This presentation about Hadoop architecture will help you understand the architecture of Apache Hadoop in detail. In this video, you will learn what is Hadoop, components of Hadoop, what is HDFS, HDFS architecture, Hadoop MapReduce, Hadoop MapReduce example, Hadoop YARN and finally, a demo on MapReduce. Apache Hadoop offers a versatile, adaptable and reliable distributed computing big data framework for a group of systems with capacity limit and local computing power. After watching this video, you will also understand the Hadoop Distributed File System and its features along with the practical implementation. Below are the topics covered in this Hadoop Architecture presentation: 1. What is Hadoop? 2. Components of Hadoop 3. What is HDFS? 4. HDFS Architecture 5. Hadoop MapReduce 6. Hadoop MapReduce Example 7. Hadoop YARN 8. Demo on MapReduce What are the course objectives? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames Who should take up this Big Data and Hadoop Certification Training Course? Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: 1. Software Developers and Architects 2. Analytics Professionals 3. Senior IT professionals 4. Testing and Mainframe professionals 5. Data Management Professionals 6. Business Intelligence Professionals 7. Project Managers 8. Aspiring Data Scientists Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training

1. What’s in it for you?

2. Back in the days when there was
no internet, data used
to be less and was often
structured
This data was easily stored on a
central sever storage
How Big Data evolved?

3. But then, internet boomed and
data grew at a very high rate
A lot of semi-structured and
unstructured data was being
generated
How Big Data evolved?

4. But then, internet boomed and
data grew at a very high rate
Storing such huge volumes of
data on a single server was not
an efficient way
How Big Data evolved?

5. But then, internet boomed and
data grew at a very high rate
There was a need for distributed
storage machines where data
could be stored and processed
parallelly
How Big Data evolved?

6. But then, internet boomed and
data grew at a very high rate
Data can be stored and
processed on multiple machines
How Big Data evolved?

7. But then, internet boomed and
data grew at a very high rate
Hadoop is a framework that allows
distributed storage and parallel processing
of big data
BIG DATA
TECHNOLOGIES
How Big Data evolved?
Solution

8. What’s in it for you?
HDFS Architecture
Components of Hadoop
Demo on MapReduce
What is Hadoop?
What is HDFS?
Hadoop MapReduce
Hadoop MapReduce Example
Hadoop YARN

9. What is Hadoop?
What is Hadoop?

10. What is Hadoop?
Hadoop is a framework that allows you to store large
volumes of data on several node machines
It also helps in processing the data in a parallel manner
1 TB
3 TB
Data
1 TB 1 TB

11. What is Hadoop?
Components of Hadoop

12. Components of Hadoop
Storing data
Cluster resource
management
Data processing

13. What is Hadoop?
What is HDFS?

14. What is HDFS?
Hadoop Distributed File System (HDFS) is the storage layer of Hadoop
that stores data in multiple data servers
Data is divided into multiple blocks
Stores them over multiple nodes of the cluster

15. What is HDFS?
Hadoop Distributed File System (HDFS) is the storage layer of Hadoop
that stores data in multiple data servers
Namenode
Secondary
Namenode
Slavenode
Master node contains
metadata in ram and disk
Has a copy of Namenode’s
metadata in disk
Contains the actual data in
the form of blocks
3 core components

16. What is Hadoop?
HDFS Blocks

17. HDFS Blocks
128 MB 128 MB 128 MB 128 MB 30 MB
542 MB
HDFS divides large data into different blocks Each block by default has 128 MB’s of data
Suppose, we have a 542 MB file

18. What is Hadoop?
Data Replication

19. Data Replication in HDFS
C
DN -------> Datanode
-------> Block AA
B -------> Block B
-------> Block C
-------> Block DD
DN 9
DN 10
DN 11
DN 12
Rack 1
Rack 3
C
B
B
D
DN 5
DN 6
DN 7
DN 8
Rack 1
Rack 2
A
B
A
C
DN 1
DN 2
DN 3
DN 4
Rack 1
Rack 1
A
C
D
D
Do you understand what’s
happening here?
Each block of data is being replicated thrice on different
datanodes present in different racks

20. Data Replication in HDFS
C
DN -------> Datanode
-------> Block AA
B -------> Block B
-------> Block C
-------> Block DD
DN 1
DN 2
DN 3
DN 4
Rack 1
Rack 1
A
C
D
D
Initial copy of Block A is created
in Rack 1
DN 5
DN 6
DN 7
DN 8
Rack 1
Rack 2
A
B
A
C
Initial copy of Block B is created
in Rack 2
DN 9
DN 10
DN 11
DN 12
Rack 1
Rack 3
C
B
B
D
Initial copy of Block C and D is
created in Rack 3
Two identical blocks cannot be placed on the same datanode

21. Data Replication in HDFS
C
DN -------> Datanode
-------> Block AA
B -------> Block B
-------> Block C
-------> Block DD
DN 1
DN 2
DN 3
DN 4
Rack 1
Rack 1
A
C
D
D
Initial copy of Block A is created
in Rack 1
DN 5
DN 6
DN 7
DN 8
Rack 1
Rack 2
A
B
A
C
Initial copy of Block B is created
in Rack 2
DN 9
DN 10
DN 11
DN 12
Rack 1
Rack 3
C
B
B
D
Initial copy of Block C and D is
created in Rack 3
When cluster is rack aware, all the replicas of a block will not be placed on the same
rack

22. Data Replication in HDFS
C
DN -------> Datanode
-------> Block AA
B -------> Block B
-------> Block C
-------> Block DD
DN 9
DN 10
DN 11
DN 12
Rack 1
DN 5
DN 6
DN 7
DN 8
Rack 1
Rack 2 Rack 3
CDN 1
DN 2
DN 3
DN 4
Rack 1
Rack 1
A
A
B
B
B
A
DC
C
D
D
Suppose, datanode 7
crashes

23. Data Replication in HDFS
C
DN -------> Datanode
-------> Block AA
B -------> Block B
-------> Block C
-------> Block DD
DN 9
DN 10
DN 11
DN 12
Rack 1
DN 5
DN 6
DN 7
DN 8
Rack 1
Rack 2 Rack 3
CDN 1
DN 2
DN 3
DN 4
Rack 1
Rack 1
A
A
B
B
B
A
DC
C
D
D
We will still have 2 copies of Block C data on DN 4 of Rack 1 and
DN 9 of Rack 3
Suppose, datanode 7
crashes

24. What is Hadoop?
HDFS Architecture

25. HDFS Architecture
Secondary
Namenode
Namenode
Datanode 1
B1 B2
Datanode 2
B1 B3
Datanode 3
B2 B3
Datanode N
……….
Master
Slave
Metadata in Disk
Edit log Fsimage
Metadata in RAM
Metadata (Name, replicas,….):
/home/foo/data, 3, …
DN1: B1, B2
DN2: B1, B3
DN3: B2, B3

26. HDFS - Namenode
Namenode
Datanode 1
B1 B2
Datanode 2
B1 B3
Datanode 3
B2 B3
Datanode N
……….
File.txt
Namenode is the master server. In a non high availability cluster, there can be only one
Namenode. In a Hadoop cluster, 2 Namenodes are possible
File system
Metadata in Disk
Edit log Fsimage
Metadata in RAM
Metadata (Name, replicas,….):
/home/foo/data, 3, …

27. HDFS - Namenode
Namenode
Datanode 1
B1 B2
Datanode 2
B1 B3
Datanode 3
B2 B3
Datanode N
……….
File.txt
File system
Metadata in Disk
Edit log Fsimage
Metadata in RAM
Metadata (Name, replicas,….):
/home/foo/data, 3, …
Namenode holds metadata information about the various
Datanodes, their location, the size of each block, etc.

28. HDFS - Namenode
Namenode
Datanode 1
B1 B2
Datanode 2
B1 B3
Datanode 3
B2 B3
Datanode N
……….
File.txt
File system
Metadata in Disk
Edit log Fsimage
Metadata in RAM
Metadata (Name, replicas,….):
/home/foo/data, 3, …
Helps to execute file system namespace operations –
opening, closing, renaming files and directories

29. HDFS - Namenode
Namenode
Datanode 1
B1 B2
Datanode 2
B1 B3
Datanode 3
B2 B3
Datanode N
……….
Datanodes send block reports to Namenode every 10 seconds
File.txt
File system
Metadata in Disk
Edit log Fsimage
Metadata in RAM
Metadata (Name, replicas,….):
/home/foo/data, 3, …

30. HDFS - Datanode
Namenode
Datanode is a multiple instance server. There can be N number of
Datanode servers
Client
Metadata ops
Metadata (Name, replicas, ….):
/home/foo/data, 3, …
Datanode 1
B1 B2
Datanode 2
B1 B3
Datanode 3
B2 B3
Datanode 4 Datanode 5
B4 B2 B4B3
Client

31. HDFS - Datanode
Namenode
Datanode stores and maintains the data blocks
Client
Metadata ops
Metadata (Name, replicas, ….):
/home/foo/data, 3, …
Datanode 1
B1 B2
Datanode 2
B1 B3
Datanode 3
B2 B3
Datanode 4 Datanode 5
B4 B2 B4B3
Client

32. HDFS - Datanode
Namenode
Datanode stores and retrieves the blocks when asked by the Namenode
Client
Metadata ops
Metadata (Name, replicas, ….):
/home/foo/data, 3, …
Block ops
Datanode 1
B1 B2
Datanode 2
B1 B3
Datanode 3
B2 B3
Datanode 4 Datanode 5
B4 B2 B4B3
Client

33. HDFS - Datanode
Namenode
Datanode 1
B1 B2
Datanode 2
B1 B3
Datanode 3
B2 B3
It reads and writes client’s request and performs block creation, deletion and
replication on instruction from the Namenode
Datanode 4 Datanode 5
B4 B2 B4B3
Client
Metadata ops
Metadata (Name, replicas, ….):
/home/foo/data, 3, …
Client
Block ops
Read
Write
Replication
Response from the Namenode
that the operation was
successful

34. HDFS – Secondary Namenode
Namenode
Datanode 1 Datanode 2 Datanode 3
Secondary Namenode server is responsible for maintaining a copy of
Metadata in disk
SecondaryN
amenode
Maintains
Metadata in Disk
Edit log Fsimage
Performs
checkpointing

35. HDFS Architecture
Namenode
MetaData (Name, replicas, ….):
/home/foo/data, 3, ….
Block ops
DatanodesDatanodes
Metadata ops
Client
Read
Rack 1 Rack 2
Replication
Client
Write Write

36. Hadoop Cluster – Rack Based Architecture
Hadoop Cluster
Core Switch Core Switch
Rack Switch
Node 1
Node 2
Node N
Rack Switch
Node 1
Node 2
Node N
Rack Switch
Node 1
Node 2
Node N
Rack 1 Rack NRack 2

37. What is Hadoop?
HDFS Read Mechanism

38. HDFS Read Mechanism
Namenode
4. Read
Datanodes
2. Get block locationsHDFS
Client
1. Open Distributed
FileSystem
FSData
InputStream
client JVM
client node

39. HDFS Read Mechanism
client JVM
client node
HDFS Client
Namenode
DN
1
DN 2
DN 3
DN 4
DN 5
DN 6
DN 7
DN 8
DN 9
Block A
Block A
Block ABlock B
Block B Block B
Request to read Block A and B
Sends the location of the blocks
(DN1 and DN2)
Block A Block B
Data to be read
Rack switch Rack switch Rack switch
Core switch
Rack 1 Rack 2 Rack 3
1
2
3
5
4

40. HDFS Read Mechanism
client JVM
client node
HDFS Client
Namenode
DN
1
DN 2
DN 3
DN 4
DN 5
DN 6
DN 7
DN 8
DN 9
Block A
Block A
Block ABlock B
Block B Block B
Request to read Block A and B
Sends the location of the blocks
(DN1 and DN2)
Block A Block B
Data to be read
Block A and B is read from DN1 and
DN2 as they are closest and have the
least network bandwidth
Rack switch Rack switch Rack switch
Core switch
Rack 1 Rack 2 Rack 3
1
2
5
7
6

41. What is Hadoop?
HDFS Write Mechanism

42. HDFS Write Mechanism
Namenode
4.1 Write packet
Datanodes
2. Create
1. CreateHDFS
Client
Distributed
FileSystem
FSData
OutputStream
client JVM
Pipeline of
datanodes
5.3 Acknowledgement
7. Complete
4.2
5.2
4.3
5.1
ack ack
client node

43. HDFS Write Mechanism
client JVM
client node
HDFS Client
Namenode
Rack switch
DN
1
DN 2
DN 3
Rack switch
DN 4
DN 5
DN 6
Rack switch
DN 7
DN 8
DN 9
Request to write data on Block A
Sends the location of the Datanodes
(DN1, DN6, DN8)
Block A
Data to be written
Core switch
Block A
Block A
Block A
Rack 1 Rack 2 Rack 3
1
2
3
4

44. HDFS Write Mechanism
client JVM
client node
HDFS Client
Namenode
Rack switch
DN
1
DN 2
DN 3
Rack switch
DN 4
DN 5
DN 6
Rack switch
DN 7
DN 8
DN 9
Request to write data on Block A
Sends the location of the Datanodes
(DN1, DN6, DN8)
Block A
Data to be written
Block A
replica 1
Block A
replica 2
Core switch
Block A
Block A
Block A
Rack 1 Rack 2 Rack 3
5 6
1
2
3
4

45. HDFS Write Mechanism
client JVM
client node
HDFS Client
Namenode
DN
1
DN 2
DN 3
Rack switch
DN 4
DN 5
DN 6
Rack switch
DN 7
DN 8
DN 9
Block A
Data to be written
Core switch
Ack
DN 1, DN 6, DN 8
Ack
DN 1
Ack
DN 8
Ack
DN 6
Write operation successful
Block A
Block A
Block A
Rack 1 Rack 2 Rack 3
7
8
9
10
11
Rack switch

46. What is Hadoop?
Hadoop MapReduce

47. Hadoop MapReduce
MapReduce is a framework that performs distributed and parallel processing of large
volumes of data
Map Reduce
Data
block
Read and
process
Generates key-value
pairs (key, value)
Shuffle
and sort
(K1, v1)
(k2, v2)
(k3, v3)
Receives key-
value pairs from
map jobs
Aggregate key-value
pairs into smaller sets

48. Hadoop MapReduce
MapReduce is a framework that performs distributed and parallel processing of large
volumes of data
Input Data Output Data
map()
map()
map()
Shuffle and
Sort
reduce()
reduce()

49. MapReduce Job Execution
Input data
stored on
HDFS
Input
Format
Shuffling
and sorting
Output
Format
Inputsplit
Inputsplit
Inputsplit
……
RecordReader
RecordReader
RecordReader
……
Combiner
Combiner
Combiner
……
Partitioner
Partitioner
Partitioner
……
Reducer
Reducer
……..
Mapper
Mapper
Mapper
……
Output data
stored on
HDFS
Input key
value pair
Intermediate
key value pair
Substitute
intermediate
key value pair

50. MapReduce Example
Big data comes in various
formats. This data can be
stored in multiple data servers
Big data comes in
various formats
This data can be
stored in multiple
data servers
Big, 1
data, 1
comes, 1
in, 1
various, 1
formats, 1
This, 1
data, 1
can, 1
be, 1
stored, 1
in, 1
multiple, 1
data, 1
servers, 1
Input Split Map
be, (1)
Big, (1)
be, (1)
can, (1)
data, (1,1)
comes, (1)
formats, (1)
in, (1,1)
multiple, (1)
servers, (1)
stored, (1)
This, (1)
various, (1)
Shuffle
be, (1)
Big, (1)
be, (1)
can, (1)
comes, (1)
data, (2)
formats, (1)
in, (2)
multiple, (1)
servers, (1)
stored, (1)
This, (1)
various, (1)
Reduce

51. What is Hadoop?
Hadoop YARN

52. Hadoop YARN
YARN ---------> Yet Another Resource Negotiator
Introduced in Hadoop 2.0 version
It is the middle layer between HDFS
and MapReduce
Manages cluster resources (memory,
network bandwidth, disk IO, CPU)

53. YARN Architecture
Resource
ManagerClient
Node
Manager
container
App Master
App Master
container
Node
Manager
Node
Manager
container container
Job Submission
Node Status
MapReduce Status
Resource Request
Submit job
request

54. YARN Architecture – Resource Manager
Resource
ManagerClient
Node
Manager
container
App Master
App Master
container
Node
Manager
Node
Manager
container container
Job Submission
Node Status
MapReduce Status
Resource Request
Submit job
request
Resource Manager manages the resource
allocation in the cluster

55. YARN Architecture – Resource Manager
Resource
ManagerClient
Node
Manager
container
App Master
App Master
container
Node
Manager
Node
Manager
container container
Job Submission
Node Status
MapReduce Status
Resource Request
Submit job
request
Resource manager has 2 components:
Scheduler and Application Manager
Scheduler
Applications
Manager

56. YARN Architecture – Scheduler
Resource
ManagerClient
Node
Manager
container
App Master
App Master
container
Node
Manager
Node
Manager
container container
Job Submission
Node Status
MapReduce Status
Resource Request
Submit job
request
Scheduler
• Scheduler allocates resources to various
running applications
• Schedules resources based on the
requirements of the applications
• Does not monitor or track the status of the
applications
Applications
Manager

57. YARN Architecture – Applications Manager
Resource
ManagerClient
Node
Manager
container
App Master
App Master
container
Node
Manager
Node
Manager
container container
Job Submission
Node Status
MapReduce Status
Resource Request
Submit job
request
Scheduler
Applications
Manager
• Applications Manager accepts job
submissions
• Monitors and restarts application masters
in case of failure

58. YARN Architecture – Node Manager
Resource
ManagerClient
container
App Master
App Master
container
container container
Job Submission
Node Status
MapReduce Status
Resource Request
Submit job
request
Node
Manager
Node
Manager
Node
Manager
• Node Manager is a tracker that tracks the
jobs running
• Monitors each container’s resource
utilization

59. YARN Architecture – App Master
Resource
ManagerClient
container
App Master
App Master
container
container container
Job Submission
Node Status
MapReduce Status
Resource Request
Submit job
request
Node
Manager
Node
Manager
Node
Manager• Application Master manages resource
needs of individual applications
• Interacts with Scheduler to acquire
required resources
• Interacts with Node Manager to execute
and monitor tasks

60. YARN Architecture - Container
Resource
ManagerClient
container
App Master
App Master
container
container container
Job Submission
Node Status
MapReduce Status
Resource Request
Submit job
request
Node
Manager
Node
Manager
Node
Manager
• Container is a collection of resources
like RAM, CPU, Network Bandwidth
• Provides rights to an application to use
specific amount of resources

61. What is Hadoop?
Use case – Word Count
using MapReduce