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What is MapReduce

What is MapReduce
What is MapReduce? About MapReduce MapReduce is the heart of Hadoop®. It is this programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. Stay on top of all the changes including, Hadoop-based analytics, streaming analytics, warehousing (including BigSQL), data asset discovery, integration, and governance For people new to this topic, it can be somewhat difficult to grasp, because it’s not typically something people have been exposed to previously. The term MapReduce actually refers to two separate and distinct tasks that Hadoop programs perform. An example of MapReduce Let’s look at a simple example. Toronto, 20 Whitby, 25 New York, 22 Rome, 32 Toronto, 4 Rome, 33 New York, 18 Out of all the data we have collected, we want to find the maximum tem­perature for each city across all of the data files (note that each file might have the same city represented multiple times). (Toronto, 20) (Whitby, 25) (New York, 22) (Rome, 33) Related:  ks567IS331003 Database

Map-Reduce — MongoDB Manual 2.6.4 Map-reduce is a data processing paradigm for condensing large volumes of data into useful aggregated results. For map-reduce operations, MongoDB provides the mapReduce database command. Consider the following map-reduce operation: In this map-reduce operation, MongoDB applies the map phase to each input document (i.e. the documents in the collection that match the query condition). All map-reduce functions in MongoDB are JavaScript and run within the mongod process. Note For most aggregation operations, the Aggregation Pipeline provides better performance and more coherent interface. Map-Reduce JavaScript Functions In MongoDB, map-reduce operations use custom JavaScript functions to map, or associate, values to a key. The use of custom JavaScript functions provide flexibility to map-reduce operations. Map-Reduce Behavior In MongoDB, the map-reduce operation can write results to a collection or return the results inline. MongoDB supports map-reduce operations on sharded collections.

3150-map-reduce-for-machine-learning-on-multicore MapReduce Overview[edit] MapReduce is a framework for processing parallelizable problems across huge datasets using a large number of computers (nodes), collectively referred to as a cluster (if all nodes are on the same local network and use similar hardware) or a grid (if the nodes are shared across geographically and administratively distributed systems, and use more heterogenous hardware). Processing can occur on data stored either in a filesystem (unstructured) or in a database (structured). "Map" step: Each worker node applies the "map()" function to the local data, and writes the output to a temporary storage. MapReduce allows for distributed processing of the map and reduction operations. Another way to look at MapReduce is as a 5-step parallel and distributed computation: These five steps can be Logically thought of as running in sequence – each step starts only after the previous step is completed – although in practice they can be interleaved as long as the final result is not affected.

When NoSQL Databases Are — Yes — Good For You And Your Company The proliferation of non-relational databases in the tech sector these days could lead you to think that these data management tools (also known as NoSQL databases) are eventually going to make traditional relational databases extinct. Not so. Each of these database types is best suited for very different types of workloads, and that's going to prevent either one from tromping the other into the dust. Which means that IT and other managers are going to have to figure out which approach is best suited for the task at hand. In this two-part series, I'll examine the capabilities of both NoSQL and relational databases to help you make the right decisions for your organization. "NoSQL"? Right off the bat, NoSQL databases are unique because they are usually independent from Structured Query Language (SQL) found in relational databases. See also: Relational Databases Aren't Dead—Heck, They're Not Even Sleeping NoSQL databases are designed to excel in speed and volume. Go Big Or Go Home Downtime?

dean MapReduce-MPI Library MapReduce Tutorial This section provides a reasonable amount of detail on every user-facing aspect of the MapReduce framework. This should help users implement, configure and tune their jobs in a fine-grained manner. However, please note that the javadoc for each class/interface remains the most comprehensive documentation available; this is only meant to be a tutorial. Let us first take the Mapper and Reducer interfaces. Applications typically implement them to provide the map and reduce methods. We will then discuss other core interfaces including JobConf, JobClient, Partitioner, OutputCollector, Reporter, InputFormat, OutputFormat, OutputCommitter and others. Finally, we will wrap up by discussing some useful features of the framework such as the DistributedCache, IsolationRunner etc. Payload Applications typically implement the Mapper and Reducer interfaces to provide the map and reduce methods. Mapper Mapper maps input key/value pairs to a set of intermediate key/value pairs. How Many Maps? Reducer Shuffle

Explaining Non-Relational Databases To My Mom | Ignored by Dinosaurs I was on the phone with Mom yesterday, and we got to talking about technology - a thing that actually happens fairly frequently. Being an only kid, she’s genuinely interested in everything that I do and it’s been helpful to have someone who’s mostly non-technical to bounce explanations off of when I’m getting my head around a new piece of gear. The piece of gear that I was explaining the other day was something called Mongo DB. Mongo’s parent company is called 10gen, and they landed on the startup scene about 5 years ago or so with their flagship product, Mongo DB. The Relational model The relational model of storing data has been around for more than 40 years. The classic example I gave to my mom was that of a common blog. The relational model typically comes into play when you visit a blog that has comments. Issues with the relational model For the purposes of this simplistic example, this hopefully isn’t that hard to get your head around. Very good. The non-relational model

Apache Hadoop 2.5.1 - MapReduce Tutorial This section provides a reasonable amount of detail on every user-facing aspect of the MapReduce framework. This should help users implement, configure and tune their jobs in a fine-grained manner. However, please note that the javadoc for each class/interface remains the most comprehensive documentation available; this is only meant to be a tutorial. Let us first take the Mapper and Reducer interfaces. We will then discuss other core interfaces including Job, Partitioner, InputFormat, OutputFormat, and others. Finally, we will wrap up by discussing some useful features of the framework such as the DistributedCache, IsolationRunner etc. Payload Applications typically implement the Mapper and Reducer interfaces to provide the map and reduce methods. Mapper Mapper maps input key/value pairs to a set of intermediate key/value pairs. Maps are the individual tasks that transform input records into intermediate records. Output pairs do not need to be of the same types as input pairs. Reducer Sort

MySQL vs. MongoDB: Looking At Relational and Non-Relational Databases | Neon Rain Interactive When building a custom web application you need to consider the type of database that best suits the data. Here's a quick guide on the differences between MySQL (Relational) and MongoDB (Non-Relational / NoSQL). It was back in 2004 that Ruby on Rails first came out and popularized web application frameworks. What you might not know, is that it also popularized ORM (Object-Relational Mapping) layers with its ActiveRecord object. An ORM layer basically provides an object oriented interface to a relational database. That means that instead of writing a query to insert or update a record, you assign some properties to an object and call a save method. For example, if you have a "post" object that represents a blog post, you can access it's comments through the property "post.comments". Thankfully, we never jumped on to the ORM bandwagon. Data Representation MySQL represents data in tables and rows. MongoDB represents data as collections of JSON documents. Querying MongoDB uses object querying.

MongoDB MongoDB (from "humongous") is a cross-platform document-oriented database. Classified as a NoSQL database, MongoDB eschews the traditional table-based relational database structure in favor of JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster. Released under a combination of the GNU Affero General Public License and the Apache License, MongoDB is free and open-source software. First developed by the software company 10gen (now MongoDB Inc.) in October 2007 as a component of a planned platform as a service product, the company shifted to an open source development model in 2009, with 10gen offering commercial support and other services.[1] Since then, MongoDB has been adopted as backend software by a number of major websites and services, including Brave Collective, Craigslist, eBay, Foursquare, SourceForge, Viacom, and the New York Times, among others. Licensing and support[edit]

Nonrelational Databases in a Big Data Environment Nonrelational databases do not rely on the table/key model endemic to RDBMSs (relational database management systems). In short, specialty data in the big data world requires specialty persistence and data manipulation techniques. Although these new styles of databases offer some answers to your big data challenges, they are not an express ticket to the finish line. One emerging, popular class of nonrelational database is called not only SQL (NoSQL). Originally the originators envisioned databases that did not require the relational model and SQL. As these products were introduced into the market, the definition softened a bit and now they are thought of as “not only SQL,” again bowing to the ubiquity of SQL. The other class is databases that do not support the relational model, but rely on SQL as a primary means of manipulating the data within.

nosql - Non-Relational Database Design Map Reduce - A really simple introduction « Kaushik Sathupadi Ever since google published its research paper on map reduce, you have been hearing about it. Here and there. If you have uptil now considered map-reduce a mysterious buzzword, and ignored it, Know that its not. The basic concept is really very simple. and in this tutorial I try to explain it in the simplest way that I can. Note that I have intentionally missed out some deeper details to make it really friendly to a beginner. Chapter 1: Your CEO’s Strange itch: Imagine this. Dear <Your Name>, As you know we are building the blogging platform blogger2.com, I need some statistics. Picture yourself in that position for a moment. Occurance of one character words – Around 937688399933 Occurance of two chracter words – Around 23388383830753434 .. hence forth till 10 If homicide, suicide or resigining the job is not an option, how would you solve it? You decide to take leave for the day, go home, sleep over it, and the next day wake up with the greatest Idea ever. Each take 4 days.

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