If you followed my previous articles, probably at this stage you should have common understanding of primary components of Hadoop ecosystem and basics of distributed calculations. But in order implement performant processing we first need to prepare a strong data foundation for it. Hadoop provides a number of solutions for this purpose and HBase data store is one of the best products in this ecosystem which allows you to organize big amounts of data in a single place. In this article I want to tell about some techniques of working with HBase – how to import the data, how to read it through native API and how to simplify its consumption through another Hadoop product called Hive.
At this stage you probably have a general idea of what Hadoop is in technical scene. But why do we really need such a huge and complicated platform for doing such simple things like searching, counting or sorting our data. According to the research provided by Cisco last year annual global IP traffic will reach 2.3 zettabytes per year by 2020. Another research forecast performed by International Data Corporation few years ago stated that up to 2020 people will have to operate with 44 zettabytes of data. Can we really handle such capacities with our current hardware and algorithms? Hadoop is probably the best attempt to handle that problem at this time.
There is quite an interesting competition which exists in the world of Big Data called Terasort. It appeared in 2008 with the general idea to generate, sort and validate 1TB of data. At that period the result was 3 minute 48 seconds on Hadoop cluster of 910 nodes. By the time the amount of data increased to 100TB and just few month ago we got a new record of sorting 100TB of data for 98.8 seconds in the cluster of 512 nodes. The actual results are available Sort Benchmark Home page.