📝 Final Quiz: Production & Architecture (Chapters 30-35)
1. What is the 3-2-1 backup rule?
2. Why should you NEVER concatenate user input into SQL strings?
3. What's the optimal connection pool size for a 4-core server?
4. What does CREATE INDEX CONCURRENTLY do differently?
5. What is the "cache stampede" problem?
Chapter 36: Choosing the Right Database for Your System
This final chapter ties everything together. Given a system to build, how do you pick the right database(s)?
The Decision Framework
Step 1: What's your data model?
Structured with relationships → Relational (PostgreSQL)
Documents with varying schema → Document (MongoDB)
Simple key lookups → Key-Value (Redis)
Highly connected data → Graph (Neo4j)
Time-stamped metrics → Time-Series (TimescaleDB)
Step 2: What are your access patterns?
Read-heavy → Add read replicas + caching
Write-heavy → Consider LSM-tree (Cassandra) or partitioning
Mixed OLTP → PostgreSQL handles this well
Analytics → Column-store (ClickHouse) or data warehouse
Step 3: What are your scale requirements?
< 1TB, < 10K QPS → Single PostgreSQL (seriously, this handles a LOT)
1-10TB → PostgreSQL with partitioning + read replicas
> 10TB or > 100K QPS → Distributed DB or sharding
Step 4: What are your consistency requirements?
Financial/critical → Strong consistency (PostgreSQL, CockroachDB)
Social/content → Eventual consistency acceptable (Cassandra, DynamoDB)
Common Architecture Patterns
Pattern 1: Simple Web App
App → PostgreSQL
That's it. Don't over-engineer.Pattern 2: High-Traffic Web App
App → Redis (cache) → PostgreSQL (primary) → PostgreSQL (read replicas)
Cache hot data, scale reads with replicas.Pattern 3: Microservices (Telecom CNF)
Service A → PostgreSQL A (sessions)
Service B → Redis (state cache) + PostgreSQL B (config)
Service C → TimescaleDB (metrics/telemetry)
All → Kafka (event streaming between services)Pattern 4: Analytics Platform
OLTP: PostgreSQL (operational data)
→ CDC (Change Data Capture) →
OLAP: ClickHouse (analytics queries on billions of rows)The Golden Rules
💡 Database Selection Rules
- Start with PostgreSQL until you have a measured reason not to
- Add Redis when you need caching or sub-millisecond lookups
- Add a specialized DB only when PostgreSQL demonstrably can't handle the workload
- Polyglot persistence (multiple DBs) adds operational complexity — justify it
- Your team's expertise matters — a well-operated MySQL beats a poorly-operated CockroachDB
What You've Learned
You now understand:
- How databases store data physically (pages, B-trees, WAL)
- How to design schemas (normalization, keys, constraints)
- How to write efficient SQL (JOINs, CTEs, window functions)
- How to make queries fast (indexes, EXPLAIN ANALYZE)
- How transactions work (ACID, isolation, MVCC)
- How databases scale (replication, partitioning, consensus)
- How to operate databases in production (backup, monitoring, security)
- How to integrate databases into applications (pooling, caching)
- How to choose the right database for a given problem
🎓 Congratulations!
You've completed the Database Zero to Hero guide. You now have the knowledge to design, query, optimize, and operate databases at a professional level. The next step is practice — build something, run queries, break things, fix them. The concepts in this guide will serve you for your entire career.