📦Key Takeaways
- 1NoSQL = Not Only SQL; includes document stores, key-value, wide-column, graph, and time-series databases
- 2NoSQL trades ACID for scalability, flexibility, and performance in specific access patterns
- 3Choose based on access pattern: key-value for simple lookups, document for flexible schema, wide-column for time-series, graph for relationships
- 4Most production systems use polyglot persistence — different databases for different needs
The NoSQL Landscape
NoSQL databases emerged to solve problems that relational databases struggle with: horizontal scaling to millions of QPS, flexible schemas that evolve without migrations, and specialized data models that match specific access patterns better than tables.
The term 'NoSQL' is misleading — it doesn't mean 'no SQL.' It means 'not only SQL.' Many NoSQL databases actually support SQL-like query languages (CQL for Cassandra, N1QL for Couchbase).
NoSQL Database Types
Simple get/set by key. Extremely fast (~100K-1M QPS). Think of it as a giant hash map.
Examples: Redis, DynamoDB, Memcached, etcd.
Use cases: Caching, session storage, feature flags, leaderboards.
Limitations: No complex queries, no joins, limited indexing.
NoSQL vs SQL Decision Matrix
| Factor | Choose SQL | Choose NoSQL |
|---|---|---|
| Data model | Highly relational, many joins | Flexible, denormalized |
| Transactions | Multi-table ACID required | Single-entity operations OK |
| Scale | <50K QPS writes | >50K QPS writes needed |
| Schema | Stable, well-defined | Evolving, varies per record |
| Query patterns | Ad-hoc, complex analytics | Known, optimized access patterns |
| Consistency | Strong consistency required | Eventual consistency acceptable |
💡Polyglot Persistence
Modern systems use multiple databases: PostgreSQL for user accounts (ACID), Redis for caching, Elasticsearch for search, Cassandra for event logs, Neo4j for social graph. This is called polyglot persistence. Each database is optimized for its specific access pattern.
Advantages
- •Horizontal scaling is a first-class feature
- •Flexible schemas enable rapid development
- •Optimized for specific access patterns
- •Often higher throughput than SQL for targeted workloads
Disadvantages
- •No universal query language
- •Weaker consistency guarantees
- •Denormalization leads to data duplication
- •Each type requires learning its specific data model
🧪 Test Your Understanding
Knowledge Check1/2
Which NoSQL type is best for storing time-series data?