Introduce the basic concepts and characteristics of Hadoop.
Hadoop is an open-source distributed computing framework used for storing and processing large-scale datasets. It can run on massive clusters to achieve high performance and reliable data processing.
The basic concepts of Hadoop include the following key points:
- Distributed storage: Hadoop utilizes the Hadoop Distributed File System (HDFS) to store data, breaking it into multiple blocks and storing them on various computing nodes to achieve redundant backups and high reliability.
- Distributed computing: Hadoop uses the MapReduce programming model to process data, breaking tasks into several smaller tasks and processing them in parallel on different computing nodes, ultimately consolidating the results to obtain the final outcome.
- Automatic fault tolerance: Hadoop can automatically detect and recover from failures in computing nodes, ensuring smooth execution of computing tasks, and enhancing the system’s reliability and stability.
The characteristics of Hadoop include:
- High reliability: Hadoop utilizes data redundancy backup and automatic fault tolerance mechanisms to ensure the security and reliability of data.
- High scalability: Hadoop can easily scale the size of the cluster to support large-scale data processing and storage needs.
- High performance: Hadoop utilizes distributed computing and parallel processing technology to achieve high-performance data processing and computational capabilities.
- Open Source: Hadoop is an open-source software framework that is available for free use and modification, and is widely supported and used by a large community of developers and users.
Overall, Hadoop is a distributed computing framework suitable for handling large-scale datasets, with characteristics such as high reliability, scalability, performance, and being open source. In the era of big data, Hadoop has become one of the standard tools for processing large datasets.
