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What is Hadoop and why is it used?
Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is used for storage and processing of big data.
Can you explain the architecture of Hadoop?
Hadoop has a master-slave architecture. The master consists of a Job Tracker, Task Tracker, NameNode, and DataNode. The slave is a cluster of nodes that store and process data.
What are the main components of Hadoop?
The main components of Hadoop are HDFS (Hadoop Distributed File System) and MapReduce. HDFS is used for storage, and MapReduce is used for processing data.
How does HDFS work?
HDFS works by dividing large files into blocks and distributing them across a cluster. This allows the system to process large datasets efficiently by parallelizing the tasks across many nodes.
What is MapReduce and how does it function?
MapReduce is a programming model used for processing large datasets with a parallel, distributed algorithm on a cluster. It functions in two steps: the Map step processes input data into key-value pairs, and the Reduce step aggregates these pairs into a smaller set of results.
How do you ensure data replication in Hadoop?
In Hadoop, data replication is handled by HDFS. Each data block is replicated across multiple nodes based on a specific replication factor to ensure availability and fault tolerance.
What is a NameNode and why is it important?
A NameNode is the centerpiece of an HDFS because it stores metadata and manages the file system namespace operations like opening, closing, and renaming files and directories.
How do you perform a Hadoop cluster capacity planning?
Cluster capacity planning involves assessing the storage and processing requirements, taking into account the data volume, growth over time, replication factor, and workload distribution to ensure the cluster handles current and future demands.
What is the function of YARN in Hadoop?
YARN stands for Yet Another Resource Negotiator. It is responsible for cluster resource management and job scheduling of various distributed applications outside the scope of MapReduce.
How can you enhance the performance of a Hadoop cluster?
Performance can be enhanced by optimizing MapReduce algorithms, ensuring efficient distribution of data blocks, tuning HDFS configurations, using better hardware, and expanding the cluster to handle more data and processing requirements.
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