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MapReduce

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). MapReduce can take advantage of locality of data, processing it on or near the storage assets in order to reduce the distance over which it must be transmitted. "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: Logical view[edit] Map(k1,v1) → list(k2,v2) Examples[edit] Related:  voidhazeIS331003 Database

Networks In mathematical terms, a network is a graph in which the nodes and edges have values associated with them. A graph is defined as a pair of sets , where is a set of nodes (vertices or points within the graph) labelled and is a set of edges (links (vi, vj ) that connect pairs of elements vi, vj within ). The degree to which the nodes of a network are directly connected is called connectivity. is the number of edges and is the number of nodes in the network, the following equation is used; The degree of a node in a network is the number of edges or connections to that node (Newman 2003). Shortest average path length The average path length, ( ) of a network is the average number of edges, or connections between nodes, that must be crossed in the shortest path between any 2 nodes (Watts 2003). where is the minimum distance between nodes i and j. The diameter of a network is the longest shortest path within a network. A common property of many social networks is cliques. consisting of the set of nodes

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. The MapReduce concept is fairly simple to understand for those who are familiar with clustered scale-out data processing solutions. 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 (Toronto, 20) (Whitby, 25) (New York, 22) (Rome, 33) (Toronto, 32) (Whitby, 27) (New York, 33) (Rome, 38)

Higher-Order Perl 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]

Baking Pi - Operating Systems Development This course has not yet been updated to work with the Raspberry Pi models B+ and A+. Some elements may not work, in particular the first few lessons about the LED. It has also not been updated for Raspberry Pi v2. Welcome to Baking Pi: Operating Systems Development! You can now help contribute to this tutorial on GitHub. This website is here to guide you through the process of developing very basic operating systems on the Raspberry Pi! This course takes you through the basics of operating systems development in assembly code. Rather than leading the reader through the full details of creating an Operating System, these tutorials focus on achieving a few common tasks separately. 1 Requirements 1.1 Hardware In order to complete this course you will need a Raspberry Pi with an SD card and power supply. 1.2 Software In terms of software, you require a GNU compiler toolchain that targets ARMv6 processors. 2 Lessons

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 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. Maps are the individual tasks that transform input records into intermediate records. How Many Maps? Sort

PDF, Let Me Count the Ways… In this post, I show how basic features of the PDF language can be used to generate polymorphic variants of (malicious) PDF documents. If you code a PDF parser, write signatures (AV, IDS, …) or analyze (malicious) PDF documents, you should to be aware of these features. Official language specifications are interesting documents, I used to read them from front to back. I especially appreciate the inclusion of a formal language description, for example in Backus–Naur form. But nowadays, I don’t take the time to do this anymore. While browsing through the official PDF documentation, I took particular interest in the rules to express lexemes. Building a test file Before I show some examples, let’s build a test PDF file that will start the default browser and navigate to a site each time the document is opened. Opening a web page from a PDF file can be done with an URI action, like this: This is the same type of object used in the malicious mailto PDF files. Name representation Or #55#52#49

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. 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. Chapter 2: Your proclamation: Let there be caste The next day, You stand with a mike on the dias before 50,000 and proclaim.

Design Patterns It has been highly influential to the field of software engineering and is regarded as an important source for object-oriented design theory and practice. More than 500,000 copies have been sold in English and in 13 other languages. The authors are often referred to as the Gang of Four (GoF).[1] History[edit] Introduction, Chapter 1[edit] Chapter 1 is a discussion of object-oriented design techniques, based on the authors' experience, which they believe would lead to good object-oriented software design, including: clients remain unaware of the specific types of objects they use, as long as the object adheres to the interfaceclients remain unaware of the classes that implement these objects; clients only know about the abstract class(es) defining the interface Use of an interface also leads to dynamic binding and polymorphism, which are central features of object-oriented programming. The authors admit that delegation and parameterization are very powerful but add a warning: Formatting[edit]

Get Involved - MongoDB Getting involved in the MongoDB community is a great way to build relationships with other talented engineers, increase awareness for the interesting work that you are doing, sharpen your skills, or give back. Here are some of the ways that you can contribute to the MongoDB ecosystem. Discuss MongoDB through Community Forums Discuss, learn about, and get help with MongoDB through community-supported forums. We also offer office hours and paid support options. Join (or Start) a MongoDB User Group MongoDB User Groups (MUGs) are a great way to network, learn from one another about MongoDB best practices, and have fun. Find a user group near you Contribute to the Docs We run the MongoDB docs like an open source project. Learn more about contributing to the docs Write Code Open source projects benefit from the contributions of the developer community. Share MongoDB If you love MongoDB, consider telling others about it. Attend a MongoDB Sponsored Event Check out the next event in your area Other Ideas?

Phreaking The term phreak is a portmanteau of the words phone and freak, and may also refer to the use of various audio frequencies to manipulate a phone system. Phreak, phreaker, or phone phreak are names used for and by individuals who participate in phreaking. Because identities were usually masked, an exact percentage of prevalence cannot be calculated. History[edit] Phone phreaking got its start in the late 1950s. Switch hook and tone dialer[edit] Possibly one of the first phreaking methods was switch-hooking. By rapidly clicking the hook for a variable number of times at roughly 5 to 10 clicks per second, separated by intervals of roughly one second, the caller can dial numbers as if they were using the rotary dial. Such key-locked telephones, if wired to a modern DTMF capable exchange, can also be exploited by a tone dialer that generates the DTMF tones used by modern keypad units. 2600 hertz[edit] Multi frequency[edit] Blue boxes[edit] Computer hacking[edit] Toll fraud[edit] Diverters[edit]

Oracle NoSQL Database Technical Overview The Oracle NoSQL Database is a distributed key-value database. It is designed to provide highly reliable, scalable and available data storage across a configurable set of systems that function as storage nodes. Data is stored as key-value pairs, which are written to particular storage node(s), based on the hashed value of the primary key. Storage nodes are replicated to ensure high availability, rapid failover in the event of a node failure and optimal load balancing of queries. Customer applications are written using an easy-to-use Java/C API to read and write data. Oracle NoSQL Driver links with the customer application, providing access to the data via appropriate storage node for the requested key. News! Need help getting started. Product Overview White Papers / Presentations Data Sheets Use Cases Online Tutorials / Videos Online Webinars Competitive Resources Additional Resources Partners Follow Us

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