moai-domain-database

Multi-database architecture combines relational (PostgreSQL), document (MongoDB), and in-memory (Redis) stores in a single system. Design involves selecting the right database for each data type, establishing cross-database routing logic, managing connections, and synchronizing data across stores to optimize for scalability and query performance.

Query optimization differs between PostgreSQL's relational indexes and MongoDB's document indexing. Strategies include identifying hot queries, building composite indexes, analyzing execution plans, denormalizing selectively in MongoDB, and leveraging aggregation pipelines to reduce data transfer and improve response times across both systems.

Redis provides in-memory caching and real-time analytics in polyglot stacks by storing frequently accessed data, materialized aggregations, and session state. Configure Redis alongside PostgreSQL and MongoDB to reduce query load, enable fast data retrieval, and support real-time dashboards without overwhelming primary databases.

Data synchronization across PostgreSQL, MongoDB, and Redis requires consistent write ordering, transaction boundaries, change data capture, and idempotent operations. Implement unified operations through a manager layer, monitor replication lag, version schemas, and test rollback scenarios to maintain integrity during migrations and ongoing sync.

Connection pooling and monitoring work across all three databases through a centralized DatabaseManager that routes connections, tracks pool saturation, logs query latency, and alerts on failures. This unified approach prevents connection exhaustion, identifies bottlenecks, and ensures reliable operations across your entire multi-database stack.

Partition when single-node throughput hits limits, data exceeds available memory, or query latency becomes unacceptable. PostgreSQL supports declarative partitioning by range or hash; MongoDB uses shard keys. Partitioning distributes load, enables parallel queries, and supports horizontal scaling for high-volume applications, but adds operational complexity.