nosql-patterns

Choosing the right NoSQL database requires analyzing your throughput, consistency, and access pattern needs. Use decision trees to evaluate document, key-value, wide-column, graph, and time-series stores to ensure your selection aligns with production scale requirements.

Common NoSQL anti-patterns include unbounded arrays, hot partitions, tombstone accumulation, and full table scans. Mitigating these requires access-pattern-driven schema design and applying specific database optimizations like proper indexing and partition key selection.

Designing a DynamoDB single-table schema involves structuring partition and sort keys to support multiple access patterns simultaneously. You must carefully plan GSIs, LSIs, and optimistic locking to avoid hot partitions and expensive full table scans.

For time-series IoT data, Cassandra wide-column data modeling is generally preferred over MongoDB document modeling. Cassandra allows you to design time-bucketed partition keys and configure TTLs to automatically expire old sensor data while preventing large partitions and tombstone buildup.

Yes, Redis caching patterns specifically support distributed locks, rate limiting, and leaderboards. Integrating Redis as a complementary key-value store provides high-speed in-memory data access while your primary database handles persistent storage.

You should not use NoSQL schema modeling if your application requires complex multi-record transactions or extensive join operations across highly relational data. NoSQL databases optimize for specific access patterns and scalability rather than strict relational consistency.