [f(a), f(b), f(c)]
map sq [1, 2, 3] = [sq(1), sq(2), sq(3)]
= [1,4,9]
 Returns a list constructed by applying a function (the first
argument) to all items in a list passed as the second
argument
22

Origin: Functional Programming
reduce f [a, b, c] = f(a, b, c)
reduce sum [1, 4, 9] = sum(1, sum(4,sum(9,sum(NULL))))
= 14
 Returns a list constructed by applying a function (the first
argument) on the list passed as the second argument
 Can be identity (do nothing)
23

Sum of squares example
[1,2,3,4]
Sq (1) Sq (2) Sq (3) Sq (4)
16941
30
Input
Intermediate
output
Output
MAPPER
REDUCER
M1 M2 M3 M4
R1
24

Sum of squares of even and odd
[1,2,3,4]
Sq (1) Sq (2) Sq (3) Sq (4)
(even, 16)(odd, 9)(even, 4)(odd, 1)
(even, 20) (odd, 10)
Input
Intermediate
output
Output
MAPPER
REDUCER
M1 M2 M3 M4
R1 R2
25

Programming model- key, value pairs
Format of input- output
(key, value)
Map: (k1, v1) → list (k2, v2)
Reduce: (k2, list v2) → list (k3, v3)
26

Sum of squares of even and odd and prime
[1,2,3,4]
Sq (1) Sq (2) Sq (3) Sq (4)
(even, 16)(odd, 9)
(prime, 9)
(even, 4)
(prime, 4)
(odd, 1)
(even, 20) (odd, 10)
(prime, 13)
Input
Intermediate
output
Output
R2R1
R3
27

Many keys, many values
Format of input- output
(key, value)
Map: (k1, v1) → list (k2, v2)
Reduce: (k2, list v2) → list (k3, v3)
28

Fibonacci sequence
 f(n) = f(n-1) + f(n-2)
i.e. f(5) = f(4) + f(3)
 0, 1, 1, 2, 3, 5, 8, 13,…
f(5)
f(4) f(3)
f(2)f(3)
f(1)f(2)
f(2) f(1)
f(0)f(1)
•MapReduce will not work
on this kind of calculation
•No inter-process
communication
•No data sharing
29

Input:
1TB text file containing color
names- Blue, Green, Yellow,
Purple, Pink, Red, Maroon, Grey,
Desired
output:
Occurrence of colors Blue
and Green
30

N1 f.001
Blue
Purple
Blue
Red
Green
Blue
Maroon
Green
Yellow
N1 f.001
Blue
Blue
Green
Blue
Green
grep Blue|Green
Nn
f.00n
Green
Blue
Blue
Blue
Green
Blue= 3000
Green= 5500
Blue=500
Green=200
Blue=420
Green=200
sort |unique -c
awk ‘{arr[$1]+=$2;}
END{print arr[Blue], arr[Green]}’
COMBINER
MAPPER
REDUCER
awk ‘{arr[$1]++;}
END{print arr[Blue], arr[Green]}’Nn f.00n
Blue
Purple
Blue
Red
Green
Blue
Maroon
Green
Yellow
31

Input
data
Map
Map
Map
Reduce
Reduce
Output
INPUT MAP SHUFFLE REDUCE OUTPUT
Works on a record
Works on output of Map
32
MapReduce Overview

Input
data
Combine
Combine
Combine
Map
Map
Map
Reduce
Reduce
Output
INPUT MAP REDUCE OUTPUT
Works on output of Map Works on output of Combiner
33
MapReduce Overview

 Mapper, reducer and combiner act on <key, value>
pairs
 Mapper gets one record at a time as an input
 Combiner (if present) works on output of map
 Reducer works on output of map (or combiner, if
present)
 Combiner can be thought of local-reducer
 Reduces output of maps that are executed on same
node
35
MapReduce Summary

What Hadoop is not..
 It is not a POSIX file system
 It is not a SAN file system
 It is not for interactive file accessing
 It is not meant for a large number of small files-
it is for a small number of large files
 MapReduce cannot be used for any and all
applications
36

Hadoop: Take Home
 Takes computation to data
 Suitable for large data centric operations
 Scalable on demand
 Fault tolerant and highly transparent
37

Questions?
38
 Coming up next …
 First hadoop program
 Second hadoop program

Your first program in hadoop
Open up any tutorial on hadoop and first program
you see will be of wordcount 
Task:
 Given a text file, generate a list of words with the
number of times each of them appear in the file
Input:
 Plain text file
Expected Output:
 <word, frequency> pairs for all words in the file
hadoop is a framework written in java
hadoop supports parallel processing
and is a simple framework
<hadoop, 2>
<is, 2>
<a , 2>
<java , 1>
<framework , 2>
<written , 1>
<in , 1>
<and,1>
<supports , 1>
<parallel , 1>
<processing. , 1>
<simple,1>
39

Your second program in hadoop
Task:
 Given a text file containing numbers, one
per line, count sum of squares of odd, even
and prime
Input:
 File containing integers, one per line
Expected Output:
 <type, sum of squares> for odd, even, prime
1
2
5
3
5
6
3
7
9
4
<odd, 302>
<even, 278>
<prime, 323 >
40

File on HDFS
41
3
9
6
2
3
7
8
Map: square
3 <odd,9>
7 <odd,49>
2
6 <even,36>
9 <odd,81>
3 <odd,9>
8 <even,64>
<prime,4>
<prime,9>
<prime,9>
<even,4>
Reducer: sum
prime:<9,4,9>
odd:<9,81,9,49>
even:<,36,4,64>
<odd,148>
<even,104>
<prime,22>
Input
Value
Output
(Key,Value)
Input (Key, List of Values)
Output
(Key,Value)

42
Map
(Invoked on a record)
Reduce
(Invoked on a key)
void map (int x){
int sq = x * x;
if(x is odd)
print(“odd”,sq);
if(x is even)
print(“even”,sq);
if(x is prime)
print(“prime”,sq);
}
void reduce(List l ){
for(y in List l){
sum += y;
}
print(Key, sum);
}
Library functions
boolean odd(int x){ …}
boolean even(int x){ …}
boolean prime(int x){ …}

43
Map
Map
void map (int x){
int sq = x * x;
if(x is odd)
print(“odd”,sq);
if(x is even)
print(“even”,sq);
if(x is prime)
print(“prime”,sq);
}

Your second program in hadoop: Reduce
44
Reduce
(Invoked on a key)
Reduce
(Invoked on a
key)
void reduce(List l ){
for(y in List l){
sum += y;
}
print(Key, sum);
}

public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = new Job(conf, “OddEvenPrime");
job.setJarByClass(OddEvenPrime.class);
job.setMapperClass(OddEvenPrimeMapper.class);
job.setCombinerClass(OddEvenPrimeReducer.class);
job.setReducerClass(OddEvenPrimeReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setInputFormatClass(TextInputFormat.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
45

Questions?
46
 Coming up next …
 More examples
 Hadoop Ecosystem

Example: Counting Fans
48
Problem: Give Crowd
statistics
Count fans
supporting India and
Pakistan

49
45882 67917
 Traditional Way
 Central Processing
 Every fan comes to the
centre and presses
India/Pak button
 Issues
 Slow/Bottlenecks
 Only one processor
 Processing time
determined by the
speed at which
people(data) can
move

50
Hadoop Way
 Appoint processors per
block (MAPPER)
 Send them to each block
and ask them to send a
signal for each person
 Central processor will
aggregate the results
(REDUCER)
 Can make the processor
smart by asking him/her
to aggregate locally and
only send aggregated
value (COMBINER)
Reducer
Combiner

Who all are using Hadoop
54
http://wiki.apache.org/hadoop/PoweredBy

References
For understanding Hadoop
 Official Hadoop website- http://hadoop.apache.org/
 Hadoop presentation wiki-
http://wiki.apache.org/hadoop/HadoopPresentations?a
ction=AttachFile
 http://developer.yahoo.com/hadoop/
 http://wiki.apache.org/hadoop/
 http://www.cloudera.com/hadoop-training/
 http://developer.yahoo.com/hadoop/tutorial/module2.
html#basics
55

References
Setup and Installation
 Installing on Ubuntu
 http://www.michael-
noll.com/wiki/Running_Hadoop_On_Ubuntu_Linux_
(Single-Node_Cluster)
 http://www.michael-
noll.com/wiki/Running_Hadoop_On_Ubuntu_Linux_
(Multi-Node_Cluster)
 Installing on Debian
 http://archive.cloudera.com/docs/_apt.html
56

 Hadoop: The Definitive Guide: Tom White
 http://developer.yahoo.com/hadoop/tutorial/
 http://www.cloudera.com/content/cloudera-
content/cloudera-docs/HadoopTutorial/CDH4/Hadoop-
Tutorial.html
57
Further Reading

59
Acknowledgements
Surabhi Pendse
Sayali Kulkarni
Parth Shah

Introduction to Hadoop

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (9)

Similaire à Introduction to Hadoop

Similaire à Introduction to Hadoop (20)

Plus de Apache Apex

Plus de Apache Apex (20)

Dernier

Dernier (20)

Introduction to Hadoop