Hadoop MapReduce has evolved as an application development framework for data transformation and computation. Hadoop applications have been developed using a combination of HDFS and MapReduce to achieve high parallel processing of tasks where the data sets involved are extraordinarily large in size. The MapReduce platform was designed to handle internet search queries. Its efficiency can be inferred from the accuracy of a system designed to process data in such quantities. There are several benefits of implementing Hadoop MapReduce into your existing database management solution.
- First, it provides a much lowered latency for client data process requests through the use of MapReduce framework. The basic principle behind MapReduce is splitting a large task into smaller tasks and assigning these to different computing clusters to process. It is followed by combining the independent results into a single answer. The latency of a process is the amount of time taken for it to produce an answer for the end user. A sound system has extremely low latency, which can also be called high responsiveness. This is essential where data is time bound, and results have to be attained within the stipulated time constraints.
- Second, the throughput provided by a Hadoop application is high. The throughput is the amount of data processed within given time. Ideally, a system should have high throughput as this creates efficiency. High throughput in this architecture is provided by the dispersed and parallel storage and processing of data. This advantage is especially pertinent as the data to be processed increases.
- Third, it can be employed in a simple environment. The framework is designed to be used on existing commodity hardware. The end user does not need to have complex systems to deploy the platform in a functional manner. Furthermore, the user is only expected to code the MapReduce task they need to process. The HDFS storage is already catered for which also eases the requirement for a specific database related skills on the part of the user.
- Fourth, the cost of the framework created is affordable. The application platform uses commodity hardware; that is available server computers within the already existing structure. It does not require the acquisition of any specialized machines. It employs whichever machines are added to the network. This allows for easy scaling up where needed by requisitioning additional resources from adjacent systems.
- Last, the distribution of the workload helps prevent network overload. This enables a network system that has many user sessions created to function continuously and efficiently.