Technology Choices & Exhaustive Comparisons

This document provides exhaustive comparisons across 8 major architectural categories, detailing the chosen technology versus all realistic industry alternatives.

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1. Concurrency Control: Atomic SQL Conditional Updates vs Redis Lua vs Pessimistic SELECT FOR UPDATE vs Optimistic Locking (OCC)

Evaluation Factor	Atomic Conditional SQL [This Project]	Redis Lua Scripts	Postgres SELECT FOR UPDATE	Optimistic Concurrency (OCC)
Throughput (Contended Single SKU)	8,000 - 15,000 RPS	50,000+ RPS	500 - 1,200 RPS	Poor under high contention (<500 RPS)
Oversell Guarantee	100% Guaranteed by DB Kernel	100% Guaranteed (Single Thread)	100% Guaranteed (Row Lock)	Guaranteed via version checks
Crash Durability	Instant (ACID WAL)	Asynchronous (AOF/RDB loss risk)	Instant (ACID WAL)	Instant (ACID WAL)
Deadlock Risk	Zero (Single atomic statement)	Zero	High (Multi-row locks)	Zero (Retry loop aborts)
Implementation Complexity	Low	Medium	Low	Medium

• Why I Used This: Atomic conditional updates (UPDATE ... WHERE available >= :q) execute in a single SQL round-trip, leveraging Postgres row locks for microsecond durations while avoiding lock wait queues.
• Why I Did Not Use Alternatives:
  • Redis Lua: While faster in raw memory, Redis requires complex two-phase commit synchronization with Postgres. If Redis crashes before flushing to Postgres, reservation state is lost.
  • SELECT FOR UPDATE: Holds row locks across multiple application round-trips, starving connection pools under 50,000 RPS.
  • Optimistic Concurrency Control (OCC): Under 90%+ contention, 95% of OCC threads fail version matching and enter expensive retry loops, consuming excessive CPU.
• 30-Second Spoken Answer:

  "I chose atomic conditional SQL updates with database CHECK constraints because they guarantee zero overselling at the database engine level without holding open pessimistic lock queues. Unlike Redis alone, there is zero risk of cache drift or lost reservation state during crash recovery."

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2. Web Framework: FastAPI (Python 3.13) vs Express.js vs Spring Boot vs Go Gin vs NestJS

Evaluation Factor	FastAPI [This Project]	Express.js (Node)	Spring Boot (Java)	Go Gin
Async Performance	High (uvloop ASGI)	High (Node Event Loop)	Very High (Virtual Threads)	Ultra High (Goroutines)
Type Safety & Schemas	Pydantic v2 (Rust-backed)	Manual / Joi / Zod	Static Strong Typing	Static Strong Typing
Auto OpenAPI Docs	Built-in Interactive Swagger	Manual Swagger configuration	Springfox / Springdoc	Manual Swagger
Developer Velocity	Very High	High	Medium	Medium

• Why I Used This: FastAPI provides native Python async/await, automated Pydantic schema validation compiled in Rust, and instant Swagger API documentation.
• Why I Did Not Use Alternatives:
  • Express.js: Lacks native schema validation and automatic OpenAPI generation; requires callback/promise chaining wrappers for clean error propagation.
  • Go Gin: High performance, but slower developer iteration time for complex state machine logic.

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3. Database Layer: PostgreSQL 16 + Async SQLAlchemy vs MongoDB vs DynamoDB vs MySQL

Evaluation Factor	PostgreSQL 16 [This Project]	MongoDB	AWS DynamoDB	MySQL 8.0
Data Integrity / CHECKs	Strict Table CHECK Constraints	Document validation rules	Condition expressions	Basic CHECK constraints
Isolation Guarantees	Full ACID (Read Committed to Serializable)	Multi-document ACID (High latency)	Eventual consistency default	Full ACID (InnoDB)
Connection Pooling	Asyncpg connection pooling	Mongoose driver pool	HTTP Request-based	aiomysql pool

• Why I Used This: Flash sales require absolute mathematical conservation invariants. PostgreSQL's check constraints (CHECK (available + reserved + sold == total_allocated)) guarantee that corrupted states are rejected by the database engine.