Outgrowing Postgres: Handling increased user concurrency

As applications grow, managing increased user concurrency becomes a challenge, especially with Postgres, which wasn't designed for thousands of simultaneous connections. While vertical scaling aids data volume, connection management requires a different strategy. Tinybird offers a solution by allowing Postgres event tables to be imported, streamed, or connected via Kafka, using SQL to define real-time API endpoints with low latency. Postgres uses a process-based model for connections, leading to limitations like resource use, connection limits, and context switching. To manage more users, key strategies include reducing connection duration through query optimization, caching, and tuning configurations; increasing connection capacity via connection pooling with PgBouncer and read replicas; and decreasing connection demand with asynchronous processing, connection sharing, and application-level caching. Despite these optimizations, signs like connection timeouts or high CPU usage may indicate the need for more advanced solutions such as vertical scaling, sharding, or specialized databases. The text offers a comprehensive guide to scaling Postgres effectively, highlighting the importance of starting with basic optimizations before considering more complex solutions.

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