Scenarios

Last Updated: Dec 07, 2017

  • Read/write splitting

    The ApsaraDB for MongoDB service uses a three-node replica set highly available architecture. The three data nodes are located on different physical servers and synchronize data automatically. The primary and secondary nodes provide service. The two nodes provide independent domain names and, with the MongoDB Driver, can independently allocate read pressure.

  • Business flexibility

    MongoDB uses a No-Schema method, making it suitable for businesses in initial stages because it avoids the need to change table structures. By storing fixed, structured data in RDS, flexible business data in MongoDB, and frequently accessed data in ApsaraDB for Memcache or ApsaraDB for Redis, you can achieve efficient data storage and reduce investment costs.

  • Mobile applications

    ApsaraDB for MongoDB supports two-dimensional space indexes, providing great support for location-based mobile app businesses. At the same time, the dynamic storage method of MongoDB is especially suitable for storing heterogeneous data from multiple systems, satisfying the needs of mobile apps.

  • IoT applications

    ApsaraDB for MongoDB provides an asynchronous data writing function. It can provide memory database performance that is effective for special scenarios such as IoT high concurrency writing. At the same time, MongoDB map-reduce function can perform aggregated analysis on large data volumes.

    ApsaraDB for MongoDB supports cluster versions to dynamically add mongos and shard components and resize their configurations, allowing unlimited performance and storage space scalability. This is well-suited for IoT scenarios with massive data volumes and high concurrency and performance requirements.

  • Core log systems

    In asynchronous disk scenarios, ApsaraDB for MongoDB can provide excellent plugin performance and has memory database processing capabilities. MongoDB provides a secondary index function to meet the need for dynamic queries. It can use the map-reduce aggregate framework to perform multidimensional data analysis.

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