Application Scenarios
Last Updated：2021-04-27
| Scenario Type | Recommended Instance Type | Descriptions |
|---|---|---|
| Personal website/Enterprise website/E-commerce | General instance | It is applicable to various types and scales of enterprise-level applications, small and medium database system, cache, and search clusters. |
| Relational database/distributed caching | Memory type instance | It is applicable to high-performance database, memory database, data analysis and mining, and distributed memory caching. |
| NoSQL database | Local SSD optimized instances | It is applicable to I/O intensive application scenarios with high requirements for disk read and write and latency, such as Cassandra, cloud database, and MongoDB. * You can use a BCC with a higher configuration. Meanwhile, the use of CDS realizes high I/O concurrence response and higher data reliability. * Also, you can adopt lower-middle configurations and have the instances equipped with a load balance to build a high-availability underlying architecture. * For more details, see [CDS](https://cloud.baidu.com/doc/CDS/s/rjwvyachw#%E5%A6%82%E4%BD%95%E9%80%89%E6%8B%A9%E4%BA%91%E7%A3%81%E7%9B%98) and [Load Balance](https://cloud.baidu.com/doc/BLB/s/Ajwvxno34). |
| Massive multiplayer online game | Computing instance | It is applicable to application scenarios, such as Web frontend server, massive multi-player online (MMO) frontend, data analysis, batch computing, video coding, high-performance science and engineering applications, etc. |
| LVB | It is applicable to high network packet receiving and sending scenarios, such as video barrage and telecommunication service forwarding. | |
| Deep learning/image processing | GPU computing instance | It can significantly improve the running speed of the large-scale computing architectures, such as machine learning and scientific computation. It provides basic architecture support for AI and high-performance computing platform. |
| Hadoop/Spark/Elastic Search | Memory optimized instance family | It is applicable to the Hadoop and Spark clusters and other enterprise applications with large memory requirements. |