Editing Openai/692e1a61-9f08-800b-89a7-b2d4c506df31 (section)

==== 2. Key benefits vs traditional SQL/relational DB ====

===== A. Horizontal scalability and high throughput =====

Relational DBs (PostgreSQL, MySQL, SQL Server) can scale vertically (bigger box) reasonably well, and can do some read scaling with replicas. But at very high scale, sharding relational data is complex.

Many NoSQL systems are designed from the ground up to:
* Distribute data across many nodes (“sharding”) automatically
* Add capacity by adding more servers (scale-out, not just scale-up)
* Handle very high write and read throughput with predictable latency

Example: A high-traffic web app with millions of users and huge write rates (logging, clickstream, IoT signals) may find it much easier to meet throughput/latency requirements with a distributed NoSQL store than with a single relational instance.

===== B. Flexible schema (schema-less or schema-light) =====

Relational DBs:
* Require a predefined schema: tables, columns, constraints
* Schema changes (ALTER TABLE) can be painful in large production systems
* Encourage normalized data, which is great for consistency and analytics but can be awkward for constantly changing, nested structures

NoSQL (especially document and key-value stores):
* Store data as JSON-like documents or arbitrary blobs
* Different records can have different fields
* You can evolve your data model without a migration that locks a table

This is valuable when:
* The product/domain is changing rapidly
* You have semi-structured data or many “optional” fields
* You’re ingesting data from varied external sources with different shapes

===== C. Data models closer to the application domain =====

Relational:
* Typically normalize into multiple tables and join them
* Application often has to assemble domain objects from rows across tables

NoSQL:
* Document stores: one document can represent an entire aggregate/object with nested sub-documents and arrays (e.g., a User with addresses, preferences, sessions)
* Wide-column: optimized for specific query patterns (e.g., time-series by user)
* Graph: optimized for entities and relationships, traversals, shortest paths, etc.

This can:
* Simplify application code (fewer joins)
* Improve performance (data for one request is often in one document/partition)
* Make some queries far more natural (graph traversals vs multi-join SQL)

===== D. Availability and partition tolerance (CAP trade-offs) =====

Many NoSQL databases are designed around AP or tunable consistency models in the CAP theorem sense:
* Highly available even during network partitions
* Often accept eventual consistency or tunable consistency (read-your-writes, quorum reads/writes, etc.)
* Automatic replication and fault tolerance are core design features

Relational DBs can be highly available too (HA clusters, replication, etc.), but NoSQL systems often:
* Make cross-region distribution, replication, and failover more “built in”
* Offer stronger guarantees about durability and availability in massive distributed deployments

===== E. High write and read performance for certain workloads =====

Because NoSQL DBs are often optimized for specific access patterns and may relax some constraints (e.g., ACID transactions across arbitrary tables), they can:
* Handle huge write loads (log ingestion, metrics, telemetry)
* Serve large volumes of simple reads with low latency
* Optimize storage layout (append-only, LSM trees, columnar layouts, etc.) for those workloads

Relational DBs can also be very fast and are often more than enough, but at extreme scale or with specific workloads, NoSQL can deliver better performance per dollar.