iot.pptx

This document summarizes the MapReduce programming model and its associated implementation for processing large datasets across distributed systems. It describes how MapReduce allows users to express computations over large datasets in a simple way, while hiding the complexity of parallelization, fault-tolerance, and data distribution. The core abstractions of Map and Reduce are explained, along with how an implementation like Google's leverages a distributed file system to parallelize tasks across clusters and provide fault-tolerance through replication.

1) Stratosphere is a distributed data processing system that extends the MapReduce model by supporting more operators and advanced data flow graphs composed of operators. 2) It has components like a query parser, compiler, and optimizer that translate queries into execution plans composed of operators like Map, Reduce, Join, Cross, CoGroup, and Union. 3) Stratosphere supports arbitrary data flows while MapReduce only supports MapReduce, and Stratosphere has better performance through in-memory processing and pipelining compared to MapReduce which always writes to disk.

Abstract: The presentation describes - What is the BigData problem - How Hadoop helps to solve BigData problems - The main principles of the Hadoop architecture as a distributed computational platform - History and definition of the MapReduce computational model - Practical examples of how to write MapReduce programs and run them on Hadoop clusters The talk is targeted to a wide audience of engineers who do not have experience using Hadoop.

This presentation discusses the following topics: Introduction Components of Hadoop MapReduce Map Task Reduce Task Anatomy of a Map Reduce

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.

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.

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 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.

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.