Doug Cutting created Apache Hadoop in 2005 after naming it after his son's stuffed elephant "Hadoop". Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It consists of modules for distributed file system (HDFS), resource management (YARN), and distributed processing (MapReduce). HDFS stores large files across nodes and provides high throughput even if nodes fail, while MapReduce allows parallel processing of large datasets using a map and reduce model.
Hadoop Institutes : kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
Accompanying slides for the class “Introduction to Hadoop” at the PRACE Autumn school 2020 - HPC and FAIR Big Data organized by the faculty of Mechanical Engineering of the University of Ljubljana (Slovenia).
Hadoop Institutes : kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
Accompanying slides for the class “Introduction to Hadoop” at the PRACE Autumn school 2020 - HPC and FAIR Big Data organized by the faculty of Mechanical Engineering of the University of Ljubljana (Slovenia).
These slides cover the very basics of Hadoop architecture, in particular HDFS. This was my presentation in the first Delhi Hadoop User Group (DHUG) meetup held at Gurgaon on 10th September 2011. Loved the positive feedback. I'll also upload a more elaborate version covering Hadoop mapreduce architecture as well soon. Most of the stuff covered in these slides can be found in Tom White's book as well (See the last slide)
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Hadoop Interview Questions and Answers by rohit kapakapa rohit
Hadoop Interview Questions and Answers - More than 130 real time questions and answers covering hadoop hdfs,mapreduce and administrative concepts by rohit kapa
There's a big shift in both at the architecture and api level from Hadoop 1 vs Hadoop 2, particularly YARN and we had our first meetup to talk about this (http://www.meetup.com/Atlanta-YARN-User-Group/) on 10/13/2013.
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
More about Hadoop
www.beinghadoop.com
https://www.facebook.com/hadoopinfo
This PPT Gives information about
Complete Hadoop Architecture and
information about
how user request is processed in Hadoop?
About Namenode
Datanode
jobtracker
tasktracker
Hadoop installation Post Configurations
Modern applications, often called “big-data” analysis, require us to manage immense amounts of data quickly. To deal with applications such as these, a new software stack has evolved.
These slides cover the very basics of Hadoop architecture, in particular HDFS. This was my presentation in the first Delhi Hadoop User Group (DHUG) meetup held at Gurgaon on 10th September 2011. Loved the positive feedback. I'll also upload a more elaborate version covering Hadoop mapreduce architecture as well soon. Most of the stuff covered in these slides can be found in Tom White's book as well (See the last slide)
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learn data analytics online;
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tools that can be used to analyse data;
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basic data analytics;
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tools of data analysis;
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statistical analysis software;
tools to analyse data;
online data analysis;
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how to do data analytics;
statistical data analysis tools;
data analyst tools;
business data analysis;
tools and techniques of data analysis;
education data analysis;
advanced data analytics;
study data analysis;
spreadsheet data analysis;
learn data analysis in excel;
software for data analysis;
shared data warehouse;
what are data analysis tools;
data analytics and statistics;
data analyse;
analysis courses;
data analysis tools for research;
research data analysis tools;
big data analysis;
data mining programs;
applications of data analytics;
data analysis tools and techniques;
Hadoop Interview Questions and Answers by rohit kapakapa rohit
Hadoop Interview Questions and Answers - More than 130 real time questions and answers covering hadoop hdfs,mapreduce and administrative concepts by rohit kapa
There's a big shift in both at the architecture and api level from Hadoop 1 vs Hadoop 2, particularly YARN and we had our first meetup to talk about this (http://www.meetup.com/Atlanta-YARN-User-Group/) on 10/13/2013.
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
More about Hadoop
www.beinghadoop.com
https://www.facebook.com/hadoopinfo
This PPT Gives information about
Complete Hadoop Architecture and
information about
how user request is processed in Hadoop?
About Namenode
Datanode
jobtracker
tasktracker
Hadoop installation Post Configurations
Modern applications, often called “big-data” analysis, require us to manage immense amounts of data quickly. To deal with applications such as these, a new software stack has evolved.
Harnessing Hadoop: Understanding the Big Data Processing Options for Optimizi...Cognizant
A guide to using Apache Hadoop as your open source big data platform of choice, including the vendors that make various Hadoop flavors, related open source tools, Hadoop capabilities and suitable applications.
The data management industry has matured over the last three decades, primarily based on relational database management system(RDBMS) technology. Since the amount of data collected, and analyzed in enterprises has increased several folds in volume, variety and velocityof generation and consumption, organisations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. In this paper we will trace the origin of new class of system called Hadoop to handle Big data.
Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications. A lot of challenges such as capture, curation, storage, search, sharing, analysis, and visualization can be encountered while handling Big Data. On the other hand the Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Big Data certification is one of the most recognized credentials of today.
For more details Click http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
This presentation provides a comprehensive introduction to the Hadoop Distributed System, a powerful and widely used framework for distributed storage and processing of large-scale data. Hadoop has revolutionized the way organizations manage and analyze data, making it a crucial tool in the field of big data and data analytics.
In this presentation, we explore the key components and features of Hadoop, shedding light on the fundamental building blocks that enable its exceptional data processing capabilities. We cover essential topics, including the Hadoop Distributed File System (HDFS), MapReduce, YARN (Yet Another Resource Negotiator), and Hadoop Ecosystem components like Hive, Pig, and Spark.
Asserting that Big Data is vital to business is an understatement. Organizations have generated more and more data for years, but struggle to use it effectively. Clearly Big Data has more important uses than ensuring compliance with regulatory requirements. In addition, data is being generated with greater velocity, due to the advent of new pervasive devices (e.g., smartphones, tablets, etc.), social Web sites (e.g., Facebook, Twitter, LinkedIn, etc.) and other sources like GPS, Google Maps, heat/pressure sensors, etc.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Big data overview of apache hadoop
1. Downloaded from: justpaste.it/50l53
Big Data: Overview of apache Hadoop
Apache Hadoop is an open-source software framework for storage and large scale processing of data-sets on
clusters of commodity hardware. Hadoop is an Apache top-level project being built and used by a global
community of contributors and users. It is licensed under the Apache License 2.0.
Hadoop was created by Doug Cutting and Mike Cafarella in 2005. Cutting, who was working at Yahoo! at the time,
named it after his son's toy elephant. It was originally developed to support distribution for the Nutch search engine
project. No one knows that better than Doug Cutting, chief architect of Cloudera and one of the curious story
behind Hadoop. When he was creating the open source software that supports the processing of large data sets,
Cutting knew the project would need a good name. Cutting's son, then 2, was just beginning to talk and called his
beloved stuffed yellow elephant "Hadoop" (with the stress on the first syllable). Fortunately, he had one up his
sleeve—thanks to his son. The son (who's now 12) frustrated with this. He's always saying 'Why don't you say my
name, and why don't I get royalties? I deserve to be famous for this :)
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The Apache Hadoop framework is composed of the following modules :
1] Hadoop Common - contains libraries and utilities needed by other Hadoop modules
2] Hadoop Distributed File System (HDFS) - a distributed file-system that stores data on the commodity
machines, providing very high aggregate bandwidth across the cluster.
3] Hadoop YARN - a resource-management platform responsible for managing compute resources in clusters and
using them for scheduling of users' applications.
4] Hadoop MapReduce - a programming model for large scale data processing.
All the modules in Hadoop are designed with a fundamental assumption that hardware failures (of individual
machines, or racks of machines) are common and thus should be automatically handled in software by the
framework. Apache Hadoop's MapReduce and HDFS components originally derived respectively from Google's
MapReduce and Google File System (GFS) papers.
Beyond HDFS, YARN and MapReduce, the entire Apache Hadoop “platform” is now commonly considered to
consist of a number of related projects as well – Apache Pig, Apache Hive, Apache HBase, and others
2. For the end-users, though MapReduce Java code is common, any programming language can be used with
"Hadoop Streaming" to implement the "map" and "reduce" parts of the user's program. Apache Pig, Apache Hive
among other related projects expose higher level user interfaces like Pig latin and a SQL variant respectively. The
Hadoop framework itself is mostly written in the Java programming language, with some native code in C and
command line utilities written as shell-scripts.
HDFS & MapReduce :
There are two primary components at the core of Apache Hadoop 1.x : the Hadoop Distributed File System
(HDFS) and the MapReduce parallel processing framework. These open source projects, inspired by technologies
created inside Google.
Hadoop distributed file system :
The Hadoop distributed file system (HDFS) is a distributed, scalable, and portable file-system written in Java for
the Hadoop framework. Each node in a Hadoop instance typically has a single namenode; a cluster of datanodes
form the HDFS cluster. The situation is typical because each node does not require a datanode to be present.
Each datanode serves up blocks of data over the network using a block protocol specific to HDFS. The file system
uses the TCP/IP layer for communication. Clients use Remote procedure call (RPC) to communicate between
each other.
HDFS stores large files (typically in the range of gigabytes to terabytes) across multiple machines. It achieves
reliability by replicating the data across multiple hosts, and hence does not require RAID storage on hosts. With
3. the default replication value, 3, data is stored on three nodes: two on the same rack, and one on a different rack.
Data nodes can talk to each other to rebalance data, to move copies around, and to keep the replication of data
high. HDFS is not fully POSIX-compliant, because the requirements for a POSIX file-system differ from the target
goals for a Hadoop application. The tradeoff of not having a fully POSIX-compliant file-system is increased
performance for data throughput and support for non-POSIX operations such as Append.
HDFS added the high-availability capabilities for release 2.x allowing the main metadata server (the NameNode)
to be failed over manually to a backup in the event of failure- automatic fail-over.
The HDFS file system includes a so-called secondary namenode, which misleads some people into thinking that
when the primary namenode goes offline, the secondary namenode takes over. In fact, the secondary namenode
regularly connects with the primary namenode and builds snapshots of the primary namenode's directory
information, which the system then saves to local or remote directories. These checkpointed images can be used
to restart a failed primary namenode without having to replay the entire journal of file-system actions, then to edit
the log to create an up-to-date directory structure. Because the namenode is the single point for storage and
management of metadata, it can become a bottleneck for supporting a huge number of files, especially a large
number of small files. HDFS Federation, a new addition, aims to tackle this problem to a certain extent by allowing
multiple name-spaces served by separate namenodes.
An advantage of using HDFS is data awareness between the job tracker and task tracker. The job tracker
schedules map or reduce jobs to task trackers with an awareness of the data location. For example: if node A
contains data (x,y,z) and node B contains data (a,b,c), the job tracker schedules node B to perform map or reduce
tasks on (a,b,c) and node A would be scheduled to perform map or reduce tasks on (x,y,z). This reduces the
amount of traffic that goes over the network and prevents unnecessary data transfer. When Hadoop is used with
other file systems this advantage is not always available. This can have a significant impact on job-completion
times, which has been demonstrated when running data-intensive jobs.HDFS was designed for mostly immutable
files and may not be suitable for systems requiring concurrent write-operations.
Another limitation of HDFS is that it cannot be mounted directly by an existing operating system. Getting data into
and out of the HDFS file system, an action that often needs to be performed before and after executing a job, can
be inconvenient. A Filesystem in Userspace (FUSE) virtual file system has been developed to address this
problem, at least for Linux and some other Unix systems.
File access can be achieved through the native Java API, the Thrift API to generate a client in the language of the
users' choosing (C++, Java, Python, PHP, Ruby, Erlang, Perl, Haskell, C#, Cocoa, Smalltalk, and OCaml), the
command-line interface, or browsed through the HDFS-UI webapp over HTTP.
JobTracker and TaskTracker: the MapReduce engine:
Above the file systems comes the MapReduce engine, which consists of one JobTracker, to which client
applications submit MapReduce jobs. The JobTracker pushes work out to available TaskTracker nodes in the
4. cluster, striving to keep the work as close to the data as possible. With a rack-aware file system, the JobTracker
knows which node contains the data, and which other machines are nearby. If the work cannot be hosted on the
actual node where the data resides, priority is given to nodes in the same rack. This reduces network traffic on the
main backbone network. If a TaskTracker fails or times out, that part of the job is rescheduled. The TaskTracker on
each node spawns off a separate Java Virtual Machine process to prevent the TaskTracker itself from failing if the
running job crashes the JVM. A heartbeat is sent from the TaskTracker to the JobTracker every few minutes to
check its status. The Job Tracker and TaskTracker status and information is exposed by Jetty and can be viewed
from a web browser.
Hadoop 1.x MapReduce System is composed of the JobTracker, which is the master, and the per-
node slaves- TaskTrackers
If the JobTracker failed on Hadoop 0.20 or earlier, all ongoing work was lost. Hadoop version 0.21 added some
checkpointing to this process; the JobTracker records what it is up to in the file system. When a JobTracker starts
up, it looks for any such data, so that it can restart work from where it left off.
Known limitations of this approach in Hadoop 1.x are:
The allocation of work to TaskTrackers is very simple. Every TaskTracker has a number of available slots (such as
"4 slots"). Every active map or reduce task takes up one slot. The Job Tracker allocates work to the tracker nearest
to the data with an available slot. There is no consideration of the current system load of the allocated machine,
and hence its actual availability.If one TaskTracker is very slow, it can delay the entire MapReduce job - especially
towards the end of a job, where everything can end up waiting for the slowest task. With speculative execution
enabled, however, a single task can be executed on multiple slave nodes.
Apache Hadoop NextGen MapReduce (YARN):
MapReduce has undergone a complete overhaul in hadoop-0.23 and we now have, what we call, MapReduce 2.0
(MRv2) or YARN
Apache™ Hadoop® YARN is a sub-project of Hadoop at the Apache Software Foundation introduced in Hadoop
2.0 that separates the resource management and processing components. YARN was born of a need to enable a
broader array of interaction patterns for data stored in HDFS beyond MapReduce. The YARN-based architecture
of Hadoop 2.0 provides a more general processing platform that is not constrained to MapReduce.
5. Architectural view of YARN
The fundamental idea of MRv2 is to split up the two major functionalities of the JobTracker, resource management
and job scheduling/monitoring, into separate daemons. The idea is to have a global ResourceManager (RM) and
per-application ApplicationMaster (AM). An application is either a single job in the classical sense of Map-Reduce
jobs or a DAG of jobs.
The ResourceManager and per-node slave, the NodeManager (NM), form the data-computation framework. The
ResourceManager is the ultimate authority that arbitrates resources among all the applications in the system.
The per-application ApplicationMaster is, in effect, a framework specific library and is tasked with negotiating
resources from the ResourceManager and working with the NodeManager(s) to execute and monitor the tasks.
Overview of Hadoop1.0 and Hadopp2.0
As part of Hadoop 2.0, YARN takes the resource management capabilities that were in MapReduce and packages
them so they can be used by new engines. This also streamlines MapReduce to do what it does best, process
data. With YARN, you can now run multiple applications in Hadoop, all sharing a common resource management.
Many organizations are already building applications on YARN in order to bring them IN to Hadoop.
A next-generation framework for Hadoop data processing
As part of Hadoop 2.0, YARN takes the resource management capabilities that were in MapReduce and packages
them so they can be used by new engines. This also streamlines MapReduce to do what it does best, process
data. With YARN, you can now run multiple applications in Hadoop, all sharing a common resource management.
Many organizations are already building applications on YARN in order to bring them IN to Hadoop.When
enterprise data is made available in HDFS, it is important to have multiple ways to process that data. With Hadoop
2.0 and YARN organizations can use Hadoop for streaming, interactive and a world of other Hadoop based
applications.
6. What YARN Does
YARN enhances the power of a Hadoop compute cluster in the following ways:
Scalability The processing power in data centers continues to grow quickly. Because YARN
ResourceManager focuses exclusively on scheduling, it can manage those larger clusters much more
easily.
Compatibility with MapReduce Existing MapReduce applications and users can run on top of YARN
without disruption to their existing processes.
Improved cluster utilization. The ResourceManager is a pure scheduler that optimizes cluster utilization
according to criteria such as capacity guarantees, fairness, and SLAs. Also, unlike before, there are no
named map and reduce slots, which helps to better utilize cluster resources.
Support for workloads other than MapReduceAdditional programming models such as graph processing
and iterative modeling are now possible for data processing. These added models allow enterprises to
realize near real-time processing and increased ROI on their Hadoop investments.
AgilityWith MapReduce becoming a user-land library, it can evolve independently of the underlying
resource manager layer and in a much more agile manner.
How YARN Works
The fundamental idea of YARN is to split up the two major responsibilities of the JobTracker/TaskTracker into
separate entities
a global ResourceManager
a per-application ApplicationMaster.
a per-node slave NodeManager and
a per-application Container running on a NodeManager
The ResourceManager and the NodeManager form the new, and generic, system for managing applications in a
distributed manner. The ResourceManager is the ultimate authority that arbitrates resources among all the
applications in the system. The per-application ApplicationMaster is a framework-specific entity and is tasked with
negotiating resources from the ResourceManager and working with the NodeManager(s) to execute and monitor
the component tasks. The ResourceManager has a scheduler, which is responsible for allocating resources to the
various running applications, according to constraints such as queue capacities, user-limits etc. The scheduler
performs its scheduling function based on the resource requirements of the applications. The NodeManager is the
per-machine slave, which is responsible for launching the applications’ containers, monitoring their resource usage
(cpu, memory, disk, network) and reporting the same to the ResourceManager. Each ApplicationMaster has the
responsibility of negotiating appropriate resource containers from the scheduler, tracking their status, and
monitoring their progress. From the system perspective, the ApplicationMaster runs as a normal container
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