Tech Matchups: Shared Database vs Database per Service
Overview
Imagine your app’s data as a galactic library. Shared Database, a classic approach, is a single, massive archive—every module or service reads and writes to one database, ensuring consistency and simplicity.
Database per Service, a microservices staple from the 2010s, is a network of specialized vaults—each service has its own database, promoting autonomy and isolation.
Both store your app’s knowledge, but shared database is a centralized fortress, while database per service is a distributed cluster. They shape how you manage data and scale.
Section 1 - Syntax and Core Offerings
Shared database uses one schema. A SQL query in a monolith:
Database per service isolates data. A query in a microservice:
Shared database centralizes access—example: 10 services query one Postgres DB with 1M rows. Database per service decentralizes—example: 10 services, each with a 100K-row DB (e.g., MongoDB, MySQL). Shared simplifies joins; per-service needs APIs or events for cross-service data.
Core difference: Shared database unifies data; database per service isolates it.
Section 2 - Scalability and Performance
Shared database scales vertically—upgrade a 16-core DB to handle 10K queries/second (e.g., 20ms reads). Performance is predictable but risks bottlenecks—example: a slow join halts all services.
Database per service scales horizontally—add DB instances per service for 50K queries/second (e.g., 10ms reads). Performance excels with isolation but adds complexity—example: sync via Kafka takes 50ms. Tools like Vitess help.
Scenario: Shared database powers a 1K-user app with simple data; database per service fuels a 1M-user platform like Netflix. Shared’s easier to manage; per-service handles diverse loads.
Section 3 - Use Cases and Ecosystem
Shared database suits monoliths—example: A 10K-user CRM with a single DB. It’s ideal for tightly coupled apps or small teams. Tools like PostgreSQL or Oracle lead here.
Database per service shines in microservices—think Uber’s 100+ services, each with a DB. It’s great for autonomous teams and diverse data needs. Ecosystems include MongoDB, DynamoDB, and Kafka for sync.
Ecosystem-wise, shared database integrates with ORMs—Django, Hibernate. Per-service uses lightweight drivers—mongoose, boto3. Example: Shared logs to one table; per-service uses ELK for metrics. Choose based on team size and autonomy.
Section 4 - Learning Curve and Community
Shared database is simple—learn SQL in hours, transactions in days. Communities are vast: PostgreSQL docs and Stack Overflow have 10K+ DB posts.
Database per service is complex—grasp APIs and sync in a week, eventual consistency in a month. Communities are vibrant: MongoDB forums and DZone offer guides. Example: AWS’s DynamoDB tutorials ease adoption.
Adoption’s quick for shared in small teams; per-service suits DevOps-heavy orgs. Newbies start with shared basics; intermediates tackle per-service’s complexity. Shared has broader resources; per-service’s are modern.
Section 5 - Comparison Table
| Aspect | Shared Database | Database per Service |
|---|---|---|
| Data Access | Centralized | Decentralized |
| Scalability | Vertical, unified | Horizontal, isolated |
| Ecosystem | Monolithic (Postgres, Oracle) | Microservices (Mongo, Kafka) |
| Learning Curve | Simple, SQL-focused | Complex, sync-heavy |
| Best For | Small, cohesive apps | Large, autonomous systems |
Shared database unifies; database per service isolates. Pick shared for simplicity, per-service for scale.
Conclusion
Shared database and database per service are data’s cosmic keepers. Shared is your choice for simple, cohesive apps—ideal for startups or monoliths. Database per service excels in scalable, autonomous systems—perfect for microservices. Weigh simplicity vs. flexibility and team size—shared for quick wins, per-service for growth.
For a small app, shared keeps it tight. For a global platform, per-service scales wide. Test both—use Postgres for shared, DynamoDB for per-service—to find your data’s home.