The document discusses performance issues on Hadoop clusters and proposes three solutions:
1) Data placement - Distributing data across heterogeneous cluster nodes according to their computing capabilities to reduce data transfer time.
2) Prefetching - Improving performance by preloading required data before tasks are assigned to reduce CPU stall time.
3) Preshuffling - Minimizing data shuffling during reduce by pipelining intermediate data between map and reduce tasks.
This document provides an overview of Hadoop storage perspectives from different stakeholders. The Hadoop application team prefers direct attached storage for performance reasons, as Hadoop was designed for affordable internet-scale analytics where data locality is important. However, IT operations has valid concerns about reliability, manageability, utilization, and integration with other systems when data is stored on direct attached storage instead of shared storage. There are tradeoffs to both approaches that depend on factors like the infrastructure, workload characteristics, and priorities of the organization.
Hive provides a SQL-like interface to query large datasets stored in Hadoop. Pig is a dataflow language for transforming datasets. HBase is a distributed, scalable, big data store that provides random real-time read/write access to datasets.
The document is a slide deck for a training on Hadoop fundamentals. It includes an agenda that covers what big data is, an introduction to Hadoop, the Hadoop architecture, MapReduce, Pig, Hive, Jaql, and certification. It provides overviews and explanations of these topics through multiple slides with images and text. The slides also describe hands-on labs for attendees to complete exercises using these big data technologies.
PRACE Autumn school 2021 - Big Data with Hadoop and Keras
27-30 September 2021
Fakulteta za strojništvo
Europe/Ljubljana
Data and scripts are available at: https://www.events.prace-ri.eu/event/1226/timetable/
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
Introduction to MapReduce | MapReduce Architecture | MapReduce FundamentalsSkillspeed
This Hadoop MapReduce tutorial will unravel MapReduce Programming, MapReduce Commands, MapReduce Fundamentals, Driver Class, Mapper Class, Reducer Class, Job Tracker & Task Tracker.
At the end, you'll have a strong knowledge regarding Hadoop MapReduce Basics.
PPT Agenda:
✓ Introduction to BIG Data & Hadoop
✓ What is MapReduce?
✓ MapReduce Data Flows
✓ MapReduce Programming
----------
What is MapReduce?
MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java.
----------
What are MapReduce Components?
It has the following components:
1. Combiner: The combiner collates all the data from the sample set based on your desired filters. For example, you can collate data based on day, week, month and year. After this, the data is prepared and sent for parallel processing.
2. Job Tracker: This allocates the data across multiple servers.
3. Task Tracker: This executes the program across various servers.
4. Reducer: It will isolate the desired output from across the multiple servers.
----------
Applications of MapReduce
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
This document summarizes key differences between SQL and NoSQL databases. It discusses the history and advantages of SQL, including its ubiquitous use, standardization, and ability to optimize queries based on schema. However, SQL has limitations for sparse and complex data. The document then introduces NoSQL databases and how they are designed for scale-out. It uses HBase as an example to illustrate how NoSQL databases can solve SQL's distributed join challenges by co-locating related data without schemas. While this improves performance, it also has disadvantages for modeling relationships and exploring data.
This document provides an overview of Hadoop storage perspectives from different stakeholders. The Hadoop application team prefers direct attached storage for performance reasons, as Hadoop was designed for affordable internet-scale analytics where data locality is important. However, IT operations has valid concerns about reliability, manageability, utilization, and integration with other systems when data is stored on direct attached storage instead of shared storage. There are tradeoffs to both approaches that depend on factors like the infrastructure, workload characteristics, and priorities of the organization.
Hive provides a SQL-like interface to query large datasets stored in Hadoop. Pig is a dataflow language for transforming datasets. HBase is a distributed, scalable, big data store that provides random real-time read/write access to datasets.
The document is a slide deck for a training on Hadoop fundamentals. It includes an agenda that covers what big data is, an introduction to Hadoop, the Hadoop architecture, MapReduce, Pig, Hive, Jaql, and certification. It provides overviews and explanations of these topics through multiple slides with images and text. The slides also describe hands-on labs for attendees to complete exercises using these big data technologies.
PRACE Autumn school 2021 - Big Data with Hadoop and Keras
27-30 September 2021
Fakulteta za strojništvo
Europe/Ljubljana
Data and scripts are available at: https://www.events.prace-ri.eu/event/1226/timetable/
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
Introduction to MapReduce | MapReduce Architecture | MapReduce FundamentalsSkillspeed
This Hadoop MapReduce tutorial will unravel MapReduce Programming, MapReduce Commands, MapReduce Fundamentals, Driver Class, Mapper Class, Reducer Class, Job Tracker & Task Tracker.
At the end, you'll have a strong knowledge regarding Hadoop MapReduce Basics.
PPT Agenda:
✓ Introduction to BIG Data & Hadoop
✓ What is MapReduce?
✓ MapReduce Data Flows
✓ MapReduce Programming
----------
What is MapReduce?
MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java.
----------
What are MapReduce Components?
It has the following components:
1. Combiner: The combiner collates all the data from the sample set based on your desired filters. For example, you can collate data based on day, week, month and year. After this, the data is prepared and sent for parallel processing.
2. Job Tracker: This allocates the data across multiple servers.
3. Task Tracker: This executes the program across various servers.
4. Reducer: It will isolate the desired output from across the multiple servers.
----------
Applications of MapReduce
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
This document summarizes key differences between SQL and NoSQL databases. It discusses the history and advantages of SQL, including its ubiquitous use, standardization, and ability to optimize queries based on schema. However, SQL has limitations for sparse and complex data. The document then introduces NoSQL databases and how they are designed for scale-out. It uses HBase as an example to illustrate how NoSQL databases can solve SQL's distributed join challenges by co-locating related data without schemas. While this improves performance, it also has disadvantages for modeling relationships and exploring data.
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.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.
Hadoop is an open source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses Google's MapReduce programming model and Google File System for reliability. The Hadoop architecture includes a distributed file system (HDFS) that stores data across clusters and a job scheduling and resource management framework (YARN) that allows distributed processing of large datasets in parallel. Key components include the NameNode, DataNodes, ResourceManager and NodeManagers. Hadoop provides reliability through replication of data blocks and automatic recovery from failures.
The slides are created for the "Hadoop User Group Vienna", a Meetup that gathers Hadoop users in Vienna on September 6, 2017. The content of the slides correspond to the first talk, which discussed the concepts, terminology and disaster recovery capabilities in the Hadoop ecosystem.
Introduction to Hadoop - The EssentialsFadi Yousuf
This document provides an introduction to Hadoop, including:
- A brief history of Hadoop and how it was created to address limitations of relational databases for big data.
- An overview of core Hadoop concepts like its shared-nothing architecture and using computation near storage.
- Descriptions of HDFS for distributed storage and MapReduce as the original programming framework.
- How the Hadoop ecosystem has grown to include additional frameworks like Hive, Pig, HBase and tools like Sqoop and Zookeeper.
- A discussion of YARN which separates resource management from job scheduling in Hadoop.
This presentation provides an overview of Hadoop, including:
- A brief history of data and the rise of big data from various sources.
- An introduction to Hadoop as an open source framework used for distributed processing and storage of large datasets across clusters of computers.
- Descriptions of the key components of Hadoop - HDFS for storage, and MapReduce for processing - and how they work together in the Hadoop architecture.
- An explanation of how Hadoop can be installed and configured in standalone, pseudo-distributed and fully distributed modes.
- Examples of major companies that use Hadoop like Amazon, Facebook, Google and Yahoo to handle their large-scale data and analytics needs.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses challenges in handling large amounts of data in a scalable, cost-effective manner. While early adoption was in web companies, enterprises are increasingly adopting Hadoop to gain insights from new sources of big data. However, Hadoop deployment presents challenges for enterprises in areas like setup/configuration, skills, integration, management at scale, and backup/recovery. Greenplum HD addresses these challenges by providing an enterprise-ready Hadoop distribution with simplified deployment, flexible scaling of compute and storage, seamless analytics integration, and advanced management capabilities backed by enterprise support.
This document provides an introduction to Hadoop and HDFS. It defines big data and Hadoop, describing how Hadoop uses a scale-out approach to distribute data and processing across clusters of commodity servers. It explains that HDFS is the distributed file system of Hadoop, which splits files into blocks and replicates them across multiple nodes for reliability. HDFS is optimized for large streaming reads and writes of large files. The document also gives an overview of the Hadoop ecosystem and common Hadoop distributions.
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
The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.
This document provides an introduction and overview of Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop uses MapReduce and HDFS to parallelize workloads and store data redundantly across nodes to solve issues around hardware failure and combining results. Key aspects covered include how HDFS distributes and replicates data, how MapReduce isolates processing into mapping and reducing functions to abstract communication, and how Hadoop moves computation to the data to improve performance.
No, combiner and reducer logic cannot be same.
Combiner is an optional step that performs local aggregation of the intermediate key-value pairs generated by the mappers. Its goal is to reduce the amount of data transferred from the mapper to the reducer.
Reducer performs the final aggregation of the values associated with a particular key. It receives the intermediate outputs from all the mappers, groups them by key, and produces the final output.
So while combiner and reducer both perform aggregation, their scopes of operation are different - combiner works locally on mapper output to minimize data transfer, whereas reducer operates globally on all mapper outputs to produce the final output. The logic needs to be optimized for their respective purposes.
The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
In YARN, the functionality of JobTracker has been replaced by ResourceManager and ApplicationMaster.
The ResourceManager replaces the JobTracker and manages the resources across the cluster. It schedules the applications on the nodes based on their resource requirements and availability.
The ApplicationMaster coordinates and manages the execution of individual applications submitted to YARN, such as MapReduce jobs. It negotiates resources from the ResourceManager and works with the NodeManagers to execute and monitor the tasks.
So in summary, the JobTracker's functionality is replaced by:
- ResourceManager (for resource management and scheduling)
- ApplicationMaster (for coordinating individual application execution)
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.
Hadoop is being used across organizations for a variety of purposes like data staging, analytics, security monitoring, and manufacturing quality assurance. However, most organizations still have separate systems optimized for specific workloads. Hadoop has the potential to relieve pressure on these systems by handling data staging, archives, transformations, and exploration. Going forward, Hadoop will need to provide enterprise-grade capabilities like high performance, security, data protection, and support for both analytical and operational workloads to fully replace specialized systems and become the main enterprise data platform.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created to support applications handling large datasets operating on many servers. Key Hadoop technologies include MapReduce for distributed computing, and HDFS for distributed file storage inspired by Google File System. Other related Apache projects extend Hadoop capabilities, like Pig for data flows, Hive for data warehousing, and HBase for NoSQL-like big data. Hadoop provides an effective solution for companies dealing with petabytes of data through distributed and parallel processing.
1. The document discusses the evolution of computing from mainframes to smaller commodity servers and PCs. It then introduces cloud computing as an emerging technology that is changing the technology landscape, with examples like Google File System and Amazon S3.
2. It discusses the need for large data processing due to increasing amounts of data from sources like the stock exchange, Facebook, genealogy sites, and scientific experiments.
3. Hadoop is introduced as a framework for distributed computing and reliable shared storage and analysis of large datasets using its Hadoop Distributed File System (HDFS) for storage and MapReduce for analysis.
HDFS provides a filesystem that can scale to tens of petabytes, with high IO bandwidth and data replication across machines. Locality is important for performance in Hadoop. There are ongoing issues with data loss, handling full disks, underreplication of data, and limits to scaling the namenode. Failover handling and security also need improvement before HDFS is ready for broad production use.
This document introduces Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop uses HDFS for scalable storage and MapReduce for distributed processing. Key components are introduced, including how HDFS stores data in replicated blocks and how MapReduce executes jobs by splitting data, mapping tasks, shuffling, and reducing results. A word count example demonstrates the MapReduce process.
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.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.
Hadoop is an open source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses Google's MapReduce programming model and Google File System for reliability. The Hadoop architecture includes a distributed file system (HDFS) that stores data across clusters and a job scheduling and resource management framework (YARN) that allows distributed processing of large datasets in parallel. Key components include the NameNode, DataNodes, ResourceManager and NodeManagers. Hadoop provides reliability through replication of data blocks and automatic recovery from failures.
The slides are created for the "Hadoop User Group Vienna", a Meetup that gathers Hadoop users in Vienna on September 6, 2017. The content of the slides correspond to the first talk, which discussed the concepts, terminology and disaster recovery capabilities in the Hadoop ecosystem.
Introduction to Hadoop - The EssentialsFadi Yousuf
This document provides an introduction to Hadoop, including:
- A brief history of Hadoop and how it was created to address limitations of relational databases for big data.
- An overview of core Hadoop concepts like its shared-nothing architecture and using computation near storage.
- Descriptions of HDFS for distributed storage and MapReduce as the original programming framework.
- How the Hadoop ecosystem has grown to include additional frameworks like Hive, Pig, HBase and tools like Sqoop and Zookeeper.
- A discussion of YARN which separates resource management from job scheduling in Hadoop.
This presentation provides an overview of Hadoop, including:
- A brief history of data and the rise of big data from various sources.
- An introduction to Hadoop as an open source framework used for distributed processing and storage of large datasets across clusters of computers.
- Descriptions of the key components of Hadoop - HDFS for storage, and MapReduce for processing - and how they work together in the Hadoop architecture.
- An explanation of how Hadoop can be installed and configured in standalone, pseudo-distributed and fully distributed modes.
- Examples of major companies that use Hadoop like Amazon, Facebook, Google and Yahoo to handle their large-scale data and analytics needs.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses challenges in handling large amounts of data in a scalable, cost-effective manner. While early adoption was in web companies, enterprises are increasingly adopting Hadoop to gain insights from new sources of big data. However, Hadoop deployment presents challenges for enterprises in areas like setup/configuration, skills, integration, management at scale, and backup/recovery. Greenplum HD addresses these challenges by providing an enterprise-ready Hadoop distribution with simplified deployment, flexible scaling of compute and storage, seamless analytics integration, and advanced management capabilities backed by enterprise support.
This document provides an introduction to Hadoop and HDFS. It defines big data and Hadoop, describing how Hadoop uses a scale-out approach to distribute data and processing across clusters of commodity servers. It explains that HDFS is the distributed file system of Hadoop, which splits files into blocks and replicates them across multiple nodes for reliability. HDFS is optimized for large streaming reads and writes of large files. The document also gives an overview of the Hadoop ecosystem and common Hadoop distributions.
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
The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.
This document provides an introduction and overview of Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop uses MapReduce and HDFS to parallelize workloads and store data redundantly across nodes to solve issues around hardware failure and combining results. Key aspects covered include how HDFS distributes and replicates data, how MapReduce isolates processing into mapping and reducing functions to abstract communication, and how Hadoop moves computation to the data to improve performance.
No, combiner and reducer logic cannot be same.
Combiner is an optional step that performs local aggregation of the intermediate key-value pairs generated by the mappers. Its goal is to reduce the amount of data transferred from the mapper to the reducer.
Reducer performs the final aggregation of the values associated with a particular key. It receives the intermediate outputs from all the mappers, groups them by key, and produces the final output.
So while combiner and reducer both perform aggregation, their scopes of operation are different - combiner works locally on mapper output to minimize data transfer, whereas reducer operates globally on all mapper outputs to produce the final output. The logic needs to be optimized for their respective purposes.
The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
In YARN, the functionality of JobTracker has been replaced by ResourceManager and ApplicationMaster.
The ResourceManager replaces the JobTracker and manages the resources across the cluster. It schedules the applications on the nodes based on their resource requirements and availability.
The ApplicationMaster coordinates and manages the execution of individual applications submitted to YARN, such as MapReduce jobs. It negotiates resources from the ResourceManager and works with the NodeManagers to execute and monitor the tasks.
So in summary, the JobTracker's functionality is replaced by:
- ResourceManager (for resource management and scheduling)
- ApplicationMaster (for coordinating individual application execution)
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.
Hadoop is being used across organizations for a variety of purposes like data staging, analytics, security monitoring, and manufacturing quality assurance. However, most organizations still have separate systems optimized for specific workloads. Hadoop has the potential to relieve pressure on these systems by handling data staging, archives, transformations, and exploration. Going forward, Hadoop will need to provide enterprise-grade capabilities like high performance, security, data protection, and support for both analytical and operational workloads to fully replace specialized systems and become the main enterprise data platform.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created to support applications handling large datasets operating on many servers. Key Hadoop technologies include MapReduce for distributed computing, and HDFS for distributed file storage inspired by Google File System. Other related Apache projects extend Hadoop capabilities, like Pig for data flows, Hive for data warehousing, and HBase for NoSQL-like big data. Hadoop provides an effective solution for companies dealing with petabytes of data through distributed and parallel processing.
1. The document discusses the evolution of computing from mainframes to smaller commodity servers and PCs. It then introduces cloud computing as an emerging technology that is changing the technology landscape, with examples like Google File System and Amazon S3.
2. It discusses the need for large data processing due to increasing amounts of data from sources like the stock exchange, Facebook, genealogy sites, and scientific experiments.
3. Hadoop is introduced as a framework for distributed computing and reliable shared storage and analysis of large datasets using its Hadoop Distributed File System (HDFS) for storage and MapReduce for analysis.
HDFS provides a filesystem that can scale to tens of petabytes, with high IO bandwidth and data replication across machines. Locality is important for performance in Hadoop. There are ongoing issues with data loss, handling full disks, underreplication of data, and limits to scaling the namenode. Failover handling and security also need improvement before HDFS is ready for broad production use.
This document introduces Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop uses HDFS for scalable storage and MapReduce for distributed processing. Key components are introduced, including how HDFS stores data in replicated blocks and how MapReduce executes jobs by splitting data, mapping tasks, shuffling, and reducing results. A word count example demonstrates the MapReduce process.
The document outlines the hierarchy and structure of authority in a typical garment industry. It lists 10 grades of authority from highest to lowest, including positions like Chairman, Managing Director, and Executive Director at the top levels. Lower levels include General Managers, Managers, Assistant Managers, Line Chiefs, Supervisors, and Operators and Workers. It also describes the main sections within a garment industry like Sample, Cutting, Sewing, Finishing, Store, and Maintenance Sections.
This document is Errin Johnson's resume and portfolio which outlines her education, skills, work history and accomplishments in web development and software. It shows that she graduated with an associate's degree in computer accounting and has continued education in web development through programs like Code Louisville and Treehouse. Her skills include HTML, CSS, JavaScript, PHP, Laravel and Sass. She has work experience in clerical, administrative and coordinator roles and seeks to expand her skills as a full stack developer.
IBM Big Data for Social Good Challenge - Submission ShowcaseIBM Analytics
Big Issues. Big Data. Big Solutions.
Big data exists in just about everything we use and do. It comes from phones, cars, roads, power lines, waterways, food crates, and innumerable other items you'd never think of as computers.
This data speaks volumes about our collective behavior and society - so let's use it to do something incredible!
IBM invited developers and data enthusiasts to take a deep dive into real world civic issues using big data and IBM Bluemix's Analytics for Hadoop service. They analyzed one of our curated datasets or brought their own and used Hadoop to create clickable and interactive data visualizations highlighting insights they found.
Join us for a Google+ Hangout with the participants to learn why they chose their projects, hear some of their challenges and delve deeper into the story behind their applications. #Hadoop4Good
April 1 and 8: Participants Journeys
April 15: Winner Announced
See slide 17 for more info.
Get started with IBM Analytics Hadoop: https://console.ng.bluemix.net/?cm_mmc=developerWorks-_-dWdevcenter-_-hadoop-_-lp _CMP=pubsec_ss_hadoop4good_slideshare
Hadoop Summit 2010 Tuning Hadoop To Deliver Performance To Your ApplicationYahoo Developer Network
This document provides guidelines for tuning Hadoop for performance. It discusses key factors that influence Hadoop performance like hardware configuration, application logic, and system bottlenecks. It also outlines various configuration parameters that can be tuned at the cluster and job level to optimize CPU, memory, disk throughput, and task granularity. Sample tuning gains are shown for a webmap application where tuning multiple parameters improved job execution time by up to 22%.
This document provides an agenda and overview for a presentation on Hadoop 2.x configuration and MapReduce performance tuning. The presentation covers hardware selection and capacity planning for Hadoop clusters, key configuration parameters for operating systems, HDFS, and YARN, and performance tuning techniques for MapReduce applications. It also demonstrates the Hadoop Vaidya performance diagnostic tool.
The document discusses optimizing performance in MapReduce jobs. It covers understanding bottlenecks through metrics and logs, tuning parameters to reduce spills during the map task sort and spill phase like io.sort.mb and io.sort.record.percent, and tips for reducer fetch tuning. The goal is to help developers understand and address bottlenecks in their MapReduce jobs to improve performance.
Networking lecture 4 Data Link Layer by Mamun sirsharifbdp
The document summarizes key aspects of the data link layer. It discusses how the data link layer provides a well-defined interface to the network layer, deals with frame transmission and errors, and regulates frame flow. It also describes common data link layer functions like framing, error detection, flow control, and link management. Finally, it discusses different data link protocols and how they handle issues like channel access, error handling, and window flow control.
Updated version of my talk about Hadoop 3.0 with the newest community updates.
Talk given at the codecentric Meetup Berlin on 31.08.2017 and on Data2Day Meetup on 28.09.2017 in Heidelberg.
Big data refers to large datasets that are difficult to process using traditional database management tools. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It provides reliable data storage with the Hadoop Distributed File System (HDFS) and high-performance parallel data processing using MapReduce. The Hadoop ecosystem includes components like HDFS, MapReduce, Hive, Pig, and HBase that provide distributed data storage, processing, querying and analysis capabilities at scale.
The document discusses HDFS (Hadoop Distributed File System) including typical workflows like writing and reading files from HDFS. It describes the roles of key HDFS components like the NameNode, DataNodes, and Secondary NameNode. It provides examples of rack awareness, file replication, and how the NameNode manages metadata. It also discusses Yahoo's practices for HDFS including hardware used, storage allocation, and benchmarks. Future work mentioned includes automated failover using Zookeeper and scaling the NameNode.
1. Introduction to Apache Accumulo provides an overview of the key-value store Accumulo.
2. Accumulo is a sorted, distributed key-value store that enables interactive access to trillions of records across hundreds to thousands of servers. It provides cell-based access control and customizable server-side processing.
3. The document discusses Accumulo's history and architecture, including how it uses Hadoop for storage and Zookeeper for coordination. It also covers Accumulo's features like iterators for server-side programming and cell-level access control labels.
Hadoop is an open-source framework for distributed processing of large datasets across clusters of computers. It allows for the parallel processing of large datasets stored across multiple servers. Hadoop uses HDFS for reliable storage and MapReduce as a programming model for distributed computing. HDFS stores data reliably in blocks across nodes, while MapReduce processes data in parallel using map and reduce functions.
This document provides an overview and introduction to Hadoop, an open-source framework for storing and processing large datasets in a distributed computing environment. It discusses what Hadoop is, common use cases like ETL and analysis, key architectural components like HDFS and MapReduce, and why Hadoop is useful for solving problems involving "big data" through parallel processing across commodity hardware.
This document discusses information processing architectures and models. It covers topics like online transaction processing, online analytical processing, complex event processing, and massively parallel processing. It also discusses shared nothing, shared disk and shared everything infrastructure models. The document then covers database architectures, tradeoffs in task scheduling, and the map reduce approach used for big data processing. Other topics discussed include ACID, BASE and CAP theories; distributed information management; data warehousing models; business intelligence models; multi-tier enterprise applications; mobile data progress; social, mobile and cloud computing; and cloud infrastructures for information processing.
(Berkeley CS186 guest lecture) Big Data Analytics Systems: What Goes Around C...Reynold Xin
(Berkeley CS186 guest lecture)
Big Data Analytics Systems: What Goes Around Comes Around
Introduction to MapReduce, GFS, HDFS, Spark, and differences between "Big Data" and database systems.
With Hadoop-3.0.0-alpha2 being released in January 2017, it's time to have a closer look at the features and fixes of Hadoop 3.0.
We will have a look at Core Hadoop, HDFS and YARN, and answer the emerging question whether Hadoop 3.0 will be an architectural revolution like Hadoop 2 was with YARN & Co. or will it be more of an evolution adapting to new use cases like IoT, Machine Learning and Deep Learning (TensorFlow)?
Big data | Hadoop | components of hadoop |Rahul Gulab SingRahul Singh
This document provides an overview of big data concepts and the Hadoop framework. It discusses the volume, variety, velocity, and veracity challenges of big data. It introduces Hadoop distributed file system (HDFS) and MapReduce programming model for processing large datasets across clusters of computers. Examples are given of how Google, Facebook, and the New York Times use Hadoop to manage petabytes of data and perform tasks like processing web searches, photos, and converting historical newspaper articles to PDF.
This document contains a quiz on topics related to big data analytics and business intelligence. It includes 10 multiple choice questions covering concepts like how Hadoop works, common big data tools like Pig and Hive, and basic terms in data analytics processes and R programming. The questions test understanding of mapper and reducer functions in Hadoop, phases of data analytics lifecycles, and popular Hadoop distributions like Cloudera's CDH.
Design, Scale and Performance of MapR's Distribution for Hadoopmcsrivas
Details the first ever Exabyte-scale system that can hold a Trillion large files. Describes MapR's Distributed NameNode (tm) architecture, and how it scales very easily and seamlessly. Shows map-reduce performance across a variety of benchmarks like dfsio, pig-mix, nnbench, terasort and YCSB.
Hadoop is an open source framework for distributed storage and processing of large datasets across commodity hardware. It has two main components - the Hadoop Distributed File System (HDFS) for storage, and MapReduce for processing. HDFS stores data across clusters in a redundant and fault-tolerant manner. MapReduce allows distributed processing of large datasets in parallel using map and reduce functions. The architecture aims to provide reliable, scalable computing using commodity hardware.
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
Big data is a popular term used to define the exponential evolution and availability of data, includes both structured and unstructured data. The volatile progression of demands on big data processing imposes heavy burden on computation, communication and storage in geographically distributed data centers. Hence it is necessary to minimize the cost of big data processing, which also includes fault tolerance cost. Big Data processing involves two types of faults: node failure and data loss. Both the faults can be recovered using heartbeat messages. Here heartbeat messages acts as an acknowledgement messages between two servers. This paper depicts about the study of node failure and recovery, data replication and heartbeat messages.
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
Big data is a popular term used to define the exponential evolution and availability of data, includes both structured and unstructured data. The volatile progression of demands on big data processing imposes heavy burden on computation, communication and storage in geographically distributed data centers. Hence it is necessary to minimize the cost of big data processing, which also includes fault tolerance cost. Big Data processing involves two types of faults: node failure and data loss. Both the faults can be recovered using heartbeat messages. Here heartbeat messages acts as an acknowledgement messages between two servers. This paper depicts about the study of node failure and recovery, data replication and heartbeat messages.
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
Big data is a popular term used to define the exponential evolution and availability of data, includes both structured and unstructured data. The volatile progression of demands on big data processing imposes heavy burden on computation, communication and storage in geographically distributed data centers. Hence it is necessary to minimize the cost of big data processing, which also includes fault tolerance cost. Big Data processing involves two types of faults: node failure and data loss. Both the faults can be recovered using heartbeat messages. Here heartbeat messages acts as an acknowledgement messages between two servers. This paper depicts about the study of node failure and recovery, data replication and heartbeat messages.
The document provides an introduction to big data and Hadoop. It defines big data as large datasets that are difficult to process using traditional software tools due to their size and complexity. It describes the characteristics of big data using the original 3Vs model (volume, velocity, variety) as well as additional attributes. The text then explains the architecture and components of Hadoop, the open-source framework for distributed storage and processing of big data, including HDFS, MapReduce, and other related tools. It provides an overview of how Hadoop addresses the challenges of big data through scalable and fault-tolerant distributed processing of data across commodity hardware.
Scheduling in distributed systems - Andrii VozniukAndrii Vozniuk
My EPFL candidacy exam presentation: http://wiki.epfl.ch/edicpublic/documents/Candidacy%20exam/vozniuk_andrii_candidacy_writeup.pdf
Here I present how schedulers work in three distributed data processing systems and their possible optimizations. I consider Gamma - a parallel database, MapReduce - a data-intensive system and Condor - a compute-intensive system.
This talk is based on the following papers:
1) Batch Scheduling in Parallel Database Systems by Manish Mehta, Valery Soloviev and David J. DeWitt
2) Improving MapReduce performance in heterogeneous environments by Matei Zaharia, Andy Konwinski, Anthony D. Joseph, Randy Katz and Ion Stoica
3) Batch Scheduling in Parallel Database Systems by Manish Mehta, Valery Soloviev and David J. DeWitt
SQL? NoSQL? NewSQL?!? What’s a Java developer to do? - JDC2012 Cairo, EgyptChris Richardson
This document discusses different database options for developers including SQL, NoSQL, and NewSQL databases. It provides an overview of why developers may choose NoSQL databases like MongoDB or Cassandra over traditional SQL databases, as well as when a NewSQL database could be a better option. The document uses a fictional example of a food delivery application to illustrate how different database choices would work for persisting and querying application data.
Similar to Performance Issues on Hadoop Clusters (20)
In this workshop, we explore ways to prepare for internship applications and interviews. In the workshop you will:
Learn how to apply for internships
Prepare for interview questions
Follow-up with employers
Receive tips that help you secure internships
An earlier version 1.0 can be found here: https://www.slideshare.net/xqin74/how-to-write-papers-part-1-principles/edit?src=slideview
5 Simple Steps to Write a Good Research Paper Title
1. Ask yourself these questions and make note of the answers What is my paper about? What techniques/ designs were used? Who/what is studied? What were the results?
2. Use your answers to list key words.
3. Create a sentence that includes the key words you listed.
4. Delete all unnecessary/repetitive words and link the remaining.
5. Delete non-essential information and reword the title.
Making a competitive nsf career proposal: Part 2 WorksheetXiao Qin
Dear Colleagues,
I created a worksheet to assist you to contrive the framework of your CAREER proposal. Answering the questions in the worksheet may streamline your thoughts when you are about to develop key components for your proposal. Any feedback on this worksheet is highly appreciated. I will have this worksheet revised in the future by incorporating your comments and suggestions.
Xiao (xqin@auburn.edu)
Making a competitive nsf career proposal: Part 1 TipsXiao Qin
A Caveat: This document consists of a list of the evaluation criteria of winning CAREER proposals. The following essential tips illustrate "what tasks" you should undertake rather than "how" to perform these tasks.
About This Document
" Proposal preparation phase: Sections 1 (Foundations), 2 (Preliminaries), and 6 (Other Suggestions) offer a list of tips on how to prepare your proposals.
" Proposal writing phase: Sections 3 (Key Components) and 4 (Writing) are comprised of a list of proposal components and writing styles.
" Proposal proofreading phase: Section 5 (Polishing a Proposal Draft) is a final proposal checklist.
In this training session, we provide new CSSE faculty with introduction on (1) policies related to graduate programs, (2) requirements and regulations, (3) teaching strategies, and (4) how to balance research and teaching. Please note that other CSSE policies (e.g., proposal submissions, startup account, CSSE committees) aren't covered in this session.
The document is a slide deck for a CSSE graduate student orientation at Auburn University. It provides information about degree requirements, faculty research areas, funding opportunities, academic policies, and advice for getting started. It also introduces key department staff and encourages students to find an advisor in their area of interest to help guide them through their degree. Interactive polling questions are included to gauge students' backgrounds and goals.
This progress report summarizes the work of the Graduate Programs Committee from February 6 to August 13, 2017. Some of their key achievements include admitting new graduate students, developing assessment reports on enrollment and program objectives, proposing updates to the qualifying exam policy, and recruiting students. They also worked on developing study guides for the qualifying exam, a new TA assignment policy, and revisions to the non-thesis M.S. program options. Moving forward, they will continue assessing programs and considering whether to keep the existing M.S. non-thesis program or transition to an M.S. in Software Engineering degree.
Note: I rebuilt the kernel by adding "hello world!" into the boot message. In what follows, I summarize my process of rebuilding the OS161 kernel. You may also found the three common mistakes at the end of this document.
I rebuilt the kernel by adding "hello world!" into the boot message. In what follows, I summarize my process of rebuilding the OS161 kernel. You may also found the three common mistakes at the end of this document.
Project 2 how to install and compile os161Xiao Qin
README: After installed VirtualBox on my Windows machine, I installed CentOS 6.5 on VirtualBox. Next, I successfully installed cs161-binutils-1.4 and cs161-gcc-1.5.tar. Unfortunately, I encountered an error "configure: error: no termcap library found". As Dustin suggested, installing the missing package can solve this problem. Please use the following command to install the package:
yum install ncurses-devel
You don't have to install CentOS 6.5, because I believe that you can install all the OS161 tools on CentOS 7. You don't have to install VirtualBox neither. Nevertheless, if you decide to install CentOS on VirtualBox, please refer to my installation log below.
This module shows you how to install a software development framework for OS/161.
Lecture: 30 minutes – Slides 1-20.
Demo: 20 minutes
1. Project 2 Specification.docx Preview the documentView in a new window 2. How to build tool chain: The MIPS toolchain for os161.txtPreview the documentView in a new window 3. How to build and run sys161.htmlView in a new window 4. gdb.htm View in a new window and cvs.htmView in a new window 5. Configuration file: sys161.confView in a new window Below, you can find five source code packages: 6. os161-1.10.tar.gzView in a new window 7. cs161-binutils-1.4.tarView in a new window 8. Download cs161-gcc-1.4.tar from: https://dl.dropboxusercontent.com/u/24238235/cs161-gcc-1.4.tar 9. Download cs161-gdb-1.4.tar from: https://dl.dropboxusercontent.com/u/24238235/cs161-gdb-1.4.tar 10. sys161-1.12.tar.gzView in a new window
Understanding what our customer wants-slideshareXiao Qin
This document discusses the two types of requirements for senior design projects - product requirements and process requirements. It emphasizes prioritizing requirements and avoiding vague requirements when developing design documents and source code. The document also asks about the reader's progress on their senior design project.
Project 2 in COMP3500 Operating Systems class at Auburn University. The objectives of this project are:
• Use your installed CentOS to build OS/161 and run Sys/161
• Configure and build OS/161 kernels
• Discover important design aspects of OS/161 by examining its source code
• Manage OS/161 using a version control system called cvs; apply cvs to create a repository and tracking your source code changes
• Use GDB to debug OS/161
How to survive a group project in COMP4710 Senior Design Project? This is a training module in the second lecture of week 1. The module takes approximately 20 minutes. After the training session is done, please check the progress of the development groups.
Watch this video at: https://www.youtube.com/watch?v=3u4AAGo31a8
Recorded on March 14, 2015. After having followed the Alfred’s adult piano course books for three years, I made a radical decision to learn a popular worship song called “Stream of Praise” [1]. A decade ago, I first learned how to sing this song when I was an assistant professor at New Mexico Tech, where minister Anna Tai [4] shared a Stream of Praise CD with me. I have listened this CD more than a few hundred times. The music video of this spiritual and emotional song can be found here https://www.youtube.com/watch?v=KIt9n2Wjlf8 [1] on YouTube.
It is worth mentioning that this song is a simple piano version of “Stream of Praise”. An advanced version of the song can be found here https://www.youtube.com/watch?v=DAOrSvexSJ8 [3]. It must take me at least 50 hours to learn this advanced version.
This video is a pilot project for me, because “Stream of Praise” is the first song I learned outside the Alfred’s-piano-book world. When I stayed away from Alfred’s piano books, I faced three grand challenges. First, it is non-trivial to choose a song that meets my current skill level. Second, there is no fingering suggestion marked on the sheet music. Last, no sample video found on YouTube. I tried various finger positions before finalizing my own style, which is marked on the sheet music posted in this video.
I am grateful to my colleague – Dr. Jeffrey Overbey [2] – for teaching me the correct finger positions of bars 4-5. I was amazed by Dr. Overbey’s sight reading skill; he read the sheet music for two seconds and immediately played the song. It took me over 19 hours to learn and practice; in contrast, he could play this song by sight-reading on the first attempt.
I would like to express my gratitude to Mike eKim (https://www.youtube.com/user/mbut123) [5], who offered insightful advice on how to play the first five measures. Mike demonstrated how to play these bars in a video (https://www.youtube.com/watch?v=_QeTQFviE88) posted on his YouTube channel [6].
I would like to thank Sean Fox for his advice on the fingering and tempo issues. He pointed out that I should play the sixteenth notes in bars 4-5 faster.
Bars 1-5 are very difficult; I could not make them musically sound until a practice of two hours. Fortunately, Mike's magic fingering position solved this problem (see [6] for the solution). Currently, I am learning how to play and sing at the same time. Ying enjoys singing this song when I play it on our piano.
The recording success rate is 19.2%, which is slightly higher than that (i.e., 12.5%) of the previous song. The tempo of this song is 83 BPM, which is marginally faster than the ideal one (i.e., 80 BPM).
A Summary of the Learning Process:
Tempo: 83 BPM (Ideal tempo: 80 BPM)
Recording: 47 minutes (26 takes, 5 acceptable videos)
Success Rate: 5/26 = 19.2%
Data center specific thermal and energy saving techniquesXiao Qin
Abstract: Data centers are ever increasing as we become more reliant of web based transactions. The benefits of such massive computing are obvious by the speed and ease we can get most media or information. A challenge is that new large data centers introduce a level of energy consumption that the world has not seen before. The obvious energy cost of running the computers is a billion dollar problem, but there are hidden costs like running cooling systems as well. To help combat the problems of large data centers, we aim at developing solutions that can work for each type of data center. This could entail creating tools that are generic enough to work for all data centers, or focusing on specific tools the type of software running in the data center. In this talk, we present a thermal model that is flexible enough to be applicable for all data centers; we show how our model can be used to save energy. We also discuss new energy saving techniques for Hadoop clusters specifically, where we focus on very data centric implementations of Hadoop to gain a significant energy savings.
This document provides advice for students on how to do research from Xiao Qin, an associate professor at Auburn University. It outlines Qin's career path in research from undergraduate to current position. The document then gives 10 pieces of advice for being a successful research assistant, including managing your time well, developing intellectual discipline, being proactive, learning to communicate, developing an intellectual community, networking, choosing a good research problem, understanding faculty, studying successful people, and having a life outside of research. It directs students to Qin's webpage and slideshares for further resources.
This document discusses C++ header files. It contains the following key points:
- Header files are used to build libraries of classes and functions that can be reused across multiple programs. They are similar to predefined libraries.
- There are three main rules for header file inclusion: 1) Be tolerant of duplicate inclusions, 2) Use forward declarations when possible instead of includes, 3) Header inclusion order is not important.
- Forward declarations can be used when a class only uses references or pointers to another class, but includes are needed when a class derives from or uses objects of another class.
HDFS-HC2: Analysis of Data Placement Strategy based on Computing Power of Nod...Xiao Qin
Hadoop and the term 'Big Data' go hand in hand. The information explosion caused
due to cloud and distributed computing lead to the curiosity to process and analyze massive
amount of data. The process and analysis helps to add value to an organization or derive
valuable information.
The current Hadoop implementation assumes that computing nodes in a cluster are
homogeneous in nature. Hadoop relies on its capability to take computation to the nodes
rather than migrating the data around the nodes which might cause a signicant network
overhead. This strategy has its potential benets on homogeneous environment but it might
not be suitable on an heterogeneous environment. The time taken to process the data on a
slower node on a heterogeneous environment might be signicantly higher than the sum of
network overhead and processing time on a faster node. Hence, it is necessary to study the
data placement policy where we can distribute the data based on the processing power of
a node. The project explores this data placement policy and notes the ramications of this
strategy based on running few benchmark applications.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
1. Performance Issues on
Hadoop Clusters
Jiong Xie
Advisor: Dr. Xiao Qin
Committee Members:
Dr. Cheryl Seals
Dr. Dean Hendrix
University Reader:
Dr. Fa Foster Dai
05/08/12 1
2. Overview of My Research
Data locality Data movement Data shuffling
Data Placement Prefetching Data
Reduce network
on Heterogeneous from Disk
congest
Cluster to Memory
[To Be Submitted]
[HCW 10] [Submit to IPDPS]
05/08/12 2
6. Hadoop Overview
--Mapreduce Running System
(J. Dean and S. Ghemawat. Mapreduce: Simplified data processing on large clusters.
OSDI ’04, pages 137–150)
05/08/12 6
9. Existing Hadoop Clusters
• Observation 1: Cluster nodes are
dedicated
– Data locality issues
– Data transfer time
• Observation 2: The number of nodes is
increased
d Scalability issues
e Shuffling overhead goes up
05/08/12 9
11. Solutions
P1: Data placement
Offline, distributed data, heterogeneous node
P2: Prefetching P3: Preshuffling
Online, data preloading Intermediate data movement,
reducing traffic
05/08/12 11
12. Improving MapReduce
Performance through Data
Placement in Heterogeneous
Hadoop Clusters
05/08/12 12
13. Motivational Example
Node A 1 task/min
(fast)
Node B 2x slower
(slow)
Node C 3x slower
(slowest)
Time (min)
05/08/12 13
14. The Native Strategy
Node A 6 tasks
Node B 3 tasks
Node C 2 tasks
Time (min)
Loading Transferring Processing
05/08/12 14
15. Our Solution
--Reducing data transfer time
Node A
6 tasks
Node A’
Node B’ 3 tasks
Node C’ 2 tasks
Time (min)
Loading Transferring Processing
05/08/12 15
16. Challenges
• Does distribution strategy depend on
applications?
• Initialization of data distribution
• The data skew problems
– New data arrival
– Data deletion
– Data updating
– New joining nodes
05/08/12 16
17. Measure Computing Ratios
• Computing ratio
• Fast machines process large data sets
1 task/min
Node A
Node B 2x slower
Node C 3x slower
Time
05/08/12 17
18. Measuring Computing Ratios
1. Run an application, collect response time
2. Set ratio of a node offering the shortest response time as 1
3. Normalize ratios of other nodes
4. Calculate the least common multiple of these ratios
5. Determine the amount of data processed by each node
Node Response Ratio # of File Speed
time(s) Fragments
Node A 10 1 6 Fastest
Node B 20 2 3 Average
Node C 30 3 2 Slowest
05/08/12 18
19. Initialize Data Distribution
Portions
3:2:1
• Input files split into 64MB Namenode
File1
blocks 1
2
4
5
3 6
• Round-Robin data
distribution algorithm
A B C
7 a
8 c
b
9
Datanodes
05/08/12 19
20. Data Redistribution 4
3
2
1
1.Get network topology, Namenode
ratio, and utilization L1 A C
2.Build and sort two lists:
under-utilized node list L1 L2 B
over-utilized node list L2
3. Select the source and A B C Portion
destination node from 3:2:1
the lists.
1 4 7 a 6
4.Transfer data 2 5 8 b
9 c
5.Repeat step 3, 4 until the 3
list is empty.
05/08/12 20
21. Experimental Environment
Node CPU Model CPU(Hz) L1 Cache(KB)
Node A Intel core 2 Duo 2*1G=2G 204
Node B Intel Celeron 2.8G 256
Node C Intel Pentium 3 1.2G 256
Node D Intel Pentium 3 1.2G 256
Node E Intel Pentium 3 1.2G 256
Five nodes in a Hadoop heterogeneous cluster
05/08/12 21
22. Benckmarks
• Grep: a tool searching for a regular
expression in a text file
• WordCount: a program used to count
words in a text file
• Sort: a program used to list the inputs in
sorted order.
05/08/12 22
23. Response Time of Grep and
Wordcount in Each Node
Application dependence
Computing ratio is
Data size independence
05/08/12 23
24. Computing Ratio for Two
Applications
Computing Node Ratios for Grep Ratios for Wordcount
Node A 1 1
Node B 2 2
Node C 3.3 5
Node D 3.3 5
Node E 3.3 5
Computing ratio of the five nodes with respective of Grep and
Wordcount applications
05/08/12 24
26. Impact of data placement on
performance of Grep
05/08/12 26
27. Impact of data placement on
performance of WordCount
05/08/12 27
28. Summary of Data Placement
P1: Data Placement Strategy
• Motivation: Fast machines process large data sets
• Problem: Data locality issue in heterogeneous
clusters
• Contributions: Distribute data according to
computing capability
– Measure computing ratio
– Initialize data placement
– Redistribution
05/08/12 28
30. Prefetching
• Goal: Improving performance
• Approach
– Best effort to guarantee data locality.
– Keeping data close to computing nodes
– Reducing the CPU stall time
05/08/12 30
31. Challenges
• What to prefetch?
• How to prefetch?
• What is the size of blocks to be
prefetched?
05/08/12 31
32. Dataflow in Hadoop
1.Submit job
t
ea
k
rtb
tas
2.Schedule
ea
xt
5.h
Ne
6.
3.Read Input map Local
reduce
FS
Block 1
HDFS
Block 2 Local
map FS reduce
7.Read new file
4. Run map
05/08/12 32
33. Dataflow in Hadoop
1.Submit job
t
2.Schedule
ea
k
rtb
+ more task
tas
ea
+ meta
xt
5.h
Ne
data
6.
3.Read Input map Local
reduce
FS
Block 1
HDFS
Block 2 Local
map FS reduce
5.1.Read new file
4. Run map
05/08/12 33
41. Summary
P2: Predictive Scheduler and Prefetching
• Goal: Moving data before task assigns
• Problem: Synchronization task and data
• Contributions: Preloading the required data early
than the task assigned
– Predictive scheduler
– Prefetching mechanism
– Worker thread
05/08/12 41
43. Preshuffling
• Observation 1: Too much data move from
Map worker to Reduce worker
– Solution1: Map nodes apply pre-shuffling
functions to their local output
• Observation 2: No reduce can start until a
map is complete.
– Solution2: Intermediate data is pipelined
between mappers and reducers.
05/08/12 43
44. Preshuffling
• Goal : Minimize data shuffle during
Reduce
• Approach
– Pipeline
– Overlap between map and data movement
– Group map and reduce
• Challenges
– Synchronize map and reduce
– Data locality
05/08/12 44
45. Dataflow in Hadoop
1.Submit job 2.Schedule
2.
at
Ne
e
rtb
k
w
tas
tas
ea
xt
5.h
k
Ne
6.
3.Read Input 5.Write data
map Local
FS
reduce
Block 1
HTTP GET HDFS
HDFS
Block 2 Local
map FS reduce
3. Request data
4. Run map 4. Send data
05/08/12 45
46. PreShuffle
map reduce
map reduce
Data request
05/08/12 46
56. Run Time affected by Network Condition
Experiment result conducted by Yixian Yang
05/08/12 56
57. Traffic Volume affected by Network
Condition
Experiment result conducted by Yixian Yang
05/08/12 57
Editor's Notes
Comment: three colors.
1. 將要執行的 MapReduce 程式複製到 Master 與每一臺 Worker 機器中。 2.Master 決定 Map 程式與 Reduce 程式,分別由哪些 Worker 機器執行。 3. 將所有的資料區塊,分配到執行 Map 程式的 Worker 機器中進行 Map 。 4. 將 Map 後的結果存入 Worker 機器的本地磁碟。 5. 執行 Reduce 程式的 Worker 機器,遠端讀取每一份 Map 結果,進行彙整與排序,同時執行 Reduce 程式。 6. 將使用者需要的運算結果輸出。 Hadoop MapReduce Single master node, many worker nodes Client submits a job to master node Master splits each job into tasks (map/reduce), and assigns tasks to worker nodes
Hadoop MapReduce Single master node, many worker nodes Client submits a job to master node Master splits each job into tasks (map/reduce), and assigns tasks to worker nodes Hadoop Distributed File System (HDFS) Single name node, many data nodes Files stored as large, fixed-size (e.g. 64MB) blocks HDFS typically holds map input and reduce output
Comment: no “want to”
90
强调
Comments: differences between shuffling overhead and data transfer time.
Comment: what is computing ratio.
Comment 1: make step descriptions short. Comment 2: animations map to steps.
blocks
More io feature
Higher resolution
Comment: Titles of all the slides must use the same format Up to 33.1% abd 10.2% with average of 17.3% and 7.1%
Up to 33.1% abd 10.2% with average of 17.3% and 7.1%
Up to 33.1% abd 10.2% with average of 17.3% and 7.1%
which data block should be loaded and where is it? how to synchronize computation process with prefetching data. When the data is loaded to the cache ahead the requirement for a long time, it wastes the resource. On the contrary, if the data arrives later than the computation, it is useless. how to optimize the prefetching rate. What is the best percentage of data pre-loading of each machine? The MapReduce can maximal benefit from the best prefetching rate while minimizing the prefetching overhead. When to prefetch is to control how early to trigger the prefetching action of which node. In our previous research, we observed that one node process the same block size data in a fix time. Before the last block finishes, the next block starts loading data. In this design, we estimate the execution time of each block in each node. The execution time for the same application in various node may be different. we collect and adapt the average the running time for a block in each node. What to prefech is to determine which block load to the memory. Initially, the predictive scheduler assign two tasks to the each node. when the preloading work was trigger, it can access the data according to the required information from second task. How much to prefech is decide the amount of prepared data preload to the memory. According to the scheduler, when one task in running, one or more tasks are waiting in the node, the meta data is loaded in the memory of this node. When the prefetching action is triggered, the preloading work automatically load data form disk to memory. In view of large size of HDFS blocks, the number should be not so aggressive. Here we set the number of task is two, the prefetching block is one.
Comment: add animations.
Comment: add animations.
Figure 4.3: Comparing the excution time of Grep in native and prefetching Mapreduce Grep 9.5% 1G 8.5% 2G WordCount 8.9% 8.1% 2G
Above single large file 9% Small files 24% Increase the number of computing node we can get the same improvement through psp
第三部分动画比较典型,但 slide44-slide53 的关系还需要提炼,最好能有所加强。
One map task for each block of the input file Applies user-defined map function to each record in the block Record = <key, value> User-defined number of reduce tasks Each reduce task is assigned a set of record groups Record group = all records with same key For each group, apply user-defined reduce function to the record values in that group Reduce tasks read from every map task Each read returns the record groups for that reduce task
To reduce the amount of transfer data, the map worker apply combiner functions to their local outputs before storing or transferring the intermediate data so as to reduce the traffic utilization. The combining strategy can minimize the amount of data that needs to be transferred to the reducers and speed up the execution time of the overall job. To reduce the communication between nodes, intermediate results are pipelined between fixed mappers and reducers. The reducer do not need to ask for each map node in the cluster. Moreover, the reducers begin processing data as soon as it is produced by mappers. 57 To use the overlapping between data movement and map computation. Our experiment shows that shuffle is always much longer than map computation time. Especially, when the network is saturated, overlapping time will disappear consequently. Secondly, reducing latency in the network also improve throughput but also increase the possibility of network conflict. Pipeline idea can be lend to hide the overlapping. To limit the communication between map and reduce. In MapReduce’s parallel execu- tion, a node’s execution includes map computation, waiting for shuffle, and reduce compu- tation, and the critical path is the slowest node. To improve performance, the critical path has to be shortened. However, the natural overlap of one node’s shuffle wait with another node’s execution does not shorten the critical path. Instead, the operations within a single node need to be overlapped. The reduce check all the avail data from all the map node in the cluster. If some reduce node and map node can group with particular key-value pairs. the network communicate costs can be shorten.
The execution time of native Hadoop for 1GB data WordCount Star. Figure 5.4.2 illustrates the average response time for 1GB data WordCount of native Hadoop. The Reduce does not launch the progress until the all the map tasks finish. Figure 5.1(b) illustrates the average response time for 1GB data WordCount of preshuffling Hadoop. The Reduce task receive map outputs a little earlier and can begin sorting earlier so as to reduce the time to required for the final merge can achieve a better cluster utilization by 14%. In the native Hadoop, Hadoop can finish all the map tasks earlier than preshuffling hadoop because preshuffling allow reduce tasks to begin executing more quickly; the reduce tasks prepare for longing required resources, causing the map phase take longer.
Figure 5.2 reports the improvement of Preshuffling compared with native Hadoop for WordCount. With the increase of block size, Preshuffling can achieve better improvement. when the block size is less than 16MB, the Preshuffling algorithm did not gain a significant performance improvement. The same results can be found in Figure 5.3. When the block size increases to 128MB or more, the improvement also arrive to a fix number. Preshuffling Hadoop can gain average 14% for WordCount and 12.5% for Sort. Figure 5.2: The improvement of Preshuffling with different block size for WordCount. Figure 5.2: The improvement of Preshuffling with different block size for Sort.
title
Average 14% improvement
Hadoop Archive Hadoop Archive 或者 HAR ,是一个高效地将小文件放入 HDFS 块中的文件存档工具,它能够将多个小文件打包成一个 HAR 文件,这样在减少 namenode 内存使用的同时,仍然允许对文件进行透明的访问。 对某个目录 /foo/bar 下的所有小文件存档成 /outputdir/ zoo.har : hadoop archive -archiveName zoo.har -p /foo/bar /outputdir 当然,也可以指定 HAR 的大小 ( 使用 -Dhar.block.size) 。 HAR 是在 Hadoop file system 之上的一个文件系统,因此所有 fs shell 命令对 HAR 文件均可用,只不过是文件路径格式不一样, HAR 的访问路径可以是以下两种格式: Sequence file sequence file 由一系列的二进制 key/value 组成,如果为 key 小文件名, value 为文件内容,则可以将大批小文件合并成一个大文件。 Hadoop-0.21.0 中提供了 SequenceFile ,包括 Writer , Reader 和 SequenceFileSorter 类进行写,读和排序操作。如果 hadoop 版本低于 0.21.0 的版本,实现方法可参见 [3] 。 CombineFileInputFormat CombineFileInputFormat 是一种新的 inputformat ,用于将多个文件合并成一个单独的 split ,另外,它会考虑数据的存储位置。