Understanding MapReduce
MapReduce is a programming software used to write and run applications. It easily composes programs that process huge levels of data on large clusters associated with commodity hardware making it reliable. It generally divides the actual data-set into independent chunks prepared through the map tasks in a parallel manner.
MapReduce is always dedicated to performing a specific or a given function. It is made up of a Map () procedure that completes functions such as sorting and filtering data. It also contains a Reduce () function, which is used in executing summary functions such as the calculation of the number of students in a particular set. The Reduce function always takes place after the Map has been carried out.
It’s important to note that MapReducecan is written in several languages because of its open-source implementation and also because it is part of Hadoop.
MapReduce works in the following way:
Tokenizing – This is the process of transforming the sequence of a specific number of characters into a sequence of tokens.
Mapping – Mapping is the process of converting data types that are not compatible with a compatible one. This is made possible by the use of object-oriented programming. Therefore, mapping comes in as the second step after tokenizing.
Sorting and shuffling – Shuffling and tokenizing are very important since they help in the analyzing and processing of the data.
Usage of MapReduce
- MapReduce is used in different applications such as document clustering, web link-graph reversal, and distributed sorting.
- It can also be used for distributed pattern-based search
- MapReduce can also be used in machine learning
- Google used MapReduce in the regeneration of Google’s index in the World Wide Web.
- It can also be used in many computing environments like multi-cluster, mobile environment, and multi-core.
Advantages of MapReduce
The use of MapReduce is essential in programming. Here are some of the advantages of this model:
It’s very fast – With MapReduce, especially when one is working to locate data within a cluster it’s a piece of cake. This is because it can be done within seconds. It reduces the time people use it trying to sort data in a specific way.
Flexibility – You can use MapReduce in different setups regardless of the context. Whether it is in a classroom sorting students based on traits or whether it is at work managing salaries of employees. You can use it anywhere.
It’s highly scalable – MapReduce has an innate ability to distribute and store large sets of data on different servers. The best thing about these services is that they can be operated in a parallel setup.
Execution of parallel processing – The most important function of MapReduce is its ability to decisively divide tasks and execute to run a program in a limited amount of time.
Disadvantages of MapReduce?
- The MapReduce framework is rigid. Its lack of flexibility is a major setback
- MapReduce semantics are hidden in the map and in the reduced functions. It, therefore, becomes very hard to optimize, maintain, or extend them.
- In MapReduce, a lot of manual coding is needed. Even for operations that you would term as common such as filter, aggregates, and sorting you need coding.