PostgreSQL's internal architecture handles data updates in a way that distinguishes it from many other database systems. A central mechanism for optimizing these updates is the Heap Only Tuple (HOT) path. This process is designed to improve performance by minimizing the overhead associated with updating records, particularly when those updates do not change the values of columns that are indexed. By understanding how PostgreSQL manages these internal movements, developers can better predict how the database will behave under heavy write loads.
The HOT update mechanism functions under specific conditions: it only triggers when the updated columns are not part of any index and the new version of the data (the tuple) can fit on the same physical page as the old version. If these conditions are met, PostgreSQL can update the record without needing to update every associated index, significantly reducing I/O. However, this optimization is sensitive to the 'fillfactor' setting. If a table's pages are too full, there is no room for the new tuple, and the HOT update fails, forcing a standard update that requires full index maintenance. Commenters on Hacker News noted that lowering the fillfactor (for example, to 80-90) on update-heavy tables can ensure enough free space remains on each page for HOT updates to occur.
Beyond the HOT path, the database's behavior is further defined by its zHeap implementation and its handling of TOAST (The Oversized-Attribute Storage Technique). These components provide a mental model of what PostgreSQL does and does not do regarding data storage and retrieval. Additionally, the database's Snapshot Isolation (SSI) implementation—which ensures consistency during concurrent transactions—has been a point of technical scrutiny. For instance, a 2020 Jepsen report identified a long-standing bug in the SSI implementation, highlighting the complexities involved in maintaining strict consistency across distributed or concurrent operations. While these internals are often abstracted away from daily use, they represent the fundamental trade-offs between storage efficiency and update performance in PostgreSQL.