The document discusses big data and distributed computing. It provides examples of the large amounts of data generated daily by organizations like the New York Stock Exchange and Facebook. It explains how distributed computing frameworks like Hadoop use multiple computers connected via a network to process large datasets in parallel. Hadoop's MapReduce programming model and HDFS distributed file system allow users to write distributed applications that process petabytes of data across commodity hardware clusters.
Hadoop is the popular open source like Facebook, Twitter, RFID readers, sensors, and implementation of MapReduce, a powerful tool so on.Your management wants to derive designed for deep analysis and transformation of information from both the relational data and thevery large data sets. Hadoop enables you to unstructuredexplore complex data, using custom analyses data, and wants this information as soon astailored to your information and questions. possible.Hadoop is the system that allows unstructured What should you do? Hadoop may be the answer!data to be distributed across hundreds or Hadoop is an open source project of the Apachethousands of machines forming shared nothing Foundation.clusters, and the execution of Map/Reduce It is a framework written in Java originallyroutines to run on the data in that cluster. Hadoop developed by Doug Cutting who named it after hishas its own filesystem which replicates data to sons toy elephant.multiple nodes to ensure if one node holding data Hadoop uses Google’s MapReduce and Google Filegoes down, there are at least 2 other nodes from System technologies as its foundation.which to retrieve that piece of information. This It is optimized to handle massive quantities of dataprotects the data availability from node failure, which could be structured, unstructured orsomething which is critical when there are many semi-structured, using commodity hardware, thatnodes in a cluster (aka RAID at a server level). is, relatively inexpensive computers. This massive parallel processing is done with greatWhat is Hadoop? performance. However, it is a batch operation handling massive quantities of data, so theThe data are stored in a relational database in your response time is not immediate.desktop computer and this desktop computer As of Hadoop version 0.20.2, updates are nothas no problem handling this load. possible, but appends will be possible starting inThen your company starts growing very quickly, version 0.21.and that data grows to 10GB. Hadoop replicates its data across differentAnd then 100GB. computers, so that if one goes down, the data areAnd you start to reach the limits of your current processed on one of the replicated computers.desktop computer. Hadoop is not suitable for OnLine Transaction So you scale-up by investing in a larger computer, Processing workloads where data are randomly and you are then OK for a few more months. accessed on structured data like a relational When your data grows to 10TB, and then 100TB. database.Hadoop is not suitable for OnLineAnd you are fast approaching the limits of that Analytical Processing or Decision Support Systemcomputer. workloads where data are sequentially accessed onMoreover, you are now asked to feed your structured data like a relational database, to application with unstructured data coming from generate reports that provide business sources intelligence. Hadoop is used for Big Data. It complements OnLine Transaction Processing and OnLine Analytical Pro
Hadoop is the popular open source like Facebook, Twitter, RFID readers, sensors, and implementation of MapReduce, a powerful tool so on.Your management wants to derive designed for deep analysis and transformation of information from both the relational data and thevery large data sets. Hadoop enables you to unstructuredexplore complex data, using custom analyses data, and wants this information as soon astailored to your information and questions. possible.Hadoop is the system that allows unstructured What should you do? Hadoop may be the answer!data to be distributed across hundreds or Hadoop is an open source project of the Apachethousands of machines forming shared nothing Foundation.clusters, and the execution of Map/Reduce It is a framework written in Java originallyroutines to run on the data in that cluster. Hadoop developed by Doug Cutting who named it after hishas its own filesystem which replicates data to sons toy elephant.multiple nodes to ensure if one node holding data Hadoop uses Google’s MapReduce and Google Filegoes down, there are at least 2 other nodes from System technologies as its foundation.which to retrieve that piece of information. This It is optimized to handle massive quantities of dataprotects the data availability from node failure, which could be structured, unstructured orsomething which is critical when there are many semi-structured, using commodity hardware, thatnodes in a cluster (aka RAID at a server level). is, relatively inexpensive computers. This massive parallel processing is done with greatWhat is Hadoop? performance. However, it is a batch operation handling massive quantities of data, so theThe data are stored in a relational database in your response time is not immediate.desktop computer and this desktop computer As of Hadoop version 0.20.2, updates are nothas no problem handling this load. possible, but appends will be possible starting inThen your company starts growing very quickly, version 0.21.and that data grows to 10GB. Hadoop replicates its data across differentAnd then 100GB. computers, so that if one goes down, the data areAnd you start to reach the limits of your current processed on one of the replicated computers.desktop computer. Hadoop is not suitable for OnLine Transaction So you scale-up by investing in a larger computer, Processing workloads where data are randomly and you are then OK for a few more months. accessed on structured data like a relational When your data grows to 10TB, and then 100TB. database.Hadoop is not suitable for OnLineAnd you are fast approaching the limits of that Analytical Processing or Decision Support Systemcomputer. workloads where data are sequentially accessed onMoreover, you are now asked to feed your structured data like a relational database, to application with unstructured data coming from generate reports that provide business sources intelligence. Hadoop is used for Big Data. It complements OnLine Transaction Processing and OnLine Analytical Pro
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
This Hadoop will help you understand the different tools present in the Hadoop ecosystem. This Hadoop video will take you through an overview of the important tools of Hadoop ecosystem which include Hadoop HDFS, Hadoop Pig, Hadoop Yarn, Hadoop Hive, Apache Spark, Mahout, Apache Kafka, Storm, Sqoop, Apache Ranger, Oozie and also discuss the architecture of these tools. It will cover the different tasks of Hadoop such as data storage, data processing, cluster resource management, data ingestion, machine learning, streaming and more. Now, let us get started and understand each of these tools in detail.
Below topics are explained in this Hadoop ecosystem presentation:
1. What is Hadoop ecosystem?
1. Pig (Scripting)
2. Hive (SQL queries)
3. Apache Spark (Real-time data analysis)
4. Mahout (Machine learning)
5. Apache Ambari (Management and monitoring)
6. Kafka & Storm
7. Apache Ranger & Apache Knox (Security)
8. Oozie (Workflow system)
9. Hadoop MapReduce (Data processing)
10. Hadoop Yarn (Cluster resource management)
11. Hadoop HDFS (Data storage)
12. Sqoop & Flume (Data collection and ingestion)
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart 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. Learn Spark SQL, creating, transforming, and querying Data frames
14. Understand the common use-cases of Spark and the various interactive algorithms
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
Introduction To Hadoop | What Is Hadoop And Big Data | Hadoop Tutorial For Be...Simplilearn
This presentation about Hadoop will help you learn the basics of Hadoop and its components. First, you will see what is Big Data and the significant challenges in it. Then, you will understand how Hadoop solved those challenges. You will have a glance at the History of Hadoop, what is Hadoop, the different companies using Hadoop, the applications of Hadoop in different companies, etc. Finally, you will learn the three essential components of Hadoop – HDFS, MapReduce, and YARN, along with their architecture. Now, let us get started with Introduction to Hadoop.
Below topics are explained in this Hadoop presentation:
1. Big Data and its challenges
2. Hadoop as a solution
3. History of Hadoop
4. What is Hadoop
5. Applications of Hadoop
6. Components of Hadoop
7. Hadoop Distributed File System
8. Hadoop MapReduce
9. Hadoop 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 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/introduction-to-big-data-and-hadoop-certification-training.
Introduction to Big Data & Hadoop Architecture - Module 1Rohit Agrawal
Learning Objectives - In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem; How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
this presentation describes the company from where I did my summer training and what is bigdata why we use big data, big data challenges, the issue in big data, the solution of big data issues, hadoop, docker , Ansible etc.
introduction to data processing using Hadoop and PigRicardo Varela
In this talk we make an introduction to data processing with big data and review the basic concepts in MapReduce programming with Hadoop. We also comment about the use of Pig to simplify the development of data processing applications
YDN Tuesdays are geek meetups organized the first Tuesday of each month by YDN in London
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
This Hadoop will help you understand the different tools present in the Hadoop ecosystem. This Hadoop video will take you through an overview of the important tools of Hadoop ecosystem which include Hadoop HDFS, Hadoop Pig, Hadoop Yarn, Hadoop Hive, Apache Spark, Mahout, Apache Kafka, Storm, Sqoop, Apache Ranger, Oozie and also discuss the architecture of these tools. It will cover the different tasks of Hadoop such as data storage, data processing, cluster resource management, data ingestion, machine learning, streaming and more. Now, let us get started and understand each of these tools in detail.
Below topics are explained in this Hadoop ecosystem presentation:
1. What is Hadoop ecosystem?
1. Pig (Scripting)
2. Hive (SQL queries)
3. Apache Spark (Real-time data analysis)
4. Mahout (Machine learning)
5. Apache Ambari (Management and monitoring)
6. Kafka & Storm
7. Apache Ranger & Apache Knox (Security)
8. Oozie (Workflow system)
9. Hadoop MapReduce (Data processing)
10. Hadoop Yarn (Cluster resource management)
11. Hadoop HDFS (Data storage)
12. Sqoop & Flume (Data collection and ingestion)
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart 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. Learn Spark SQL, creating, transforming, and querying Data frames
14. Understand the common use-cases of Spark and the various interactive algorithms
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
Introduction To Hadoop | What Is Hadoop And Big Data | Hadoop Tutorial For Be...Simplilearn
This presentation about Hadoop will help you learn the basics of Hadoop and its components. First, you will see what is Big Data and the significant challenges in it. Then, you will understand how Hadoop solved those challenges. You will have a glance at the History of Hadoop, what is Hadoop, the different companies using Hadoop, the applications of Hadoop in different companies, etc. Finally, you will learn the three essential components of Hadoop – HDFS, MapReduce, and YARN, along with their architecture. Now, let us get started with Introduction to Hadoop.
Below topics are explained in this Hadoop presentation:
1. Big Data and its challenges
2. Hadoop as a solution
3. History of Hadoop
4. What is Hadoop
5. Applications of Hadoop
6. Components of Hadoop
7. Hadoop Distributed File System
8. Hadoop MapReduce
9. Hadoop 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 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/introduction-to-big-data-and-hadoop-certification-training.
Introduction to Big Data & Hadoop Architecture - Module 1Rohit Agrawal
Learning Objectives - In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem; How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
this presentation describes the company from where I did my summer training and what is bigdata why we use big data, big data challenges, the issue in big data, the solution of big data issues, hadoop, docker , Ansible etc.
introduction to data processing using Hadoop and PigRicardo Varela
In this talk we make an introduction to data processing with big data and review the basic concepts in MapReduce programming with Hadoop. We also comment about the use of Pig to simplify the development of data processing applications
YDN Tuesdays are geek meetups organized the first Tuesday of each month by YDN in London
Hadoop, Pig, and Twitter (NoSQL East 2009)Kevin Weil
A talk on the use of Hadoop and Pig inside Twitter, focusing on the flexibility and simplicity of Pig, and the benefits of that for solving real-world big data problems.
Apache Hive provides SQL-like access to your stored data in Apache Hadoop. Apache HBase stores tabular data in Hadoop and supports update operations. The combination of these two capabilities is often desired, however, the current integration show limitations such as performance issues. In this talk, Enis Soztutar will present an overview of Hive and HBase and discuss new updates/improvements from the community on the integration of these two projects. Various techniques used to reduce data exchange and improve efficiency will also be provided.
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
‘Grids’areanapproachforbuildingdynamicallyconstructedproblem-solvingenvironmentsusing
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Detailed presentation on big data hadoop +Hadoop Project Near Duplicate Detec...Ashok Royal
Bigdata Hadoop, Its components and a Hadoop project is described in Details.
Visit http://hadoop-beginners.blogspot.com to see Hadoop Tutorials.
Thanks for the visit. :)
Hadoop Training, Enhance your Big data subject knowledge with Online Training without wasting your time. Register for Free LIVE DEMO Class.
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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 Online Training : kelly technologies is the bestHadoop online Training Institutes in Bangalore. ProvidingHadoop online Training by real time faculty in Bangalore.
There is a growing trend of applications that ought to handle huge information. However, analysing huge information may be a terribly difficult drawback nowadays. For such data many techniques can be considered. The technologies like Grid Computing, Volunteering Computing, and RDBMS can be considered as potential techniques to handle such data. We have a still in growing phase Hadoop Tool to handle such data also. We will do a survey on all this techniques to find a potential technique to manage and work with Big Data.
Panchayat Season 3 - Official Trailer.pdfSuleman Rana
The dearest series "Panchayat" is set to make a victorious return with its third season, and the fervor is discernible. The authority trailer, delivered on May 28, guarantees one more enamoring venture through the country heartland of India.
Jitendra Kumar keeps on sparkling as Abhishek Tripathi, the city-reared engineer who ends up functioning as the secretary of the Panchayat office in the curious town of Phulera. His nuanced depiction of a young fellow exploring the difficulties of country life while endeavoring to adjust to his new environmental factors has earned far and wide recognition.
Neena Gupta and Raghubir Yadav return as Manju Devi and Brij Bhushan Dubey, separately. Their dynamic science and immaculate acting rejuvenate the hardships of town administration. Gupta's depiction of the town Pradhan with an ever-evolving outlook, matched with Yadav's carefully prepared exhibition, adds profundity and credibility to the story.
New Difficulties and Experiences
The trailer indicates new difficulties anticipating the characters, as Abhishek keeps on wrestling with his part in the town and his yearnings for a superior future. The series has reliably offset humor with social editorial, and Season 3 looks ready to dig much more profound into the intricacies of rustic organization and self-awareness.
Watchers can hope to see a greater amount of the enchanting and particular residents who have become fan top picks. Their connections and the one of a kind cut of-life situations give a reviving and interesting portrayal of provincial India, featuring the two its appeal and its difficulties.
A Mix of Humor and Heart
One of the signs of "Panchayat" is its capacity to mix humor with sincere narrating. The trailer features minutes that guarantee to convey giggles, as well as scenes that pull at the heartstrings. This equilibrium has been a critical calculate the show's prosperity, resounding with crowds across different socioeconomics.
Creation Greatness
The creation quality remaining parts first rate, with the beautiful setting of Phulera town filling in as a scenery that upgrades the narrating. The meticulousness in portraying provincial life, joined with sharp composition and solid exhibitions, guarantees that "Panchayat" keeps on hanging out in the packed web series scene.
Expectation and Delivery
As the delivery date draws near, expectation for "Panchayat" Season 3 is at a record-breaking high. The authority trailer has previously created critical buzz, with fans enthusiastically anticipating the continuation of Abhishek Tripathi's excursion and the new undertakings that lie ahead in Phulera.
All in all, the authority trailer for "Panchayat" Season 3 recommends that watchers are in for another drawing in and engaging ride. Yet again with its charming characters, convincing story, and ideal mix of humor and show, the new season is set to enamor crowds. Write in your schedules and prepare to get back to the endearing universe of "Panchayat."
From the Editor's Desk: 115th Father's day Celebration - When we see Father's day in Hindu context, Nanda Baba is the most vivid figure which comes to the mind. Nanda Baba who was the foster father of Lord Krishna is known to provide love, care and affection to Lord Krishna and Balarama along with his wife Yashoda; Letter’s to the Editor: Mother's Day - Mother is a precious life for their children. Mother is life breath for her children. Mother's lap is the world happiness whose debt can never be paid.
As a film director, I have always been awestruck by the magic of animation. Animation, a medium once considered solely for the amusement of children, has undergone a significant transformation over the years. Its evolution from a rudimentary form of entertainment to a sophisticated form of storytelling has stirred my creativity and expanded my vision, offering limitless possibilities in the realm of cinematic storytelling.
From Slave to Scourge: The Existential Choice of Django Unchained. The Philos...Rodney Thomas Jr
#SSAPhilosophy #DjangoUnchained #DjangoFreeman #ExistentialPhilosophy #Freedom #Identity #Justice #Courage #Rebellion #Transformation
Welcome to SSA Philosophy, your ultimate destination for diving deep into the profound philosophies of iconic characters from video games, movies, and TV shows. In this episode, we explore the powerful journey and existential philosophy of Django Freeman from Quentin Tarantino’s masterful film, "Django Unchained," in our video titled, "From Slave to Scourge: The Existential Choice of Django Unchained. The Philosophy of Django Freeman!"
From Slave to Scourge: The Existential Choice of Django Unchained – The Philosophy of Django Freeman!
Join me as we delve into the existential philosophy of Django Freeman, uncovering the profound lessons and timeless wisdom his character offers. Through his story, we find inspiration in the power of choice, the quest for justice, and the courage to defy oppression. Django Freeman’s philosophy is a testament to the human spirit’s unyielding drive for freedom and justice.
Don’t forget to like, comment, and subscribe to SSA Philosophy for more in-depth explorations of the philosophies behind your favorite characters. Hit the notification bell to stay updated on our latest videos. Let’s discover the principles that shape these icons and the profound lessons they offer.
Django Freeman’s story is one of the most compelling narratives of transformation and empowerment in cinema. A former slave turned relentless bounty hunter, Django’s journey is not just a physical liberation but an existential quest for identity, justice, and retribution. This video delves into the core philosophical elements that define Django’s character and the profound choices he makes throughout his journey.
Link to video: https://youtu.be/GszqrXk38qk
In the vast landscape of cinema, stories have been told, retold, and reimagined in countless ways. At the heart of this narrative evolution lies the concept of a "remake". A successful remake allows us to revisit cherished tales through a fresh lens, often reflecting a different era's perspective or harnessing the power of advanced technology. Yet, the question remains, what makes a remake successful? Today, we will delve deeper into this subject, identifying the key ingredients that contribute to the success of a remake.
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Monday, June 3, 2024 - Episode 241: Sergeant Rathebe nabs a top scammer in Turfloop. Meikie is furious at her uncle's reaction to the truth about Ntswaki.
Tuesday, June 4, 2024 - Episode 242: Babeile uncovers the truth behind Rathebe’s latest actions. Leeto's announcement shocks his employees, and Ntswaki’s ordeal haunts her family.
Wednesday, June 5, 2024 - Episode 243: Rathebe blocks Babeile from investigating further. Melita warns Eunice to stay clear of Mr. Kgomo.
Thursday, June 6, 2024 - Episode 244: Tbose surrenders to the police while an intruder meddles in his affairs. Rathebe's secret mission faces a setback.
Friday, June 7, 2024 - Episode 245: Rathebe’s antics reach Kganyago. Tbose dodges a bullet, but a nightmare looms. Mr. Kgomo accuses Melita of witchcraft.
Monday, June 10, 2024 - Episode 246: Ntswaki struggles on her first day back at school. Babeile is stunned by Rathebe’s romance with Bullet Mabuza.
Tuesday, June 11, 2024 - Episode 247: An unexpected turn halts Rathebe’s investigation. The press discovers Mr. Kgomo’s affair with a young employee.
Wednesday, June 12, 2024 - Episode 248: Rathebe chases a criminal, resorting to gunfire. Turf High is rife with tension and transfer threats.
Thursday, June 13, 2024 - Episode 249: Rathebe traps Kganyago. John warns Toby to stop harassing Ntswaki.
Friday, June 14, 2024 - Episode 250: Babeile is cleared to investigate Rathebe. Melita gains Mr. Kgomo’s trust, and Jacobeth devises a financial solution.
Monday, June 17, 2024 - Episode 251: Rathebe feels the pressure as Babeile closes in. Mr. Kgomo and Eunice clash. Jacobeth risks her safety in pursuit of Kganyago.
Tuesday, June 18, 2024 - Episode 252: Bullet Mabuza retaliates against Jacobeth. Pitsi inadvertently reveals his parents’ plans. Nkosi is shocked by Khwezi’s decision on LJ’s future.
Wednesday, June 19, 2024 - Episode 253: Jacobeth is ensnared in deceit. Evelyn is stressed over Toby’s case, and Letetswe reveals shocking academic results.
Thursday, June 20, 2024 - Episode 254: Elizabeth learns Jacobeth is in Mpumalanga. Kganyago's past is exposed, and Lehasa discovers his son is in KZN.
Friday, June 21, 2024 - Episode 255: Elizabeth confirms Jacobeth’s dubious activities in Mpumalanga. Rathebe lies about her relationship with Bullet, and Jacobeth faces theft accusations.
Monday, June 24, 2024 - Episode 256: Rathebe spies on Kganyago. Lehasa plans to retrieve his son from KZN, fearing what awaits.
Tuesday, June 25, 2024 - Episode 257: MaNtuli fears for Kwaito’s safety in Mpumalanga. Mr. Kgomo and Melita reconcile.
Wednesday, June 26, 2024 - Episode 258: Kganyago makes a bold escape. Elizabeth receives a shocking message from Kwaito. Mrs. Khoza defends her husband against scam accusations.
Thursday, June 27, 2024 - Episode 259: Babeile's skillful arrest changes the game. Tbose and Kwaito face a hostage crisis.
Friday, June 28, 2024 - Episode 260: Two women face the reality of being scammed. Turf is rocked by breaking
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Monday, 3 June 2024
Episode 47
A friend is compelled to expose a manipulative scheme to prevent another from making a grave mistake. In a frantic bid to save Jojo, Phakamile agrees to a meeting that unbeknownst to her, will seal her fate.
Tuesday, 4 June 2024
Episode 48
A mother, with her son's best interests at heart, finds him unready to heed her advice. Motshabi finds herself in an unmanageable situation, sinking fast like in quicksand.
Wednesday, 5 June 2024
Episode 49
A woman fabricates a diabolical lie to cover up an indiscretion. Overwhelmed by guilt, she makes a spontaneous confession that could be devastating to another heart.
Thursday, 6 June 2024
Episode 50
Linda unwittingly discloses damning information. Nhlamulo and Vuvu try to guide their friend towards the right decision.
Friday, 7 June 2024
Episode 51
Jojo's life continues to spiral out of control. Dintle weaves a web of lies to conceal that she is not as successful as everyone believes.
Monday, 10 June 2024
Episode 52
A heated confrontation between lovers leads to a devastating admission of guilt. Dintle's desperation takes a new turn, leaving her with dwindling options.
Tuesday, 11 June 2024
Episode 53
Unable to resort to violence, Taps issues a verbal threat, leaving Mdala unsettled. A sister must explain her life choices to regain her brother's trust.
Wednesday, 12 June 2024
Episode 54
Winnie makes a very troubling discovery. Taps follows through on his threat, leaving a woman reeling. Layla, oblivious to the truth, offers an incentive.
Thursday, 13 June 2024
Episode 55
A nosy relative arrives just in time to thwart a man's fatal decision. Dintle manipulates Khanyi to tug at Mo's heartstrings and get what she wants.
Friday, 14 June 2024
Episode 56
Tlhogi is shocked by Mdala's reaction following the revelation of their indiscretion. Jojo is in disbelief when the punishment for his crime is revealed.
Monday, 17 June 2024
Episode 57
A woman reprimands another to stay in her lane, leading to a damning revelation. A man decides to leave his broken life behind.
Tuesday, 18 June 2024
Episode 58
Nhlamulo learns that due to his actions, his worst fears have come true. Caiphus' extravagant promises to suppliers get him into trouble with Ndu.
Wednesday, 19 June 2024
Episode 59
A woman manages to kill two birds with one stone. Business doom looms over Chillax. A sobering incident makes a woman realize how far she's fallen.
Thursday, 20 June 2024
Episode 60
Taps' offer to help Nhlamulo comes with hidden motives. Caiphus' new ideas for Chillax have MaHilda excited. A blast from the past recognizes Dintle, not for her newfound fame.
Friday, 21 June 2024
Episode 61
Taps is hungry for revenge and finds a rope to hang Mdala with. Chillax's new job opportunity elicits mixed reactions from the public. Roommates' initial meeting starts off on the wrong foot.
Monday, 24 June 2024
Episode 62
Taps seizes new information and recruits someone on the inside. Mary's new job
Maximizing Your Streaming Experience with XCIPTV- Tips for 2024.pdfXtreame HDTV
In today’s digital age, streaming services have become an integral part of our entertainment lives. Among the myriad of options available, XCIPTV stands out as a premier choice for those seeking seamless, high-quality streaming. This comprehensive guide will delve into the features, benefits, and user experience of XCIPTV, illustrating why it is a top contender in the IPTV industry.
Are the X-Men Marvel or DC An In-Depth Exploration.pdfXtreame HDTV
The world of comic books is vast and filled with iconic characters, gripping storylines, and legendary rivalries. Among the most famous groups of superheroes are the X-Men. Created in the early 1960s, the X-Men have become a cultural phenomenon, featuring in comics, animated series, and blockbuster movies. A common question among newcomers to the comic book world is: Are the X-Men Marvel or DC? This article delves into the history, creators, and significant moments of the X-Men to provide a comprehensive answer.
Tom Selleck Net Worth: A Comprehensive Analysisgreendigital
Over several decades, Tom Selleck, a name synonymous with charisma. From his iconic role as Thomas Magnum in the television series "Magnum, P.I." to his enduring presence in "Blue Bloods," Selleck has captivated audiences with his versatility and charm. As a result, "Tom Selleck net worth" has become a topic of great interest among fans. and financial enthusiasts alike. This article delves deep into Tom Selleck's wealth, exploring his career, assets, endorsements. and business ventures that contribute to his impressive economic standing.
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Early Life and Career Beginnings
The Foundation of Tom Selleck's Wealth
Born on January 29, 1945, in Detroit, Michigan, Tom Selleck grew up in Sherman Oaks, California. His journey towards building a large net worth began with humble origins. , Selleck pursued a business administration degree at the University of Southern California (USC) on a basketball scholarship. But, his interest shifted towards acting. leading him to study at the Hills Playhouse under Milton Katselas.
Minor roles in television and films marked Selleck's early career. He appeared in commercials and took on small parts in T.V. series such as "The Dating Game" and "Lancer." These initial steps, although modest. laid the groundwork for his future success and the growth of Tom Selleck net worth. Breakthrough with "Magnum, P.I."
The Role that Defined Tom Selleck's Career
Tom Selleck's breakthrough came with the role of Thomas Magnum in the CBS television series "Magnum, P.I." (1980-1988). This role made him a household name and boosted his net worth. The series' popularity resulted in Selleck earning large salaries. leading to financial stability and increased recognition in Hollywood.
"Magnum P.I." garnered high ratings and critical acclaim during its run. Selleck's portrayal of the charming and resourceful private investigator resonated with audiences. making him one of the most beloved television actors of the 1980s. The success of "Magnum P.I." played a pivotal role in shaping Tom Selleck net worth, establishing him as a major star.
Film Career and Diversification
Expanding Tom Selleck's Financial Portfolio
While "Magnum, P.I." was a cornerstone of Selleck's career, he did not limit himself to television. He ventured into films, further enhancing Tom Selleck net worth. His filmography includes notable movies such as "Three Men and a Baby" (1987). which became the highest-grossing film of the year, and its sequel, "Three Men and a Little Lady" (1990). These box office successes contributed to his wealth.
Selleck's versatility allowed him to transition between genres. from comedies like "Mr. Baseball" (1992) to westerns such as "Quigley Down Under" (1990). This diversification showcased his acting range. and provided many income streams, reinforcing Tom Selleck net worth.
Television Resurgence with "Blue Bloods"
Sustaining Wealth through Consistent Success
In 2010, Tom Selleck began starring as Frank Reagan i
2. Introduction
Big Data:
•Big data is a term used to describe the voluminous amount of unstructured
and semi-structured data a company creates.
•Data that would take too much time and cost too much money to load into
a relational database for analysis.
• Big data doesn't refer to any specific quantity, the term is often used when
speaking about petabytes and exabytes of data.
3. • The New York Stock Exchange generates about one terabyte of new trade data per day.
• Facebook hosts approximately 10 billion photos, taking up one petabyte of storage.
• Ancestry.com, the genealogy site, stores around 2.5 petabytes of data.
• The Internet Archive stores around 2 petabytes of data, and is growing at a rate of 20
terabytes per month.
• The Large Hadron Collider near Geneva, Switzerland, produces about 15 petabytes of
data per year.
4. What Caused The Problem?
Standard Hard Drive Size
Year (in Mb)
Data Transfer Rate
Year (Mbps)
1990 1370
1990 4.4
2010 1000000 2010 100
5. So What Is The Problem?
The transfer speed is around 100 MB/s
A standard disk is 1 Terabyte
Time to read entire disk= 10000 seconds or 3 Hours!
Increase in processing time may not be as helpful because
• Network bandwidth is now more of a limiting factor
• Physical limits of processor chips have been reached
6. So What do We Do?
•The obvious solution is that we use
multiple processors to solve the same
problem by fragmenting it into pieces.
•Imagine if we had 100 drives, each
holding one hundredth of the data.
Working in parallel, we could read the
data in under two minutes.
7. Distributed Computing Vs
Parallelization
Parallelization- Multiple processors or CPU’s
in a single machine
Distributed Computing- Multiple computers
connected via a network
8. Examples
Cray-2 was a four-processor ECL
vector supercomputer made by
Cray Research starting in 1985
9. Distributed Computing
The key issues involved in this Solution:
Hardware failure
Combine the data after analysis
Network Associated Problems
10. What Can We Do With A Distributed
Computer System?
IBM Deep Blue
Multiplying Large Matrices
Simulating several 100’s of characters-
LOTRs
Index the Web (Google)
Simulating an internet size network for
network experiments
11. Problems In Distributed Computing
• Hardware Failure:
As soon as we start using many pieces of
hardware, the chance that one will fail is fairly
high.
• Combine the data after analysis:
Most analysis tasks need to be able to combine
the data in some way; data read from one
disk may need to be combined with the data
from any of the other 99 disks.
12. To The Rescue!
Apache Hadoop is a framework for running applications on
large cluster built of commodity hardware.
A common way of avoiding data loss is through replication:
redundant copies of the data are kept by the system so that in the
event of failure, there is another copy available. The Hadoop
Distributed Filesystem (HDFS), takes care of this problem.
The second problem is solved by a simple programming model-
Mapreduce. Hadoop is the popular open source implementation
of MapReduce, a powerful tool designed for deep analysis and
transformation of very large data sets.
13. What Else is Hadoop?
A reliable shared storage and analysis system.
There are other subprojects of Hadoop that provide complementary
services, or build on the core to add higher-level abstractions The various
subprojects of hadoop include:
4. Core
5. Avro
6. Pig
7. HBase
8. Zookeeper
9. Hive
10. Chukwa
14. Hadoop Approach to Distributed
Computing
The theoretical 1000-CPU machine would cost a very large amount of
money, far more than 1,000 single-CPU.
Hadoop will tie these smaller and more reasonably priced machines together
into a single cost-effective compute cluster.
Hadoop provides a simplified programming model which allows the user to
quickly write and test distributed systems, and its’ efficient, automatic
distribution of data and work across machines and in turn utilizing the
underlying parallelism of the CPU cores.
16. MapReduce
Hadoop limits the amount of communication which can be performed by the
processes, as each individual record is processed by a task in isolation from one another
By restricting the communication between nodes, Hadoop makes the distributed system
much more reliable. Individual node failures can be worked around by restarting tasks
on other machines.
The other workers continue to operate as though nothing went wrong, leaving the
challenging aspects of partially restarting the program to the underlying Hadoop layer.
Map : (in_value,in_key)(out_key, intermediate_value)
Reduce: (out_key, intermediate_value) (out_value list)
17. What is MapReduce?
MapReduce is a programming model
Programs written in this functional style are automatically parallelized and
executed on a large cluster of commodity machines
MapReduce is an associated implementation for processing and generating
large data sets.
18. The Programming Model Of MapReduce
Map, written by the user, takes an input pair and produces a set of intermediate
key/value pairs. The MapReduce library groups together all intermediate values
associated with the same intermediate key I and passes them to the Reduce
function.
19. The Reduce function, also written by the user, accepts an intermediate key I and a set of values
for that key. It merges together these values to form a possibly smaller set of values
20. This abstraction allows us to handle lists of values that are too large to fit in memory.
Example:
// key: document name
// value: document contents
for each word w in value:
EmitIntermediate(w, "1");
reduce(String key, Iterator values):
// key: a word
// values: a list of counts
int result = 0;
for each v in values:
result += ParseInt(v);
Emit(AsString(result));
21. Orientation of Nodes
Data Locality Optimization:
The computer nodes and the storage nodes are the same. The Map-Reduce
framework and the Distributed File System run on the same set of nodes. This
configuration allows the framework to effectively schedule tasks on the nodes where
data is already present, resulting in very high aggregate bandwidth across the
cluster.
If this is not possible: The computation is done by another processor on the same
rack.
“Moving Computation is Cheaper than Moving Data”
22. How MapReduce Works
A Map-Reduce job usually splits the input data-set into independent chunks which are
processed by the map tasks in a completely parallel manner.
The framework sorts the outputs of the maps, which are then input to the reduce tasks.
Typically both the input and the output of the job are stored in a file-system. The
framework takes care of scheduling tasks, monitoring them and re-executes the failed
tasks.
A MapReduce job is a unit of work that the client wants to be performed: it consists of
the input data, the MapReduce program, and configuration information. Hadoop runs
the job by dividing it into tasks, of which there are two types: map tasks and reduce
tasks
23. Fault Tolerance
There are two types of nodes that control the job execution process: tasktrackers and
jobtrackers
The jobtracker coordinates all the jobs run on the system by scheduling tasks to run on
tasktrackers.
Tasktrackers run tasks and send progress reports to the jobtracker, which keeps a record
of the overall progress of each job.
If a tasks fails, the jobtracker can reschedule it on a different tasktracker.
24.
25. Input Splits
Input splits: Hadoop divides the input to a MapReduce job into fixed-size
pieces called input splits, or just splits. Hadoop creates one map task for each
split, which runs the user-defined map function for each record in the split.
The quality of the load balancing increases as the splits become more fine-
grained.
BUT if splits are too small, then the overhead of managing the splits and of map
task creation begins to dominate the total job execution time. For most jobs, a
good split size tends to be the size of a HDFS block, 64 MB by default.
WHY?
Map tasks write their output to local disk, not to HDFS. Map output is
intermediate output: it’s processed by reduce tasks to produce the final output,
and once the job is complete the map output can be thrown away. So storing it
in HDFS, with replication, would be a waste of time. It is also possible that the
node running the map task fails before the map output has been consumed by
the reduce task.
26. Input to Reduce Tasks
Reduce tasks don’t have the advantage of
data locality—the input to a single reduce
task is normally the output from all mappers.
30. Combiner Functions
•Many MapReduce jobs are limited by the bandwidth available on the cluster.
•In order to minimize the data transferred between the map and reduce tasks, combiner
functions are introduced.
•Hadoop allows the user to specify a combiner function to be run on the map output—the
combiner function’s output forms the input to the reduce function.
•Combiner finctions can help cut down the amount of data shuffled between the maps and
the reduces.
31. Hadoop Streaming:
•Hadoop provides an API to MapReduce that allows you to
write your map and reduce functions in languages other than
Java.
•Hadoop Streaming uses Unix standard streams as the
interface between Hadoop and your program, so you can use
any language that can read standard input and write to
standard output to write your MapReduce program.
32. Hadoop Pipes:
•Hadoop Pipes is the name of the C++ interface to Hadoop MapReduce.
•Unlike Streaming, which uses standard input and output to communicate with
the map and reduce code, Pipes uses sockets as the channel over which the
tasktracker communicates with the process running the C++ map or reduce
function. JNI is not used.
33. HADOOP DISTRIBUTED
FILESYSTEM (HDFS)
Filesystems that manage the storage across a network of machines are called
distributed filesystems.
Hadoop comes with a distributed filesystem called HDFS, which stands for
Hadoop Distributed Filesystem.
HDFS, the Hadoop Distributed File System, is a distributed file system
designed to hold very large amounts of data (terabytes or even petabytes), and
provide high-throughput access to this information.
34. Problems In Distributed File Systems
Making distributed filesystems is more complex than regular disk filesystems. This
is because the data is spanned over multiple nodes, so all the complications of
network programming kick in.
•Hardware Failure
An HDFS instance may consist of hundreds or thousands of server machines, each storing
part of the file system’s data. The fact that there are a huge number of components and that
each component has a non-trivial probability of failure means that some component of HDFS
is always non-functional. Therefore, detection of faults and quick, automatic recovery from
them is a core architectural goal of HDFS.
•Large Data Sets
Applications that run on HDFS have large data sets. A typical file in HDFS is gigabytes to
terabytes in size. Thus, HDFS is tuned to support large files. It should provide high
aggregate data bandwidth and scale to hundreds of nodes in a single cluster. It should
support tens of millions of files in a single instance.
35. Goals of HDFS
Streaming Data Access
Applications that run on HDFS need streaming access to their data sets. They are
not general purpose applications that typically run on general purpose file systems.
HDFS is designed more for batch processing rather than interactive use by users.
The emphasis is on high throughput of data access rather than low latency of data
access. POSIX imposes many hard requirements that are not needed for
applications that are targeted for HDFS. POSIX semantics in a few key areas has
been traded to increase data throughput rates.
Simple Coherency Model
HDFS applications need a write-once-read-many access model for files. A file
once created, written, and closed need not be changed. This assumption simplifies
data coherency issues and enables high throughput data access. A Map/Reduce
application or a web crawler application fits perfectly with this model. There is a plan
to support appending-writes to files in the future.
36. “Moving Computation is Cheaper than Moving Data”
A computation requested by an application is much more efficient if
it is executed near the data it operates on. This is especially true when
the size of the data set is huge. This minimizes network congestion
and increases the overall throughput of the system. The assumption is
that it is often better to migrate the computation closer to where the
data is located rather than moving the data to where the application is
running. HDFS provides interfaces for applications to move
themselves closer to where the data is located.
Portability Across Heterogeneous Hardware and Software
Platforms HDFS has been designed to be easily portable from
one platform to another. This facilitates widespread adoption
of HDFS as a platform of choice for a large set of
applications.
37. Design of HDFS
Very large files
Files that are hundreds of megabytes, gigabytes, or terabytes in size. There
are Hadoop clusters running today that store petabytes of data.
Streaming data access
HDFS is built around the idea that the most efficient data processing pattern
is a write-once, read-many-times pattern.
A dataset is typically generated or copied from source, then various
analyses are performed on that dataset over time. Each analysis will involve
a large proportion of the dataset, so the time to read the whole dataset is
more important than the latency in reading the first record.
38. Low-latency data access
Applications that require low-latency access to data, in the tens
of milliseconds
range, will not work well with HDFS. Remember HDFS is
optimized for delivering a high throughput of data, and this may
be at the expense of latency. HBase (Chapter 12) is currently a
better choice for low-latency access.
Multiple writers, arbitrary file modifications
Files in HDFS may be written to by a single writer. Writes are
always made at the end of the file. There is no support for
multiple writers, or for modifications at arbitrary offsets in the
file. (These might be supported in the future, but they are likely
to be relatively inefficient.)
39. • Lots of small files
Since the namenode holds filesystem metadata in memory, the limit to
the number of files in a filesystem is governed by the amount of
memory on the namenode. As a rule of thumb, each file, directory, and
block takes about 150 bytes. So, for example, if you had one million
files, each taking one block, you would need at least 300 MB of
memory. While storing millions of files is feasible, billions is beyond the
capability of current hardware.
40. Commodity hardware
Hadoop doesn’t require expensive, highly reliable hardware to run on.
It’s designed to run on clusters of commodity hardware for which the
chance of node failure across the cluster is high, at least for large
clusters. HDFS is designed to carry on working without a noticeable
interruption to the user in the face of such failure. It is also worth
examining the applications for which using HDFS does not work so
well. While this may change in the future, these are areas where HDFS
is not a good fit today:
42. Block Abstraction
Blocks:
• A block is the minimum amount of data that can be read or
written.
• 64 MB by default.
• Files in HDFS are broken into block-sized chunks, which are
stored as independent units.
• HDFS blocks are large compared to disk blocks, and the
reason is to minimize the cost of seeks. By making a block
large enough, the time to transfer the data from the disk can be
made to be significantly larger than the time to seek to the start
of the block. Thus the time to transfer a large file made of
multiple blocks operates at the disk transfer rate.
43. Benefits of Block Abstraction
A file can be larger than any single disk in the network. There’s
nothing that requires the blocks from a file to be stored on the
same disk, so they can take advantage of any of the disks in
the cluster.
Making the unit of abstraction a block rather than a file
simplifies the storage subsystem.
Blocks provide fault tolerance and availability. To insure against
corrupted blocks and disk and machine failure, each block is
replicated to a small number of physically separate machines
(typically three). If a block becomes unavailable, a copy can be
read from another location in a way that is transparent to the
client.
44. Hadoop Archives
HDFS stores small files inefficiently, since each file is stored in
a block, and block metadata is held in memory by the
namenode. Thus, a large number of small files can eat up a lot
of memory on the namenode.
Hadoop Archives, or HAR files, are a file archiving facility that
packs files into HDFS blocks more efficiently, thereby reducing
namenode memory usage while still allowing transparent
access to files.
Hadoop Archives can be used as input to MapReduce.
45. Limitations of Archiving
There is currently no support for archive
compression, although the files that go into
the archive can be compressed
Archives are immutable once they have been
created. To add or remove files, you must
recreate the archive
46. Namenodes and Datanodes
A HDFS cluster has two types of node operating in a master-
worker pattern: a namenode (the master) and a number of
datanodes (workers).
The namenode manages the filesystem namespace. It
maintains the filesystem tree and the metadata for all the files
and directories in the tree.
Datanodes are the work horses of the filesystem. They store
and retrieve blocks when they are told to (by clients or the
namenode), and they report back to the namenode periodically
with lists of blocks that they are storing.
47. Without the namenode, the filesystem cannot
be used. In fact, if the machine running the
namenode were obliterated, all the files on
the filesystem would be lost since there
would be no way of knowing how to
reconstruct the files from the blocks on the
datanodes.
48. Important to make the namenode resilient to failure, and
Hadoop provides two mechanisms for this:
2. is to back up the files that make up the persistent state of the
filesystem metadata. Hadoop can be configured so that the
namenode writes its persistent state to multiple filesystems.
3. Another solution is to run a secondary namenode. The
secondary namenode usually runs on a separate physical
machine, since it requires plenty of CPU and as much memory
as the namenode to perform the merge. It keeps a copy of the
merged namespace image, which can be used in the event of
the namenode failing
49. File System Namespace
HDFS supports a traditional hierarchical file organization. A user or an
application can create and remove files, move a file from one directory
to another, rename a file, create directories and store files inside these
directories.
HDFS does not yet implement user quotas or access permissions.
HDFS does not support hard links or soft links. However, the HDFS
architecture does not preclude implementing these features.
The Namenode maintains the file system namespace. Any change to
the file system namespace or its properties is recorded by the
Namenode. An application can specify the number of replicas of a file
that should be maintained by HDFS. The number of copies of a file is
called the replication factor of that file. This information is stored by the
Namenode.
50. Data Replication
The blocks of a file are replicated for fault tolerance.
The NameNode makes all decisions regarding replication of
blocks. It periodically receives a Heartbeat and a Blockreport
from each of the DataNodes in the cluster. Receipt of a
Heartbeat implies that the DataNode is functioning properly.
A Blockreport contains a list of all blocks on a DataNode.
When the replication factor is three, HDFS’s placement policy
is to put one replica on one node in the local rack, another on a
different node in the local rack, and the last on a different node
in a different rack.
51. Bibliography
1. Hadoop- The Definitive Guide, O’Reilly 2009, Yahoo! Press
2. MapReduce: Simplified Data Processing on Large Clusters,
Jeffrey Dean and Sanjay Ghemawat
3. Ranking and Semi-supervised Classification on Large Scale
Graphs Using Map-Reduce, Delip Rao, David Yarowsky, Dept.
of Computer Science, Johns Hopkins University
4. Improving MapReduce Performance in Heterogeneous
Environments, Matei Zaharia, Andy Konwinski, Anthony D.
Joseph, Randy Katz, Ion Stoica, University of California,
Berkeley
5. MapReduce in a Week By Hannah Tang, Albert Wong, Aaron
Kimball, Winter 2007
Editor's Notes
(Note, however, that small files do not take up any more disk space than is required to store the raw contents of the file. For example, a 1 MB file stored with a block size of 128 MB uses 1 MB of disk space, not 128 MB.)