In this session you will learn:
1. Meet MapReduce
2. Word Count Algorithm – Traditional approach
3. Traditional approach on a Distributed System
4. Traditional approach – Drawbacks
5. MapReduce Approach
6. Input & Output Forms of a MR program
7. Map, Shuffle & Sort, Reduce Phase
8. WordCount Code walkthrough
9. Workflow & Transformation of Data
10. Input Split & HDFS Block
11. Relation between Split & Block
12. Data locality Optimization
13. Speculative Execution
14. MR Flow with Single Reduce Task
15. MR flow with multiple Reducers
16. Input Format & Hierarchy
17. Output Format & Hierarchy
In this session you will learn:
1. History of hadoop
2. Hadoop Ecosystem
3. Hadoop Animal Planet
4. What is Hadoop?
5. Distinctions of hadoop
6. Hadoop Components
7. The Hadoop Distributed Filesystem
8. Design of HDFS
9. When Not to use Hadoop?
10. HDFS Concepts
11. Anatomy of a File Read
12. Anatomy of a File Write
13. Replication & Rack awareness
14. Mapreduce Components
15. Typical Mapreduce Job
This document provides an overview of Hadoop and its core components. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as its programming model and the Hadoop Distributed File System (HDFS) for storage. HDFS stores data redundantly across nodes for reliability. The core subprojects of Hadoop include MapReduce, HDFS, Hive, HBase, and others.
Hadoop is an open source framework for distributed storage and processing of large datasets across commodity hardware. It has two main components - the Hadoop Distributed File System (HDFS) for storage, and MapReduce for processing. HDFS stores data across clusters in a redundant and fault-tolerant manner. MapReduce allows distributed processing of large datasets in parallel using map and reduce functions. The architecture aims to provide reliable, scalable computing using commodity hardware.
Hadoop Institutes : kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
Hadoop is an open source framework for running large-scale data processing jobs across clusters of computers. It has two main components: HDFS for reliable storage and Hadoop MapReduce for distributed processing. HDFS stores large files across nodes through replication and uses a master-slave architecture. MapReduce allows users to write map and reduce functions to process large datasets in parallel and generate results. Hadoop has seen widespread adoption for processing massive datasets due to its scalability, reliability and ease of use.
The document provides an introduction to Hadoop, including an overview of its core components HDFS and MapReduce, and motivates their use by explaining the need to process large amounts of data in parallel across clusters of computers in a fault-tolerant and scalable manner. It also presents sample code walkthroughs and discusses the Hadoop ecosystem of related projects like Pig, HBase, Hive and Zookeeper.
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.
The document summarizes Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes the key components of Hadoop including the Hadoop Distributed File System (HDFS) which stores data reliably across commodity hardware, and the MapReduce programming model which allows distributed processing of large datasets in parallel. The document provides an overview of HDFS architecture, data flow, fault tolerance, and other aspects to enable reliable storage and access of very large files across clusters.
In this session you will learn:
1. History of hadoop
2. Hadoop Ecosystem
3. Hadoop Animal Planet
4. What is Hadoop?
5. Distinctions of hadoop
6. Hadoop Components
7. The Hadoop Distributed Filesystem
8. Design of HDFS
9. When Not to use Hadoop?
10. HDFS Concepts
11. Anatomy of a File Read
12. Anatomy of a File Write
13. Replication & Rack awareness
14. Mapreduce Components
15. Typical Mapreduce Job
This document provides an overview of Hadoop and its core components. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as its programming model and the Hadoop Distributed File System (HDFS) for storage. HDFS stores data redundantly across nodes for reliability. The core subprojects of Hadoop include MapReduce, HDFS, Hive, HBase, and others.
Hadoop is an open source framework for distributed storage and processing of large datasets across commodity hardware. It has two main components - the Hadoop Distributed File System (HDFS) for storage, and MapReduce for processing. HDFS stores data across clusters in a redundant and fault-tolerant manner. MapReduce allows distributed processing of large datasets in parallel using map and reduce functions. The architecture aims to provide reliable, scalable computing using commodity hardware.
Hadoop Institutes : kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
Hadoop is an open source framework for running large-scale data processing jobs across clusters of computers. It has two main components: HDFS for reliable storage and Hadoop MapReduce for distributed processing. HDFS stores large files across nodes through replication and uses a master-slave architecture. MapReduce allows users to write map and reduce functions to process large datasets in parallel and generate results. Hadoop has seen widespread adoption for processing massive datasets due to its scalability, reliability and ease of use.
The document provides an introduction to Hadoop, including an overview of its core components HDFS and MapReduce, and motivates their use by explaining the need to process large amounts of data in parallel across clusters of computers in a fault-tolerant and scalable manner. It also presents sample code walkthroughs and discusses the Hadoop ecosystem of related projects like Pig, HBase, Hive and Zookeeper.
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.
The document summarizes Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes the key components of Hadoop including the Hadoop Distributed File System (HDFS) which stores data reliably across commodity hardware, and the MapReduce programming model which allows distributed processing of large datasets in parallel. The document provides an overview of HDFS architecture, data flow, fault tolerance, and other aspects to enable reliable storage and access of very large files across clusters.
This document provides an overview of Hadoop and MapReduce. It discusses how Hadoop uses HDFS for distributed storage and replication of data blocks across commodity servers. It also explains how MapReduce allows for massively parallel processing of large datasets by splitting jobs into mappers and reducers. Mappers process data blocks in parallel and generate intermediate key-value pairs, which are then sorted and grouped by the reducers to produce the final results.
Sept 17 2013 - THUG - HBase a Technical IntroductionAdam Muise
HBase Technical Introduction. This deck includes a description of memory design, write path, read path, some operational tidbits, SQL on HBase (Phoenix and Hive), as well as HOYA (HBase on YARN).
WANdisco is a provider of non-stop software for global enterprises to meet the challenges of Big Data and distributed software development.
KEY HIGHLIGHTS, Session 1: Tuesday, Feb. 26, 5:15 p.m.-6 p.m.
Hadoop and HBase on the Cloud: A Case Study on Performance and Isolation
Cloud infrastructure is a flexible tool to orchestrate multiple Hadoop and HBase clusters, which provides strict isolation of data and compute resources for multiple customers. Most importantly, our benchmarks show that virtualized environment allows for higher average utilization of per-node resources. For more session information, visit http://na.apachecon.com/schedule/presentation/131/.
CO-PRESENTERS, Dr. Konstantin V. Shvachko, Chief Architect, Big Data, WANdisco and Jagane Sundar, CTO/VP Engineering, Big Data, WANdisco
A veteran Hadoop developer and respected author, Konstantin Shvachko is a technical expert specializing in efficient data structures and algorithms for large-scale distributed storage systems. Konstantin joined WANdisco through the AltoStor acquisition and before that he was founder and Chief Scientist at AltoScale, a Hadoop and HBase-as-a-Platform company acquired by VertiCloud. Konstantin played a lead architectural role at eBay, building two generations of the organization's Hadoop platform. At Yahoo!, he worked on the Hadoop Distributed File System (HDFS). Konstantin has dozens of publications and presentations to his credit and is currently a member of the Apache Hadoop PMC. Konstantin has a Ph.D. in Computer Science and M.S. in Mathematics from Moscow State University, Russia.
Jagane Sundar has extensive big data, cloud, virtualization, and networking experience and joined WANdisco through its AltoStor acquisition. Before AltoStor, Jagane was founder and CEO of AltoScale, a Hadoop and HBase-as-a-Platform company acquired by VertiCloud. His experience with Hadoop began as Director of Hadoop Performance and Operability at Yahoo! Jagane has such accomplishments to his credit as the creation of Livebackup, development of a user mode TCP Stack for Precision I/O, development of the NFS and PPP clients and parts of the TCP stack for JavaOS for Sun MicroSystems, and more. Jagane received his B.E. in Electronics and Communications Engineering from Anna University.
About WANdisco
WANdisco ( LSE : WAND ) is a provider of enterprise-ready, non-stop software solutions that enable globally distributed organizations to meet today's data challenges of secure storage, scalability and availability. WANdisco's products are differentiated by the company's patented, active-active data replication technology, serving crucial high availability (HA) requirements, including Hadoop Big Data and Application Lifecycle Management (ALM). Fortune Global 1000 companies including AT&T, Motorola, Intel and Halliburton rely on WANdisco for performance, reliability, security and availability. For additional information, please visit www.wandisco.com.
Hadoop is an open source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses Google's MapReduce programming model and Google File System for reliability. The Hadoop architecture includes a distributed file system (HDFS) that stores data across clusters and a job scheduling and resource management framework (YARN) that allows distributed processing of large datasets in parallel. Key components include the NameNode, DataNodes, ResourceManager and NodeManagers. Hadoop provides reliability through replication of data blocks and automatic recovery from failures.
Scaling HDFS to Manage Billions of Files with Key-Value StoresDataWorks Summit
The document discusses scaling HDFS to manage billions of files. It describes how HDFS usage has grown from millions of files in 2007 to potentially billions of files in the future. To address this, the speakers propose storing HDFS metadata in a key-value store like LevelDB instead of solely in memory. They evaluate this approach and find comparable performance to HDFS for most operations. Future work includes improving operations like compaction and failure recovery in the new architecture.
This document provides an agenda and overview for a presentation on Hadoop 2.x configuration and MapReduce performance tuning. The presentation covers hardware selection and capacity planning for Hadoop clusters, key configuration parameters for operating systems, HDFS, and YARN, and performance tuning techniques for MapReduce applications. It also demonstrates the Hadoop Vaidya performance diagnostic tool.
The document discusses Apache HCatalog, which provides metadata services and a unified view of data across Hadoop tools like Hive, Pig, and MapReduce. It allows sharing of data and metadata between tools and external systems through a consistent schema. HCatalog simplifies data management by allowing tools to access metadata like the schema, location, and format of data from a shared metastore instead of encoding that information within each application.
The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.
This document provides an overview of Hadoop and how it addresses the challenges of big data. It discusses how Hadoop uses a distributed file system (HDFS) and MapReduce programming model to allow processing of large datasets across clusters of computers. Key aspects summarized include how HDFS works using namenodes and datanodes, how MapReduce leverages mappers and reducers to parallelize processing, and how Hadoop provides fault tolerance.
This document compares the Google File System (GFS) and the Hadoop Distributed File System (HDFS). It discusses their motivations, architectures, performance measurements, and role in larger systems. GFS was designed for Google's data processing needs, while HDFS was created as an open-source framework for Hadoop applications. Both divide files into blocks and replicate data across multiple servers for reliability. The document provides details on their file structures, data flow models, consistency approaches, and benchmark results. It also explores how systems like MapReduce/Hadoop utilize these underlying storage systems.
Apache Drill is an open source SQL query engine for analysis of large scale datasets. It is modeled after Google's Dremel system and allows for interactive analysis of data stored in Hadoop files. Drill uses a columnar data format and execution engine to enable fast query performance on large datasets. It also supports pluggable components for different data formats, query languages, and data sources to provide a flexible and extensible platform.
Introduction to Hadoop and Hadoop component rebeccatho
This document provides an introduction to Apache Hadoop, which is an open-source software framework for distributed storage and processing of large datasets. It discusses Hadoop's main components of MapReduce and HDFS. MapReduce is a programming model for processing large datasets in a distributed manner, while HDFS provides distributed, fault-tolerant storage. Hadoop runs on commodity computer clusters and can scale to thousands of nodes.
This presentation about Hadoop architecture will help you understand the architecture of Apache Hadoop in detail. In this video, you will learn what is Hadoop, components of Hadoop, what is HDFS, HDFS architecture, Hadoop MapReduce, Hadoop MapReduce example, Hadoop YARN and finally, a demo on MapReduce. Apache Hadoop offers a versatile, adaptable and reliable distributed computing big data framework for a group of systems with capacity limit and local computing power. After watching this video, you will also understand the Hadoop Distributed File System and its features along with the practical implementation.
Below are the topics covered in this Hadoop Architecture presentation:
1. What is Hadoop?
2. Components of Hadoop
3. What is HDFS?
4. HDFS Architecture
5. Hadoop MapReduce
6. Hadoop MapReduce Example
7. Hadoop YARN
8. Demo on MapReduce
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Who should take up this Big Data and Hadoop Certification Training Course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. Senior IT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The document discusses Hadoop, its components, and how they work together. It covers HDFS, which stores and manages large files across commodity servers; MapReduce, which processes large datasets in parallel; and other tools like Pig and Hive that provide interfaces for Hadoop. Key points are that Hadoop is designed for large datasets and hardware failures, HDFS replicates data for reliability, and MapReduce moves computation instead of data for efficiency.
Distributed Computing with Apache Hadoop is a technology overview that discusses:
1) Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of commodity hardware.
2) Hadoop addresses limitations of traditional distributed computing with an architecture that scales linearly by adding more nodes, moves computation to data instead of moving data, and provides reliability even when hardware failures occur.
3) Core Hadoop components include the Hadoop Distributed File System for storage, and MapReduce for distributed processing of large datasets in parallel on multiple machines.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses challenges in big data by providing reliability, scalability, and fault tolerance. Hadoop allows distributed processing of large datasets across clusters using MapReduce and can scale from single servers to thousands of machines, each offering local computation and storage. It is widely used for applications such as log analysis, data warehousing, and web indexing.
In this session you will learn:
Meet MapReduce
Word Count Algorithm – Traditional approach
Traditional approach on a Distributed System
Traditional approach – Drawbacks
MapReduce Approach
Input & Output Forms of a MR program
Map, Shuffle & Sort, Reduce Phase
WordCount Code walkthrough
Workflow & Transformation of Data
Input Split & HDFS Block
Relation between Split & Block
Data locality Optimization
Speculative Execution
MR Flow with Single Reduce Task
MR flow with multiple Reducers
Input Format & Hierarchy
Output Format & Hierarchy
To know more, click here: https://www.mindsmapped.com/courses/big-data-hadoop/big-data-and-hadoop-training-for-beginners/
In these slides we analyze why the aggregate data models change the way data is stored and manipulated. We introduce MapReduce and its open source implementation Hadoop. We consider how MapReduce jobs are written and executed by Hadoop.
Finally we introduce spark using a docker image and we show how to use anonymous function in spark.
The topics of the next slides will be
- Spark Shell (Scala, Python)
- Shark Shell
- Data Frames
- Spark Streaming
- Code Examples: Data Processing and Machine Learning
Map reduce - simplified data processing on large clustersCleverence Kombe
The document describes MapReduce, a programming model and software framework for processing large datasets in a distributed computing environment. It discusses how MapReduce allows users to specify map and reduce functions to parallelize tasks across large clusters of machines. It also covers how MapReduce handles parallelization, fault tolerance, and load balancing transparently through an easy-to-use programming interface.
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.
Challenges of Building a First Class SQL-on-Hadoop EngineNicolas Morales
Challenges of Building a First Class SQL-on-Hadoop Engine:
Why and what is Big SQL 3.0?
Overview of the challenges
How we solved (some of) them
Architecture and interaction with Hadoop
Query rewrite
Query optimization
Future challenges
This document provides an overview of Hadoop and MapReduce. It discusses how Hadoop uses HDFS for distributed storage and replication of data blocks across commodity servers. It also explains how MapReduce allows for massively parallel processing of large datasets by splitting jobs into mappers and reducers. Mappers process data blocks in parallel and generate intermediate key-value pairs, which are then sorted and grouped by the reducers to produce the final results.
Sept 17 2013 - THUG - HBase a Technical IntroductionAdam Muise
HBase Technical Introduction. This deck includes a description of memory design, write path, read path, some operational tidbits, SQL on HBase (Phoenix and Hive), as well as HOYA (HBase on YARN).
WANdisco is a provider of non-stop software for global enterprises to meet the challenges of Big Data and distributed software development.
KEY HIGHLIGHTS, Session 1: Tuesday, Feb. 26, 5:15 p.m.-6 p.m.
Hadoop and HBase on the Cloud: A Case Study on Performance and Isolation
Cloud infrastructure is a flexible tool to orchestrate multiple Hadoop and HBase clusters, which provides strict isolation of data and compute resources for multiple customers. Most importantly, our benchmarks show that virtualized environment allows for higher average utilization of per-node resources. For more session information, visit http://na.apachecon.com/schedule/presentation/131/.
CO-PRESENTERS, Dr. Konstantin V. Shvachko, Chief Architect, Big Data, WANdisco and Jagane Sundar, CTO/VP Engineering, Big Data, WANdisco
A veteran Hadoop developer and respected author, Konstantin Shvachko is a technical expert specializing in efficient data structures and algorithms for large-scale distributed storage systems. Konstantin joined WANdisco through the AltoStor acquisition and before that he was founder and Chief Scientist at AltoScale, a Hadoop and HBase-as-a-Platform company acquired by VertiCloud. Konstantin played a lead architectural role at eBay, building two generations of the organization's Hadoop platform. At Yahoo!, he worked on the Hadoop Distributed File System (HDFS). Konstantin has dozens of publications and presentations to his credit and is currently a member of the Apache Hadoop PMC. Konstantin has a Ph.D. in Computer Science and M.S. in Mathematics from Moscow State University, Russia.
Jagane Sundar has extensive big data, cloud, virtualization, and networking experience and joined WANdisco through its AltoStor acquisition. Before AltoStor, Jagane was founder and CEO of AltoScale, a Hadoop and HBase-as-a-Platform company acquired by VertiCloud. His experience with Hadoop began as Director of Hadoop Performance and Operability at Yahoo! Jagane has such accomplishments to his credit as the creation of Livebackup, development of a user mode TCP Stack for Precision I/O, development of the NFS and PPP clients and parts of the TCP stack for JavaOS for Sun MicroSystems, and more. Jagane received his B.E. in Electronics and Communications Engineering from Anna University.
About WANdisco
WANdisco ( LSE : WAND ) is a provider of enterprise-ready, non-stop software solutions that enable globally distributed organizations to meet today's data challenges of secure storage, scalability and availability. WANdisco's products are differentiated by the company's patented, active-active data replication technology, serving crucial high availability (HA) requirements, including Hadoop Big Data and Application Lifecycle Management (ALM). Fortune Global 1000 companies including AT&T, Motorola, Intel and Halliburton rely on WANdisco for performance, reliability, security and availability. For additional information, please visit www.wandisco.com.
Hadoop is an open source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses Google's MapReduce programming model and Google File System for reliability. The Hadoop architecture includes a distributed file system (HDFS) that stores data across clusters and a job scheduling and resource management framework (YARN) that allows distributed processing of large datasets in parallel. Key components include the NameNode, DataNodes, ResourceManager and NodeManagers. Hadoop provides reliability through replication of data blocks and automatic recovery from failures.
Scaling HDFS to Manage Billions of Files with Key-Value StoresDataWorks Summit
The document discusses scaling HDFS to manage billions of files. It describes how HDFS usage has grown from millions of files in 2007 to potentially billions of files in the future. To address this, the speakers propose storing HDFS metadata in a key-value store like LevelDB instead of solely in memory. They evaluate this approach and find comparable performance to HDFS for most operations. Future work includes improving operations like compaction and failure recovery in the new architecture.
This document provides an agenda and overview for a presentation on Hadoop 2.x configuration and MapReduce performance tuning. The presentation covers hardware selection and capacity planning for Hadoop clusters, key configuration parameters for operating systems, HDFS, and YARN, and performance tuning techniques for MapReduce applications. It also demonstrates the Hadoop Vaidya performance diagnostic tool.
The document discusses Apache HCatalog, which provides metadata services and a unified view of data across Hadoop tools like Hive, Pig, and MapReduce. It allows sharing of data and metadata between tools and external systems through a consistent schema. HCatalog simplifies data management by allowing tools to access metadata like the schema, location, and format of data from a shared metastore instead of encoding that information within each application.
The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.
This document provides an overview of Hadoop and how it addresses the challenges of big data. It discusses how Hadoop uses a distributed file system (HDFS) and MapReduce programming model to allow processing of large datasets across clusters of computers. Key aspects summarized include how HDFS works using namenodes and datanodes, how MapReduce leverages mappers and reducers to parallelize processing, and how Hadoop provides fault tolerance.
This document compares the Google File System (GFS) and the Hadoop Distributed File System (HDFS). It discusses their motivations, architectures, performance measurements, and role in larger systems. GFS was designed for Google's data processing needs, while HDFS was created as an open-source framework for Hadoop applications. Both divide files into blocks and replicate data across multiple servers for reliability. The document provides details on their file structures, data flow models, consistency approaches, and benchmark results. It also explores how systems like MapReduce/Hadoop utilize these underlying storage systems.
Apache Drill is an open source SQL query engine for analysis of large scale datasets. It is modeled after Google's Dremel system and allows for interactive analysis of data stored in Hadoop files. Drill uses a columnar data format and execution engine to enable fast query performance on large datasets. It also supports pluggable components for different data formats, query languages, and data sources to provide a flexible and extensible platform.
Introduction to Hadoop and Hadoop component rebeccatho
This document provides an introduction to Apache Hadoop, which is an open-source software framework for distributed storage and processing of large datasets. It discusses Hadoop's main components of MapReduce and HDFS. MapReduce is a programming model for processing large datasets in a distributed manner, while HDFS provides distributed, fault-tolerant storage. Hadoop runs on commodity computer clusters and can scale to thousands of nodes.
This presentation about Hadoop architecture will help you understand the architecture of Apache Hadoop in detail. In this video, you will learn what is Hadoop, components of Hadoop, what is HDFS, HDFS architecture, Hadoop MapReduce, Hadoop MapReduce example, Hadoop YARN and finally, a demo on MapReduce. Apache Hadoop offers a versatile, adaptable and reliable distributed computing big data framework for a group of systems with capacity limit and local computing power. After watching this video, you will also understand the Hadoop Distributed File System and its features along with the practical implementation.
Below are the topics covered in this Hadoop Architecture presentation:
1. What is Hadoop?
2. Components of Hadoop
3. What is HDFS?
4. HDFS Architecture
5. Hadoop MapReduce
6. Hadoop MapReduce Example
7. Hadoop YARN
8. Demo on MapReduce
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Who should take up this Big Data and Hadoop Certification Training Course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. Senior IT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The document discusses Hadoop, its components, and how they work together. It covers HDFS, which stores and manages large files across commodity servers; MapReduce, which processes large datasets in parallel; and other tools like Pig and Hive that provide interfaces for Hadoop. Key points are that Hadoop is designed for large datasets and hardware failures, HDFS replicates data for reliability, and MapReduce moves computation instead of data for efficiency.
Distributed Computing with Apache Hadoop is a technology overview that discusses:
1) Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of commodity hardware.
2) Hadoop addresses limitations of traditional distributed computing with an architecture that scales linearly by adding more nodes, moves computation to data instead of moving data, and provides reliability even when hardware failures occur.
3) Core Hadoop components include the Hadoop Distributed File System for storage, and MapReduce for distributed processing of large datasets in parallel on multiple machines.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses challenges in big data by providing reliability, scalability, and fault tolerance. Hadoop allows distributed processing of large datasets across clusters using MapReduce and can scale from single servers to thousands of machines, each offering local computation and storage. It is widely used for applications such as log analysis, data warehousing, and web indexing.
In this session you will learn:
Meet MapReduce
Word Count Algorithm – Traditional approach
Traditional approach on a Distributed System
Traditional approach – Drawbacks
MapReduce Approach
Input & Output Forms of a MR program
Map, Shuffle & Sort, Reduce Phase
WordCount Code walkthrough
Workflow & Transformation of Data
Input Split & HDFS Block
Relation between Split & Block
Data locality Optimization
Speculative Execution
MR Flow with Single Reduce Task
MR flow with multiple Reducers
Input Format & Hierarchy
Output Format & Hierarchy
To know more, click here: https://www.mindsmapped.com/courses/big-data-hadoop/big-data-and-hadoop-training-for-beginners/
In these slides we analyze why the aggregate data models change the way data is stored and manipulated. We introduce MapReduce and its open source implementation Hadoop. We consider how MapReduce jobs are written and executed by Hadoop.
Finally we introduce spark using a docker image and we show how to use anonymous function in spark.
The topics of the next slides will be
- Spark Shell (Scala, Python)
- Shark Shell
- Data Frames
- Spark Streaming
- Code Examples: Data Processing and Machine Learning
Map reduce - simplified data processing on large clustersCleverence Kombe
The document describes MapReduce, a programming model and software framework for processing large datasets in a distributed computing environment. It discusses how MapReduce allows users to specify map and reduce functions to parallelize tasks across large clusters of machines. It also covers how MapReduce handles parallelization, fault tolerance, and load balancing transparently through an easy-to-use programming interface.
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.
Challenges of Building a First Class SQL-on-Hadoop EngineNicolas Morales
Challenges of Building a First Class SQL-on-Hadoop Engine:
Why and what is Big SQL 3.0?
Overview of the challenges
How we solved (some of) them
Architecture and interaction with Hadoop
Query rewrite
Query optimization
Future challenges
Hadoop and HBase experiences in perf log projectMao Geng
This document discusses experiences using Hadoop and HBase in the Perf-Log project. It provides an overview of the Perf-Log data format and architecture, describes how Hadoop and HBase were configured, and gives examples of using MapReduce jobs and HBase APIs like Put and Scan to analyze log data. Key aspects covered include matching Hadoop and HBase versions, running MapReduce jobs, using column families in HBase, and filtering Scan results.
Challenges of Implementing an Advanced SQL Engine on HadoopDataWorks Summit
Big SQL 3.0 is IBM's SQL engine for Hadoop that addresses challenges of building a first class SQL engine on Hadoop. It uses a modern MPP shared-nothing architecture and is architected from the ground up for low latency and high throughput. Key challenges included data placement on Hadoop, reading and writing Hadoop file formats, query optimization with limited statistics, and resource management with a shared Hadoop cluster. The architecture utilizes existing SQL query rewrite and optimization capabilities while introducing new capabilities for statistics, constraints, and pushdown to Hadoop file formats and data sources.
This document provides an overview of MapReduce and HBase in big data processing. It discusses how MapReduce distributes tasks across nodes in a cluster and uses map and reduce functions to process large datasets in parallel. It also explains how HBase can be used for storage with MapReduce, providing fast access and retrieval of large amounts of flexible, column-oriented data.
The document discusses MapReduce, a framework for processing large datasets in a distributed manner. It begins by explaining how MapReduce addresses issues around scaling computation across large networks. It then provides details on the key features and working of MapReduce, including how it divides jobs into map and reduce phases that operate in parallel on data blocks. Examples are given to illustrate how MapReduce can be used to count word frequencies in text and tally population statistics from a census.
Apache Spark - San Diego Big Data Meetup Jan 14th 2015cdmaxime
This document provides an introduction to Apache Spark presented by Maxime Dumas of Cloudera. It discusses:
1. What Cloudera does including distributing Hadoop components with enterprise tooling and support.
2. An overview of the Apache Hadoop ecosystem including why Hadoop is used for scalability, efficiency, and flexibility with large amounts of data.
3. An introduction to Apache Spark which improves on MapReduce by being faster, easier to use, and supporting more types of applications such as machine learning and graph processing. Spark can be 100x faster than MapReduce for certain applications.
Apache Spark - Santa Barbara Scala Meetup Dec 18th 2014cdmaxime
This document provides an introduction to Apache Spark, a general purpose cluster computing framework. It discusses how Spark improves upon MapReduce by offering better performance, support for iterative algorithms, and an easier developer experience. Spark retains MapReduce's advantages like scalability, fault tolerance, and data locality, but offers more by leveraging distributed memory and supporting directed acyclic graphs of tasks. Examples demonstrate how Spark can run programs up to 100x faster than Hadoop MapReduce and how it supports machine learning algorithms and streaming data analysis.
MapReduce is a software framework introduced by Google that enables automatic parallelization and distribution of large-scale computations. It hides the details of parallelization, data distribution, load balancing, and fault tolerance. MapReduce allows programmers to specify 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. It then automatically parallelizes the computation across large clusters of machines.
This document provides an overview of Hadoop MapReduce concepts including:
- The MapReduce paradigm with mappers processing input splits in parallel during the map phase and reducers processing grouped intermediate outputs in parallel during the reduce phase.
- Key classes involved include the main driver class, mapper class, reducer class, input format class, output format class, and job configuration class.
- An example word count job is described that counts the number of occurrences of each word by emitting (word, 1) pairs from mappers and summing the counts by word from reducers.
- The timeline of a MapReduce job including map and reduce phases is covered along with details of map and reduce task execution.
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This document discusses MapReduce and how it can be used to parallelize a word counting task over large datasets. It explains that MapReduce programs have two phases - mapping and reducing. The mapping phase takes input data and feeds each element to mappers, while the reducing phase aggregates the outputs from mappers. It also describes how Hadoop implements MapReduce by splitting files into splits, assigning splits to mappers across nodes, and using reducers to aggregate the outputs.
isca22-feng-menda_for sparse transposition and dataflow.pptxssuser30e7d2
MeNDA is a near-memory architecture that uses processing units deployed in DIMM buffer chips to perform sparse matrix transposition and SpMV through a multi-way merge algorithm. It presents a scalable solution by exploiting rank-level and DIMM-level parallelism. Evaluation shows MeNDA achieves speedups of 19x, 12x and 8x over CPU, GPU, and state-of-the-art SpMV accelerator implementations, respectively. It also reduces the transposition overhead in graph analytics from 126% to 5% by enabling in-situ processing.
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.
The document describes Hadoop MapReduce and its key concepts. It discusses how MapReduce allows for parallel processing of large datasets across clusters of computers using a simple programming model. It provides details on the MapReduce architecture, including the JobTracker master and TaskTracker slaves. It also gives examples of common MapReduce algorithms and patterns like counting, sorting, joins and iterative processing.
This document provides an overview of big data processing techniques including batch processing using MapReduce and Hive, iterative batch processing using Spark, stream processing using Apache Storm, and OLAP over big data using Dremel and Druid. It discusses techniques such as MapReduce, Hive, Spark RDDs, and Storm tuples for processing large datasets and compares small versus big data approaches. Example usages and technologies for different processing types are also outlined.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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In pioneer days they used oxen for heavy pulling, and when on ox couldn’t
budge a log,they didn’t try to grow a larger ox. We shouldn’t be trying for
bigger computers, but for more systems of computers.
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Meet MapReduce
• MapReduce is a programming model for distributed processing
• Advantage - easy scaling of data processing over multiple computing nodes
• The basic entities in this model are – mappers & reducers
• Decomposing a data processing application into mappers and reducers
is the task of developer
• once you write an application in the MapReduce form, scaling the
application to run over hundreds, thousands, or even tens of thousands of
machines in a cluster is merely a configuration change
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WordCount – Traditional Approach
• The program loops through all the documents. For each document, the
words are extracted one by one using a tokenization process. For each
word, its corresponding entry in a multiset called wordCount is
incremented by one. At the end, a display ()function prints out all the
entries in wordCount.
• A multiset is a set where each element also has a count. The word count
we’re trying to generate is a canonical example of a multiset. In practice, it’s
usually implemented as a hash table.
define wordCount as Multiset;
for each document in documentSet {
T = tokenize(document);
for each token in T {
wordCount[token]++;
}
}
display(wordCount);
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Traditional Approach – Distributed Processing
define wordCount as Multiset;
for each document in documentSubset {
< same code as in perv.slide>
}
sendToSecondPhase(wordCount);
define totalWordCount as Multiset;
for each wordCount received from firstPhase {
multisetAdd (totalWordCount, wordCount);
}
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Traditional Approach – Drawbacks
• Central Storage – bottleneck in bandwidth of the server
• Multiple Storage – handling splits
• Program runs in memory
• When processing large document sets, the number of unique words can
exceed the RAM storage of a machine
• Phase 2 handling by one machine?
• If Multiple machines are used for phase-2, how to partition the data?
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Mapreduce Approach
• Has two execution phases – mapping & reducing
• These phases are defined by data processing functions called – mapper &
reducer
• Mapping phase – MR takes the input data and feeds each data element to
the mapper
• Reducing phase – reducer processes all the outputs from the mapper and
arrives at a final result
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Input & Output forms:
• In order for mapping, reducing, partitioning, and shuffling (and a few others
that were not mentioned) to seamlessly work together, we need to agree
on a common structure for the data being processed
• InputFormat class is responsible for creating input splits and dividing them
into records()
Input Output
map() <k1, v1> list(<k2, v2>)
reduce() <k2, list(v2)> list(<k3, v3>)
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Input & Output forms:
• Input & output forms should be flexible and powerful enough to handle
most of the targeted data processing applications. MapReduce
uses lists and(key/value) pairs as its main data primitives.
• The keys and values are often integers or strings but can also be dummy
values to be ignored or complex object types.
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MR - Work flow & Transformation of data
From i/p
files to the
mapper
From the
Mapper to
the
intermediate
results
From
intermediate
results to
the reducer
From the
reducer to
output files
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Word Count: Source Code
• Key points to note:
1.In MR, map() processes one record at a time, where as traditional
approaches process one document at a time.
2.The new classes that we have seen (Text, IntWritable, LongWritable etc.,)
have additional serialization capabilities. (Will discuss in detail later)
• Source Code: http://hadoop.apache.org/docs/current/hadoop-mapreduce-
client/hadoop-mapreduce-client-core/MapReduceTutorial.html
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Relation Between Input Split & Hdfs Block
1 2 3 4 76 8 1
0
95
File
Lin
es
Block
Bounda
ry
Block
Bounda
ry
Block
Bounda
ry
Block
Bounda
ry
Split Split Split
• Logical records do not fit neatly into the HDFS blocks.
• Logical records are lines that cross the boundary of the blocks.
• First split contains line 5 although it spans across blocks.
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Data locality Optimization
• MR job is split into various map &
reduce tasks
• Map tasks run on the input splits
• Ideally, the task JVM would be
initiated in the node where the
split/block of data exists
• While in some scenarios, JVMs might
not be free to accept another task.
• In that case, Task Tracker will be
initiated at a different location.
• Scenario a) Same node execution
• Scenario b) Off-node execution
• Scenario c) Off-rack execution
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Speculative execution
• MR job is split into various map & reduce tasks and they get executed in
parallel.
• Overall job execution time is pulled down by the slowest task.
• Hadoop doesn’t try to diagnose and fix slow-running tasks; instead, it tries
to detect when a task is running slower than expected and launches
another equivalent task as a backup. This is
termed speculative execution of tasks.
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Combiner
• A combiner is a mini-reducer
• It gets executed on the mapper output at the mapper side
• Combiner’s output is fed to Reducer
• As the mapper output is further refined using combiner, data that has to be
shuffled across the cluster is minimized
• Because the combiner function is an optimization, Hadoop does not
provide a guarantee of how many times it will call it for a particular map
output record,
if at all
• So, calling the combiner function zero, one, or many times should produce
the same output from the reducer.
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Combiner’s Contract
• Only those functions that obey commutative & associative properties can
use combiners.
• Because
max(0, 20, 10, 25, 15) = max(max(0, 20, 10), max(25, 15)) = max(20,
25) = 25
where as,
mean(0, 20, 10, 25, 15) = 14 and
mean(mean(0, 20, 10), mean(25, 15)) = mean(10, 20) = 15
Can a combiner replace a reducer?
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Partitioner
• We know that a unique key will always go to a unique reducer.
• Partitioner is responsible for sending key, value pairs to a reducer based on
the key content.
• The default partitioner is Hash-partitioner. It takes mapper output, create a
Hash value for each key and divide it modulo by the number of reducers.
The output of this calculation will determine the reducer that this particular
key would go to
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Partitioner
public class HashPartitioner<K, V> extends Partitioner<K, V> {
public int getPartition(K key, V value, int numReduceTasks) {
return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;
}
}
2%3 = 1
3%3=0
4%3=1
5%3=2
6%3=0
2%4 = 2
3%4=1
4%4=0
5%4=1
6%4=2
7%4=3
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InputFormat Hierarchy
• An Input split is a chunk of the input that is processed by a single map. Each
map processes a single split. Each split is divided into records, and the map
processes each record—a key-value pair—in turn. Splits and records are
logical: there is nothing that requires them to be tied to files, for example,
although in their most common incarnations, they are.
• In a database context, a split might correspond to a range of rows from a
table and a record to a row in that range.
• An InputFormat is responsible for creating the input splits and dividing
them into records.
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InputFormat Hierarchy
public abstract class InputFormat<K, V> {
public abstract List<InputSplit> getSplits(JobContext context) throws
IOException, InterruptedException;
public abstract RecordReader<K, V> createRecordReader(InputSplit split,
TaskAttemptContext context) throws
IOException, InterruptedException;
}
Client calls getSplits() & map task calls createRecordReader()
• FileInputFormat is the base class for all implementations
of InputFormat that use files as their data source
• It provides two things: a place to define which files are included as the input
to a job, and an implementation for generating splits for the input files. The
job of dividing splits into records is performed by subclasses.
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InputFormat Hierarchy
public static void addInputPath(Job job, Path path)
public static void addInputPaths(Job job, String commaSeparatedPaths)
public static void setInputPaths(Job job, Path... inputPaths)
public static void setInputPaths(Job job, String commaSeparatedPaths)
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Counters
• Map input records
• Map output records
• Filesystem bytes read
• Launched map tasks
• Failed map tasks
• Killed reduce tasks
• Counters are a useful channel for
gathering statistics about the job:
for quality control or for
application-level statistics.
• Often used for debugging purpose.
• eg: Count number of Good records,
bad records in the input
• Two types – Built-in & Custom
Counters
• Examples of Built-in Counters:
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Joins
• Map-side join(Replication): A map-side join that works in situations
where one of the datasets is small enough to cache
• Reduce-side join(Repartition join): A reduce-side join for situations where
you’re joining two or more large datasets together
• Semi-join(A map-side join): Another map-side join where one dataset is
initially too large to fit into memory, but after some filtering
can be reduced down to a size that can fit in memory
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Distributed Cache
• Side data can be defined as extra read-only data needed by a job to process
the main dataset
• To make side data available to all map or reduce tasks, we distribute those
datasets using Hadoop’s Distributed Cache mechanism.