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Hadoop Cluster

What Is a Hadoop Cluster?

Apache Hadoop is an open-source, Java-based software framework and parallel data processing engine. It allows big data analytics processing tasks to be divided into smaller tasks that can be executed in parallel using an algorithm (such as the MapReduce algorithm) and distributed across a Hadoop cluster.

A Hadoop cluster is a group of computers, called nodes, networked together to perform parallel computations on large data sets. Unlike other computer clusters, Hadoop clusters are specifically designed to store and analyze vast amounts of structured and unstructured data in a distributed computing environment.

Hadoop ecosystems are further distinguished from other computer clusters by their unique structure and architecture. Hadoop clusters consist of a network of connected master and slave nodes that utilize high availability, low-cost commodity hardware. Their ability to scale linearly and quickly add or remove nodes as volume demands makes them well-suited for big data analytics jobs with varying data set sizes.

Hadoop Cluster Architecture

Hadoop clusters are composed of a network of master and worker nodes that coordinate and execute various jobs across the Hadoop distributed file system. The master nodes typically use higher quality hardware and include a NameNode, Secondary NameNode, and JobTracker, each running on a separate machine.

The workers consist of virtual machines, running both DataNode and TaskTracker services on commodity hardware, and perform the actual work of storing and processing the jobs as directed by the master nodes. The final part of the system is the Client Nodes, responsible for loading the data and fetching the results.

  • Master nodes store data in HDFS and oversee key operations, such as running parallel computations on the data using MapReduce.
  • Worker nodes make up most of the virtual machines in a Hadoop cluster and perform the tasks of storing data and running computations. Each worker node runs the DataNode and TaskTracker services, which receive instructions from the master nodes.
  • Client nodes load data into the cluster. They first submit MapReduce jobs describing how data should be processed and then retrieve the results once processing is complete.

What is Cluster Size in Hadoop?

A Hadoop cluster size is a set of metrics that define storage and compute capabilities for running Hadoop workloads, namely:

  • Number of nodes: number of Master nodes, number of Edge Nodes, number of Worker Nodes.
  • Configuration of each node type: number of cores per node, RAM, and Disk Volume.

Advantages of a Hadoop Cluster

  • Hadoop clusters can accelerate the processing speed of many big data analytics jobs due to their ability to break down large computational tasks into smaller tasks that can be run in parallel and distributed.
  • Hadoop clusters are easily scalable and can quickly add nodes to increase throughput and maintain processing speed when faced with growing data volumes.
  • The use of low-cost, high availability commodity hardware makes Hadoop clusters relatively simple and affordable to set up and maintain.
  • Hadoop clusters replicate data sets across the distributed file system, making them resistant to data loss and cluster failure.
  • Hadoop clusters enable the integration and utilization of data from various source systems and data formats.
  • Hadoop can be deployed using a single-node installation for evaluation purposes.

Challenges of a Hadoop Cluster

  • Small file issue: Hadoop struggles with large volumes of small files, overloading the Namenode.
  • High processing overhead: Reading and writing operations in Hadoop can become costly, especially when processing large amounts of data.
  • Batch processing only: Hadoop is built for processing small volumes of large files in batches, limiting support for streaming data and real-time processing.
  • Iterative Processing: Hadoop's data flow structure is set up in sequential stages, making it impossible to perform iterative processing or use for machine learning.