Understanding MapReduce Programming
Considered one of the most confusing paradigms of programming, we’re going to have to keep it as simple as possible for MapReduce to be understood. Perhaps you just recently heard it in class and you were scratching your head wondering what the heck was the professor saying? Don’t worry, we know it sounded like Mandarin; it’s happened to the best of us. In fact, MapReduce is considered one of the stepping stones to making yourself a great programmer. If you can master how to do a MapReduce programming assignment, then you are definitely on your way to becoming a top-tier programmer!
What Is MapReduce?
Normally, MapReduce is made up of a Map() procedure that executes functions such as performing filtering and sorting of data (for example if you are a group of students). Hmm, let’s dive deeper into that. The map job, therefore, functions to work on a set of data and to transform it into a new set of data that can then be transformed yet again to another set of data!
The Reduce() method, on the other hand, is geared to executing summary functions such as calculating the total number of students in a given set and etcetera. That being said, marshalling the two together gives us the famous (or shall we say infamous?) MapReduce. In Leigh man terms, it takes output that was present in one map as input and then collects the data tuples combining them into a smaller set. This is done always after the Map() has been carried out!
One thing you have to be aware of is that the library of MapReduce has been written in multiple languages thanks to its open-source implementation and the fact that is also part of Apache Hadoop.
How Does the MapReduce Algorithm Work?
The MapReduce algorithm also known as the split-apply-combine strategy is inherently important. In it, there are two important functions as stated earlier which are Map() and Reduce().
NOTE: The map task is executed by the Mapper Class while the reduce task is done by the Reducer Class.
That being said, the algorithm works in the following way. First of all, the Mapper Class gets a hold of the input and does the following to it:
- Tokenizing – Tokenizing is the process of transforming the sequence of a given number of characters to that of a sequence of tokens.
- Mapping – By now, it’s pretty clear that mapping is the processing of converting an incompatible data type to one that is compatible all thanks to the use of object-oriented programming. So mapping is applied as the second step.
- Shuffling and sorting – These are some of the most important functions in programming that are essential in analyzing and processing data.
After this, we head on to the reducer class where the main functions are searching and reducing. As we have initially stated what the reducing procedure is, let’s only focus on searching. In simple terms, searching is an important method used to determine a desired criteria in the MapReduce algorithm. Here is where you can be able to perform a summary operation as well!
What Are the Advantages of MapReduce?
Now that you’ve familiarized yourself with Map Reduce programming homework, before we begin diving into examples and much more, let’s look at some of the reasons why using MapReduce is essential for infrastructure running the various tasks of programming systems. We will be looking at the advantages of MapReduce in Apache Hadoop as it is an integral part of this open-source platform:
- Highly scalable – All this stems from the fact that it has innate ability to store and further distribute large sets of data to multiple servers. The best thing is that these servers do not have to be expensive and they can be operated in a parallel setup to enable additional processing power every time a server is included.
- Extremely flexible – MapReduce can be utilized in quite a number of setups regardless of the context. Whether it’s a business looking to manage the salaries of its employees to a simple classroom for sorting students according to specific traits!
- Superfast – With the implementation of mapping in MapReduce to locate data within a cluster, data processing becomes a piece of cake and is executed within seconds. So don’t bat an eye if you hear of MapReduce processing terabytes and petabytes of data in a matter of minutes!
- Ability to execute parallel processing – Perhaps the Holy Grail of MapReduce is its ability to decisively divide tasks and to execute them to be able to run a program in less amount of time!
Disadvantages of MapReduce
Though not too many, there are some shortcomings of programming using MapReduce even in MapReduce programming assignments. The difficulty with real-time processing- Perhaps it’s Achilles heel, the fact that MapReduce has high latency makes it almost unusable for real-time instances and processes. This makes it practically impossible for it to be applied to some spheres of computer science and modern day life!
A Few Pointers on MapReduce Programming Assignment Help
Here are some few pointers on MapReduce that will make it easier for you to understand and implement it.
- The default size of distributed cache MapReduce programming – The size of an input slip in Hadoop is 64 mb. You should definitely keep this in mind when dealing with your big data MapReduce assignment.
- Most common MapReduce question – It is a fact that the most frequently asked question in most MapReduce in big data assignment and exams is normally “When is the reduce method first called in the MapReduce job?”. It’s a given that this question might appear in your big data MapReduce assignment and exam. Hmm so why not check it out?
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