When Google faced the trouble of analyzing huge datasets, such as web access logs and the page rank of a website. They used to employ an algorithmic approach which used to take up a lot of time and had to be re-done for every problem. So to get rid of it all, they tasked their software developers to develop a program which splits the data, forwards it to the participating nodes along with the code, check for errors and then retrieves as well as organizes the results. After a couple of trials, MapReduce was born.
Simply put: “It is an abstraction which arranges the parallelizable jobs”
With MapReduce, Google pioneered the tech which allowed to easily perform computations while hiding keeping the mess of parallelization in the background. It handles the fault-tolerance, load balancing and the data distribution in a library. You can say that Hadoop was born in 2004 as it replaced the heuristics and indexing algorithms.
The MapR Programming Model
The computations of MapR take a set of input key values and produces the set of output key values. Almost every computation involves two primary parts: Map and Reduce.
Written by the programmer, the Map takes an input pair of values and produces a set of intermediary values. The MapR library groups together all these intermediate values and then passes them to the Reduce function.
Also written by the user, this function takes the intermediate keys as arguments along with a set of values for the key. It merges them together to form an even smaller set of values. Usually, zero or just a single value is produced as the result of invoking the Reduce function. The intermediate values are supplied to the user written Reduce function through an iterator, which allows easy handling of the values which are too huge to fit in the memory.
One of the most basic advantages of MapR is that it allows the user to achieve results faster than the traditional databases, however, it doesn’t necessarily mean that they’d replace them. It is just a better way of doing something. With MapR programming becomes easier, making the job run faster and smoother with parallelization in service hardware.
When working with MapR, programmers will have lesser issues programming jobs between the or within the clusters. The reason is that proper communication is ensured within the clusters, also efficient monitoring along with effective fault handling. This makes it the best framework for carrying out simulation projects and the tasks which encompass analytical requirements.
No matter platform you are used to, using MapR isn’t very difficult. If you have worked with any programming language such as Python, Java, Ruby, C++ or even C#, you can easily program a job. So this being a huge part of the Hadoop Environment, allows programmers to effectively and efficiently handle their jobs which would have taken up immense time periods if done manually. For anyone, the MapR framework will take care of all the data transport between the node as well as the node coordination, making it a crucial part of Hadoop.