Introduction to the three core components of hadoop

The three core components of Hadoop are: Hadoop Distributed File System (HDFS), MapReduce and Yet Another Resource Negotiator (YARN).

1. Hadoop Distributed File System (HDFS):

   • HDFS is Hadoop’s distributed file system, used to store large-scale data sets. It splits large files into multiple data blocks and distributes and stores these data blocks on multiple nodes in the cluster. HDFS provides high-capacity, high-reliability and high-throughput data storage solutions and is the foundation of the Hadoop distributed computing framework.

2. MapReduce:

   • MapReduce is Hadoop's distributed computing framework for parallel processing of large-scale data sets. It is based on the functional programming model and decomposes the computing task into two stages: Map and Reduce. The Map stage divides the input data into independent tasks for processing, while the Reduce stage combines the results of the Map tasks into the final output. MapReduce provides fault tolerance, scalability, and parallel processing capabilities.

3. Yet Another Resource Negotiator (YARN):

   • YARN is the resource manager of Hadoop, responsible for the scheduling and management of resources in the cluster. It can allocate and manage computing resources for multiple applications, thereby improving the utilization of computing resources. YARN divides the computing resources in the cluster into multiple containers and provides appropriate resources for different applications while monitoring and managing the running status of each application.

These three components together form the core of the Hadoop distributed computing framework, making Hadoop good at offline data analysis. In cloud computing, Hadoop is combined with big data and virtualization technology to provide powerful support for data processing.

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