A breakdown of how Netflix, Instagram, and Twitter chose their databases based on access patterns rather than popularity. Netflix uses Apache Cassandra for its massive write throughput (3M writes/sec) via denormalized, partition-key-based data modeling. Instagram runs PostgreSQL at billion-user scale using PgBouncer, read replicas, partitioning, and smart indexing because its workload is read-heavy and relational. Twitter uses Redis as a cache layer with fan-out-on-write to serve pre-built timelines in microseconds, while keeping a durable store as the source of truth. A hybrid fan-out approach handles celebrity accounts with millions of followers. The key framework: identify your primary access pattern, understand what trade-offs you accept, and avoid over-engineering before scale demands it.
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