Author: Zen and the Art of Computer Programming
1 Introduction
As Internet enterprise data grows, application scenarios become more complex, and computing resources become more powerful, traditional data storage architecture can no longer meet business needs. In the cloud native era, containerization and microservice architecture are becoming more and more popular. How to build a highly available distributed data storage system is an important topic. Data containerization refers to managing data as an independent entity. Containerization technology can achieve a high degree of resource isolation and elastic scaling, achieving goals such as fault tolerance, elastic expansion, and high performance. Data containerization technology helps enterprises improve data processing efficiency and reduce operating costs. It also makes data storage more convenient and is no longer limited by the hardware limitations of traditional data centers. Currently, many companies are exploring data containerization technology, including Internet companies such as Alibaba, Tencent, Huawei, Baidu, NetEase, and Meituan. This article will elaborate on data containerization technology from the following three aspects:
2. Data storage classification and characteristics
3. Introduction to containerization technology
4. Data containerization solution in Kubernetes
5. Cassandra data containerization practice cases
6. Zookeeper data containerization practice cases
7. Summary and outlook
2. Data storage classification and characteristics
The types of data storage are mainly divided into relational databases (RDBMS), NoSQL databases (such as MongoDB, Redis) and search engine databases (such as ElasticSearch). Different types of databases have their own characteristics. The following is a brief introduction to RDBMS. Relational database (Relational Database Management System) is based on table structure, which uses structured