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From nw
Guides database selection with RDBMS vs NoSQL catalogs, CAP/ACID/BASE theory, OLTP/OLAP distinctions, and specifics for PostgreSQL, MySQL, MongoDB, Redis, DynamoDB.
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/nw:nw-database-technology-selectionThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Start with these questions:
Start with these questions:
Strengths: Full ACID, advanced cost-based optimizer, rich indexes (B-tree, Hash, GiST, GIN, BRIN), JSONB | Best for: complex queries, mixed OLTP/analytics, geospatial (PostGIS), JSON+relational hybrid | Scaling: read replicas, partitioning, PgBouncer, Citus for horizontal | Watch: write-heavy needs tuning, vertical scaling limits
Strengths: RAC clustering, Data Guard, Flashback, mature optimizer, partitioning | Best for: enterprise OLTP, mission-critical with vendor support, large-scale DW | Scaling: RAC horizontal, partitioning, Active Data Guard read replicas | Watch: licensing cost, vendor lock-in
Strengths: BI integration (SSRS/SSAS/SSIS), Always On AG, TDE built-in, columnstore indexes | Best for: Microsoft ecosystem, BI-heavy, hybrid OLTP/analytics | Scaling: Always On AG for HA, read-scale replicas, partitioning | Watch: Windows-centric, licensing model
Strengths: Simplicity, wide adoption, InnoDB ACID, good read performance, easy replication | Best for: web apps, read-heavy, simple transactional systems | Scaling: primary-replica, Group Replication, MySQL Router | Watch: less sophisticated optimizer than PostgreSQL, limited window functions in older versions
JSON-like documents, flexible schemas | Best for: CMS, catalogs, user profiles, rapid prototyping | Query: MongoDB aggregation pipeline, Couchbase N1QL | Indexing: compound (ESR rule: Equality-Sort-Range), text, geospatial | Trade-offs: flexible schema vs consistency enforcement, $lookup joins expensive
Simple key-value pairs, values can be complex structures | Best for: caching, sessions, leaderboards, shopping carts | Redis: in-memory sub-ms, FT.SEARCH/FT.AGGREGATE | DynamoDB: single-digit ms at any scale, Query on partition+sort key | Trade-offs: Redis limited by RAM, DynamoDB requires careful partition key design
Wide columns grouped into column families, partitioned by partition key | Best for: write-heavy, time-series, IoT, event logging, audit trails | Cassandra CQL: SQL-like, must include partition key, no joins | Linear horizontal scaling, SAI indexing 43% throughput gain over SASI | Trade-offs: query flexibility limited to partition key, query-first schema design, strong consistency causes up to 95% perf degradation
Nodes and edges with properties, index-free adjacency | Best for: social networks, recommendations, fraud detection, knowledge graphs | Neo4j Cypher (pattern matching), ArangoDB AQL (multi-model) | Relationship traversals far more efficient than recursive SQL CTEs | Trade-offs: not suited for aggregation-heavy analytics, scaling more complex
Atomicity: all-or-nothing | Consistency: valid state transitions | Isolation: concurrent transactions don't interfere (levels: READ UNCOMMITTED/COMMITTED, REPEATABLE READ, SERIALIZABLE) | Durability: committed data survives failures | Use when: financial transactions, inventory, order processing, data correctness non-negotiable
Basically Available | Soft state (may change without input) | Eventually consistent | Use when: availability > immediate consistency (social feeds, recommendations, activity streams)
During network partition, choose:
PACELC extension: even without partitions, latency vs consistency trade-off exists.
Many short atomic transactions (INSERT/UPDATE/DELETE) | Normalized 3NF | Simple queries, few rows, ms response | High write concurrency, ACID required | DBs: PostgreSQL, MySQL, Oracle, SQL Server
Complex analytical queries with aggregations | Denormalized star/snowflake | Complex SELECTs with JOINs, GROUP BY, window functions, seconds-minutes response | Read-heavy, fewer concurrent users | DBs: Snowflake, Redshift, BigQuery, Druid, ClickHouse
Combines OLTP+OLAP in single system | Examples: TiDB, CockroachDB, SingleStore, SQL Server with columnstore | Trade-off: convenience vs potential performance compromise for both workloads
npx claudepluginhub nwave-ai/nwave --plugin nwAdvises on database selection, schema design, indexing, query optimization, and migrations for SQL/NoSQL databases like PostgreSQL, MySQL, MongoDB, Redis, and ORMs including Prisma, Drizzle.
Guides database selection for services: PostgreSQL as default, Redis for ephemeral data, NoSQL/specialized DB comparisons, polyglot persistence. Includes references, schema analysis script, decision matrix.
Choose the right database technology for specific workloads. Evaluate relational, NoSQL, data warehouses, and search engines. Use when selecting storage systems for new features or optimizing existing ones.