In general, a computer cluster is a collection of various computers that work collectively as a single system.
“A hadoop cluster is a collection of independent components connected through a dedicated network to work as a single centralized data processing resource. “
“A hadoop cluster can be referred to as a computational computer cluster for storing and analysing big data (structured, semi-structured and unstructured) in a distributed environment.”
“A computational computer cluster that distributes data analysis workload across various cluster nodes that work collectively to process the data in parallel.”
Hadoop clusters are also known as “Shared Nothing” systems because nothing is shared between the nodes in a hadoop cluster except for the network which connects them. The shared nothing paradigm of a hadoop cluster reduces the processing latency so when there is a need to process queries on huge amounts of data the cluster-wide latency is completely minimized.
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A hadoop cluster architecture consists of a data centre, rack and the node that actually executes the jobs. Data centre consists of the racks and racks consists of nodes. A medium to large cluster consists of a two or three level hadoop cluster architecture that is built with rack mounted servers. Every rack of servers is interconnected through 1 gigabyte of Ethernet (1 GigE). Each rack level switch in a hadoop cluster is connected to a cluster level switch which are in turn connected to other cluster level switches or they uplink to other switching infrastructure.
Hadoop cluster consists of three components -
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As the name says, Single Node Hadoop Cluster has only a single machine whereas a Multi-Node Hadoop Cluster will have more than one machine.
In a single node hadoop cluster, all the daemons i.e. DataNode, NameNode, TaskTracker and JobTracker run on the same machine/host. In a single node hadoop cluster setup everything runs on a single JVM instance. The hadoop user need not make any configuration settings except for setting the JAVA_HOME variable. For any single node hadoop cluster setup the default replication factor is 1.
In a multi-node hadoop cluster, all the essential daemons are up and run on different machines/hosts. A multi-node hadoop cluster setup has a master slave architecture where in one machine acts as a master that runs the NameNode daemon while the other machines acts as slave or worker nodes to run other hadoop daemons. Usually in a multi-node hadoop cluster there are cheaper machines (commodity computers) that run the TaskTracker and DataNode daemons while other services are run on powerful servers. For a multi-node hadoop cluster, machines or computers can be present in any location irrespective of the location of the physical server.
If you would like to understand the steps for Hadoop Installation on your system, check out the free hadoop tutorials on Single Node Hadoop Cluster Setup / Installation and Multinode Node Hadoop Cluster Setup / Installation.
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Hadoop’s performance depends on various factors based on the hardware resources which use hard drive (I/O storage), CPU, memory, network bandwidth and other well-configured software layers. Building a Hadoop cluster is a complex task that requires consideration of several factors like choosing the right hardware, sizing the hadoop cluster and configuring it correctly.
Many organizations are in a predicament when setting up hadoop infrastructure as they are not aware on what kind of machines they need to purchase for setting up an optimized hadoop environment and what is the ideal configuration they must use. The foremost thing that bothers users is deciding on the hardware for the hadoop cluster. Hadoop runs on industry-standard hardware but there is no ideal cluster configuration like providing a list of hardware specifications to setup cluster hadoop. The hardware chosen for a hadoop cluster setup should provide a perfect balance between performance and economy for a particular workload. Choosing the right hardware for a hadoop cluster is a standard chicken-and-egg problem that requires complete understanding of the workloads (IO bound or CPU bound workloads) to fully optimize it after thorough testing and validation. The number of machines or the hardware specification of machines depends on factors like –
The data volume that the hadoop users will process on the hadoop cluster should be a key consideration when sizing the hadoop cluster. Knowing the data volume to be processed helps decide as to how many nodes or machines would be required to process the data efficiently and how much memory capacity will be required for each machine. The best practice to size a hadoop cluster is sizing it based on the amount of storage required. Whenever a new node is added to the hadoop cluster, more computing resources will be added to the new storage capacity.
To obtain maximum performance from a Hadoop cluster, it needs to be configured correctly. However, finding the ideal configuration for a hadoop cluster is not easy. Hadoop framework needs to be adapted to the cluster it is running and also to the job. The best way to decide on the ideal configuration for the cluster is to run the hadoop jobs with the default configuration available to get a baseline. After that the job history log files can be analysed to see if there is any resource weakness or if the time taken to run the jobs is higher than expected. Repeating the same process can help fine tune the hadoop cluster configuration in such a way that it best fits the business requirements. The number of CPU cores and memory resources that need to be allocated to the daemons also has a great impact on the performance of the cluster. In case of small to medium data context, one CPU core is reserved on each DataNode whereas 2 CPU cores are reserved on each DataNode for HDFS and MapReduce daemons, in case of huge data context.
Having listed out the benefits of a hadoop cluster setup, it is extremely important to understand if it is ideal to use a hadoop cluster setup for all data analysis needs. For example, if a company has intense data analysis requirements but has relatively less data then under such circumstances the company might not benefit from using Hadoop cluster setup. A hadoop cluster setup is always optimized for large datasets. For instance, 10MB of data when fed to a hadoop cluster for processing will take more time to process when compared to traditional systems.
Hadoop clusters make an assumption that data can be torn apart and analysed by parallel processes running on different cluster nodes. Thus, a hadoop cluster is the right tool for analysis only in a parallel processing environment. The answer to when you should consider building a hadoop cluster depends on whether or not your organization’s data analysis needs can be met by the capabilities of a hadoop cluster setup.
Here are a few scenarios where hadoop cluster setup might not be a right fit –