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Distrubuted computingDistributed computing is a field of computer science that studies distributed systems. A distributed system is a software system in which components located on networked computers communicate and coordinate their actions by passing messages. The components interact with each other in order to achieve a common goal. There are many alternatives for the message passing mechanism, including RPC-like connectors and message queues. Three significant characteristics of distributed systems are: concurrency of components, lack of a global clock, and independent failure of components. An important goal and challenge of distributed systems is location transparency. Examples of distributed systems vary from SOA-based systems to massively multiplayer online games to peer-to-peer applications.
A computer program that runs in a distributed system is called a distributed program, and distributed programming is the process of writing such programs.
Distributed computing also refers to the use of distributed systems to solve computational problems. In distributed computing, a problem is divided into many tasks, each of which is solved by one or more computers, which communicate with each other by message passing.
BigData need Distributed computingNot all problems require distributed computing. If a big time constraint doesn’t exist, complex processing can done via a specialized service remotely. When companies needed to do complex data analysis, IT would move data to an external service or entity where lots of spare resources were available for processing.
It wasn’t that companies wanted to wait to get the results they needed; it just wasn’t economically feasible to buy enough computing resources to handle these emerging requirements. In many situations, organizations would capture only selections of data rather than try to capture all the data because of costs. Analysts wanted all the data but had to settle for snapshots, hoping to capture the right data at the right time.
Key hardware and software breakthroughs revolutionized the data management industry. First, innovation and demand increased the power and decreased the price of hardware. New software emerged that understood how to take advantage of this hardware by automating processes like load balancing and optimization across a huge cluster of nodes.
The software included built-in rules that understood that certain workloads required a certain performance level. The software treated all the nodes as though they were simply one big pool of computing, storage, and networking assets, and moved processes to another node without interruption if a node failed, using the technology of virtualization.
Réf: Bigtable: A Distributed Storage System for Structured Data