Help Center/ GeminiDB/ GeminiDB Redis API/ Service Overview/ Application Scenarios
Updated on 2024-08-06 GMT+08:00
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Application Scenarios

E-Commerce

  • For e-commerce applications, some commodity data is more frequently queried than others. GeminiDB Redis API stores frequently queried commodity information in memory as hot data, and cold data in the shared storage pool. This not only meets the quick access requirements of popular commodities, but also avoid excessive in-memory storage costs
  • GeminiDB Redis API can permanently store massive amounts of historical order data of e-commerce applications. It allows you to access data through the Redis API and provides TB-level storage.
  • There may be a large number of concurrent access requests within a short period of time during an e-commerce promotion. GeminiDB Redis API works as a front-end cache (large memory required) to help back-end databases handle service peaks. You can easily add compute nodes in seconds to handle the expected peak traffic.

Gaming

  • The schema of gaming services is simple. You can select GeminiDB Redis API as a persistent database and use simple Redis APIs to quickly develop and launch services. For example, the sorted set structure of Redis can be used to display game rankings in real time.
  • In delay-sensitive gaming scenarios, GeminiDB Redis API can be used as the front-end cache (large memory required) to accelerate access to applications.

Live Streaming

The most-viewed live streaming content usually dominates most traffic of the live streaming applications. GeminiDB Redis API can efficiently use memory resources by retaining popular live streaming data in the memory and other data in the shared storage, reducing your business costs.

Online Education

Online education applications store a large amount of data such as courses and Qs&As. However, only hot data (including most-viewed courses, latest question libraries, and lectures by famous teachers) is frequently accessed. GeminiDB Redis API can save data in memory or shared storage based on data access frequency, achieving a balance between performance and costs.

Persistent Storage for Other Applications

With the rapid development of the Internet, various large-scale applications have increasing requirements for persistent storage. Specifically, a massive amount of data needs to be stored, including historical orders, feature engineering, log records, location coordinates, machine learning, and user profiles. A common feature of these scenarios is large data volume and long validity period. Therefore, a large-capacity and low-cost key-value storage service is required to collect and transfer data. Redis is the most widely used key-value service. Its various data structures and operation APIs have innate advantages in storing such data. However, the native Redis can only be used as a cache and cannot guarantee persistence.

In addition to compatibility with Redis APIs, GeminiDB Redis API provides large-capacity, low-cost, and high-reliability data storage capabilities, making it well-suited to persistent storage scenarios.

Parent topic: Service Overview