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INTRODUCTION
Computing in its purest form, has changed hands multiple times. First, from near the beginning mainframes were predicted to be the future of computing. Indeed mainframes and large scale machines were built and used, and in some circumstances are used similarly today. The trend, however, turned from bigger and more expensive, to smaller and more affordable commodity PCs and servers.
Most of our data is stored on local networks with servers that may be clustered and sharing storage. This approach has had time to be developed into stable architecture, and provide decent redundancy when deployed right. A newer emerging technology, cloud computing, has shown up demanding attention and quickly is changing the direction of the technology landscape. Whether it is Google’s unique and scalable Google File System, or Amazon’s robust Amazon S3 cloud storage model, it is clear that cloud computing has arrived with much to be gleaned from.
Cloud computing is a style of computing in which dynamically scalable and often virtualize resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure in the "cloud" that supports them.
Need for large data processing
We live in the data age. It’s not easy to measure the total volume of data stored electronically, but an IDC estimate put the size of the “digital universe” at 0.18 zettabytes in 2006, and is forecasting a tenfold growth by 2011 to 1.8 zettabytes.
Some of the large data processing needed areas include:-
• The New York Stock Exchange generates about one terabyte of new trade data per day.
• Facebook hosts approximately 10 billion photos, taking up one petabyte of storage.
• Ancestry.com, the genealogy site, stores around 2.5 petabytes of data.
• The Internet Archive stores around 2 petabytes of data, and is growing at a rate of 20 terabytes per month.
• The Large Hadron Collider near Geneva, Switzerland, will produce about 15 petabytes of data per year.
The problem is that while the storage capacities of hard drives have increased massively over the years, access speeds—the rate at which data can be read from drives have not kept up. One typical drive from 1990 could store 1370 MB of data and had a transfer speed of 4.4 MB/s,§ so we could read all the data from a full drive in around five minutes. Almost 20 years later one terabyte drives are the norm, but the transfer speed is around 100 MB/s, so it takes more than two and a half hours to read all the data off the disk. This is a long time to read all data on a single drive—and writing is even slower. The obvious way to reduce the time is to read from multiple disks at once. Imagine if we had 100 drives, each holding one hundredth of the data. Working in parallel, we could read the data in under two minutes.This shows the significance of distributed computing.
Challenges in distributed computing --- meeting hadoop
Various challenges are faced while developing a distributed application. The first problem to solve is hardware failure: as soon as we start using many pieces of hardware, the chance that one will fail is fairly high. A common way of avoiding data loss is through replication: redundant copies of the data are kept by the system so that in the event of failure, there is another copy available. This is how RAID works, for instance, although Hadoop’s filesystem, the Hadoop Distributed Filesystem(HDFS), takes a slightly different approach.
The second problem is that most analysis tasks need to be able to combine the data in some way; data read from one disk may need to be combined with the data from any of the other 99 disks. Various distributed systems allow data to be combined from multiple sources, but doing this correctly is notoriously challenging. MapReduce provides a programming model that abstracts the problem from disk reads and writes transforming it into a computation over sets of keys and values.
This, in a nutshell, is what Hadoop provides: a reliable shared storage and analysis system. The storage is provided by HDFS, and analysis by MapReduce. There are other parts to Hadoop, but these capabilities are its kernel.
Hadoop is the popular open source implementation of MapReduce, a powerful tool designed for deep analysis and transformation of very large data sets. Hadoop enables you to explore complex data, using custom analyses tailored to your information and questions. Hadoop is the system that allows unstructured data to be distributed across hundreds or thousands of machines forming shared nothing clusters, and the execution of Map/Reduce routines to run on the data in that cluster. Hadoop has its own filesystem which replicates data to multiple nodes to ensure if one node holding data goes down, there are at least 2 other nodes from which to retrieve that piece of information. This protects the data availability from node failure, something which is critical when there are many nodes in a cluster (aka RAID at a server level).