Using Python to Scale PostgreSQL From Monolith to Shards

PostgreSQL sharding scales databases in high-growth Python apps by horizontally partitioning data across servers, improving performance and handling large data volumes. Key considerations include choosing an appropriate shard key, evenly distributing data, and ensuring queries include the shard key. While SQLAlchemy enables application-level sharding, extensions like Citus simplify implementation. Sharding adds complexity and should be pursued after optimizing queries, adding indexes, and exploring vertical scaling. When done effectively, sharding allows Python apps to handle massive user bases and data growth.