Hadoop is an open-source framework for storing and processing large datasets across clusters of computers. It has two main components: HDFS for storage, and MapReduce for processing. MapReduce allows parallel processing of data stored in HDFS. It uses map and reduce tasks that can run on multiple machines in parallel. Hadoop 2.0 features include HDFS federation for scalability, high availability of the namenode, and YARN which splits job tracking from resource management for increased flexibility.
Apache Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. The core of Hadoop consists of HDFS for storage and MapReduce for processing. Hadoop has been expanded with additional projects including YARN for job scheduling and resource management, Pig and Hive for SQL-like queries, HBase for column-oriented storage, Zookeeper for coordination, and Ambari for provisioning and managing Hadoop clusters. Hadoop provides scalable and cost-effective solutions for storing and analyzing massive amounts of data.
- Data is a precious resource that can last longer than the systems themselves (Tim Berners-Lee)
- Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It provides reliability, scalability and flexibility.
- Hadoop consists of HDFS for storage and MapReduce for processing. The main nodes include NameNode, DataNodes, JobTracker and TaskTrackers. Tools like Hive, Pig, HBase extend its capabilities for SQL-like queries, data flows and NoSQL access.
Optimal Execution Of MapReduce Jobs In Cloud - Voices 2015Deanna Kosaraju
Optimal Execution Of MapReduce Jobs In Cloud
Anshul Aggarwal, Software Engineer, Cisco Systems
Session Length: 1 Hour
Tue March 10 21:30 PST
Wed March 11 0:30 EST
Wed March 11 4:30:00 UTC
Wed March 11 10:00 IST
Wed March 11 15:30 Sydney
Voices 2015 www.globaltechwomen.com
We use MapReduce programming paradigm because it lends itself well to most data-intensive analytics jobs run on cloud these days, given its ability to scale-out and leverage several machines to parallel process data. Research has demonstrates that existing approaches to provisioning other applications in the cloud are not immediately relevant to MapReduce -based applications. Provisioning a MapReduce job entails requesting optimum number of resource sets (RS) and configuring MapReduce parameters such that each resource set is maximally utilized.
Each application has a different bottleneck resource (CPU :Disk :Network), and different bottleneck resource utilization, and thus needs to pick a different combination of these parameters based on the job profile such that the bottleneck resource is maximally utilized.
The problem at hand is thus defining a resource provisioning framework for MapReduce jobs running in a cloud keeping in mind performance goals such as Optimal resource utilization with Minimum incurred cost, Lower execution time, Energy Awareness, Automatic handling of node failure and Highly scalable solution.
This document discusses large scale computing with MapReduce. It provides background on the growth of digital data, noting that by 2020 there will be over 5,200 GB of data for every person on Earth. It introduces MapReduce as a programming model for processing large datasets in a distributed manner, describing the key aspects of Map and Reduce functions. Examples of MapReduce jobs are also provided, such as counting URL access frequencies and generating a reverse web link graph.
The document provides an introduction to Apache Hadoop, including:
1) It describes Hadoop's architecture which uses HDFS for distributed storage and MapReduce for distributed processing of large datasets across commodity clusters.
2) It explains that Hadoop solves issues of hardware failure and combining data through replication of data blocks and a simple MapReduce programming model.
3) It gives a brief history of Hadoop originating from Doug Cutting's Nutch project and the influence of Google's papers on distributed file systems and MapReduce.
This document summarizes a proposal to improve fault tolerance in Hadoop clusters. It proposes adding a "Backup" state to store intermediate MapReduce data, so reducers can continue working even if mappers fail. It also proposes a "supernode" protocol where neighboring slave nodes communicate task information. If one node fails, a neighbor can take over its tasks without involving the JobTracker. This would improve fault tolerance by allowing computation to continue locally between nodes after failures.
Hadoop/MapReduce is an open source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce, a programming model where input data is processed by "map" functions in parallel, and results are combined by "reduce" functions, to process and generate outputs from large amounts of data and nodes. The core components are the Hadoop Distributed File System for data storage, and the MapReduce programming model and framework. MapReduce jobs involve mapping data to intermediate key-value pairs, shuffling and sorting the data, and reducing to output results.
Apache Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. The core of Hadoop consists of HDFS for storage and MapReduce for processing. Hadoop has been expanded with additional projects including YARN for job scheduling and resource management, Pig and Hive for SQL-like queries, HBase for column-oriented storage, Zookeeper for coordination, and Ambari for provisioning and managing Hadoop clusters. Hadoop provides scalable and cost-effective solutions for storing and analyzing massive amounts of data.
- Data is a precious resource that can last longer than the systems themselves (Tim Berners-Lee)
- Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It provides reliability, scalability and flexibility.
- Hadoop consists of HDFS for storage and MapReduce for processing. The main nodes include NameNode, DataNodes, JobTracker and TaskTrackers. Tools like Hive, Pig, HBase extend its capabilities for SQL-like queries, data flows and NoSQL access.
Optimal Execution Of MapReduce Jobs In Cloud - Voices 2015Deanna Kosaraju
Optimal Execution Of MapReduce Jobs In Cloud
Anshul Aggarwal, Software Engineer, Cisco Systems
Session Length: 1 Hour
Tue March 10 21:30 PST
Wed March 11 0:30 EST
Wed March 11 4:30:00 UTC
Wed March 11 10:00 IST
Wed March 11 15:30 Sydney
Voices 2015 www.globaltechwomen.com
We use MapReduce programming paradigm because it lends itself well to most data-intensive analytics jobs run on cloud these days, given its ability to scale-out and leverage several machines to parallel process data. Research has demonstrates that existing approaches to provisioning other applications in the cloud are not immediately relevant to MapReduce -based applications. Provisioning a MapReduce job entails requesting optimum number of resource sets (RS) and configuring MapReduce parameters such that each resource set is maximally utilized.
Each application has a different bottleneck resource (CPU :Disk :Network), and different bottleneck resource utilization, and thus needs to pick a different combination of these parameters based on the job profile such that the bottleneck resource is maximally utilized.
The problem at hand is thus defining a resource provisioning framework for MapReduce jobs running in a cloud keeping in mind performance goals such as Optimal resource utilization with Minimum incurred cost, Lower execution time, Energy Awareness, Automatic handling of node failure and Highly scalable solution.
This document discusses large scale computing with MapReduce. It provides background on the growth of digital data, noting that by 2020 there will be over 5,200 GB of data for every person on Earth. It introduces MapReduce as a programming model for processing large datasets in a distributed manner, describing the key aspects of Map and Reduce functions. Examples of MapReduce jobs are also provided, such as counting URL access frequencies and generating a reverse web link graph.
The document provides an introduction to Apache Hadoop, including:
1) It describes Hadoop's architecture which uses HDFS for distributed storage and MapReduce for distributed processing of large datasets across commodity clusters.
2) It explains that Hadoop solves issues of hardware failure and combining data through replication of data blocks and a simple MapReduce programming model.
3) It gives a brief history of Hadoop originating from Doug Cutting's Nutch project and the influence of Google's papers on distributed file systems and MapReduce.
This document summarizes a proposal to improve fault tolerance in Hadoop clusters. It proposes adding a "Backup" state to store intermediate MapReduce data, so reducers can continue working even if mappers fail. It also proposes a "supernode" protocol where neighboring slave nodes communicate task information. If one node fails, a neighbor can take over its tasks without involving the JobTracker. This would improve fault tolerance by allowing computation to continue locally between nodes after failures.
Hadoop/MapReduce is an open source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce, a programming model where input data is processed by "map" functions in parallel, and results are combined by "reduce" functions, to process and generate outputs from large amounts of data and nodes. The core components are the Hadoop Distributed File System for data storage, and the MapReduce programming model and framework. MapReduce jobs involve mapping data to intermediate key-value pairs, shuffling and sorting the data, and reducing to output results.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It has two main components:
1) The Hadoop Distributed File System (HDFS) which stores data reliably across commodity hardware. It divides files into blocks and replicates them for fault tolerance.
2) MapReduce, which processes data in parallel. It handles scheduling, input partitioning, and failover. Users write mapping and reducing functions. Mappers process key-value pairs and output new pairs shuffled to reducers, which combine values for each key.
Hadoop is an open source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses Google's MapReduce programming model and Google File System for reliability. The Hadoop architecture includes a distributed file system (HDFS) that stores data across clusters and a job scheduling and resource management framework (YARN) that allows distributed processing of large datasets in parallel. Key components include the NameNode, DataNodes, ResourceManager and NodeManagers. Hadoop provides reliability through replication of data blocks and automatic recovery from failures.
This document provides an overview of Hadoop, its core components HDFS and MapReduce, and how they work. It discusses that Hadoop is an open-source framework used for storing and processing huge datasets across clusters of commodity hardware. The two core concepts of Hadoop are HDFS for distributed storage and MapReduce for distributed processing. HDFS provides reliable storage with replication and MapReduce allows processing of large datasets in parallel by dividing work across nodes and integrating results.
we are interested in performing Big Data analytics, we need to
learn Hadoop to perform operations with Hadoop MapReduce. In this Presentation, we
will discuss what MapReduce is, why it is necessary, how MapReduce programs can
be developed through Apache Hadoop, and more.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a master-slave architecture with the NameNode as master and DataNodes as slaves. The NameNode manages file system metadata and the DataNodes store data blocks. Hadoop also includes a MapReduce engine where the JobTracker splits jobs into tasks that are processed by TaskTrackers on each node. Hadoop saw early adoption from companies handling big data like Yahoo!, Facebook and Amazon and is now widely used for applications like advertisement targeting, search, and security analytics.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It implements Google's MapReduce programming model and the Hadoop Distributed File System (HDFS) for reliable data storage. Key components include a JobTracker that coordinates jobs, TaskTrackers that run tasks on worker nodes, and a NameNode that manages the HDFS namespace and DataNodes that store application data. The framework provides fault tolerance, parallelization, and scalability.
Apache hadoop, hdfs and map reduce OverviewNisanth Simon
This document provides an overview of Apache Hadoop, HDFS, and MapReduce. It describes how Hadoop uses a distributed file system (HDFS) to store large amounts of data across commodity hardware. It also explains how MapReduce allows distributed processing of that data by allocating map and reduce tasks across nodes. Key components discussed include the HDFS architecture with NameNodes and DataNodes, data replication for fault tolerance, and how the MapReduce engine works with a JobTracker and TaskTrackers to parallelize jobs.
The document provides an overview of Hadoop including what it is, how it works, its architecture and components. Key points include:
- Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers using simple programming models.
- It consists of HDFS for storage and MapReduce for processing via parallel computation using a map and reduce technique.
- HDFS stores data reliably across commodity hardware and MapReduce processes large amounts of data in parallel across nodes in a cluster.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a simple programming model called MapReduce that automatically parallelizes and distributes work across nodes. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and MapReduce execution engine for processing. HDFS stores data as blocks replicated across nodes for fault tolerance. MapReduce jobs are split into map and reduce tasks that process key-value pairs in parallel. Hadoop is well-suited for large-scale data analytics as it scales to petabytes of data and thousands of machines with commodity hardware.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It has four main modules - Hadoop Common, HDFS, YARN and MapReduce. HDFS provides a distributed file system that stores data reliably across commodity hardware. MapReduce is a programming model used to process large amounts of data in parallel. Hadoop architecture uses a master-slave model, with a NameNode master and DataNode slaves. It provides fault tolerance, high throughput access to application data and scales to thousands of machines.
The document discusses fault tolerance in Apache Hadoop. It describes how Hadoop handles failures at different layers through replication and rapid recovery mechanisms. In HDFS, data nodes regularly heartbeat to the name node, and blocks are replicated across racks. The name node tracks block locations and initiates replication if a data node fails. HDFS also supports name node high availability. In MapReduce v1, task and task tracker failures cause re-execution of tasks. YARN improved fault tolerance by removing the job tracker single point of failure.
This presentation will give you Information about :
1.Configuring HDFS
2.Interacting With HDFS
3.HDFS Permissions and Security
4.Additional HDFS Tasks
HDFS Overview and Architecture
5.HDFS Installation
6.Hadoop File System Shell
7.File System Java API
Hadoop is an open-source framework that uses clusters of commodity hardware to store and process big data using the MapReduce programming model. It consists of four main components: MapReduce for distributed processing, HDFS for storage, YARN for resource management and scheduling, and common utilities. HDFS stores large files as blocks across nodes for fault tolerance. MapReduce jobs are split into map and reduce phases to process data in parallel. YARN schedules resources and manages job execution. The common utilities provide libraries and scripts used by all Hadoop components. Major companies use Hadoop to analyze large amounts of data.
This document provides an overview of the Apache Spark framework. It discusses how Spark allows distributed processing of large datasets across computer clusters using simple programming models. It also describes how Spark can scale from single servers to thousands of machines. Spark is designed to provide high availability by detecting and handling failures at the application layer. The document also summarizes Resilient Distributed Datasets (RDDs), which are Spark's fundamental data abstraction, and transformations and actions that can be performed on RDDs.
This presentation provides an overview of Hadoop, including what it is, how it works, its architecture and components, and examples of its use. Hadoop is an open-source software platform for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable and distributed processing of large datasets through its core components - the Hadoop Distributed File System (HDFS) for storage, and MapReduce for processing.
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The document provides an overview of Hadoop, including:
- A brief history of Hadoop and its origins from Google and Apache projects
- An explanation of Hadoop's architecture including HDFS, MapReduce, JobTracker, TaskTracker, and DataNodes
- Examples of how large companies like Yahoo, Facebook, and Amazon use Hadoop for applications like log processing, searches, and advertisement targeting
The document provides an overview of Hadoop, including:
- A brief history of Hadoop and its origins at Google and Yahoo
- An explanation of Hadoop's architecture including HDFS, MapReduce, JobTracker, TaskTracker, and DataNodes
- Examples of how large companies like Facebook and Amazon use Hadoop to process massive amounts of data
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It has two main components:
1) The Hadoop Distributed File System (HDFS) which stores data reliably across commodity hardware. It divides files into blocks and replicates them for fault tolerance.
2) MapReduce, which processes data in parallel. It handles scheduling, input partitioning, and failover. Users write mapping and reducing functions. Mappers process key-value pairs and output new pairs shuffled to reducers, which combine values for each key.
Hadoop is an open source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses Google's MapReduce programming model and Google File System for reliability. The Hadoop architecture includes a distributed file system (HDFS) that stores data across clusters and a job scheduling and resource management framework (YARN) that allows distributed processing of large datasets in parallel. Key components include the NameNode, DataNodes, ResourceManager and NodeManagers. Hadoop provides reliability through replication of data blocks and automatic recovery from failures.
This document provides an overview of Hadoop, its core components HDFS and MapReduce, and how they work. It discusses that Hadoop is an open-source framework used for storing and processing huge datasets across clusters of commodity hardware. The two core concepts of Hadoop are HDFS for distributed storage and MapReduce for distributed processing. HDFS provides reliable storage with replication and MapReduce allows processing of large datasets in parallel by dividing work across nodes and integrating results.
we are interested in performing Big Data analytics, we need to
learn Hadoop to perform operations with Hadoop MapReduce. In this Presentation, we
will discuss what MapReduce is, why it is necessary, how MapReduce programs can
be developed through Apache Hadoop, and more.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a master-slave architecture with the NameNode as master and DataNodes as slaves. The NameNode manages file system metadata and the DataNodes store data blocks. Hadoop also includes a MapReduce engine where the JobTracker splits jobs into tasks that are processed by TaskTrackers on each node. Hadoop saw early adoption from companies handling big data like Yahoo!, Facebook and Amazon and is now widely used for applications like advertisement targeting, search, and security analytics.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It implements Google's MapReduce programming model and the Hadoop Distributed File System (HDFS) for reliable data storage. Key components include a JobTracker that coordinates jobs, TaskTrackers that run tasks on worker nodes, and a NameNode that manages the HDFS namespace and DataNodes that store application data. The framework provides fault tolerance, parallelization, and scalability.
Apache hadoop, hdfs and map reduce OverviewNisanth Simon
This document provides an overview of Apache Hadoop, HDFS, and MapReduce. It describes how Hadoop uses a distributed file system (HDFS) to store large amounts of data across commodity hardware. It also explains how MapReduce allows distributed processing of that data by allocating map and reduce tasks across nodes. Key components discussed include the HDFS architecture with NameNodes and DataNodes, data replication for fault tolerance, and how the MapReduce engine works with a JobTracker and TaskTrackers to parallelize jobs.
The document provides an overview of Hadoop including what it is, how it works, its architecture and components. Key points include:
- Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers using simple programming models.
- It consists of HDFS for storage and MapReduce for processing via parallel computation using a map and reduce technique.
- HDFS stores data reliably across commodity hardware and MapReduce processes large amounts of data in parallel across nodes in a cluster.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a simple programming model called MapReduce that automatically parallelizes and distributes work across nodes. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and MapReduce execution engine for processing. HDFS stores data as blocks replicated across nodes for fault tolerance. MapReduce jobs are split into map and reduce tasks that process key-value pairs in parallel. Hadoop is well-suited for large-scale data analytics as it scales to petabytes of data and thousands of machines with commodity hardware.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It has four main modules - Hadoop Common, HDFS, YARN and MapReduce. HDFS provides a distributed file system that stores data reliably across commodity hardware. MapReduce is a programming model used to process large amounts of data in parallel. Hadoop architecture uses a master-slave model, with a NameNode master and DataNode slaves. It provides fault tolerance, high throughput access to application data and scales to thousands of machines.
The document discusses fault tolerance in Apache Hadoop. It describes how Hadoop handles failures at different layers through replication and rapid recovery mechanisms. In HDFS, data nodes regularly heartbeat to the name node, and blocks are replicated across racks. The name node tracks block locations and initiates replication if a data node fails. HDFS also supports name node high availability. In MapReduce v1, task and task tracker failures cause re-execution of tasks. YARN improved fault tolerance by removing the job tracker single point of failure.
This presentation will give you Information about :
1.Configuring HDFS
2.Interacting With HDFS
3.HDFS Permissions and Security
4.Additional HDFS Tasks
HDFS Overview and Architecture
5.HDFS Installation
6.Hadoop File System Shell
7.File System Java API
Hadoop is an open-source framework that uses clusters of commodity hardware to store and process big data using the MapReduce programming model. It consists of four main components: MapReduce for distributed processing, HDFS for storage, YARN for resource management and scheduling, and common utilities. HDFS stores large files as blocks across nodes for fault tolerance. MapReduce jobs are split into map and reduce phases to process data in parallel. YARN schedules resources and manages job execution. The common utilities provide libraries and scripts used by all Hadoop components. Major companies use Hadoop to analyze large amounts of data.
This document provides an overview of the Apache Spark framework. It discusses how Spark allows distributed processing of large datasets across computer clusters using simple programming models. It also describes how Spark can scale from single servers to thousands of machines. Spark is designed to provide high availability by detecting and handling failures at the application layer. The document also summarizes Resilient Distributed Datasets (RDDs), which are Spark's fundamental data abstraction, and transformations and actions that can be performed on RDDs.
This presentation provides an overview of Hadoop, including what it is, how it works, its architecture and components, and examples of its use. Hadoop is an open-source software platform for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable and distributed processing of large datasets through its core components - the Hadoop Distributed File System (HDFS) for storage, and MapReduce for processing.
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The document provides an overview of Hadoop, including:
- A brief history of Hadoop and its origins from Google and Apache projects
- An explanation of Hadoop's architecture including HDFS, MapReduce, JobTracker, TaskTracker, and DataNodes
- Examples of how large companies like Yahoo, Facebook, and Amazon use Hadoop for applications like log processing, searches, and advertisement targeting
The document provides an overview of Hadoop, including:
- A brief history of Hadoop and its origins at Google and Yahoo
- An explanation of Hadoop's architecture including HDFS, MapReduce, JobTracker, TaskTracker, and DataNodes
- Examples of how large companies like Facebook and Amazon use Hadoop to process massive amounts of data
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Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
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An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
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2. Hadoop Mapreduce paradigm
• Hadoop is an open-source software framework
for storing and processing large datasets ranging
in size from gigabytes to petabytes.
• developed at the Apache Software Foundation.
• basically two components in Hadoop:
1. Massive data storage
2. Faster data processing
2
3. Hadoop Mapreduce paradigm
• Hadoop distributed File System (HDFS):
• It allows you to store data of various formats
across a cluster.
• Map-Reduce:
• For resource management in Hadoop. It allows
parallel processing over the data stored across
HDFS.
3
13. Map tasks
• Process independent chunks in a parallel manner
• Out of map task stored as intermediate data on
local disk of that server
13
• Out of mapper automatically shuffled and stored
by framework
• Sorts the output based on key
• Provide reduced output by combining the output
f various mappers
Reduce task
16. JobTracker
• Master daemon
• Single JobTracker per Hadoop cluster
• Provide connectivity between Hadoop and client
application
• Execution plan creation(which task to assign to
which node)
• Monitor all running tasks
• If task failed then rescheduling
16
17. Task Tracker
• Responsible for executing individual task which
is assigned by JobTracker
• Single Task Tracker per slave
• Continuously sends heartbeat message to Job
Tracker
• If no heartbeat message then task will be
allocated to other Task Trackers
17
19. Mapper
• Mapper maps the input key-value pairs into a set of
intermediate key-value pairs
• Phases:
1. RecordReader:
• Converts tasks with key value pairs
• <Key , value> <positional information, chunk of
data that constitutes the record>
2. Map:
• generate zero or more intermediate key-value pairs
19
20. 3. Combiner
• Optimization technique for mapreduce job,
applies user specific aggregate function to only
that mapper
• Also known as Local reducer
4. Partitioner
• Intermediate key-value pairs
• Usually Number of partitions are equal to the
number of reducer
20
Mapper
21. Reducer
1. Shuffle and sort:
• consumes the output of Mapping phase
• consolidate the relevant records from Mapping
phase output.
• the same words are clubbed together along with
their respective frequency.
21
22. Reducer
2. Reducer:
• Grouped data produced by the shuffle and sort phase
• Apply reduce function
• Process one group at a time
• Reducer function iterate all the values associated with that key
• Aggregation, filtering,combining
22
3. Output format:
• Separates key value pair with tab
• Write it out to a file using record writer
24. API
• Main Class file Packages
• Mapper Class Packages
• Reducer Class Packages
24
25. Main class file packages
25
• import org.apache.hadoop.conf.Configured; (Configuration of system parameters)
• import org.apache.hadoop.fs.Path; (Configuration of file system path)
• import org.apache.hadoop.io.IntWritable; (Input/output package to display in output screen)
• import org.apache.hadoop.io.Text; ( to read and write the text)
• import org.apache.hadoop.mapred.FileInputFormat; ( MapRed file input format)
• import org.apache.hadoop.mapred.FileOutputFormat; ; ( MapRed file output format)
• import org.apache.hadoop.mapred.JobClient; ( assign the input job and process)
• import org.apache.hadoop.mapred.JobConf; (configuration file to execute I/O process)
26. • import org.apache.hadoop.util.Tool; (interface
(command line options) used to access MapRed
functions)
• import org.apache.hadoop.util.ToolRunner;
( Interface use to call run function)
26
27. Mapper File Packages
• import java.io.IOException; ( Exception handle)
• import org.apache.hadoop.io.IntWritable; ( to read the integer file)
• import org.apache.hadoop.io.LongWritable; (to read files range exceeding integer)
• import org.apache.hadoop.io.Text; (Input and output text)
• import org.apache.hadoop.mapred.MapReduceBase;( Inherited class of MapReduce functions)
• import org.apache.hadoop.mapred.Mapper; (Mapper Class)
• import org.apache.hadoop.mapred.OutputCollector; ( to collect and display class)
• import org.apache.hadoop.mapred.Reporter; (to display the information)
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28. Reducer file Package
• import java.io.IOException; ( Exception handle)
• import java.util.Iterator; (to call utility function has more elements from iterator class)
• import org.apache.hadoop.io.IntWritable; ( to read the integer file)
• import org.apache.hadoop.io.Text; (Input and output text)
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29. Reducer file Package
• import org.apache.hadoop.mapred.MapReduceBase; ( Inherited class of
MapReduce functions)
• import org.apache.hadoop.mapred.OutputCollector; ( to collect and
display class)
• import org.apache.hadoop.mapred.Reducer; (Reducer Class)
• import org.apache.hadoop.mapred.Reporter; (to display the
information)
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30. Hadoop 2.0 features
• HDFS Federation – horizontal scalability of
NameNode
• NameNode High Availability – NameNode is no
longer a Single Point of Failure
• YARN – ability to process Terabytes and
Petabytes of data available in HDFS using Non-
MapReduce applications such as MPI, GIRAPH
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31. Hadoop 2.0 features
• Resource Manager – splits up the two major
functionalities of overburdened JobTracker
(resource management and job
scheduling/monitoring) into two separate
daemons: a global Resource Manager and per-
application ApplicationMaster
• Capacity Scheduler
• Data Snapshot
• Support for Windows
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32. Namenode high availability
• Hadoop 1.x, NameNode was single point of failure
• Hadoop Administrators need to manually recover
the NameNode using Secondary NameNode.
• Hadoop 2.0 Architecture supports multiple
NameNodes to remove this bottleneck
• Passive Standby NameNode support.
• In case of Active NameNode failure, the passive
NameNode becomes the Active NameNode and
starts writing to the shared storage
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33. YARN(Yet Another Resource Negotiator)
• Main idea is splitting the JobTracker
responsibility of resource management and Job
scheduling into separate daemons.
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34. YARN daemons
1. Global resource manager:
a) Scheduler(allocation of resources among
various running applications)
b) Application manager(Accepting job
submission, restarting application master in
case of failure)
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35. YARN daemons
2. Node manager:
• Pre machine slave daemon
• Launching application container for application
execution
• Report usage of resources to the global resource
manager
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36. YARN daemons
3. Application master:
• Application specific entity
• Negotiate required resources for execution from
the resource manager
• Works with node manager for executing and
monitoring component tasks
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38. YARN workflow
1. Client submits an application
2. The Resource Manager allocates a container to start the
Application Manager
3. The Application Manager registers itself with the Resource
Manager
4. The Application Manager negotiates containers from the Resource
Manager
5. The Application Manager notifies the Node Manager to launch
containers
6. Application code is executed in the container
7. Client contacts Resource Manager/Application Manager to
monitor application’s status
8. Once the processing is complete, the Application Manager un-
registers with the Resource Manager
38