database-management-patterns

Schema design balances normalization for consistency in PostgreSQL against denormalization for performance in MongoDB. The patterns guide when to normalize relational data and when to embed documents, with trade-offs between query speed and storage efficiency.

Indexing strategies include B-tree indexes for range queries in PostgreSQL and GIN indexes for full-text search. MongoDB patterns cover similar optimization, with guidance on when each index type reduces latency in production workloads.

Transactions enforce ACID guarantees in PostgreSQL and multi-document transactions in MongoDB. Replication patterns ensure data consistency and availability, with trade-offs between strong consistency and partition tolerance for distributed systems.

Sharding distributes data across multiple nodes when a single database reaches capacity limits. The patterns specify shard key selection, rebalancing constraints, and performance trade-offs between query complexity and write throughput in large-scale deployments.

Many patterns apply to both databases—schema normalization, indexing, and replication—but implementation differs. PostgreSQL enforces schemas at the database layer, while MongoDB allows flexible schemas, requiring distinct approaches to achieve production readiness.

Production optimization covers connection pooling, query execution planning, index tuning, and monitoring metrics. The patterns prescribe when to denormalize, cache, or shard, with constraints and examples for e-commerce and high-throughput applications.