HDFS is a distributed file system designed for large data sets and high throughput access. It uses a master/slave architecture with a Namenode managing the file system namespace and Datanodes storing file data blocks. Blocks are replicated across Datanodes for fault tolerance. The system is highly scalable, handling large clusters and files sizes ranging from gigabytes to terabytes.
The document discusses the key features and architecture of the Hadoop File System (HDFS). HDFS is designed for large data sets and high fault tolerance. It uses a master/slave architecture with one namenode that manages file metadata and multiple datanodes that store file data blocks. HDFS replicates blocks across datanodes for reliability and provides interfaces for applications to access file data.
Hadoop Institutes : kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
The document describes the key features and architecture of the Hadoop Distributed File System (HDFS). HDFS is designed to reliably store very large data sets across clusters of commodity hardware. It uses a master/slave architecture with a single NameNode that manages file system metadata and regulates client access. Numerous DataNodes store file system blocks and replicate data for fault tolerance. The document outlines HDFS properties like high fault tolerance, data replication, and APIs.
Hadoop-professional-software-development-course-in-mumbaiUnmesh Baile
The document provides an overview of the Hadoop Distributed File System (HDFS). It describes HDFS's key features like fault tolerance, high throughput, and support for large datasets. The core architecture uses a master/slave model with a Namenode managing metadata and Datanodes storing file data blocks. HDFS employs replication for reliability and data is placed across racks for high availability. The document also discusses HDFS protocols, APIs, and how it ensures data integrity and handles failures.
Hadoop professional-software-development-course-in-mumbaiUnmesh Baile
Vibrant Technologies is headquarted in Mumbai,India.We are the best Hadoop training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Hadoop classes in Mumbai according to our students and corporates
HDFS is a distributed file system that stores large data across multiple nodes in a Hadoop cluster. It divides files into blocks and replicates them across nodes for reliability. The NameNode manages the file system namespace and regulates client access, while DataNodes store data blocks. HDFS provides interfaces for applications to access data blocks efficiently and is highly fault tolerant due to replication.
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
A simple replication-based mechanism has been used to achieve high data reliability of Hadoop Distributed File System (HDFS). However, replication based mechanisms have high degree of disk storage requirement since it makes copies of full block without consideration of storage size. Studies have shown that erasure-coding mechanism can provide more storage space when used as an alternative to replication. Also, it can increase write throughput compared to replication mechanism. To improve both space efficiency and I/O performance of the HDFS while preserving the same data reliability level, we propose HDFS+, an erasure coding based Hadoop Distributed File System. The proposed scheme writes a full block on the primary DataNode and then performs erasure coding with Vandermonde-based Reed-Solomon algorithm that divides data into m data fragments and encode them into n data fragments (n>m), which are saved in N distinct DataNodes such that the original object can be reconstructed from any m fragments. The experimental results show that our scheme can save up to 33% of storage space while outperforming the original scheme in write performance by 1.4 times. Our scheme provides the same read performance as the original scheme as long as data can be read from the primary DataNode even under single-node or double-node failure. Otherwise, the read performance of the HDFS+ decreases to some extent. However, as the number of fragments increases, we show that the performance degradation becomes negligible.
The document discusses the key features and architecture of the Hadoop File System (HDFS). HDFS is designed for large data sets and high fault tolerance. It uses a master/slave architecture with one namenode that manages file metadata and multiple datanodes that store file data blocks. HDFS replicates blocks across datanodes for reliability and provides interfaces for applications to access file data.
Hadoop Institutes : kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
The document describes the key features and architecture of the Hadoop Distributed File System (HDFS). HDFS is designed to reliably store very large data sets across clusters of commodity hardware. It uses a master/slave architecture with a single NameNode that manages file system metadata and regulates client access. Numerous DataNodes store file system blocks and replicate data for fault tolerance. The document outlines HDFS properties like high fault tolerance, data replication, and APIs.
Hadoop-professional-software-development-course-in-mumbaiUnmesh Baile
The document provides an overview of the Hadoop Distributed File System (HDFS). It describes HDFS's key features like fault tolerance, high throughput, and support for large datasets. The core architecture uses a master/slave model with a Namenode managing metadata and Datanodes storing file data blocks. HDFS employs replication for reliability and data is placed across racks for high availability. The document also discusses HDFS protocols, APIs, and how it ensures data integrity and handles failures.
Hadoop professional-software-development-course-in-mumbaiUnmesh Baile
Vibrant Technologies is headquarted in Mumbai,India.We are the best Hadoop training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Hadoop classes in Mumbai according to our students and corporates
HDFS is a distributed file system that stores large data across multiple nodes in a Hadoop cluster. It divides files into blocks and replicates them across nodes for reliability. The NameNode manages the file system namespace and regulates client access, while DataNodes store data blocks. HDFS provides interfaces for applications to access data blocks efficiently and is highly fault tolerant due to replication.
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
A simple replication-based mechanism has been used to achieve high data reliability of Hadoop Distributed File System (HDFS). However, replication based mechanisms have high degree of disk storage requirement since it makes copies of full block without consideration of storage size. Studies have shown that erasure-coding mechanism can provide more storage space when used as an alternative to replication. Also, it can increase write throughput compared to replication mechanism. To improve both space efficiency and I/O performance of the HDFS while preserving the same data reliability level, we propose HDFS+, an erasure coding based Hadoop Distributed File System. The proposed scheme writes a full block on the primary DataNode and then performs erasure coding with Vandermonde-based Reed-Solomon algorithm that divides data into m data fragments and encode them into n data fragments (n>m), which are saved in N distinct DataNodes such that the original object can be reconstructed from any m fragments. The experimental results show that our scheme can save up to 33% of storage space while outperforming the original scheme in write performance by 1.4 times. Our scheme provides the same read performance as the original scheme as long as data can be read from the primary DataNode even under single-node or double-node failure. Otherwise, the read performance of the HDFS+ decreases to some extent. However, as the number of fragments increases, we show that the performance degradation becomes negligible.
HDFS is Hadoop's implementation of a distributed file system designed to store large amounts of data across clusters of machines. It is based on Google's GFS and addresses limitations of other distributed file systems like NFS. HDFS uses a master/slave architecture with a NameNode master storing metadata and DataNodes storing data blocks. Data is replicated across multiple DataNodes for reliability. The file system is optimized for large, sequential reads and writes of entire files rather than random access or updates.
In this session you will learn:
History of Hadoop
Hadoop Ecosystem
Hadoop Animal Planet
What is Hadoop?
Distinctions of Hadoop
Hadoop Components
The Hadoop Distributed Filesystem
Design of HDFS
When Not to use Hadoop?
HDFS Concepts
Anatomy of a File Read
Anatomy of a File Write
Replication & Rack awareness
Mapreduce Components
Typical Mapreduce Job
To know more, click here: https://www.mindsmapped.com/courses/big-data-hadoop/big-data-and-hadoop-training-for-beginners/
The document summarizes the Hadoop Distributed File System (HDFS), which is designed to reliably store and stream very large datasets at high bandwidth. It describes the key components of HDFS, including the NameNode which manages the file system metadata and mapping of blocks to DataNodes, and DataNodes which store block replicas. HDFS allows scaling storage and computation across thousands of servers by distributing data storage and processing tasks.
HDFS is a distributed file system designed for storing very large data files across commodity servers or clusters. It works on a master-slave architecture with one namenode (master) and multiple datanodes (slaves). The namenode manages the file system metadata and regulates client access, while datanodes store and retrieve block data from their local file systems. Files are divided into large blocks which are replicated across datanodes for fault tolerance. The namenode monitors datanodes and replicates blocks if their replication drops below a threshold.
This document summarizes key aspects of the Hadoop Distributed File System (HDFS). HDFS is designed for storing very large files across commodity hardware. It uses a master/slave architecture with a single NameNode that manages file system metadata and multiple DataNodes that store application data. HDFS allows for streaming access to this distributed data and can provide higher throughput than a single high-end server by parallelizing reads across nodes.
Hadoop Interview Questions And Answers Part-1 | Big Data Interview Questions ...Simplilearn
This video on Hadoop interview questions part-1 will take you through the general Hadoop questions and questions on HDFS, MapReduce and YARN, which are very likely to be asked in any Hadoop interview. It covers all the topics on the major components of Hadoop. This Hadoop tutorial will give you an idea about the different scenario-based questions you could face and some multiple-choice questions as well. Now, let us dive into this Hadoop interview questions video and gear up for youe next Hadoop Interview.
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. It employs a Master and Slave architecture with a NameNode that manages metadata and DataNodes that store data blocks. The NameNode tracks locations of data blocks and regulates access to files, while DataNodes store file blocks and manage read/write operations as directed by the NameNode. HDFS provides high-performance, scalable access to data across large Hadoop clusters.
Hadoop Distributed File System (HDFS) is modeled after Google File System and optimized for large data sets and high throughput. HDFS uses a master/slave architecture with a NameNode that manages file system metadata and DataNodes that store data blocks. The NameNode tracks locations of data blocks and the DataNodes provide streaming access to blocks for batch processing applications. HDFS replicates data blocks across multiple DataNodes for fault tolerance.
Apache Hadoop is an open-source software framework that supports large-scale distributed applications and processing of multi-petabyte datasets across thousands of commodity servers. It implements the MapReduce programming model for distributed processing and the Hadoop Distributed File System (HDFS) for reliable data storage. HDFS stores data across commodity servers, provides high aggregate bandwidth, and detects/recovers from failures automatically.
Hadoop Distributed Filesystem (HDFS) is a distributed filesystem designed for storing very large files across commodity hardware. It is optimized for streaming data access and is a good fit for large files, terabytes or petabytes in size, with streaming write-once and read-many access patterns. HDFS uses a master-slave architecture with a Namenode managing the filesystem metadata and Datanodes storing and retrieving block data. Blocks are replicated across Datanodes for reliability. The Namenode tracks block locations and clients read/write data by communicating with the Namenode and Datanodes in a pipeline.
Big data interview questions and answersKalyan Hadoop
This document provides an overview of the Hadoop Distributed File System (HDFS), including its goals, design, daemons, and processes for reading and writing files. HDFS is designed for storing very large files across commodity servers, and provides high throughput and reliability through replication. The key components are the NameNode, which manages metadata, and DataNodes, which store data blocks. The Secondary NameNode assists the NameNode in checkpointing filesystem state periodically.
This document outlines the course objectives and modules for a Big Data Analytics course. The course focuses on Hadoop Distributed File System concepts, Hadoop tools, business intelligence applications, and data mining techniques including decision trees, text mining, and social network analysis. Key topics covered include HDFS architecture, MapReduce programming, YARN, and ensuring high availability of HDFS through techniques like rack awareness and NameNode replication.
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
Big data refers to large and complex datasets that are difficult to process using traditional methods. Key challenges include capturing, storing, searching, sharing, and analyzing large datasets in domains like meteorology, physics simulations, biology, and the internet. Hadoop is an open-source software framework for distributed storage and processing of big data across clusters of computers. It allows for the distributed processing of large data sets in a reliable, fault-tolerant and scalable manner.
The document provides an introduction to the key concepts of Big Data including Hadoop, HDFS, and MapReduce. It defines big data as large volumes of data that are difficult to process using traditional methods. Hadoop is introduced as an open-source framework for distributed storage and processing of large datasets across clusters of computers. HDFS is described as Hadoop's distributed file system that stores data across clusters and replicates files for reliability. MapReduce is a programming model where data is processed in parallel across clusters using mapping and reducing functions.
The Hadoop Distributed File System (HDFS) has a master/slave architecture with a single NameNode that manages the file system namespace and regulates client access, and multiple DataNodes that store and retrieve blocks of data files. The NameNode maintains metadata and a map of blocks to files, while DataNodes store blocks and report their locations. Blocks are replicated across DataNodes for fault tolerance following a configurable replication factor. The system uses rack awareness and preferential selection of local replicas to optimize performance and bandwidth utilization.
We have entered an era of Big Data. Huge information is for the most part accumulation of information sets so extensive and complex that it is exceptionally hard to handle them utilizing close by database administration devices. The principle challenges with Big databases incorporate creation, curation, stockpiling, sharing, inquiry, examination and perception. So to deal with these databases we require, "exceedingly parallel software's". As a matter of first importance, information is procured from diverse sources, for example, online networking, customary undertaking information or sensor information and so forth. Flume can be utilized to secure information from online networking, for example, twitter. At that point, this information can be composed utilizing conveyed document frameworks, for example, Hadoop File System. These record frameworks are extremely proficient when number of peruses are high when contrasted with composes.
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.
Threats in network that can be noted in securityssuserec53e73
Network security threats fall into four main categories: external threats from outside organizations or individuals, internal threats from employees, structured threats from organized cybercriminals, and unstructured attacks from amateurs. Threats are attempts to breach a network, while vulnerabilities are weaknesses in systems that threats can exploit. Common network threats include phishing attacks, ransomware, malware, DDoS attacks, advanced persistent threats, and SQL injection. Organizations can identify threats and vulnerabilities by monitoring their own network, using threat intelligence, conducting penetration testing, managing permissions, using firewalls, and continuously monitoring their network.
HDFS is Hadoop's implementation of a distributed file system designed to store large amounts of data across clusters of machines. It is based on Google's GFS and addresses limitations of other distributed file systems like NFS. HDFS uses a master/slave architecture with a NameNode master storing metadata and DataNodes storing data blocks. Data is replicated across multiple DataNodes for reliability. The file system is optimized for large, sequential reads and writes of entire files rather than random access or updates.
In this session you will learn:
History of Hadoop
Hadoop Ecosystem
Hadoop Animal Planet
What is Hadoop?
Distinctions of Hadoop
Hadoop Components
The Hadoop Distributed Filesystem
Design of HDFS
When Not to use Hadoop?
HDFS Concepts
Anatomy of a File Read
Anatomy of a File Write
Replication & Rack awareness
Mapreduce Components
Typical Mapreduce Job
To know more, click here: https://www.mindsmapped.com/courses/big-data-hadoop/big-data-and-hadoop-training-for-beginners/
The document summarizes the Hadoop Distributed File System (HDFS), which is designed to reliably store and stream very large datasets at high bandwidth. It describes the key components of HDFS, including the NameNode which manages the file system metadata and mapping of blocks to DataNodes, and DataNodes which store block replicas. HDFS allows scaling storage and computation across thousands of servers by distributing data storage and processing tasks.
HDFS is a distributed file system designed for storing very large data files across commodity servers or clusters. It works on a master-slave architecture with one namenode (master) and multiple datanodes (slaves). The namenode manages the file system metadata and regulates client access, while datanodes store and retrieve block data from their local file systems. Files are divided into large blocks which are replicated across datanodes for fault tolerance. The namenode monitors datanodes and replicates blocks if their replication drops below a threshold.
This document summarizes key aspects of the Hadoop Distributed File System (HDFS). HDFS is designed for storing very large files across commodity hardware. It uses a master/slave architecture with a single NameNode that manages file system metadata and multiple DataNodes that store application data. HDFS allows for streaming access to this distributed data and can provide higher throughput than a single high-end server by parallelizing reads across nodes.
Hadoop Interview Questions And Answers Part-1 | Big Data Interview Questions ...Simplilearn
This video on Hadoop interview questions part-1 will take you through the general Hadoop questions and questions on HDFS, MapReduce and YARN, which are very likely to be asked in any Hadoop interview. It covers all the topics on the major components of Hadoop. This Hadoop tutorial will give you an idea about the different scenario-based questions you could face and some multiple-choice questions as well. Now, let us dive into this Hadoop interview questions video and gear up for youe next Hadoop Interview.
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. It employs a Master and Slave architecture with a NameNode that manages metadata and DataNodes that store data blocks. The NameNode tracks locations of data blocks and regulates access to files, while DataNodes store file blocks and manage read/write operations as directed by the NameNode. HDFS provides high-performance, scalable access to data across large Hadoop clusters.
Hadoop Distributed File System (HDFS) is modeled after Google File System and optimized for large data sets and high throughput. HDFS uses a master/slave architecture with a NameNode that manages file system metadata and DataNodes that store data blocks. The NameNode tracks locations of data blocks and the DataNodes provide streaming access to blocks for batch processing applications. HDFS replicates data blocks across multiple DataNodes for fault tolerance.
Apache Hadoop is an open-source software framework that supports large-scale distributed applications and processing of multi-petabyte datasets across thousands of commodity servers. It implements the MapReduce programming model for distributed processing and the Hadoop Distributed File System (HDFS) for reliable data storage. HDFS stores data across commodity servers, provides high aggregate bandwidth, and detects/recovers from failures automatically.
Hadoop Distributed Filesystem (HDFS) is a distributed filesystem designed for storing very large files across commodity hardware. It is optimized for streaming data access and is a good fit for large files, terabytes or petabytes in size, with streaming write-once and read-many access patterns. HDFS uses a master-slave architecture with a Namenode managing the filesystem metadata and Datanodes storing and retrieving block data. Blocks are replicated across Datanodes for reliability. The Namenode tracks block locations and clients read/write data by communicating with the Namenode and Datanodes in a pipeline.
Big data interview questions and answersKalyan Hadoop
This document provides an overview of the Hadoop Distributed File System (HDFS), including its goals, design, daemons, and processes for reading and writing files. HDFS is designed for storing very large files across commodity servers, and provides high throughput and reliability through replication. The key components are the NameNode, which manages metadata, and DataNodes, which store data blocks. The Secondary NameNode assists the NameNode in checkpointing filesystem state periodically.
This document outlines the course objectives and modules for a Big Data Analytics course. The course focuses on Hadoop Distributed File System concepts, Hadoop tools, business intelligence applications, and data mining techniques including decision trees, text mining, and social network analysis. Key topics covered include HDFS architecture, MapReduce programming, YARN, and ensuring high availability of HDFS through techniques like rack awareness and NameNode replication.
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
Big data refers to large and complex datasets that are difficult to process using traditional methods. Key challenges include capturing, storing, searching, sharing, and analyzing large datasets in domains like meteorology, physics simulations, biology, and the internet. Hadoop is an open-source software framework for distributed storage and processing of big data across clusters of computers. It allows for the distributed processing of large data sets in a reliable, fault-tolerant and scalable manner.
The document provides an introduction to the key concepts of Big Data including Hadoop, HDFS, and MapReduce. It defines big data as large volumes of data that are difficult to process using traditional methods. Hadoop is introduced as an open-source framework for distributed storage and processing of large datasets across clusters of computers. HDFS is described as Hadoop's distributed file system that stores data across clusters and replicates files for reliability. MapReduce is a programming model where data is processed in parallel across clusters using mapping and reducing functions.
The Hadoop Distributed File System (HDFS) has a master/slave architecture with a single NameNode that manages the file system namespace and regulates client access, and multiple DataNodes that store and retrieve blocks of data files. The NameNode maintains metadata and a map of blocks to files, while DataNodes store blocks and report their locations. Blocks are replicated across DataNodes for fault tolerance following a configurable replication factor. The system uses rack awareness and preferential selection of local replicas to optimize performance and bandwidth utilization.
We have entered an era of Big Data. Huge information is for the most part accumulation of information sets so extensive and complex that it is exceptionally hard to handle them utilizing close by database administration devices. The principle challenges with Big databases incorporate creation, curation, stockpiling, sharing, inquiry, examination and perception. So to deal with these databases we require, "exceedingly parallel software's". As a matter of first importance, information is procured from diverse sources, for example, online networking, customary undertaking information or sensor information and so forth. Flume can be utilized to secure information from online networking, for example, twitter. At that point, this information can be composed utilizing conveyed document frameworks, for example, Hadoop File System. These record frameworks are extremely proficient when number of peruses are high when contrasted with composes.
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.
Threats in network that can be noted in securityssuserec53e73
Network security threats fall into four main categories: external threats from outside organizations or individuals, internal threats from employees, structured threats from organized cybercriminals, and unstructured attacks from amateurs. Threats are attempts to breach a network, while vulnerabilities are weaknesses in systems that threats can exploit. Common network threats include phishing attacks, ransomware, malware, DDoS attacks, advanced persistent threats, and SQL injection. Organizations can identify threats and vulnerabilities by monitoring their own network, using threat intelligence, conducting penetration testing, managing permissions, using firewalls, and continuously monitoring their network.
Lsn21_NumPy in data science using pythonssuserec53e73
This document provides an overview of NumPy, a fundamental package for scientific computing in Python. It introduces NumPy's multidimensional array object and its core features like basic linear algebra functions and tools for integrating C/C++ and Fortran code. The document covers how to create and manipulate NumPy arrays using their shape, data types, indexing, slicing, and common methods. It lists NumPy documentation resources and provides examples of creating, indexing, slicing arrays and using NumPy functions. The objective is for students to learn to use the NumPy package.
OpenSSL is an open source toolkit that implements the SSL and TLS protocols for secure network communication as well as cryptography functions. It includes both a command line interface for key generation, encryption, signing and other operations as well as programming APIs for C/C++, Perl, PHP and Python. OpenSSL is used to secure communications with SSL/TLS and provides cryptographic algorithms like AES, DES and RSA. It is commonly used to add SSL support to applications like the Apache web server.
Hash functions, digital signatures and hmacssuserec53e73
This document discusses hash functions, HMACs, and digital signatures. It begins by explaining that while encryption provides confidentiality, it does not prevent message modification. Hash functions map messages to fixed-length values and can detect message tampering, but secure cryptographic hash functions with properties like collision resistance are required. HMACs use a key to authenticate messages hashed with a shared key. Digital signatures use public key cryptography for a sender to authenticate a message by encrypting its hash with their private key.
Asian Elephant Adaptations - Chelsea P..pptxssuserec53e73
The Asian elephant has several adaptations for survival including its massive size, specialized trunks for manipulating objects, tough tusks for defense, floppy ears to regulate temperature, the ability to communicate using infrasound calls, an interesting diet of plants, and resources were found at http://animals.pawnation.com/adaptations-survival-elephants6658.html.
The document provides an introduction to object-oriented programming concepts in Python, including class definitions, objects/instances, methods, inheritance, encapsulation, polymorphism, method overriding, and operator overloading. Key points covered are:
1. A class defines the blueprint for an object, while an instance is a specific object created from a class. Methods are functions defined inside a class.
2. Inheritance allows a child class to inherit attributes and behaviors from a parent class. Encapsulation restricts access to attributes and methods.
3. Polymorphism enables using a common interface for multiple forms or data types. Method overriding allows redefining inherited methods. Operator overloading customizes operator behaviors for user
Here are the key steps to solve this cryptarithmetic puzzle as a constraint satisfaction problem:
1. Define the variables - In this case, the variables are the letters A,E,N,R,S,T. Each variable can take on the values 0-9.
2. Define the constraints - The constraints are that the letters must add up correctly based on the sum, no two letters can have the same value, and M=1 is given.
3. Specify the domain of possible values for each variable. In this case, the domain is 0-9 for each variable.
4. Systematically assign values to the variables while making sure each assignment is consistent with the constraints. Backtrack and
The document discusses planning in artificial intelligence. It introduces the Strips planning framework which uses operators with preconditions, add lists, and delete lists to describe how actions change the world. It describes how Strips can be used to solve a blocks world problem. The document also discusses partial order planning as an alternative to Strips, and represents planning problems using the situation calculus logical framework. It provides an example of representing the monkeys and bananas problem in the situation calculus.
The document discusses different types of knowledge and representations in artificial intelligence, including declarative and procedural knowledge. It provides examples to illustrate the differences between declarative and procedural representations. Specifically, it examines how the order assertions are examined can impact the answer obtained when using a procedural representation compared to a declarative representation. The document also discusses logic programming and the differences between logical assertions and Prolog representations.
Dr. Jose Reena K received a Certificate of Participation for attending a guest lecture titled "Teaching for Tomorrow: Embracing a Research-Oriented Approach" organized by the Internal Quality Assurance Cell of VISTAS on December 18, 2023. The certificate was signed by Dr. N. Kanya of the IQAC, Dr. Malini Pande the Director of IQAC and ASC, and Dr. C.B. Palanivelu the Registrar.
Enumeration involves querying target systems to gather information that can help attackers find vulnerabilities to exploit. It provides details like IP addresses, hostnames, network services, shares, and more. Common enumeration techniques include NetBIOS, SNMP, LDAP, NTP, SMTP, and DNS enumeration, which use tools and protocols like network scanners, SNMP agents, LDAP directories, and cleartext protocols to extract usernames, passwords, device details, email addresses and other data from systems and services. The information gathered through enumeration serves as an initial step for attackers to identify entry points for further attacks.
This document discusses techniques for footprinting and scanning target systems during the early stages of a penetration test or cyber attack. It describes footprinting as gathering open source information on a target through methods like searching online databases and websites. Scanning involves using tools like ping sweeps, port scanning and OS detection to learn more about a target's network configuration and running services. The document provides examples of specific tools attackers can use and also discusses some countermeasures organizations can take to limit the information available through these techniques.
The document discusses concurrency control in database management systems. Concurrency control ensures that transactions are performed concurrently without conflicting results by using methods like locking and timestamps. It prevents issues like lost updates, dirty reads, and non-repeatable reads. The main concurrency control protocols discussed are lock-based protocols using techniques like two-phase locking, and timestamp-based protocols.
The document discusses the key components and technologies that enable the Internet of Things (IoT). It defines IoT as physical objects embedded with sensors and software that connect and exchange data over the internet. The main technologies that make IoT possible are sensors and actuators to interact with the physical world, various connectivity technologies, cloud computing infrastructure to store and analyze vast amounts of data, big data analytics tools to extract insights from data, and security technologies to protect connected devices and data.
This document provides an overview of an IoT reference architecture, describing its functional, information, and deployment views. The functional view outlines several functional groups including device and application, communication, IoT service, virtual entity, process management, service organization, security, and management. The information view describes different patterns for handling information between functional components, such as push, request/response, subscribe/notify, and publish/subscribe. The deployment view focuses on the main real-world components that make up the system.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Macroeconomics- Movie Location
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The simplified electron and muon model, Oscillating Spacetime: The Foundation...
HDFS.ppt
1. B . R A M A M U R T H Y
Hadoop File System
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2. Reference
The Hadoop Distributed File System: Architecture
and Design by Apache Foundation Inc.
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3. Basic Features: HDFS
Highly fault-tolerant
High throughput
Suitable for applications with large data sets
Streaming access to file system data
Can be built out of commodity hardware
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4. Fault tolerance
Failure is the norm rather than exception
A HDFS instance may consist of thousands of server
machines, each storing part of the file system’s data.
Since we have huge number of components and that
each component has non-trivial probability of failure
means that there is always some component that is
non-functional.
Detection of faults and quick, automatic recovery
from them is a core architectural goal of HDFS.
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5. Data Characteristics
Streaming data access
Applications need streaming access to data
Batch processing rather than interactive user access.
Large data sets and files: gigabytes to terabytes size
High aggregate data bandwidth
Scale to hundreds of nodes in a cluster
Tens of millions of files in a single instance
Write-once-read-many: a file once created, written and
closed need not be changed – this assumption simplifies
coherency
A map-reduce application or web-crawler application fits
perfectly with this model.
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8. Namenode and Datanodes
Master/slave architecture
HDFS cluster consists of a single Namenode, a master server that
manages the file system namespace and regulates access to files by
clients.
There are a number of DataNodes usually one per node in a
cluster.
The DataNodes manage storage attached to the nodes that they run
on.
HDFS exposes a file system namespace and allows user data to be
stored in files.
A file is split into one or more blocks and set of blocks are stored in
DataNodes.
DataNodes: serves read, write requests, performs block creation,
deletion, and replication upon instruction from Namenode.
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10. File system Namespace
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Hierarchical file system with directories and files
Create, remove, move, rename etc.
Namenode maintains the file system
Any meta information changes to the file system
recorded by the Namenode.
An application can specify the number of replicas of
the file needed: replication factor of the file. This
information is stored in the Namenode.
11. Data Replication
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HDFS is designed to store very large files across
machines in a large cluster.
Each file is a sequence of blocks.
All blocks in the file except the last are of the same
size.
Blocks are replicated for fault tolerance.
Block size and replicas are configurable per file.
The Namenode receives a Heartbeat and a
BlockReport from each DataNode in the cluster.
BlockReport contains all the blocks on a Datanode.
12. Replica Placement
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The placement of the replicas is critical to HDFS reliability and performance.
Optimizing replica placement distinguishes HDFS from other distributed file
systems.
Rack-aware replica placement:
Goal: improve reliability, availability and network bandwidth utilization
Research topic
Many racks, communication between racks are through switches.
Network bandwidth between machines on the same rack is greater than those in
different racks.
Namenode determines the rack id for each DataNode.
Replicas are typically placed on unique racks
Simple but non-optimal
Writes are expensive
Replication factor is 3
Another research topic?
Replicas are placed: one on a node in a local rack, one on a different node in the
local rack and one on a node in a different rack.
1/3 of the replica on a node, 2/3 on a rack and 1/3 distributed evenly across
remaining racks.
13. Replica Selection
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Replica selection for READ operation: HDFS tries to
minimize the bandwidth consumption and latency.
If there is a replica on the Reader node then that is
preferred.
HDFS cluster may span multiple data centers:
replica in the local data center is preferred over the
remote one.
14. Safemode Startup
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On startup Namenode enters Safemode.
Replication of data blocks do not occur in Safemode.
Each DataNode checks in with Heartbeat and
BlockReport.
Namenode verifies that each block has acceptable
number of replicas
After a configurable percentage of safely replicated
blocks check in with the Namenode, Namenode exits
Safemode.
It then makes the list of blocks that need to be replicated.
Namenode then proceeds to replicate these blocks to
other Datanodes.
15. Filesystem Metadata
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The HDFS namespace is stored by Namenode.
Namenode uses a transaction log called the EditLog
to record every change that occurs to the filesystem
meta data.
For example, creating a new file.
Change replication factor of a file
EditLog is stored in the Namenode’s local filesystem
Entire filesystem namespace including mapping of
blocks to files and file system properties is stored in a
file FsImage. Stored in Namenode’s local filesystem.
16. Namenode
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Keeps image of entire file system namespace and file
Blockmap in memory.
4GB of local RAM is sufficient to support the above data
structures that represent the huge number of files and
directories.
When the Namenode starts up it gets the FsImage and
Editlog from its local file system, update FsImage with
EditLog information and then stores a copy of the
FsImage on the filesytstem as a checkpoint.
Periodic checkpointing is done. So that the system can
recover back to the last checkpointed state in case of a
crash.
17. Datanode
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A Datanode stores data in files in its local file system.
Datanode has no knowledge about HDFS filesystem
It stores each block of HDFS data in a separate file.
Datanode does not create all files in the same directory.
It uses heuristics to determine optimal number of files
per directory and creates directories appropriately:
Research issue?
When the filesystem starts up it generates a list of all
HDFS blocks and send this report to Namenode:
Blockreport.
19. The Communication Protocol
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All HDFS communication protocols are layered on top of
the TCP/IP protocol
A client establishes a connection to a configurable TCP
port on the Namenode machine. It talks ClientProtocol
with the Namenode.
The Datanodes talk to the Namenode using Datanode
protocol.
RPC abstraction wraps both ClientProtocol and
Datanode protocol.
Namenode is simply a server and never initiates a
request; it only responds to RPC requests issued by
DataNodes or clients.
21. Objectives
Primary objective of HDFS is to store data reliably in
the presence of failures.
Three common failures are: Namenode failure,
Datanode failure and network partition.
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22. DataNode failure and heartbeat
A network partition can cause a subset of Datanodes
to lose connectivity with the Namenode.
Namenode detects this condition by the absence of a
Heartbeat message.
Namenode marks Datanodes without Hearbeat and
does not send any IO requests to them.
Any data registered to the failed Datanode is not
available to the HDFS.
Also the death of a Datanode may cause replication
factor of some of the blocks to fall below their
specified value.
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23. Re-replication
The necessity for re-replication may arise due to:
A Datanode may become unavailable,
A replica may become corrupted,
A hard disk on a Datanode may fail, or
The replication factor on the block may be increased.
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24. Cluster Rebalancing
HDFS architecture is compatible with data
rebalancing schemes.
A scheme might move data from one Datanode to
another if the free space on a Datanode falls below a
certain threshold.
In the event of a sudden high demand for a
particular file, a scheme might dynamically create
additional replicas and rebalance other data in the
cluster.
These types of data rebalancing are not yet
implemented: research issue.
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25. Data Integrity
Consider a situation: a block of data fetched from
Datanode arrives corrupted.
This corruption may occur because of faults in a
storage device, network faults, or buggy software.
A HDFS client creates the checksum of every block of
its file and stores it in hidden files in the HDFS
namespace.
When a clients retrieves the contents of file, it
verifies that the corresponding checksums match.
If does not match, the client can retrieve the block
from a replica.
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26. Metadata Disk Failure
FsImage and EditLog are central data structures of HDFS.
A corruption of these files can cause a HDFS instance to be
non-functional.
For this reason, a Namenode can be configured to maintain
multiple copies of the FsImage and EditLog.
Multiple copies of the FsImage and EditLog files are
updated synchronously.
Meta-data is not data-intensive.
The Namenode could be single point failure: automatic
failover is NOT supported! Another research topic.
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28. Data Blocks
HDFS support write-once-read-many with reads at
streaming speeds.
A typical block size is 64MB (or even 128 MB).
A file is chopped into 64MB chunks and stored.
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29. Staging
A client request to create a file does not reach
Namenode immediately.
HDFS client caches the data into a temporary file.
When the data reached a HDFS block size the client
contacts the Namenode.
Namenode inserts the filename into its hierarchy and
allocates a data block for it.
The Namenode responds to the client with the
identity of the Datanode and the destination of the
replicas (Datanodes) for the block.
Then the client flushes it from its local memory.
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30. Staging (contd.)
The client sends a message that the file is closed.
Namenode proceeds to commit the file for creation
operation into the persistent store.
If the Namenode dies before file is closed, the file is
lost.
This client side caching is required to avoid network
congestion; also it has precedence is AFS (Andrew
file system).
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31. Replication Pipelining
When the client receives response from Namenode,
it flushes its block in small pieces (4K) to the first
replica, that in turn copies it to the next replica and
so on.
Thus data is pipelined from Datanode to the next.
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33. Application Programming Interface
HDFS provides Java API for application to use.
Python access is also used in many applications.
A C language wrapper for Java API is also available.
A HTTP browser can be used to browse the files of a
HDFS instance.
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34. FS Shell, Admin and Browser Interface
HDFS organizes its data in files and directories.
It provides a command line interface called the FS
shell that lets the user interact with data in the
HDFS.
The syntax of the commands is similar to bash and
csh.
Example: to create a directory /foodir
/bin/hadoop dfs –mkdir /foodir
There is also DFSAdmin interface available
Browser interface is also available to view the
namespace.
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35. Space Reclamation
When a file is deleted by a client, HDFS renames file
to a file in be the /trash directory for a configurable
amount of time.
A client can request for an undelete in this allowed
time.
After the specified time the file is deleted and the
space is reclaimed.
When the replication factor is reduced, the
Namenode selects excess replicas that can be
deleted.
Next heartbeat(?) transfers this information to the
Datanode that clears the blocks for use. 10/26/2022
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36. Summary
We discussed the features of the Hadoop File
System, a peta-scale file system to handle big-data
sets.
What discussed: Architecture, Protocol, API, etc.
Missing element: Implementation
The Hadoop file system (internals)
An implementation of an instance of the HDFS (for use by
applications such as web crawlers).
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