Hadoop cluster is a special type of cloud designed especially for operating and storing huge amount of unstructured data. The main function of Hadoop cluster is to increase speed and performance of data applications. Because of its powerful features, it helps greatly in Hadoop technology. A Hadoop cluster is made up of three Nodes, namely Master Node, Client Node and Worker Node. Each of these Nodes has a specific function. And the Node type decides how many numbers of that Node you require in Planning your cluster.
Master Node, it controls the operations that comprise Hadoop, like allocating data in Hadoop Distributed File system and parallel running computations on data using MapRedure big data tools. The JobTracker together with the parallel processing of data supervise the processes using MapReduce and similarly NameNode coordinates with data storage system.
Worker Node, it creates the maximum of virtual machines and stores the data and other running computations. Each worker node performs operation both as DataNode and TaskTracker service. Every Worker Nodes of Hadoop Cluster works under Master Node.
The Client node is the last node of Hadoop Cluster. They are fully setup in Big Data Environment. But they can neither work as Master Nodes nor as Worker Nodes. It performs a simple task of filling cluster with data using MapReduce. MapReduce tells about how data must be processed at times, and how it is retrieved or how result must be displayed after the job is finished.
Hadoop Cluster Architecture
The main function of MapReduce is to reduce the size of huge data. It is a framework which divides large datasets in small datasets in a distributed manner with multiple machines. MapReduce performs three basic steps, take the input in form of datasets, divide that data set into small data sets, shuffle the result and combine all small ones to form a single output. This component is important in Hadoop cluster setup.
Hadoop Distributed File System(HDFS)
The HDFS is a very important part of Hadoop cluster planning. It performs a variety of operations such as storage awareness, rack-tracking for the data to be stored and Fault management which makes it way better than other distributed file systems. Some key points which make HDFS better than others are:
- Detect hardware failure of cluster
- Can operated for large datasets
- Cross-platform technology
- supports both homogeneous as well as heterogeneous cluster
Inside the Hadoop Cluster, the data is broken into small units called Blocks. Then 2 copies of each block are produced and assigned to any two nodes in rack inside Hadoop ecosystem. Since now total 3 replicas of each block are present, therefore chances of failure of Hadoop cluster architecture are very less.
Yet Another Resource Negotiator (YARN)
YARN’s main function is to assign computational resources to each block for execution of an Hadoop Cluster application. A YARN provides a variety of resources such as Resource Manager (one per cluster), Application Master (one per application) and Node Manager (one per node) to perform in a better way on Hadoop cloud. Each of these resources has a different function which greatly helps in Hadoop cluster management.
Hadoop Cluster Diagram
A Hadoop cluster diagram starts with a client which can be anyone from the world. A client works on any of the architecture from HDFS, MapReduce or YARN. For each section of Hadoop cluster administration, individual nodes are created at the time of Hadoop Cluster configuration. Each of these nodes carry some data and have a task tracker attached to it if a client is working on HDFS architecture. And happens vice versa if the client works on MapReduce architecture. A Hadoop cluster diagram is basically outlet if Hadoop cluster Architecture.
A Hadoop Diagram simply flows in this manner
- At first, data is loaded into cluster using HDFS writer
- Then the input data in Analyzed using MapReduce
- Result of the outcome is stored in the cluster using HDFS writes
- Result is read from Cluster using HDFS reader
Hadoop Cluster Advantages
A Hadoop Cluster Cloud have following advantages which makes it so much of use:
- Flexibility: Hadoop is an open source environment which can work effectively for every field. Hadoop cluster enables big data businesses to easily use the resources to manage their data and applications. It can change itself with the need of the client.
- Speed: Hadoop Cluster can not only store data efficiently but it can also process the data fastly. The tools that Hadoop cluster provides on the Hadoop Cluster server are very fast and effective. They can perform a variety of operations on data in just a few seconds.
- Scalable: The size of the Hadoop cluster can be easily changed according to the requirement of the client. For a large size of data sets, Hadoop cluster builds the cloud of large size. It’s scalable property plays a very vital role for business organizations.
- Cost Effective: Creating a Hadoop cluster is not so expensive. Hadoop technology provides easy storage solutions to the business organization. Keeping a raw data in the cloud become expensive and managing it every time is quite complex in RDBMS, while Hadoop cluster saves that memory by automatic deleting that data. The solutions that Hadoop Cluster provides makes it more worthy, demanding and cost effective.
Hadoop Cluster Disadvantages
- Security Concern: Managing a so huge cloud like Hadoop Cluster is quite challenging. Since many big organizations stores huge amount of data on it, so security of that data is always an important job. Hadoop doesn’t have effective data encryption technology at storage and network level, which is an important point of concern while storing your data.
- Not Fit for Small Data: Hadoop Cluster can work undoubtedly for big data, but when it comes to small data it is not so suitable. Due to its high storage design, HDFS architecture lacks in performing its work on small data.