How Databases Survive Going Viral | Arbind Builds | Explains

One database. One million users overnight. CPU pegged at 100%, queries timing out, 502s everywhere. This is the exact 10-step playbook for scaling a relational database without losing data, compromising security, or going bankrupt: vertical scaling, query optimization and indexing, caching, read replicas, message queues, partitioning and sharding, polyglot persistence, and security — then the evolution that ties it together.

Chapters: 0:00 The viral bottleneck — 1K to 1M users, one database 0:44 Vertical scaling — same code, bigger box (and its ceiling) 1:17 Query optimization — sequential scans vs B-tree indexes 1:58 Caching — Redis, cache-aside, and invalidation 2:38 Read replicas — multiply reads, meet replication lag 3:22 Message queues — decouple writes with Kafka 4:00 Partitioning and sharding — split logs, shard users 4:45 Polyglot persistence — the right store per workload 5:22 Security and compliance — TLS, encryption, GDPR/FERPA 6:04 The evolution — scale when the data demands it

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