In full-stack development, the choice of database often determines the system's scalability, consistency, development speed, and maintenance costs. The two most common camps are SQL (relational databases) and NoSQL (non-relational databases). When should you choose one over the other? What are their differences? Let's clarify this from a practical perspective!
1. Core Comparison of SQL and NoSQL (Mainstream Views in 2026)
| Comparison Items | SQL (Relational Database) | NoSQL (Non-Relational Database) |
|---|---|---|
| Data Structure | Fixed Schema (tables, fields, types predefined) | Flexible Schema (fields can be added/modified at any time) |
| Data Model | Tables (Rows & Columns), strong support for JOINs | Documents, Key-Value, Column-family, Graphs |
| Query Language | Standard SQL (JOIN, GROUP BY, subqueries, etc.) | Varies by vendor (MongoDB uses JSON-like queries, Cassandra uses CQL) |
| Consistency Guarantee | ACID(Strong consistency: Atomicity, Consistency, Isolation, Durability) | MajorityBASE(Eventual consistency: Basically Available, Soft state, Eventual consistency) |
| Scalability Method | Primarily vertical scaling (upgrading CPU/RAM/SSD) | Horizontal scaling (adding machines, sharding) is the strongest |
| Performance Sweet Spot | Complex queries, transactions, multi-table relationships | High concurrent writes, massive data, rapid iteration |
| Representative Products (2026) | PostgreSQL, MySQL, MariaDB, SQL Server | MongoDB, Cassandra, DynamoDB, Redis, Neo4j, Pinecone (vector) |
| Typical Usage Proportions | Traditional businesses, finance, ERP still account for 75–85%. | Social, real-time, AI, big data are growing the fastest. |
2. Advantages of SQL Databases and Best Application Scenarios
Why do many people still love SQL?
- Strong data integrity: ACID guarantees allow you to confidently perform bank transfers, order payments, and inventory management.
- Unmatched complex queries: Multi-table JOINs, views, window functions, and CTEs make reporting and analysis very simple.
- Most mature ecosystem: ORM (Prisma, TypeORM, SQLAlchemy, Entity Framework) is super convenient.
- Complete administrative supportAll operations must either succeed or fail; SQL is the way to go
Suitable real-world scenarios (common in Hong Kong/Asia)
- Financial FinTech (payments, virtual banks, securities)
- E-commerce order + users + products + logistics multi-table system
- Enterprise ERP, CRM, HRM
- Businesses that require strict reporting and BI analysis (financial monthly reports, sales dashboards)
3. Advantages of NoSQL databases and best application scenarios
Where does NoSQL truly shine?
- Super strong horizontal scalabilityAdding machines can handle tens of millions or even billions of data
- Schema flexibilityProduct requirements change daily? Add fields without migrating table structures
- High concurrent writesTens of thousands of logs, chat messages, sensor data per second is no problem
- Native support for unstructured dataEasily handle JSON documents, graph relationships, vector embeddings
Suitable real-world scenarios
- Social media feeds, likes, comments, follow relationships (Graph or Document)
- Real-time chat, notifications, online game leaderboards (Redis + MongoDB)
- Log systems, monitoring data, large amounts of time series data from IoT
- E-commerce product catalog (attributes frequently change: color, size, specifications)
- AI applications: similarity search with vector databases, RAG (Pinecone, pgvector, Weaviate)
4. Practical selection recommendations for 2026 (common practices in Hong Kong/Asia teams)
| Your project type | Recommended priority selection | Common polyglot combinations (mixed use) |
|---|---|---|
| Finance, payments, order transactions | PostgreSQL(Preferred) | PostgreSQL + Redis cache + Kafka events |
| Small to medium-sized SaaS / e-commerce | PostgreSQL or MongoDB | PostgreSQL primary + MongoDB (product attribute changes) |
| Social, content platforms, feeds | MongoDB / Cassandra | MongoDB + Elasticsearch search + Redis cache |
| High concurrency real-time (chat, games) | Redis / DynamoDB | Redis state + PostgreSQL persistence |
| Logs / monitoring / big data analysis | ClickHouse / TimescaleDB | — |
| AI vector search, generative applications | pgvector / Pinecone | PostgreSQL (pgvector) or specialized vector DB |