How a Logistics Platform Partitioned a Billion-Row Database Table in Production

A nationwide logistics platform faced severe database performance issues after its core tracking events table surpassed one billion rows, rendering traditional indexing solutions ineffective. The table recorded every parcel scan and status change, growing continuously as tens of thousands of orders were processed daily. Engineers implemented range-based table partitioning in PostgreSQL, splitting data into monthly partitions by timestamp so queries could skip irrelevant date ranges through a mechanism called partition pruning. The approach required restructuring the primary key to include the partition column, which created compatibility challenges with foreign keys and ORM frameworks that assume single-column primary keys. The key takeaway was that partitioning decisions should be driven by query access patterns rather than table size alone, and that queries must include the partition key to avoid scanning all partitions simultaneously.