Basics of a Functional Hadoop Ecosystem

The Hadoop ecosystem reduces the cost of processing large data sets by employing clusters composed of simple computing power. This is accomplished by using a simple programming model to undertake distributed processing of a query. The trade name of the system is Apache Hadoop, and it is usually provided as an open source solution. Open source means that the end user can modify the source code if they are possessed of the relevant knowledge and it can suit their needs much better.

The simplified programming model employed is structured to provide scalability using network accessible server machines. Each of the machines within the functional Hadoop ecosystem provides both storage and processing functions. This enables the system to have a highly effective processing power and storage capacity because the ecosystem is based on multiple parallel processing architectures. Multiple parallel processing is where dispersed server systems undertake the processing of a single job split into tasks, at the same time. The storage function is managed by Hadoop Distributed File System HDFS while the processing function is undertaken by MapReduce in a fault tolerant manner.

The main components of the HDFS system are: NameNode, DataNode, and Secondary NameNode. The NameNodeis the system master and maintains a directory of the files and nodes available on the system. A DataNode is the slave in the machine. It provides the actual storage used for the data. It is from this component that the user read and writes requests are handled. In most systems, there is usually a periodic checkpoint system that is used to maintain system stability and functionality. This periodic checkpoint is called the Secondary NameNode. It also serves as a backup system to the NameNode. It stores information that allows the NameNode to be restarted in the event of a failure.

MapReduce is a processing paradigm that employs common clusters to process a task much faster and more efficiently. There are MapReduce tutorials available online to assist in learning about the system and its functionality. They can provide a step by step guide on how to use it and how the data processing is undertaken from the user perspective. A MapReduce tutorial explains that the processing architecture divides a complex job into easier simpler tasks. This is then processed by multiple nodes within the system concurrently and the end result then channeled to the main node, which is then transmitted to the end user. An understanding of the MapReduce functional form is important for streamlining the data processing within the system.