The document provides an introduction to MapReduce, including:
- MapReduce is a framework for executing parallel algorithms across large datasets using commodity computers. It is based on map and reduce functions.
- Mappers process input key-value pairs in parallel, and outputs are sorted and grouped by the reducers.
- Examples demonstrate how MapReduce can be used for tasks like building indexes, joins, and iterative algorithms.
This document provides an overview of MapReduce in Hadoop. It defines MapReduce as a distributed data processing paradigm designed for batch processing large datasets in parallel. The anatomy of MapReduce is explained, including the roles of mappers, shufflers, reducers, and how a MapReduce job runs from submission to completion. Potential purposes are batch processing and long running applications, while weaknesses include iterative algorithms, ad-hoc queries, and algorithms that depend on previously computed values or shared global state.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
Here is how you can solve this problem using MapReduce and Unix commands:
Map step:
grep -o 'Blue\|Green' input.txt | wc -l > output
This uses grep to search the input file for the strings "Blue" or "Green" and print only the matches. The matches are piped to wc which counts the lines (matches).
Reduce step:
cat output
This isn't really needed as there is only one mapper. Cat prints the contents of the output file which has the count of Blue and Green.
So MapReduce has been simulated using grep for the map and cat for the reduce functionality. The key aspects are - grep extracts the relevant data (map
This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
Very basic Introduction to Big Data. Touches on what it is, characteristics, some examples of Big Data frameworks. Hadoop 2.0 example - Yarn, HDFS and Map-Reduce with Zookeeper.
A very high-level introduction to scaling out wth Hadoop and NoSQL combined with some experiences on my current project. I gave this presentation at the JFall 2009 conference in the Netherlands
This document provides an overview of MapReduce in Hadoop. It defines MapReduce as a distributed data processing paradigm designed for batch processing large datasets in parallel. The anatomy of MapReduce is explained, including the roles of mappers, shufflers, reducers, and how a MapReduce job runs from submission to completion. Potential purposes are batch processing and long running applications, while weaknesses include iterative algorithms, ad-hoc queries, and algorithms that depend on previously computed values or shared global state.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
Here is how you can solve this problem using MapReduce and Unix commands:
Map step:
grep -o 'Blue\|Green' input.txt | wc -l > output
This uses grep to search the input file for the strings "Blue" or "Green" and print only the matches. The matches are piped to wc which counts the lines (matches).
Reduce step:
cat output
This isn't really needed as there is only one mapper. Cat prints the contents of the output file which has the count of Blue and Green.
So MapReduce has been simulated using grep for the map and cat for the reduce functionality. The key aspects are - grep extracts the relevant data (map
This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
Very basic Introduction to Big Data. Touches on what it is, characteristics, some examples of Big Data frameworks. Hadoop 2.0 example - Yarn, HDFS and Map-Reduce with Zookeeper.
A very high-level introduction to scaling out wth Hadoop and NoSQL combined with some experiences on my current project. I gave this presentation at the JFall 2009 conference in the Netherlands
This document discusses techniques for data reduction to reduce the size of large datasets for analysis. It describes five main strategies for data reduction: data cube aggregation, dimensionality reduction, data compression, numerosity reduction, and discretization. Data cube aggregation involves aggregating data at higher conceptual levels, such as aggregating quarterly sales data to annual totals. Dimensionality reduction removes redundant attributes. The document then focuses on attribute subset selection techniques, including stepwise forward selection, stepwise backward elimination, and combinations of the two, to select a minimal set of relevant attributes. Decision trees can also be used for attribute selection by removing attributes not used in the tree.
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
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
This document provides an introduction to Hadoop, including its motivation and key components. It discusses the scale of cloud computing that Hadoop addresses, and describes the core Hadoop technologies - the Hadoop Distributed File System (HDFS) and MapReduce framework. It also briefly introduces the Hadoop ecosystem, including other related projects like Pig, HBase, Hive and ZooKeeper. Sample code is walked through to illustrate MapReduce programming. Key aspects of HDFS like fault tolerance, scalability and data reliability are summarized.
MapReduce is a programming model for processing large datasets in a distributed system. It involves a map step that performs filtering and sorting, and a reduce step that performs summary operations. Hadoop is an open-source framework that supports MapReduce. It orchestrates tasks across distributed servers, manages communications and fault tolerance. Main steps include mapping of input data, shuffling of data between nodes, and reducing of shuffled data.
Introduction to Big Data & Hadoop Architecture - Module 1Rohit Agrawal
Learning Objectives - In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem; How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
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.
The document provides an introduction to NoSQL and HBase. It discusses what NoSQL is, the different types of NoSQL databases, and compares NoSQL to SQL databases. It then focuses on HBase, describing its architecture and components like HMaster, regionservers, Zookeeper. It explains how HBase stores and retrieves data, the write process involving memstores and compaction. It also covers HBase shell commands for creating, inserting, querying and deleting data.
This document introduces HBase, an open-source, non-relational, distributed database modeled after Google's BigTable. It describes what HBase is, how it can be used, and when it is applicable. Key points include that HBase stores data in columns and rows accessed by row keys, integrates with Hadoop for MapReduce jobs, and is well-suited for large datasets, fast random access, and write-heavy applications. Common use cases involve log analytics, real-time analytics, and messages-centered systems.
This document discusses concepts related to data streams and real-time analytics. It begins with introductions to stream data models and sampling techniques. It then covers filtering, counting, and windowing queries on data streams. The document discusses challenges of stream processing like bounded memory and proposes solutions like sampling and sketching. It provides examples of applications in various domains and tools for real-time data streaming and analytics.
The document discusses the Hadoop ecosystem, which includes core Apache Hadoop components like HDFS, MapReduce, YARN, as well as related projects like Pig, Hive, HBase, Mahout, Sqoop, ZooKeeper, Chukwa, and HCatalog. It provides overviews and diagrams explaining the architecture and purpose of each component, positioning them as core functionality that speeds up Hadoop processing and makes Hadoop more usable and accessible.
The document provides an overview of MapReduce, including:
1) MapReduce is a programming model and implementation that allows for large-scale data processing across clusters of computers. It handles parallelization, distribution, and reliability.
2) The programming model involves mapping input data to intermediate key-value pairs and then reducing by key to output results.
3) Example uses of MapReduce include word counting and distributed searching of text.
The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. It employs a Master and Slave architecture with a NameNode that manages metadata and DataNodes that store data blocks. The NameNode tracks locations of data blocks and regulates access to files, while DataNodes store file blocks and manage read/write operations as directed by the NameNode. HDFS provides high-performance, scalable access to data across large Hadoop clusters.
HDFS is a distributed file system designed to run on commodity hardware. It provides high-performance access to big data across Hadoop clusters and supports big data analytics applications in a low-cost manner. The NameNode stores metadata and manages the file system namespace, while DataNodes store file data in blocks and handle replication for fault tolerance. Clients interact with the NameNode for file operations like writing blocks to DataNodes for storage and reading file blocks.
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
Hive is a data warehouse infrastructure tool that allows users to query and analyze large datasets stored in Hadoop. It uses a SQL-like language called HiveQL to process structured data stored in HDFS. Hive stores metadata about the schema in a database and processes data into HDFS. It provides a familiar interface for querying large datasets using SQL-like queries and scales easily to large datasets.
This document summarizes a presentation on Big Data analytics using R. It introduces R as a programming language for statistics, mathematics, and data science. It is open source and has an active user community. The presentation then discusses Revolution R Enterprise, a commercial product that builds upon R to enable high performance analytics on big data across multiple platforms and data sources through parallelization, distributed computing, and integration tools. It aims to allow writing analytics code once that can be deployed anywhere.
This document provides an introduction to Hadoop and MapReduce. It describes how Hadoop is composed of MapReduce and HDFS. MapReduce is a programming model that allows processing of large datasets in a distributed, parallel manner. It works by breaking the processing into mappers that perform filtering and sorting, and reducers that perform summarization. HDFS provides a distributed file system that stores data across clusters of machines. An example of word count using MapReduce is described to illustrate how it works.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created in 2005 and is designed to reliably handle large volumes of data and complex computations in a distributed fashion. The core of Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing data in parallel across large clusters of computers. It is widely adopted by companies handling big data like Yahoo, Facebook, Amazon and Netflix.
This document discusses techniques for data reduction to reduce the size of large datasets for analysis. It describes five main strategies for data reduction: data cube aggregation, dimensionality reduction, data compression, numerosity reduction, and discretization. Data cube aggregation involves aggregating data at higher conceptual levels, such as aggregating quarterly sales data to annual totals. Dimensionality reduction removes redundant attributes. The document then focuses on attribute subset selection techniques, including stepwise forward selection, stepwise backward elimination, and combinations of the two, to select a minimal set of relevant attributes. Decision trees can also be used for attribute selection by removing attributes not used in the tree.
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
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
This document provides an introduction to Hadoop, including its motivation and key components. It discusses the scale of cloud computing that Hadoop addresses, and describes the core Hadoop technologies - the Hadoop Distributed File System (HDFS) and MapReduce framework. It also briefly introduces the Hadoop ecosystem, including other related projects like Pig, HBase, Hive and ZooKeeper. Sample code is walked through to illustrate MapReduce programming. Key aspects of HDFS like fault tolerance, scalability and data reliability are summarized.
MapReduce is a programming model for processing large datasets in a distributed system. It involves a map step that performs filtering and sorting, and a reduce step that performs summary operations. Hadoop is an open-source framework that supports MapReduce. It orchestrates tasks across distributed servers, manages communications and fault tolerance. Main steps include mapping of input data, shuffling of data between nodes, and reducing of shuffled data.
Introduction to Big Data & Hadoop Architecture - Module 1Rohit Agrawal
Learning Objectives - In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem; How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
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.
The document provides an introduction to NoSQL and HBase. It discusses what NoSQL is, the different types of NoSQL databases, and compares NoSQL to SQL databases. It then focuses on HBase, describing its architecture and components like HMaster, regionservers, Zookeeper. It explains how HBase stores and retrieves data, the write process involving memstores and compaction. It also covers HBase shell commands for creating, inserting, querying and deleting data.
This document introduces HBase, an open-source, non-relational, distributed database modeled after Google's BigTable. It describes what HBase is, how it can be used, and when it is applicable. Key points include that HBase stores data in columns and rows accessed by row keys, integrates with Hadoop for MapReduce jobs, and is well-suited for large datasets, fast random access, and write-heavy applications. Common use cases involve log analytics, real-time analytics, and messages-centered systems.
This document discusses concepts related to data streams and real-time analytics. It begins with introductions to stream data models and sampling techniques. It then covers filtering, counting, and windowing queries on data streams. The document discusses challenges of stream processing like bounded memory and proposes solutions like sampling and sketching. It provides examples of applications in various domains and tools for real-time data streaming and analytics.
The document discusses the Hadoop ecosystem, which includes core Apache Hadoop components like HDFS, MapReduce, YARN, as well as related projects like Pig, Hive, HBase, Mahout, Sqoop, ZooKeeper, Chukwa, and HCatalog. It provides overviews and diagrams explaining the architecture and purpose of each component, positioning them as core functionality that speeds up Hadoop processing and makes Hadoop more usable and accessible.
The document provides an overview of MapReduce, including:
1) MapReduce is a programming model and implementation that allows for large-scale data processing across clusters of computers. It handles parallelization, distribution, and reliability.
2) The programming model involves mapping input data to intermediate key-value pairs and then reducing by key to output results.
3) Example uses of MapReduce include word counting and distributed searching of text.
The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. It employs a Master and Slave architecture with a NameNode that manages metadata and DataNodes that store data blocks. The NameNode tracks locations of data blocks and regulates access to files, while DataNodes store file blocks and manage read/write operations as directed by the NameNode. HDFS provides high-performance, scalable access to data across large Hadoop clusters.
HDFS is a distributed file system designed to run on commodity hardware. It provides high-performance access to big data across Hadoop clusters and supports big data analytics applications in a low-cost manner. The NameNode stores metadata and manages the file system namespace, while DataNodes store file data in blocks and handle replication for fault tolerance. Clients interact with the NameNode for file operations like writing blocks to DataNodes for storage and reading file blocks.
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
Hive is a data warehouse infrastructure tool that allows users to query and analyze large datasets stored in Hadoop. It uses a SQL-like language called HiveQL to process structured data stored in HDFS. Hive stores metadata about the schema in a database and processes data into HDFS. It provides a familiar interface for querying large datasets using SQL-like queries and scales easily to large datasets.
This document summarizes a presentation on Big Data analytics using R. It introduces R as a programming language for statistics, mathematics, and data science. It is open source and has an active user community. The presentation then discusses Revolution R Enterprise, a commercial product that builds upon R to enable high performance analytics on big data across multiple platforms and data sources through parallelization, distributed computing, and integration tools. It aims to allow writing analytics code once that can be deployed anywhere.
This document provides an introduction to Hadoop and MapReduce. It describes how Hadoop is composed of MapReduce and HDFS. MapReduce is a programming model that allows processing of large datasets in a distributed, parallel manner. It works by breaking the processing into mappers that perform filtering and sorting, and reducers that perform summarization. HDFS provides a distributed file system that stores data across clusters of machines. An example of word count using MapReduce is described to illustrate how it works.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created in 2005 and is designed to reliably handle large volumes of data and complex computations in a distributed fashion. The core of Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing data in parallel across large clusters of computers. It is widely adopted by companies handling big data like Yahoo, Facebook, Amazon and Netflix.
This document presents a Hadoop project to analyze stock prices using MapReduce and Hive. The project aims to find the adjusted closing price for stock days without dividends by joining daily stock price and dividend datasets. The technical architecture uses Eclipse, Hadoop, and AWS EC2 for development and clustering. Pseudo code is provided for MapReduce jobs to parse input data and calculate adjusted closing prices for output. The results found adjusted closing prices for 44 records out of 75 total records by removing headers and dividend-only days.
The document introduces MapReduce 2 and YARN, which were designed to address limitations in MapReduce 1. YARN allows for decoupling of MapReduce processing from cluster resource management, enabling better resource utilization and support for additional applications beyond MapReduce. It separates resource management from job scheduling and processing, with a centralized ResourceManager and per-node NodeManagers. This improves scalability and high availability. The new architecture also allows for containers with variable resource limits rather than fixed slot types.
The document discusses various join algorithms that can be used in MapReduce frameworks. It begins by introducing MapReduce and Hadoop frameworks and explaining the map and reduce phases. It then outlines the objectives of comparing join algorithms. The document goes on to describe several join algorithms - map-side join, reduce-side join, repartition join, broadcast join, trojan join, and replicated join. It explains the process for each algorithm and compares their advantages and issues. Finally, it provides a decision tree for selecting the optimal join algorithm based on factors like schema knowledge, data size, and replication efficiency.
Hadoop 2 introduces the YARN framework to provide a common platform for multiple data processing paradigms beyond just MapReduce. YARN splits cluster resource management from application execution, allowing different applications like MapReduce, Spark, Storm and others to run on the same Hadoop cluster. HDFS 2 improves HDFS with features like high availability, federation and snapshots. Apache Tez provides a new data processing engine that enables pipelining of jobs to improve performance over traditional MapReduce.
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.
Hadoop is a distributed processing framework for large datasets. It stores data across clusters of commodity hardware in a Hadoop Distributed File System (HDFS) and provides tools for distributed processing using MapReduce. HDFS uses a master-slave architecture with a namenode managing metadata and datanodes storing data blocks. Data is replicated across nodes for reliability. MapReduce allows distributed processing of large datasets in parallel across clusters.
The document presents an introduction to MapReduce. It discusses how MapReduce provides an easy framework for distributed computing by allowing programmers to write simple map and reduce functions without worrying about complex distributed systems issues. It outlines Google's implementation of MapReduce and how it uses the Google File System for fault tolerance. Alternative open-source implementations like Apache Hadoop are also covered. The document discusses how MapReduce has been widely adopted by companies to process massive amounts of data and analyzes some criticism of MapReduce from database experts. It concludes by noting trends in using MapReduce as a parallel database and for multi-core processing.
This was a presentation on my book MapReduce Design Patterns, given to the Twin Cities Hadoop Users Group. Check it out if you are interested in seeing what my my book is about.
This talk was prepared for the November 2013 DataPhilly Meetup: Data in Practice ( http://www.meetup.com/DataPhilly/events/149515412/ )
Map Reduce: Beyond Word Count by Jeff Patti
Have you ever wondered what map reduce can be used for beyond the word count example you see in all the introductory articles about map reduce? Using Python and mrjob, this talk will cover a few simple map reduce algorithms that in part power Monetate's information pipeline
Bio: Jeff Patti is a backend engineer at Monetate with a passion for algorithms, big data, and long walks on the beach. Prior to working at Monetate he performed software R&D for Lockheed Martin, where he worked on projects ranging from social network analysis to robotics.
This document provides a summary of MapReduce algorithms. It begins with background on the author's experience blogging about MapReduce algorithms in academic papers. It then provides an overview of MapReduce concepts including the mapper and reducer functions. Several examples of recently published MapReduce algorithms are described for tasks like machine learning, finance, and software engineering. One algorithm is examined in depth for building a low-latency key-value store. Finally, recommendations are provided for designing MapReduce algorithms including patterns, performance, and cost/maintainability considerations. An appendix lists additional MapReduce algorithms from academic papers in areas such as AI, biology, machine learning, and mathematics.
This document provides an introduction and overview of HDFS and MapReduce in Hadoop. It describes HDFS as a distributed file system that stores large datasets across commodity servers. It also explains that MapReduce is a framework for processing large datasets in parallel by distributing work across clusters. The document gives examples of how HDFS stores data in blocks across data nodes and how MapReduce utilizes mappers and reducers to analyze datasets.
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
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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.
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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.
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Applications of MapReduce
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
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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 provides an overview of the Hadoop MapReduce Fundamentals course. It discusses what Hadoop is, why it is used, common business problems it can address, and companies that use Hadoop. It also outlines the core parts of Hadoop distributions and the Hadoop ecosystem. Additionally, it covers common MapReduce concepts like HDFS, the MapReduce programming model, and Hadoop distributions. The document includes several code examples and screenshots related to Hadoop and MapReduce.
Sam believed an apple a day keeps the doctor away. He cut an apple and used a blender to make juice, applying this process to other fruits. Sam got a job at JuiceRUs for his talent in making juice. He later implemented a parallel version of juice making that involved mapping key-value pairs to other key-value pairs, then grouping and reducing them into a list of values, like the classical MapReduce model. Sam realized he could use a combiner after reducers to create mixed fruit juices more efficiently in a side effect free way.
The document discusses big data and MapReduce frameworks like Hadoop. It provides an overview of MapReduce and how it allows distributed processing of large datasets using simple map and reduce functions. The document also covers several common design patterns for MapReduce jobs, including filtering, sorting, joins, and computing statistics.
Bobby Evans and Tom Graves, the engineering leads for Spark and Storm development at Yahoo will talk about how these technologies are used on Yahoo's grids and reasons why to use one or the other.
Bobby Evans is the low latency data processing architect at Yahoo. He is a PMC member on many Apache projects including Storm, Hadoop, Spark, and Tez. His team is responsible for delivering Storm as a service to all of Yahoo and maintaining Spark on Yarn for Yahoo (Although Tom really does most of that work).
Tom Graves a Senior Software Engineer on the Platform team at Yahoo. He is an Apache PMC member on Hadoop, Spark, and Tez. His team is responsible for delivering and maintaining Spark on Yarn for Yahoo.
This document provides an overview of MapReduce, a programming model developed by Google for processing and generating large datasets in a distributed computing environment. It describes how MapReduce abstracts away the complexities of parallelization, fault tolerance, and load balancing to allow developers to focus on the problem logic. Examples are given showing how MapReduce can be used for tasks like word counting in documents and joining datasets. Implementation details and usage statistics from Google demonstrate how MapReduce has scaled to process exabytes of data across thousands of machines.
This document provides an overview of MapReduce and how it works. It discusses:
1) Traditional enterprise systems have centralized servers that create bottlenecks for processing large datasets, which MapReduce addresses by dividing tasks across multiple computers.
2) MapReduce contains two tasks - Map converts input data into key-value pairs, and Reduce combines the outputs from Map into a smaller set of pairs.
3) The MapReduce process involves input readers passing data to mappers, optional combiners, partitioners, sorting and shuffling to reducers, and output writers, allowing large datasets to be processed efficiently in parallel across clusters.
The WordCount and Sort examples demonstrate basic MapReduce algorithms in Hadoop. WordCount counts the frequency of words in a text document by having mappers emit (word, 1) pairs and reducers sum the counts. Sort uses an identity mapper and reducer to simply sort the input files by key. Both examples read from and write to HDFS, and can be run on large datasets to benchmark a Hadoop cluster's sorting performance.
The document describes MapReduce, a programming model and associated implementation for processing large datasets across distributed systems. MapReduce allows users to specify map and reduce functions to process key-value pairs. The runtime system automatically parallelizes and distributes the computation across clusters, handling failures and communication. Hundreds of programs have been implemented using MapReduce at Google to process terabytes of data on thousands of machines.
2004 map reduce simplied data processing on large clusters (mapreduce)anh tuan
The document describes MapReduce, a programming model and associated implementation for processing large datasets across distributed systems. It allows users to specify map and reduce functions to process key-value pairs. The runtime system handles parallelization across machines, partitioning data, scheduling execution, and handling failures. Hundreds of programs have been implemented using MapReduce at Google to process terabytes of data on thousands of machines.
The document describes MapReduce, a programming model and associated implementation for processing large datasets across distributed systems. It allows users to specify map and reduce functions to process key-value pairs. The runtime system handles parallelization across machines, partitioning data, scheduling execution, and handling failures. Hundreds of programs have been implemented using MapReduce at Google to process terabytes of data on thousands of machines.
Hadoop eco system with mapreduce hive and pigKhanKhaja1
This document provides an overview of MapReduce architecture and components. It discusses how MapReduce processes data using map and reduce tasks on key-value pairs. The JobTracker manages jobs by scheduling tasks on TaskTrackers. Data is partitioned and sorted during the shuffle and sort phase before being processed by reducers. Components like Hive, Pig, partitions, combiners, and HBase are described in the context of how they integrate with and optimize MapReduce processing.
Introduction to the Map-Reduce framework.pdfBikalAdhikari4
The document provides an introduction to the MapReduce programming model and framework. It describes how MapReduce is designed for processing large volumes of data in parallel by dividing work into independent tasks. Programs are written using functional programming idioms like map and reduce operations on lists. The key aspects are:
- Mappers process input records in parallel, emitting (key, value) pairs.
- A shuffle/sort phase groups values by key to same reducer.
- Reducers process grouped values to produce final output, aggregating as needed.
- This allows massive datasets to be processed across a cluster in a fault-tolerant way.
MapReduce is a programming model for processing large datasets in a distributed system. It allows parallel processing of data across clusters of computers. A MapReduce program defines a map function that processes key-value pairs to generate intermediate key-value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. The MapReduce framework handles parallelization of tasks, scheduling, input/output handling, and fault tolerance.
This document introduces MapReduce, a programming model for processing large datasets across distributed systems. It describes how users write map and reduce functions to specify computations. The MapReduce system automatically parallelizes jobs by splitting input data, running the map function on different parts in parallel, collecting output, and running the reduce function to combine results. It handles failures and distribution of work across machines. Many common large-scale data processing tasks can be expressed as MapReduce jobs. The system has been used to process petabytes of data on thousands of machines at Google.
This document introduces MapReduce, a programming model and associated implementation for processing large datasets across distributed systems. The key aspects are:
1. Users specify map and reduce functions that process key-value pairs. The map function produces intermediate key-value pairs and the reduce function merges values for the same key.
2. The system automatically parallelizes the computation by partitioning input data and scheduling tasks on a cluster. It handles failures, data distribution, and load balancing.
3. The implementation runs on large Google clusters and is highly scalable, processing terabytes of data on thousands of machines. Hundreds of programs use MapReduce daily at Google.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It provides reliable storage through HDFS and distributed processing via MapReduce. HDFS handles storage and MapReduce provides a programming model for parallel processing of large datasets across a cluster. The MapReduce framework consists of a mapper that processes input key-value pairs in parallel, and a reducer that aggregates the output of the mappers by key.
This document provides an overview of Hadoop frameworks and concepts. It discusses distributed file systems like HDFS and how they are organized at large scale. It then explains the MapReduce execution model and how it allows distributed processing of large datasets in a fault-tolerant manner. Specific algorithms like matrix-vector multiplication are discussed as examples of how MapReduce can be used. Finally, it introduces Hadoop YARN, which separates resource management from job execution, allowing more flexible processing of different types of applications on Hadoop clusters.
This document provides an overview of MapReduce and Hadoop frameworks. It describes how MapReduce works by dividing data processing into two phases - map and reduce. The map phase processes input data in parallel and produces intermediate key-value pairs, while the reduce phase aggregates the intermediate outputs by key. Hadoop provides an implementation of MapReduce by running tasks on a distributed file system and coordinating execution across clusters.
MapReduce is a programming model used for processing large datasets in a distributed computing environment. It consists of two main tasks - the Map task which converts input data into intermediate key-value pairs, and the Reduce task which combines these intermediate pairs into a smaller set of output pairs. The MapReduce framework operates on input and output in the form of key-value pairs, with the keys and values implemented as serializable Java objects. It divides jobs into map and reduce tasks executed in parallel on a cluster, with a JobTracker coordinating task assignment and tracking progress.
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Parallel Data Processing with MapReduce: A SurveyKyong-Ha Lee
This document summarizes a survey on parallel data processing with MapReduce. It provides an overview of the MapReduce framework, including its architecture, key concepts of Map and Reduce functions, and how it handles parallel processing. It also discusses some inherent pros and cons of MapReduce, such as its simplicity but also performance limitations. Finally, it outlines approaches studied in recent literature to improve and optimize the MapReduce framework.
This document provides an introduction to MapReduce, including:
1) MapReduce is a programming model and framework that supports distributed processing of large datasets across clusters of machines. It handles parallelization, fault tolerance, and scheduling.
2) The MapReduce model consists of map and reduce functions that specify the computation. Mappers process input key-value pairs and produce intermediate outputs. Reducers combine intermediate outputs by key.
3) The execution framework handles scheduling, data/code distribution, synchronization, and fault handling to execute the user-specified map and reduce functions on large datasets in parallel across a cluster.
Computing Scientometrics in Large-Scale Academic Search Engines with MapReduceLeonidas Akritidis
This document describes using MapReduce to efficiently compute scientometrics like the h-index on large academic datasets. It introduces four MapReduce algorithms to parallelize the computation. The most efficient approach uses an in-mapper combiner to create a list of <paper, score> pairs for each unique author during the map phase. This reduces running times by at least 20% and bandwidth usage by around 13% compared to alternatives. Experiments on a 1.8 million paper dataset showed this first method performed best both in terms of runtime and I/O sizes.
**MapReduce Types:**
1. **Classic MapReduce:**
- Traditional implementation introduced by Google.
- Comprises two main functions: Map and Reduce.
- Splits data into key-value pairs, processes them, and aggregates the results.
2. **Hadoop MapReduce:**
- Apache Hadoop's open-source implementation of MapReduce.
- Scales across a cluster of machines, handling large datasets efficiently.
- Provides fault tolerance and reliability.
**MapReduce Formats:**
1. **InputFormat:**
- Describes how to read input data and convert it into key-value pairs.
- Examples include TextInputFormat for plain text and SequenceFileInputFormat for binary data.
2. **OutputFormat:**
- Specifies how to write the output of the MapReduce job.
- Examples include TextOutputFormat for plain text and SequenceFileOutputFormat for binary data.
**MapReduce Features:**
1. **Fault Tolerance:**
- MapReduce systems handle node failures by redistributing tasks to other healthy nodes.
- Checkpoints and task re-execution contribute to fault tolerance.
2. **Scalability:**
- Easily scales horizontally by adding more machines to the cluster.
- Splits data into smaller chunks processed in parallel, enhancing scalability.
3. **Data Locality:**
- Optimizes performance by processing data on nodes where it resides, minimizing data transfer over the network.
- Improves efficiency in large-scale distributed systems.
4. **Parallel Processing:**
- Divides tasks into smaller sub-tasks processed concurrently.
- Enables efficient utilization of cluster resources and reduces overall processing time.
5. **Ease of Use:**
- Abstracts the complexity of parallel and distributed computing.
- Developers focus on defining Map and Reduce functions without managing low-level details.
6. **Flexibility:**
- Supports various programming languages, such as Java, Python, and others.
- Allows developers to choose the language suitable for their application.
These features collectively contribute to the effectiveness and popularity of MapReduce frameworks in handling large-scale data processing tasks.
Kinetica is a patented, in-memory, columnar, distributed, GPU-accelerated database. It was originally developed for the US Army to identify terrorist threats in real-time by ingesting and analyzing over 200 sources of streaming data including social media, drones, and cyber data. Kinetica can ingest 200 billion new records per hour and provides real-time, actionable intelligence. It leverages GPUs for high performance and can simultaneously ingest and analyze large volumes of data at scale in real-time.
This document provides an overview of deep learning on GPUs. It discusses how GPUs are well-suited for deep learning and other computationally intensive tasks due to their massively parallel architecture. The document then describes what deep learning is, including different types of neural networks commonly used. It also discusses how deep learning can enhance analytics and big data by automating feature extraction. Examples of running deep learning on Spark clusters using frameworks like TensorFlow on Spark are presented.
Jim Scott, CHUG co-founder and Director, Enterprise Strategy and Architecture for MapR presents "Using Apache Drill". This presentation was given on August 13th, 2014 at the Nokia office in Chicago, IL.
Jim has held positions running Operations, Engineering, Architecture and QA teams. He has worked in the Consumer Packaged Goods, Digital Advertising, Digital Mapping, Chemical and Pharmaceutical industries. His work with high-throughput computing at Dow Chemical was a precursor to more standardized big data concepts like Hadoop.
Apache Drill brings the power of standard ANSI:SQL 2003 to your desktop and your clusters. It is like AWK for Hadoop. Drill supports querying schemaless systems like HBase, Cassandra and MongoDB. Use standard JDBC and ODBC APIs to use Drill from your custom applications. Leveraging an efficient columnar storage format, an optimistic execution engine and a cache-conscious memory layout, Apache Drill is blazing fast. Coordination, query planning, optimization, scheduling, and execution are all distributed throughout nodes in a system to maximize parallelization. This presentation contains live demonstrations.
The video can be found here: http://vimeo.com/chug/using-apache-drill
Using HBase Co-Processors to Build a Distributed, Transactional RDBMS - Splic...Chicago Hadoop Users Group
John Leach Co-Founder and CTO of Splice Machine with 15+ years software development and machine learning experience will discuss how to use HBase co-processors to build an ANSI-99 SQL database with 1) parallelization of SQL execution plans, 2) ACID transactions with snapshot isolation and 3) consistent secondary indexing.
Transactions are critical in traditional RDBMSs because they ensure reliable updates across multiple rows and tables. Most operational applications require transactions, but even analytics systems use transactions to reliably update secondary indexes after a record insert or update.
In the Hadoop ecosystem, HBase is a key-value store with real-time updates, but it does not have multi-row, multi-table transactions, secondary indexes or a robust query language like SQL. Combining SQL with a full transactional model over HBase opens a whole new set of OLTP and OLAP use cases for Hadoop that was traditionally reserved for RDBMSs like MySQL or Oracle. However, a transactional HBase system has the advantage of scaling out with commodity servers, leading to a 5x-10x cost savings over traditional databases like MySQL or Oracle.
HBase co-processors, introduced in release 0.92, provide a flexible and high-performance framework to extend HBase. In this talk, we show how we used HBase co-processors to support a full ANSI SQL RDBMS without modifying the core HBase source. We will discuss how endpoint transactions are used to serialize SQL execution plans over to regions so that computation is local to where the data is stored. Additionally, we will show how observer co-processors simultaneously support both transactions and secondary indexing.
The talk will also discuss how Splice Machine extended the work of Google Percolator, Yahoo Labs’ OMID, and the University of Waterloo on distributed snapshot isolation for transactions. Lastly, performance benchmarks will be provided, including full TPC-C and TPC-H results that show how Hadoop/HBase can be a replacement of traditional RDBMS solutions.
To view the accompanying slide deck: http://www.slideshare.net/ChicagoHUG/
This document summarizes a presentation about using Apache Spark for various data analytics use cases. It discusses how Spark can be used for interactive SQL queries on large datasets, log file enrichment by connecting to data stores like HBase, mixing SQL and machine learning by accessing training and query engines in the same platform, and building recommendation engines by performing ETL, training models with MLlib, and serving recommendations with NoSQL. The presentation argues that Spark helps flatten the adoption curve by providing a unified framework for all these tasks.
This document discusses choosing the right data architecture for big data projects. It begins by acknowledging big data comes in many types, from structured transactional data to unstructured text data. It then presents several big data architectures and platforms that are suitable for different data types and use cases, such as relational databases, NoSQL databases, data grids, and distributed file systems. The document emphasizes that one size does not fit all and the right choice depends on the specific data and business needs.
This document provides an overview of Apache Ambari, an open source framework for provisioning, managing and monitoring Hadoop clusters. It discusses Ambari's architecture and features for provisioning clusters, managing services, monitoring metrics and alerts, and extensibility through Ambari stacks, views and blueprints. The document also outlines Ambari's release cadence and upcoming features around operations, extensibility and troubleshooting insights.
This document discusses security challenges related to big data and Hadoop. It notes that as data grows exponentially, the complexity of managing, securing, and enforcing privacy restrictions on data sets increases. Organizations now need to control access to data scientists based on authorization levels and what data they are allowed to see. Mismanagement of data sets can be costly, as shown by incidents at AOL, Netflix, and a Massachusetts hospital that led to lawsuits and fines. The document then provides a brief history of Hadoop security, noting that it was originally developed without security in mind. It outlines the current Kerberos-centric security model and talks about some vendor solutions emerging to enhance Hadoop security. Finally, it provides guidance on developing security and privacy
This document discusses advanced features and use cases of Oozie including:
1. JMS notifications for job status and SLA notifications
2. Overriding the default launcher for actions like Pig to add custom logic
3. Unit testing Oozie workflows using MiniOozie without a live cluster
Dean Wampler presents on using Scalding, which leverages Cascading, to write MapReduce jobs in a more productive way. Cascading provides higher-level abstractions for building data pipelines and hides much of the boilerplate of the Hadoop MapReduce framework. It allows expressing jobs using concepts like joins and group-bys in a cleaner way focused on the algorithm rather than infrastructure details. Word count is shown implemented in the lower-level MapReduce API versus in Cascading Java code to demonstrate how Cascading minimizes boilerplate and exposes the right abstractions.
Adam Gugliciello, a 15-year veteran in Software Engineering and Systems Architecture specializes in highly available, parallel systems. In this session, we will answer common questions and demonstrate use cases on how Hadoop and Datameer help with asset management and risk management, fraud detection and data security. This presentation was given on January 24th at the CME Group's offices in Chicago, IL.
Boris Lublinsky and Alexey Yakubovich give us an overview of using Oozie. This presentation was given on December 13th, 2012 at the Nokia offices in Chicago, IL.
View the HD video of this talk here: http://vimeo.com/chug/oozie-overview
Impala is a massively parallel processing SQL query engine for Apache Hadoop. It allows real-time queries on large datasets by using a new execution engine written in C++ instead of Java and MapReduce. Impala can process queries in milliseconds to hours by distributing query execution across Hadoop clusters. It uses existing Hadoop file formats and metadata but is optimized for performance through techniques like runtime code generation and in-memory processing.
HCatalog provides table management capabilities for Hadoop that allow data to be shared across tools like Pig, Hive, and MapReduce. It exposes metadata about tables, partitions, columns and their properties through a metastore. This allows jobs written in different tools to access the structure and location of the data without needing to declare schemas or know file formats. HCatalog aims to simplify data integration and management in Hadoop.
This document summarizes AdGooroo's experience deploying Hadoop in a Windows environment. Key points include:
- Hadoop and Windows can integrate but require workarounds like NFS for data transfer between Linux and Windows.
- Tools like Hive and Sqoop worked as expected while others like Flume were overkill.
- Unexpected issues arose with data serialization formats like AVRO not being fully compatible between .NET and Java.
- The learning curve is steep but can be flattened by taking things one component at a time.
The document summarizes a presentation on using R and Hadoop together. It includes:
1) An outline of topics to be covered including why use MapReduce and R, options for combining R and Hadoop, an overview of RHadoop, a step-by-step example, and advanced RHadoop features.
2) Code examples from Jonathan Seidman showing how to analyze airline on-time data using different R and Hadoop options - naked streaming, Hive, RHIPE, and RHadoop.
3) The analysis calculates average departure delays by year, month and airline using each method.
The document discusses Apache Avro, a data serialization framework. It provides an overview of Avro's history and capabilities. Key points include that Avro supports schema evolution, multiple languages, and interoperability with other formats like Protobuf and Thrift. The document also covers implementing Avro, including using the generic, specific and reflect data types, and examples of writing and reading data. Performance is addressed, finding that Avro size is competitive while speed is in the top half.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
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
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
2. Doing things by the book
Boris Lublinsky, Kevin T.
Smith and Alexey
Yakubovich.
―Professional Hadoop
Solutions‖
3. What is MapReduce?
• MapReduce is a framework for executing highly parallelizable
and distributable algorithms across huge datasets using a large
number of commodity computers.
• MapReduce model originates from the map and reduce
combinators concept in functional programming languages, for
example, Lisp. In Lisp, a map takes as input a function and a
sequence of values. It then applies the function to each value
in the sequence. A reduce combines all the elements of a
sequence using a binary operation.
• MapReduce is based on divide-and-conquer principles—the
input data sets are split into independent chunks, which are
processed by the mappers in parallel. Additionally execution of
the maps is typically co-located with the data. The framework
then sorts the outputs of the maps, and uses them as an input
to the reducers.
4. MapReduce in a nutshell
• A mapper takes input in a form of key/value pairs (k1, v1) and
transforms them into another key/value pair (k2, v2).
• MapReduce framework sorts mapper’s output key/value pairs and
combines each unique key with all its values (k2, {v2, v2,…}) and
deliver these key/value combinations to reducers
• Reducer takes (k2, {v2, v2,…}) key/value combinations and translates
them into yet another key/value pair (k3, v3).
6. MapReduce framework features
• Scheduling ensures that multiple tasks from multiple jobs are
executed on the cluster. Another aspect of scheduling is
speculative execution, which is an optimization that is
implemented by MapReduce. If the job tracker notices that one
of the tasks is taking too long to execute, it can start additional
instance of the same task (using different task tracker).
• Synchronization. MapReduce execution provides
synchronization between the map and reduce phases of
processing (reduce phase cannot start until all map’ key/value
pairs are emitted).
• Error and fault handling. In order to accomplish job execution
in the environment where errors and faults are the norm job
tracker will attempt to restart failed tasks execution
8. MapReduce design
• How to break up a large problem into smaller tasks? More
specifically, how to decompose the problem so that the
smaller tasks can be executed in parallel?
• What are key/value pairs, that can be viewed as
inputs/outputs of every task
• How to bring together all data required for calculation?
More specifically how to organize processing the way that
all the data necessary for calculation is in memory at the
same time?
9. Examples of MapReduce design
• MapReduce for parallel processing (face recognition
•
•
•
•
example)
Simple data processing with MapReduce(inverted indexes
example)
Building joins using MapReduce(Road Enrichment
Example)
Building bucketing joins using MapReduce (Link elevation
example)
Iterative MapReduce applications (Stranding Bivalent
Links Example)
10. Face Recognition Example
To run face recognition on millions of pictures stored in
Hadoop in a form of sequence file a simple Map only job
can be used to parallelize execution.
Process Phase
Description
Mapper
In this job, mapper is first initialized with the
set of recognizable features. For every
image a map function invokes face
recognition algorithm’s implementation,
passing it an image itself along with a list of
recognizable features. The result of
recognition along with the original imageID
is outputted from the map.
A result of this job execution is results of
recognitions of all the images, contained in
the original images.
Result
11. Building Joins using MapReduce
• Generic join problem can be described as follows. Given multiple data sets (S1
•
•
•
•
through Sn), sharing the same key (join key) build records, containing a key and
all required data from every record.
There are two ―standard‖ implementations for joining data in MapReduce –
reduce-side join and map-side join.
A most common implementation of join is a reduce side join. In this case, all
datasets are processed by the mapper that emits a join key as the intermediate
key, and the value as the intermediate record capable of holding either of the
set’s values. Shuffle and sort will group all intermediate records by the join key.
A special case of a reducer side join is called a ―Bucketing‖ join. In this case, the
datasets might not have common keys, but support the notion of proximity, for
example geographic proximity. A proximity value can be used as a join key for the
data sets.
In the cases when one of the data sets is small enough (―baby‖ joins) to fit in
memory – a map in memory side join can be used. In this case a small data set is
brought in memory in a form of key-based hash providing fast lookups. Now it is
possible iterate over the larger data set, do all the joins in memory and output the
resulting records.
12. Inverted indexes
• An inverted index is a data structure storing a mapping
from content, such as words or numbers, to its
locations in a document or a set of documents
• To build an inverted index, it is possible to feed the
mapper each document (or lines within a document).
The Mapper will parse the words in the document to
emit [word, descriptor] pairs. The reducer can simply
be an identity function that just writes out the list, or it
can perform some statistic aggregation per word.
13. Calculation of Inverted Indexes Job
Process Phase
Mapper
Description
In this job, the role of the mapper is to build a unique record
containing a word-index and information describing this word
occurrence in the document. It reads every input document,
parses it and for every unique word in the document builds an
index descriptor. This descriptor contains a document ID,
number of times index occurs in the document and any
additional information, for example index positions as offset from
the beginning of the document. Every index descriptor pair is
written out.
Shuffle and Sort
MapReduce’ shuffle and sort will sort all the records based on
the index value, which will guarantee that a reducer will get all
the indexes with a given key together.
Reducer
The role of reducer, in this job, is to build inverted indexes
structure(s). Depending on the system requirements there can
be one or more reducers. Reducer gets all the descriptors for a
given index and builds an index record, which is written to the
desired index storage.
Result
A result of this job execution is an inverted index (s) for a set of
the original documents.
14. Road Enrichment
• Find all links connected to a given node, for example,
node N1 has links L1, L2, L3 and L4 and node N2 – L4,
L5 and L6.
• Based on the amount of lanes for every link at the node
calculate the road width at the intersection.
• Based on the road width calculate the intersection
geometry
• Based on the intersection geometry move road’s end
point to tie it to the intersection geometry.
15. Calculation of Intersection Geometry job
Process Phase
Mapper
Shuffle and Sort
Reducer
Result
Description
In this job, the role of the mapper is to build LNNi records out of the
source records - NNi and LLi. It reads every input record from both source
files and then looks at the object type. If it is a node with the key NNi, a
new record of the type LNNi is written to the output. If it is a link, keys of
the both adjacent nodes (NNi and NNj) are extracted from the link and two
records – LNNi and LNNj are written out.
MapReduce’ shuffle and sort will sort all the records based on the node’s
keys, which will guarantee that every nodes with all adjacent links will be
processed by a single reducer and that a reducer will get all the LN
records for a given key together.
The role of reducer, in this job, is to calculate intersection geometry and
move road’s ends. It gets all LN records for a given node key and stores
them in memory. Once all of them are in memory, intersection’s geometry
can be calculated and written out as a intersection record with node key
SNi. At the same time, all the link records, connected to a given node can
be converted to road records and written out with link key – R Li.
A result of this job execution is a set of intersection records ready to use
and road records that have to be merged together (every road is
connected to 2 intersections, dead ends are typically modeled with a node
that has a single link connected to it.). It is necessary to implement the
second map reduce job to merge them
16. Merge Roads Job
Process Phase
Description
Mapper
The role of mapper in this job is very simple. It is just
a pass-through (or identity mapper in Hadoop’s
terminology). It reads roads records and writes them
directly to the output
MapReduce’ shuffle and sort will sort all the records
based on the link’s keys, which will guarantee that
both road records with the same key will come to the
same reducer together.
The role of reducer, in this job, is to merge roads
with same id. Once reducer reads both records for a
given road it merges them together and writes them
out as a single road record.
A result of this job execution is a set of road records
ready to use.
Shuffle and Sort
Reducer
Result
17. Link Elevation
Given links graph and terrain
model convert two dimensional
(x,y) links into 3 dimensional (x,
y, z)
• Split every link into fixed length
(for example 10 meters)
fragments.
• For every piece. calculate
heights, from terrain model, for
both start and end point of
each link
• Combine pieces together into
original links.
18. Link Elevation in MapReduce
Process Phase
Mapper
Shuffle and Sort
Reducer
Result
Description
In this job, the role of the mapper is to build link pieces and assign
them to the individual tiles. It reads every link record and splits it into
fixed length fragments, which effectively converts link representation
into a set of points. For every point it calculates a tile this point belongs
to and produce one or more link piece records which look as follows
(tid, {lid [point]}), where tid is tile id, lid – original link id, points array is
an array of points from the original link, that belong to a given tile.
MapReduce’ shuffle and sort will sort all the records based on the tile’s
ids, which will guarantee that all of the link’s piece belonging to the
same tile will be processed by a single reducer.
The role of reducer, in this job, is to calculate elevation for every link
piece. For every tile id it loads terrain information for a given tile from
HBase and then reads all of link piece and calculates elevation for
every point. It outputs the record (lid, [point]), where lid is original link id
and points array is an array of three-dimensional points.
A result of this job execution will contain fully elevated link pieces. It is
necessary to implement the second map reduce job to merge them.
19. Stranding bivalent links
• Two connected links are called bivalent if they are connected via bivalent
node. A Bivalent node is a node that has only 2 connections. For example
nodes N6, N7, N8 and N9 are bivalent; also links L5, L6, L7, L8 and L9 are
bivalent. A degenerate case of bivalent link is link L4.
• An algorithm for calculating bivalent links stretches (Figure looks very
straightforward – any stretch of links between two not bivalent nodes is a
stretch of bivalent links.
• Build set of partial bivalent strands.
• Loop
• Combine partial strands
• If no partial strands were combined, exit the loop
• End of loop
20. Elimination of Non-Bivalent Nodes Job
Process Phase
Description
In this job, the role of the mapper is to build LNNi records out of the source
records - NNi and LLi. It reads every input record and then looks at the object
type. If it is a node with the key Ni, a new record of the type LNNi is written to
the output. If it is a link, keys of the both adjacent nodes (Ni and Nj) are
extracted from the link and two records – LNNi and LNNj are written out.
Shuffle and Sort MapReduce’ shuffle and sort will sort all the records based on the node’s
keys, which will guarantee that every nodes with all adjacent links will be
processed by a single reducer and that a reducer will get all the LN records
for a given key together.
Reducer
The role of reducer, in this job, is to eliminate non bivalent nodes and build
partial link strands. It reads all LN records for a given node key and stores
them in memory. If the amount of links for this node is 2, then this is a
bivalent node and a new strand, combining these two nodes is written to the
output (see, for example, pairs L5, L6 or L7, L8). If the amount of links is not
equal to 2 (it can be either a dead end or a node, at which multiple links
intersect), then this is a non-bivalent node. For this type of node a special
strands containing a single link, connected to this non bivalent node (see, for
example, L4 or L5) is created. The amount of strands for such node is equal
to the amount of links connected to this node.
Result
A result of this job execution contains partial strand records some of which
can be repeated (L4, for example, will appear twice – from processing N4
and N5).
Mapper
21. Merging Partial Strands Job
Process Phase
Mapper
Shuffle and
Sort
Reducer
Result
Description
The role of mapper in this job is to bring together strands sharing the
same links. For every strand it reads, it outputs several key value pairs
The key values are the keys of the links in the strand and the values
are the strands themselves.
MapReduce’ shuffle and sort will sort all the records based on end link
keys, which will guarantee that all strand records containing the same
link ID will come to the same reducer together.
The role of reducer, in this job, is to merge strands sharing the same
link id. For every link id all strands are loaded in memory and
processed based on the strand types:
If there are two strands containing the same links, the produced
strand is complete and can be written directly to the final result
directory.
Otherwise the resulting strand, strand containing all unique links, is
created and is written to the output for further processing. In this
case ―to be processed‖ counter is incremented.
A result of this job execution contains all complete strands in a separate
directory. It also contains a directory of all bivalent partial strands along
with value of ―to be processed‖ counter.
22. When to MapReduce?
MapReduce is a technique aimed to solve a relatively simple
problem; there is a lot of data and it has to be processed in parallel,
preferably on multiple machines. Alternatively it can be used for
parallelizing compute intensive calculations, where it’s not necessarily
about amount of data, but rather about overall calculation time The
following has to be true in order for MapReduce to be applicable:
• The calculations that need to be run have to be composable. This
means it should be possible to run the calculation on a subset of
data, and merge partial results.
• The dataset size is big enough (or the calculations are long enough)
that the infrastructure overhead of splitting it up for independent
computations and merging results will not hurt overall performance.
• The calculation depends mostly on the dataset being processed;
additional small data sets can be added using HBase, Distributed
cache or some other techniques.
23. When not to MapReduce?
• MapReduce is not applicable, in scenarios when the dataset has to
be accessed randomly to perform the operation. MapReduce
calculations need to be composable - it should be possible to run
the calculation on a subset of data, and merge partial results.
• MapReduce is not applicable for general recursive problems
because computation of the current value requires the knowledge
of the previous one, which means that you can’t break it apart to
sub computations that can be run independently..
• If a data size is small enough to fit on a single machine memory, it
is probably going to be faster to process it as a standalone
application. Usage of MapReduce, in this case, makes
implementation unnecessary complex and typically slower.
• MapReduce is an inherently batch implementation and as such is
not applicable for online computations
24. General design guidelines - 1
When splitting data between map tasks make sure that you do not
create too many or too few of them. Having the right number of maps
has the following advantages for applications:
• Having too many mappers creates scheduling and infrastructure
overhead and in extreme cases can even kill job tracker. Also, having
too many mappers typically increases overall resource utilization (due
to excessive JVMs creation) and execution time (due to the limited
amount of execution slots).
• Having too few mappers can lead to underutilization of the cluster and
creation of too much load on some of the nodes (where maps actually
run). Additionally, in the case of very large map tasks, retries and
speculative execution becomes very expensive and takes longer.
• Large amount of small mappers creates a lot of seeks during shuffle
of the map output to the reducers. It also creates too many
connections delivering map outputs to the reducers.
25. General design guidelines - 2
• The number of reduces configured for the application is
another crucial factor. Having too many or too few reduces can
be anti-productive:
• Having too many reducers, in addition to scheduling and infrastructure
overhead, results in creation of too many output files (remember each
reducer creates its own output file), which has negative impact on
name node. It will also make more complicated to leverage results
from this MapReduce job by other ones.
• Having too few reducers will have the same negative effects as having
too few mappers - underutilization of the cluster and very expensive
retries
• Use the job counters appropriately.
• Counters are appropriate for tracking few, important, global bits of
information. They are definitely not meant to aggregate very finegrained statistics of applications.
• Counters are fairly expensive since the JobTracker has to maintain
every counter of every map/reduce task for the entire duration of the
application.
26. General design guidelines - 3
• Do not create applications writing out multiple, small, output
files from each reduce.
• Consider compressing the application's output with an appropriate
compressor (compression speed v/s efficiency) to improve writeperformance.
• Use an appropriate file-format for the output of reduces. Using
SequenceFiles is often a best option since they are both compressable
and splittable.
• Consider using a larger output block size when the individual
input/output files are large (multiple GBs).
• Try to avoid creation of new objects instances inside map and
reduce methods. These methods are executed many times
inside the loop, which means objects creation and disposal will
increase execution time and create extra work for garbage
collector. A better approach is to create the majority of your
intermediate classes in corresponding setup method and then
repopulating them inside map/reduce methods.
27. General design guidelines - 4
• Do not write directly to the user-defined files from either mapper or
reducer. The current implementation of file writer in Hadoop is singe
threaded, which means that execution of multiple mappers/reducers
trying to write to this file will be serialized.
• Do not create MapReduce implementations scanning HBase table to
create a new HBase table (or write to the same table).
TableInputFormat used for HBase MapReduce implementation are
based on table scan which is time sensitive. HBase writes, on another
hand, can cause a significant write delays due to HBase table splits.
As a result region servers can hang up or even some data can be
lost. A better solution is to split such job into two – one scanning a
table and writing intermediate results into HDFS and another reading
from HDFS and writing to HBase.
• Do not try to re implement existing Hadoop classes - extend them and
explicitly specify your implementation in configuration. Unlike
application servers, Hadoop command is specifying user classes last,
which means that existing Hadoop classes always have precedence.