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OpenAI's ChatGPT database architecture is a single primary PostgreSQL instance with ~50 read replicas, PgBouncer connection pooling, and cascading replication on Azure. The core insight: read-heavy workloads don't need sharding — optimizing the read path is what matters.
For a book selling platform, the key decisions are search architecture (Elasticsearch vs full-text search), inventory consistency (strong vs eventual), and order state machine design.
DDIA Chapter 1's core argument: the challenge of data-intensive systems isn't big compute — it's data complexity (volume, variety, velocity). Evaluating this complexity requires precise definitions of reliability, scalability, and maintainability that are more specific than how most engineers use these terms.
Redis is an in-memory data structure server that achieves sub-millisecond latency through a single-threaded event loop, rich data types, and all-RAM storage. It's the go-to for caching, sessions, leaderboards, rate limiting — and in 2026, AI agent memory.
DuckDB improved its core OLAP operations by 4-12x over three years and can now complete TPC-H SF10,000 (10 TB) on a single laptop in about four hours. Its design boundary is clear—single-node, single-user, embedded OLAP—but within that boundary, what it can actually do keeps exceeding expectations.