| name | storage-selection |
| description | 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. |
Storage Selection
Choose optimal database technology by analyzing workload characteristics, consistency requirements, and operational complexity.
Context
You are helping select storage systems for specific data requirements. Analyze access patterns, consistency needs, data volume, and operational constraints. Read any existing schemas or architectural context provided.
Domain Context
Based on Martin Kleppmann's Designing Data-Intensive Applications:
- Relational Databases: ACID transactions, schema enforcement, complex queries; limited horizontal scaling
- Document Databases: Flexible schema, natural object mapping, good for hierarchical data; transactions often single-document only
- Key-Value Stores: Fast reads/writes, simple data model; limited query flexibility
- Column Family Stores: Optimized for analytics and time-series; sparse data; distributed
- Search Engines: Full-text search, fuzzy matching, aggregations; not transaction-oriented
- Data Warehouses: Analytical queries, columnar storage, massive scale; designed for batch not realtime
Instructions
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Catalog Data Requirements: List primary access patterns (read-heavy? write-heavy? both?), data volume growth rate, consistency needs (strong/eventual?), query types (transactional vs analytical), and operational SLAs.
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Match Workload to Storage Type: Transactional workload with complex queries → PostgreSQL. Document-oriented objects → MongoDB. Time-series metrics → Prometheus or InfluxDB. Full-text search → Elasticsearch. Analytical queries on massive datasets → BigQuery/Redshift.
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Evaluate Consistency vs Availability: Need ACID guarantees across distributed systems? Relational with replication. Can tolerate eventual consistency? DynamoDB/Cassandra. Determine RTO/RPO requirements.
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Project Operational Burden: Managed services (RDS, DynamoDB, BigQuery) reduce ops overhead. Self-hosted databases require backup, monitoring, scaling, tuning expertise.
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Cost Analysis: Storage cost (per GB), compute cost (per hour or per query), backup/replication costs. Cross-reference with projected data growth and query volume.
Anti-Patterns
- One Database for Everything: PostgreSQL solves every problem. Result: poor performance at scale, wrong tool for job. Guard: Accept polyglot persistence; different workloads need different databases.
- Choosing Based on Hype: Elasticsearch for everything because it's trendy. Result: overly complex infrastructure, unnecessary cost. Guard: Choose based on actual access patterns, not marketing.
- Ignoring Operational Complexity: Pick distributed database without ops expertise. Result: data loss, network partitions, failed migrations. Guard: Account for team operational maturity; managed services are often better than self-hosted.
- Migrating Without Strategy: Realize choice was wrong mid-project. Result: painful migration, downtime. Guard: Prototype with real data; test failure scenarios before committing.
Further Reading
- Designing Data-Intensive Applications by Martin Kleppmann — comprehensive storage system analysis
- Database Internals by Alex Petrov — deep dive on storage engines and tradeoffs
- NoSQL Databases by Dan McCreary and Ann Kelly — NoSQL patterns and when to use each type
