ApsaraDB for MongoDB supports standalone, replica set, and sharded cluster deployment architectures and provides enterprise-class capabilities such as security audit and point-in-time backup. It is used across the Internet, IoT, gaming, and finance industries.
Read/write splitting
Production workloads often hit bottlenecks when a single database node handles both writes and reads. ApsaraDB for MongoDB uses a three-node replica set architecture to provide high availability. The three data nodes are distributed across different physical servers and automatically synchronize data. The primary and secondary nodes expose separate endpoints, and MongoDB drivers distribute read/write requests between them. For more information, see Architectures and components of ApsaraDB for MongoDB.
Flexible business scenarios
Schema changes are costly in relational databases—every field addition requires a migration. ApsaraDB for MongoDB is schema-less, so the data model evolves without downtime or migrations. A typical multi-tier architecture pairs it with complementary services: store structured data in ApsaraDB RDS, flexible business data in ApsaraDB for MongoDB, and hot data in Tair (Redis OSS-compatible) or ApsaraDB for Memcache. This combination delivers high read/write throughput while reducing overall storage costs.
Location-based apps
Location-based apps must query geospatial data at scale and store heterogeneous data from multiple systems without a fixed schema. ApsaraDB for MongoDB supports two-dimensional spatial indexes for proximity queries and uses a dynamic storage mode that accommodates heterogeneous data from multiple systems.
IoT scenarios
IoT workloads generate continuous bursts of sensor data that must be ingested quickly and queried flexibly. ApsaraDB for MongoDB has features such as high performance and asynchronous data writing, and can deliver in-memory database performance in specific scenarios. ApsaraDB for MongoDB is suitable for IoT scenarios with highly concurrent write operations. In a sharded cluster instance, independently scale performance and storage by adjusting the configuration and number of mongos nodes and shards—without an upper limit on storage capacity. For configuration steps, see Overview.
After ingestion, use secondary indexes for low-latency dynamic queries and the MapReduce aggregation framework to run multidimensional data analysis across the collected data.
Applications in various fields
Game applications: Store user information, in-game equipment, and credits directly in embedded documents to facilitate queries and updates.
Logistics applications: Store order information with status changes recorded as entries in an embedded array. Read the full history of an order—from placement to delivery—in a single query, which is convenient, quick, and clear.
Social networking applications: Store user information and the information of WeChat moments published by users, and use geographical location indexes to find nearby people and places. ApsaraDB for MongoDB's rich query capabilities and fast read/write performance also make it well-suited for storing and retrieving chat history.
Live video streaming: Store user information and gift information for real-time streaming events.
Big data applications: Use ApsaraDB for MongoDB as a cloud storage system for big data. Extract and analyze data at any time to track trends across your industry.