Typical Application Scenarios

Games

Player information generated, like their equipment and bonus points, are stored in DDS databases. During peak hours, DDS cluster instances can handle large amounts of concurrent requests. DDS cluster and replica set provide high availability to ensure the games are stable in high-concurrency scenarios.

In addition, DDS is compatible with MongoDB and provides a non-schema mode, which frees you from having to change table structures when the play modes change. DDS can meet the flexible gaming requirements. You can store structured data with fixed schemas in Relational Database Service (RDS), store services with flexible schemas in DDS, and store hot data in GaussDB(for Redis), improving data efficiency and reducing data storage costs.

Advantages:

• Supports Embedded Documents: Embedded documents eliminate the need for JOIN statements, which simplifies application development. Flexible schemas also facilitate rapid development and iteration.
• Easy to Cope with Peak Pressure: Sharded clusters provide enough capacity to store data into the TB range.

IoT

DDS is compatible with MongoDB and provides high-performance and asynchronous data write functionality. In certain scenarios, DDS can process data in the memory database. In addition, cluster instances can dynamically add mongos and shard nodes or upgrade specifications. The performance and storage space can be quickly expanded, making cluster instances suitable for IoT scenarios with high concurrent writes.

Intelligent IoT terminals need to collect various types of data, store device logs, and analyze various types of information. In recent years, IoT services have grown rapidly, generating huge volumes of data and increasing access traffic. IoT has created demand for horizontal storage scaling.

DDS provides a secondary index to meet dynamic query requirements and uses the MapReduce aggregation framework, which is compatible with MongoDB, to analyze data from multiple dimensions.

Advantages:

• High Write Performance: DDS sharded clusters provide the robust write performance needed to handle terabyte-scale databases.
• High Performance and Scalability: DDS supports applications with high QPS rates, and its sharding architecture can be scaled in or out to flexibly cope with application changes.

Internet

DDS replica sets use the three-node HA architecture. Three data nodes form an anti-affinity group and are deployed on different physical servers to automatically synchronize data. The primary and secondary nodes provide services. Each node has a private IP address and works with Driver to allocate read workloads.

Many organizations need to process and store data into the TB range, requiring data to be written to databases in real time and dynamic analysis capabilities in big data computing.

Advantages:

• MapReduce: With a complete data analysis utility, you can query statements or scripts, and distribute requests to DDS.
• Excellent Scalability: DDS DB instances can be scaled up to support growing services and data volumes in content management systems.