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What’s in it for you?
Hadoop 1.0 (MR 1)
What’s in it for you?
Hadoop 1.0 (MR 1)
Limitations of Hadoop
1.0 (MR 1)
What’s in it for you?
Hadoop 1.0 (MR 1)
Limitations of Hadoop
1.0 (MR 1)
Need for YARN
Workloads running on
YARN
What’s in it for you?
Hadoop 1.0 (MR 1)
Limitations of Hadoop
1.0 (MR 1)
Need for YARN
What is YARN?
What’s in it for you?
Hadoop 1.0 (MR 1)
Limitations of Hadoop
1.0 (MR 1)
Need for YARN
What is YARN?
Workloads running on
...
What’s in it for you?
Hadoop 1.0 (MR 1)
Limitations of Hadoop
1.0 (MR 1)
Need for YARN
What is YARN?
Workloads running on
...
What’s in it for you?
Hadoop 1.0 (MR 1)
Limitations of Hadoop
1.0 (MR 1)
Need for YARN
What is YARN?
Workloads running on
...
What’s in it for you?
Hadoop 1.0 (MR 1)
Limitations of Hadoop
1.0 (MR 1)
Need for YARN
What is YARN?
Workloads running on
...
Hadoop 1.0 (MR 1)
Hadoop 1.0 (MR 1)
HDFS
(data storage)
MapReduce
(data processing)
Hadoop 1.0
In Hadoop 1.0, MapReduce performed both
data ...
Hadoop 1.0 (MR 1)
Job
Tracker
Task
Tracker
Allocated resources, performed
scheduling and monitored jobs
MapReduce consiste...
Client
Client
Job
Tracker
Clie
nt
Job Submission
Hadoop 1.0 (MR 1)
Client
Client
Job
Tracker
Clie
nt
Job Submission
Hadoop 1.0 (MR 1)
Hadoop Master
Daemon
Client
Client
Job
Tracker
Clie
nt
Task
Tracker
Task Task
Task
Tracker
Task Task
Task
Tracker
Task Task
Hadoop 1.0 (MR 1)
J...
Client
Client
Job
Tracker
Clie
nt
Task
Tracker
Task Task
Task
Tracker
Task Task
Task
Tracker
Task Task
Hadoop 1.0 (MR 1)
J...
Client
Client
Job
Tracker
Clie
nt
Task
Tracker
Task Task
Task
Tracker
Task Task
Task
Tracker
Task Task
MapReduce Status
Ha...
Client
Client
Job
Tracker
Clie
nt
Task
Tracker
Task Task
Task
Tracker
Task Task
Task
Tracker
Task Task
Hadoop 1.0 (MR 1)
S...
Limitations of Hadoop 1.0 (MR 1)
Due to a single JobTracker, scalability
became a bottleneck.
Cannot have a cluster size o...
Limitations of Hadoop 1.0 (MR 1)
JobTracker is single point of
failure. Any failure kills all queued
and running jobs. Job...
Limitations of Hadoop 1.0 (MR 1)
Due to predefined number of map
and reduce slots for each
TaskTracker, resource utilizati...
Limitations of Hadoop 1.0 (MR 1)
Problem in performing real-time
analysis and running Ad-hoc query as
MapReduce is batch d...
Need for YARN
HDFS
(data storage)
MapReduce
(data processing)
Hadoop 1.0
Before YARN
Need for YARN
Designed to run MapReduce jobs only a...
MapReduce
(data processing)
Other frameworks
(processing)
YARN
(cluster resource management)
HDFS
(data storage)
Hadoop 2....
Hadoop 2.0 (YARN)
Solution - Hadoop 2.0 (YARN)
Scalability
Can have a cluster size of
more than 10,000 nodes
and can run
more than 1,00,000
...
Solution - Hadoop 2.0 (YARN)
Scalability
Can have a cluster size of
more than 10,000 nodes
and can run
more than 1,00,000
...
Solution - Hadoop 2.0 (YARN)
Scalability
Can have a cluster size of
more than 10,000 nodes
and can run
more than 1,00,000
...
Solution - Hadoop 2.0 (YARN)
Scalability
Can have a cluster size of
more than 10,000 nodes
and can run
more than 1,00,000
...
What is YARN?
What is YARN?
YARN – Yet Another Resource Negotiator
YARN is the cluster resource management layer of the Apache Hadoop Ec...
What is YARN?
YARN – Yet Another Resource Negotiator
I want resources to
run my applications
MapReduce
Application
YARN is...
What is YARN?
YARN – Yet Another Resource Negotiator
Memory
Network CPU
YARN provides the desired
resources
I want resourc...
Workloads running on YARN
Hadoop Distributed
File System
Cluster Resource
Management
BATCH
(MapReduce)
INTERACTIVE
(Tez)
C...
YARN Components
YARN Components
Resource
ManagerClient
Node
Manager
container
App Master
App Master
container
Node
Manager
Node
Manager
co...
YARN Components
Node
Manager
Node
Manager
Node
Manager
Container App Master Container App Master Container App Master
Data...
YARN Components –
Resource Manager
YARN Components – Resource Manager
Scheduler
Applications
Manager
Resource
Manager
Ultimate authority that decides the
all...
YARN Components – Resource Manager
Scheduler
Applications
Manager
Resource
Manager
Responsible for allocating resources to...
YARN Components – Resource Manager
Scheduler
Applications
Manager
Resource
Manager
Responsible for allocating resources to...
YARN Components –
Node Manager
YARN Components – Node Manager
Container App Master
Node
Manager
Slaves track processes and running
jobs and monitor each ...
YARN Components – Node Manager
Container App Master
Node
Manager
Has a collection of resources like CPU,
memory, disk, net...
YARN Components – Node Manager
Container App Master
Node
Manager
Has a collection of resources like CPU,
memory, disk, net...
YARN Architecture
YARN Architecture
Client
YARN Architecture
Resource
ManagerClient
Job Submission
Submit job
request
YARN Architecture
Resource
ManagerClient
Node
Manager
container
App Master
App Master
container
Node
Manager
Node
Manager
...
YARN Architecture
Resource
ManagerClient
Node
Manager
container
App Master
App Master
container
Node
Manager
Node
Manager
...
YARN Architecture
Resource
ManagerClient
Node
Manager
container
App Master
App Master
container
Node
Manager
Node
Manager
...
YARN Architecture
Resource
ManagerClient
Node
Manager
container
App Master
App Master
container
Node
Manager
Node
Manager
...
Running an application in
YARN
Running an application in YARN
Client
Client submits an application to the ResourceManager1
Running an application in YARN
Client
Client submits an application to the ResourceManager
Resource
Manager
ResourceManage...
Running an application in YARN
Client
Client submits an application to the ResourceManager
Resource
Manager
ResourceManage...
Running an application in YARN
Client
Client submits an application to the ResourceManager
Resource
Manager
ResourceManage...
Running an application in YARN
Client
Client submits an application to the ResourceManager
Resource
Manager
ResourceManage...
Demo on YARN
So what’s
your next step?
Hadoop YARN | Hadoop YARN Architecture | Hadoop YARN Tutorial | Hadoop Tutorial | Simplilearn
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Hadoop YARN | Hadoop YARN Architecture | Hadoop YARN Tutorial | Hadoop Tutorial | Simplilearn

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This presentation about Hadoop YARN will help you understand the Hadoop 1.0 and Hadoop 2.0, limitations of Hadoop 1.0, need for YARN, what is YARN, workloads running on YARN, YARN components, YARN architecture and you will also go through a demo on YARN. YARN is the cluster resource management layer of the Apache Hadoop Ecosystem, which schedules jobs and assigns resources. Hadoop 1.0 is designed to run MapReduce jobs only and had issues in scalability, resource utilization, etc. whereas YARN solved those issues and users could work on multiple processing models. Now let us get started and learn YARN in detail.

Below topics are explained in this Hadoop YARN presentation:
1. Hadoop 1.0 (MapReduce 1)
2. Limitations of Hadoop 1.0 (MapReduce 1)
3. Need for YARN
4. What is YARN
5. Workloads running on YARN
6. YARN components
7. YARN architecture
8. Demo on YARN

What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.

What are the course objectives?
This course will enable you to:
1. Understand the different components of the 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

Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training

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Hadoop YARN | Hadoop YARN Architecture | Hadoop YARN Tutorial | Hadoop Tutorial | Simplilearn

  1. 1. What’s in it for you? Hadoop 1.0 (MR 1)
  2. 2. What’s in it for you? Hadoop 1.0 (MR 1) Limitations of Hadoop 1.0 (MR 1)
  3. 3. What’s in it for you? Hadoop 1.0 (MR 1) Limitations of Hadoop 1.0 (MR 1) Need for YARN Workloads running on YARN
  4. 4. What’s in it for you? Hadoop 1.0 (MR 1) Limitations of Hadoop 1.0 (MR 1) Need for YARN What is YARN?
  5. 5. What’s in it for you? Hadoop 1.0 (MR 1) Limitations of Hadoop 1.0 (MR 1) Need for YARN What is YARN? Workloads running on YARN
  6. 6. What’s in it for you? Hadoop 1.0 (MR 1) Limitations of Hadoop 1.0 (MR 1) Need for YARN What is YARN? Workloads running on YARN YARN Components
  7. 7. What’s in it for you? Hadoop 1.0 (MR 1) Limitations of Hadoop 1.0 (MR 1) Need for YARN What is YARN? Workloads running on YARN YARN Components YARN Architecture
  8. 8. What’s in it for you? Hadoop 1.0 (MR 1) Limitations of Hadoop 1.0 (MR 1) Need for YARN What is YARN? Workloads running on YARN YARN Components YARN Architecture Demo on YARN
  9. 9. Hadoop 1.0 (MR 1)
  10. 10. Hadoop 1.0 (MR 1) HDFS (data storage) MapReduce (data processing) Hadoop 1.0 In Hadoop 1.0, MapReduce performed both data processing and resource management Data processing Resource management
  11. 11. Hadoop 1.0 (MR 1) Job Tracker Task Tracker Allocated resources, performed scheduling and monitored jobs MapReduce consisted of Job Tracker and Task Tracker Task Trackers reported their progress to the Job Tracker Assigned map and reduce tasks to jobs running on Task Trackers Task Trackers processed the jobs
  12. 12. Client Client Job Tracker Clie nt Job Submission Hadoop 1.0 (MR 1)
  13. 13. Client Client Job Tracker Clie nt Job Submission Hadoop 1.0 (MR 1) Hadoop Master Daemon
  14. 14. Client Client Job Tracker Clie nt Task Tracker Task Task Task Tracker Task Task Task Tracker Task Task Hadoop 1.0 (MR 1) Job Submission
  15. 15. Client Client Job Tracker Clie nt Task Tracker Task Task Task Tracker Task Task Task Tracker Task Task Hadoop 1.0 (MR 1) Job Submission Hadoop Slave Daemons
  16. 16. Client Client Job Tracker Clie nt Task Tracker Task Task Task Tracker Task Task Task Tracker Task Task MapReduce Status Hadoop 1.0 (MR 1) Job Submission Slave daemon Slave daemon Slave daemon
  17. 17. Client Client Job Tracker Clie nt Task Tracker Task Task Task Tracker Task Task Task Tracker Task Task Hadoop 1.0 (MR 1) Slave daemon Slave daemon Slave daemonManaging jobs using a single job tracker and utilization of computational resources was inefficient in MR 1
  18. 18. Limitations of Hadoop 1.0 (MR 1) Due to a single JobTracker, scalability became a bottleneck. Cannot have a cluster size of more than 4000 nodes and cannot run more than 40000 concurrent tasks Scalability1
  19. 19. Limitations of Hadoop 1.0 (MR 1) JobTracker is single point of failure. Any failure kills all queued and running jobs. Jobs need to be resubmitted by users Availability issue2 Due to a single JobTracker, scalability became a bottleneck. Maximum cluster size – 4000 nodes Maximum concurrent tasks - 40000 Scalability1
  20. 20. Limitations of Hadoop 1.0 (MR 1) Due to predefined number of map and reduce slots for each TaskTracker, resource utilization issues occur Resource Utilization3
  21. 21. Limitations of Hadoop 1.0 (MR 1) Problem in performing real-time analysis and running Ad-hoc query as MapReduce is batch driven Limitations in running non- MapReduce applications4 Due to predefined number of map and reduce slots for each TaskTracker, resource utilization issues occur Resource Utilization3
  22. 22. Need for YARN
  23. 23. HDFS (data storage) MapReduce (data processing) Hadoop 1.0 Before YARN Need for YARN Designed to run MapReduce jobs only and had issues in scalability, resource utilization, etc.
  24. 24. MapReduce (data processing) Other frameworks (processing) YARN (cluster resource management) HDFS (data storage) Hadoop 2.0 YARN solved those issues and users could work on multiple processing models along with MapReduce HDFS (data storage) MapReduce (data processing) Hadoop 1.0 Designed to run MapReduce jobs only and had issues in scalability, resource utilization, etc. Before YARN After YARN Need for YARN
  25. 25. Hadoop 2.0 (YARN)
  26. 26. Solution - Hadoop 2.0 (YARN) Scalability Can have a cluster size of more than 10,000 nodes and can run more than 1,00,000 concurrent tasks
  27. 27. Solution - Hadoop 2.0 (YARN) Scalability Can have a cluster size of more than 10,000 nodes and can run more than 1,00,000 concurrent tasks Compatibility Applications developed for Hadoop 1 runs on YARN without any disruption or availability issues
  28. 28. Solution - Hadoop 2.0 (YARN) Scalability Can have a cluster size of more than 10,000 nodes and can run more than 1,00,000 concurrent tasks Resource utilizationCompatibility Allows dynamic allocation of cluster resources to improve resource utilization Applications developed for Hadoop 1 runs on YARN without any disruption or availability issues
  29. 29. Solution - Hadoop 2.0 (YARN) Scalability Can have a cluster size of more than 10,000 nodes and can run more than 1,00,000 concurrent tasks Resource utilization Multitenancy Can use open-source and propriety data access engines and perform real- time analysis and running ad-hoc query Compatibility Allows dynamic allocation of cluster resources to improve resource utilization Applications developed for Hadoop 1 runs on YARN without any disruption or availability issues
  30. 30. What is YARN?
  31. 31. What is YARN? YARN – Yet Another Resource Negotiator YARN is the cluster resource management layer of the Apache Hadoop Ecosystem, which schedules jobs and assigns resources
  32. 32. What is YARN? YARN – Yet Another Resource Negotiator I want resources to run my applications MapReduce Application YARN is the cluster resource management layer of the Apache Hadoop Ecosystem, which schedules jobs and assigns resources
  33. 33. What is YARN? YARN – Yet Another Resource Negotiator Memory Network CPU YARN provides the desired resources I want resources to run my applications MapReduce Application YARN is the cluster resource management layer of the Apache Hadoop Ecosystem, which schedules jobs and assigns resources
  34. 34. Workloads running on YARN Hadoop Distributed File System Cluster Resource Management BATCH (MapReduce) INTERACTIVE (Tez) Column Oriented Database (HBase) STREAMING (Storm) GRAPH (Giraph) IN-MEMORY (Spark) OTHERS (Weave) List of frameworks that runs on top of YARN:
  35. 35. YARN Components
  36. 36. YARN Components Resource ManagerClient Node Manager container App Master App Master container Node Manager Node Manager container container Submit job request A general overview of YARN architectural components Applications Manager Scheduler
  37. 37. YARN Components Node Manager Node Manager Node Manager Container App Master Container App Master Container App Master Datanode Datanode Datanode 4 main components – Resource Manager, Node Manager, Container and App Master Scheduler Applications Manager Resource Manager
  38. 38. YARN Components – Resource Manager
  39. 39. YARN Components – Resource Manager Scheduler Applications Manager Resource Manager Ultimate authority that decides the allocation of resources among all the applications in the system
  40. 40. YARN Components – Resource Manager Scheduler Applications Manager Resource Manager Responsible for allocating resources to various running applications Does not perform monitoring or tracking of status for the applications Offers no guarantee about restarting failed tasks due to hardware or application failures
  41. 41. YARN Components – Resource Manager Scheduler Applications Manager Resource Manager Responsible for allocating resources to various running applications Does not perform monitoring or tracking of status for the applications Offers no guarantee about restarting failed tasks due to hardware or application failures Responsible for accepting job- submissions Negotiates the first container for executing the application specific ApplicationMaster Provides the service for restarting the ApplicationMaster container on failure
  42. 42. YARN Components – Node Manager
  43. 43. YARN Components – Node Manager Container App Master Node Manager Slaves track processes and running jobs and monitor each container’s resource utilization
  44. 44. YARN Components – Node Manager Container App Master Node Manager Has a collection of resources like CPU, memory, disk, network, etc. Authenticates and provides rights to an application to use specific amount of resources Node Manager Monitors Resource Usage, CPU, Memory, etc.
  45. 45. YARN Components – Node Manager Container App Master Node Manager Has a collection of resources like CPU, memory, disk, network, etc. Authenticates and provides rights to an application to use specific amount of resources Application Master manages resource needs of individual applications Interacts with Scheduler to acquire required resources and Node Manager to execute and monitor tasks Node Manager Monitors Resource Usage, CPU, Memory, etc. Resource Manager Application Master Node Manager Interacts Interacts
  46. 46. YARN Architecture
  47. 47. YARN Architecture Client
  48. 48. YARN Architecture Resource ManagerClient Job Submission Submit job request
  49. 49. YARN Architecture Resource ManagerClient Node Manager container App Master App Master container Node Manager Node Manager container container Job Submission Submit job request
  50. 50. YARN Architecture Resource ManagerClient Node Manager container App Master App Master container Node Manager Node Manager container container Job Submission Node Status Submit job request
  51. 51. YARN Architecture Resource ManagerClient Node Manager container App Master App Master container Node Manager Node Manager container container Job Submission Node Status MapReduce Status Submit job request
  52. 52. 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
  53. 53. Running an application in YARN
  54. 54. Running an application in YARN Client Client submits an application to the ResourceManager1
  55. 55. Running an application in YARN Client Client submits an application to the ResourceManager Resource Manager ResourceManager allocates a container 1 2
  56. 56. Running an application in YARN Client Client submits an application to the ResourceManager Resource Manager ResourceManager allocates a container App Master ApplicationMaster contacts the related NodeManager 1 2 3
  57. 57. Running an application in YARN Client Client submits an application to the ResourceManager Resource Manager ResourceManager allocates a container App Master ApplicationMaster contacts the related NodeManager Node Manager NodeManager launches the container 1 2 3 4
  58. 58. Running an application in YARN Client Client submits an application to the ResourceManager Resource Manager ResourceManager allocates a container App Master ApplicationMaster contacts the related NodeManager Node Manager NodeManager launches the container container Container executes the ApplicationMaster 1 2 3 4 5
  59. 59. Demo on YARN
  60. 60. So what’s your next step?

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