Introduction to Map-Reduce Programming with Hadoop

This document provides an overview of Hadoop and MapReduce terminology and concepts. It describes the key components of Hadoop including HDFS, Zookeeper, and HBase. It explains the MapReduce programming model and how data is processed through mappers, reducers, and the shuffle and sort process. Finally, it provides a word count example program and describes how to run Hadoop jobs on a cluster.

An increasing number of popular applications become data-intensive in nature. In the past decade, the World Wide Web has been adopted as an ideal platform for developing data-intensive applications, since the communication paradigm of the Web is sufficiently open and powerful. Data-intensive applications like data mining and web indexing need to access ever-expanding data sets ranging from a few gigabytes to several terabytes or even petabytes. Google leverages the MapReduce model to process approximately twenty petabytes of data per day in a parallel fashion. In this talk, we introduce the Google’s MapReduce framework for processing huge datasets on large clusters. We first outline the motivations of the MapReduce framework. Then, we describe the dataflow of MapReduce. Next, we show a couple of example applications of MapReduce. Finally, we present our research project on the Hadoop Distributed File System. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Data locality has not been taken into account for launching speculative map tasks, because it is assumed that most maps are data-local. Unfortunately, both the homogeneity and data locality assumptions are not satisfied in virtualized data centers. We show that ignoring the datalocality issue in heterogeneous environments can noticeably reduce the MapReduce performance. In this paper, we address the problem of how to place data across nodes in a way that each node has a balanced data processing load. Given a dataintensive application running on a Hadoop MapReduce cluster, our data placement scheme adaptively balances the amount of data stored in each node to achieve improved data-processing performance. Experimental results on two real data-intensive applications show that our data placement strategy can always improve the MapReduce performance by rebalancing data across nodes before performing a data-intensive application in a heterogeneous Hadoop cluster.

The document introduces Hadoop and provides an overview of its key components. It discusses how Hadoop uses a distributed file system (HDFS) and the MapReduce programming model to process large datasets across clusters of computers. It also provides an example of how the WordCount algorithm can be implemented in Hadoop using mappers to extract words from documents and reducers to count word frequencies.

Hadoop became the most common systm to store big data. With Hadoop, many supporting systems emerged to complete the aspects that are missing in Hadoop itself. Together they form a big ecosystem. This presentation covers some of those systems. While not capable to cover too many in one presentation, I tried to focus on the most famous/popular ones and on the most interesting ones.

The document discusses key concepts related to Hadoop including its components like HDFS, MapReduce, Pig, Hive, and HBase. It provides explanations of HDFS architecture and functions, how MapReduce works through map and reduce phases, and how higher-level tools like Pig and Hive allow for more simplified programming compared to raw MapReduce. The summary also mentions that HBase is a NoSQL database that provides fast random access to large datasets on Hadoop, while HCatalog provides a relational abstraction layer for HDFS data.

Founding committer of Spark, Patrick Wendell, gave this talk at 2015 Strata London about Apache Spark. These slides provides an introduction to Spark, and delves into future developments, including DataFrames, Datasource API, Catalyst logical optimizer, and Project Tungsten.

This document provides an overview of MapReduce and Hadoop. It describes the Map and Reduce functions, explaining that Map applies a function to each element of a list and Reduce reduces a list to a single value. It gives examples of Map and Reduce using employee salary data. It then discusses Hadoop and its core components HDFS for distributed storage and MapReduce for distributed processing. Key aspects covered include the NameNode, DataNodes, input/output formats, and the job launch process. It also addresses some common questions around small files, large files, and accessing SQL data from Hadoop.

This presentation will give you Information about : 1. Map/Reduce Overview and Architecture Installation 2. Developing Map/Red Jobs Input and Output Formats 3. Job Configuration Job Submission 4. Practicing Map Reduce Programs (atleast 10 Map Reduce 5. Algorithms )Data Flow Sources and Destinations 6. Data Flow Transformations Data Flow Paths 7. Custom Data Types 8. Input Formats 9. Output Formats 10. Partitioning Data 11. Reporting Custom Metrics 12. Distributing Auxiliary Job Data

This document discusses Scoobi, a Scala library for developing MapReduce applications on Hadoop. Some key points: 1) Scoobi allows developers to write Hadoop MapReduce jobs using a functional programming style in Scala, inspired by Google's FlumeJava. It provides abstractions like DList and DObject to represent distributed datasets and computations. 2) Under the hood, Scoobi compiles Scala code into Java MapReduce jobs that run on Hadoop. It handles partitioning, parallelization, and distribution of data and computation across clusters. 3) Examples show how common operations like filtering, mapping, reducing can be expressed concisely using the Scoobi API, mirroring Scala

This document provides an overview of MapReduce, including: - MapReduce is a programming model for processing large datasets in parallel across clusters of computers. - It works by breaking the processing into map and reduce functions that can be run on many machines. - Examples are given like word counting, distributed grep, and analyzing web server logs.

This document provides an overview of key concepts in Hadoop including: - Hadoop was tested on a 4000 node cluster with 32,000 cores and 16 petabytes of storage. - MapReduce is Hadoop's programming model and consists of mappers that process input splits in parallel, and reducers that combine the outputs of the mappers. - The JobTracker manages jobs, TaskTrackers run tasks on slave nodes, and Tasks are individual mappers or reducers. Data is distributed to nodes implicitly based on the HDFS file distribution. Configurations are set using a JobConf object.

This document discusses big data and Hadoop. It begins by explaining how data is growing exponentially and defining what big data is. It then introduces Hadoop as an open-source framework for storing and processing big data across clusters of commodity hardware. The rest of the document provides details on the key components of Hadoop, including HDFS for distributed storage, MapReduce for distributed processing, and various related projects like Pig, Hive and HBase that build on Hadoop.

This document provides an overview of Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop was developed based on Google's MapReduce algorithm and how it uses HDFS for scalable storage and MapReduce as an execution engine. Key components of Hadoop architecture include HDFS for fault-tolerant storage across data nodes and the MapReduce programming model for parallel processing of data blocks. The document also gives examples of how MapReduce works and industries that use Hadoop for big data applications.

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 various fruits. Sam got a job at JuiceRUs for his talent in making juice. Later, he implemented a parallel version of his juice-making process to handle large volumes of fruits, realizing several optimizations for efficiency and side effect prevention.

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.