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When web applications scale to millions of active users, monolithic database servers hit physical I/O bottlenecks. Discover how enterprise architects implement read-write splitting, declarative table partitioning, PgBouncer pooling, and distributed sharding to scale PostgreSQL effortlessly.
Database bottlenecks are the #1 cause of application slowdowns and crashes during high-traffic surges. While application servers can scale horizontally with ease, scaling stateful relational databases requires disciplined Database Sharding, Read-Replica Routing, and Connection Pooling.
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Request Database Architecture ReviewRead-Replicas copy data from a primary write database to multiple read-only nodes, offloading heavy `SELECT` queries. Database Sharding horizontally splits table rows across separate physical database servers using a shard key (like `tenant_id` or `user_id`), allowing unlimited write scaling.
Each PostgreSQL connection consumes 5MB to 10MB of server RAM and spawns an OS process. PgBouncer maintains a small pool of reusable backend connections, allowing 10,000 incoming app connections without crashing the database.
By partitioning large tables by month or year, queries scanning recent dates only read the relevant small sub-table partition, bypassing hundreds of millions of historical rows.
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