The Nature of the Hardware
The name node is the commodity hardware. This is an important question. The name node is the core of Hadoop, and it is important to understand the hardware requirements for the name node clearly. To determine the hardware requirements for the namenode, we need to understand the workflow of the name node. The name node is the centrepiece of the Hadoop cluster. It is the node that manages the file system and the job tracker.
The Master Node
The name node is the centrepiece of an HDFS file system. It keeps the directory tree of all files in the file system and tracks where across the cluster the file data is kept. It also performs namespace operations such as opening, closing, and renaming files and directories.
The Worker Nodes
Worker nodes are the nodes that actually do the heavy lifting in a Hadoop cluster. These are the nodes where data is stored and processed. Worker node hardware is generally not as high-powered as the namenode, since they don’t need to perform as many calculations.
The software for the namenode
The namenode is the centerpiece of an HDFS file system. It keeps the directory tree of all files in the file system, and tracks where across the cluster the file data is kept. It also maintains the file system namespace. The namenode is a single point of failure for the HDFS file system. When the namenode goes down, the file system goes offline.
The Hadoop Distributed File System
The Hadoop Distributed File System (HDFS) is a scalable, reliable, and distributed file system designed to span large clusters of commodity servers and to provide extremely rapid data access. It is an integral part of the Apache Hadoop project and provides both a data storage mechanism and a framework for application development.
HDFS is designed to be highly fault-tolerant, providing a redundant data storage layer that can be used in conjunction with other forms of storage such as SANs and NAS devices. In addition, HDFS provides powerful tools for data replication and load balancing, making it an ideal solution for large scale data processing.
The MapReduce
MapReduce is a software programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster.
The namenode and the Datanode
The namenode is the centrepiece of an HDFS file system. It keeps the directory tree of all files in the file system. It also maintains the file system namespace. The namenode also regulates access to files by clients. It also performs some monitoring functions. The Datanode is a repository for user data.
The namenode and the Datanode
The namenode is the master node in a Hadoop cluster, and is responsible for maintaining the metadata for all the files stored in the HDFS. The datanode is a slave node in a Hadoop cluster, and is responsible for storing the actual data.
The namenode and the JobTracker
In a Hadoop cluster, there is one namenode, and possibly multiple jobtrackers. The namenode is the powerhouse of the cluster: it manages the file system namespace and regulates access to files by clients. The jobtracker is responsible for managing jobs submitted to the cluster (jobs are essentially jars, see below). When a job is submitted, the jobtracker schedules tasks to be run on individual nodes in the cluster.
The namenode and the Tasktracker
The namenode is the centerpiece of an HDFS file system. It keeps the directory tree of all files in the file system, and tracks where across the cluster the file data is kept. It also executes file system operations such as opening, closing, and renaming files and directories. The namenode is a single point of failure for the HDFS file system. When the namenode goes down, the file system goes offline.
The namenode and the Tasktracker
The namenode is the master node in a Hadoop cluster and is responsible for managing the file system. The tasktracker is a node that runs MapReduce tasks on behalf of the namenode.
The namenode and the ResourceManager
The namenode is the central node in a Hadoop cluster that manages the file system namespace and regulates access to files by clients. It also tracks where each file is located on the cluster’s nodes and manages replication of those files across the cluster.
The ResourceManager is the central node in a Hadoop cluster that manages all the resources used by applications running on the cluster. It is responsible for scheduling applications, allocating resources to them, and monitoring their progress.