moai-domain-database

Schema optimization involves analyzing table structures, identifying missing indexes, and refactoring inefficient designs. This Skill automatically reviews your PostgreSQL, MongoDB, or Redis schemas, detects bottlenecks like missing indexes or poor partitioning, and recommends specific improvements to boost query speed and reduce resource usage.

Indexing strategies depend on query patterns and data volume. This Skill recommends optimal index placement across relational, document, and in-memory databases, implementing covering indexes, composite indexes, and partitioning to accelerate lookups while minimizing write overhead and storage costs.

Database migrations require careful sequencing to maintain data integrity and availability. This Skill guides you through migration workflows, handles schema changes across PostgreSQL, MongoDB, and Redis, and ensures minimal downtime by coordinating version transitions and validating data consistency throughout the process.

Yes. This Skill handles polyglot persistence across PostgreSQL (relational), MongoDB (document), and Redis (in-memory), unifying access patterns and optimization strategies. It supports hybrid architectures, cross-system caching, and multi-database routing for enterprise backends requiring diverse storage engines.

Common tuning spans indexing, partitioning, aggregation strategies, and caching. This Skill applies unified patterns adapted to each database's strengths—SQL optimization for PostgreSQL, document aggregation for MongoDB, and cache invalidation for Redis—ensuring consistent performance across your data architecture.

Monitoring includes tracking query execution, index usage, and resource contention. This Skill provides operational best practices, configuration guidance, and tooling to identify slow queries, detect missing indexes, and validate data synchronization across PostgreSQL, MongoDB, and Redis deployments.