Why Database Merge Joins Beat Hash Joins for Sorted Data
from Database Tech with Fexingo: SQL, NoSQL, and Data Storage Conversations Hosted by Fexingo
In this episode of Database Tech with Fexingo, Lucas and Luna explore a common yet overlooked optimization: why merge joins can outperform hash joins when both input tables are pre-sorted. Using a concrete PostgreSQL example with a 10-million-row join, they break down the trade-offs in memory pressure, CPU cache behavior, and disk spill avoidance. Lucas argues that modern optimizers often underestimate the cost of building and probing hash tables, especially when the data is physically ordered by an index. They discuss real-world scenarios where switching to a merge join yielded a 4x speedup, and why leaving hints can help. The conversation also touches on the importance of understanding your data's physical order and how tools like EXPLAIN can expose suboptimal plans. Whether you're tuning SQL queries or designing schema, this episode offers a practical look at join algorithm selection. It's a must-listen for database engineers and data analysts looking to squeeze more performance from their queries without hardware upgrades. #Database #MergeJoin #HashJoin #QueryOptimization #Performance #SQL #PostgreSQL #SortMerge #Indexing #OLTP #JoinAlgorithms #MemoryManagement #DiskSpill #DatabaseTech #FexingoBusiness #BusinessPodcast #Tech #DataEngineering Keep every episode free: buymeacoffee.com/fexingo
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