OpenAI scaling PostgreSQL: lessons and alternatives

Scaling PostgreSQL to accommodate extremely high write and read volumes, as demonstrated by OpenAI, involves addressing structural challenges inherent to the database system rather than mere configuration errors. OpenAI's approach highlights the need for architectural decisions that offload analytical workloads from the OLTP engine, where tools like ClickHouse® offer significant advantages. Postgres tuning strategies, such as partitioning, connection pooling, and replica routing, have their limits, particularly when continuous large write volumes and complex analytical queries are involved. The transition to a system that can handle these demands typically involves integrating Change Data Capture (CDC) paths to offload analytical processing to a more suitable engine like ClickHouse®, which is optimized for large-scale aggregations and concurrent analytical reads. This strategic offloading allows for maintaining the integrity and performance of Postgres as an OLTP source, while leveraging ClickHouse® for efficient analytical query handling, as seen in OpenAI's implementation. The process involves careful schema design, monitoring for WAL lag, and ensuring query freshness, all while considering security and operational monitoring to manage new failure surfaces introduced by the distributed architecture.

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