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This article mainly introduces "the advantages and disadvantages of big data Hadoop technology". In daily operation, I believe many people have doubts about the advantages and disadvantages of big data Hadoop technology. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts about "the advantages and disadvantages of big data Hadoop technology". Next, please follow the editor to study!

Advantages and disadvantages of big data's entry-level Hadoop

(1) Hadoop has the high reliability of bit-by-bit data storage and processing.

(2) Hadoop distributes data through available computer clusters to complete storage and computing tasks. These clusters can be easily extended to thousands of nodes and have high scalability.

(3) Hadoop can move data dynamically between nodes and ensure the dynamic balance of each node. The processing speed is very fast and has high efficiency.

(4) Hadoop can automatically save multiple copies of data, and automatically reassign failed tasks, so it has high fault tolerance.

The shortcomings of Hadoop which hospital has a good www.sptdnk.com as a person in Zhengzhou?

(1) Hadoop is not suitable for low-latency data access.

(2) Hadoop cannot store a large number of small files efficiently.

(3) Hadoop does not support multiple users to write and modify files arbitrarily.

Core components of Hadoop

Since the birth of Hadoop, there have been three series and multiple versions of Hadoop1, Hadoop2 and Hadoop3.

HDFS and MapReduce are the core components of Hadoop1, and many components in the Hadoop ecosystem are based on HDFS and MapReduce. After Hadoop1, Hadoop2,Hadoop2 has made improvements on the basis of Hadoop1. Compared with Hadoop1,Hadoop2, the three core components are HDFS, MapReduce and Yarn. At present, most enterprises on the market use Hadoop2, and this book uses the version of Hadoop2.7.3.

A common module and three core components of Hadoop2 consist of four modules, which are briefly introduced below.

(1) HadoopCommon: provides infrastructure for other Hadoop modules.

(2) HDFS: distributed file system with high reliability and high throughput.

(3) MapReduce: based on Yarn system, distributed offline parallel computing framework.

(4) Yarn: the framework responsible for job scheduling and cluster resource management.

At this point, the study of "the advantages and disadvantages of big data Hadoop technology" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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