Database query optimizers promise to automatically select the fastest execution plan for your queries, but new testing reveals they often fall short of this goal. While these systems work well for simple scenarios, they frequently struggle with real-world data patterns, leading to performance issues that can catch developers off guard.
The Reality Behind Query Optimization
Recent performance testing across different data distributions shows that query optimizers consistently pick suboptimal plans. The testing involved simple SELECT queries with range conditions, yet even these basic operations revealed significant gaps between what optimizers choose and what actually runs fastest. The problem becomes more pronounced with complex queries involving multiple tables and joins.
The core issue lies in how optimizers make decisions. They rely on statistical summaries of your data and simplified cost models to estimate performance. However, these statistics are necessarily incomplete representations of the actual data patterns. Real-world databases contain correlations, clustering, and distribution patterns that can't be fully captured in compact statistical summaries.
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| Visual representation of how often a query optimizer selects an optimal execution plan, highlighting performance gaps in query execution |
Community Frustrations with Plan Predictability
Database professionals are increasingly vocal about optimizer unpredictability. Some argue that modern query planners have become too smart for their own good, creating situations where minor changes in data or query structure can dramatically alter performance. This unpredictability becomes especially problematic in production environments where consistent performance matters more than theoretical optimization.
The PostgreSQL community faces particular challenges since the database doesn't allow users to force specific execution plans or indexes. While this design philosophy aims to treat suboptimal plans as bugs to be fixed, it leaves developers with limited options when the optimizer makes poor choices. Extensions like pg_hint_plan provide some relief, but they're not part of the core system.
It's becoming too complex, too unpredictable. Every query and variable becomes a surprise in production.
Hardware and Environment Complications
Modern computing environments add another layer of complexity to query optimization. Cost models developed for traditional on-premise systems may not accurately reflect performance characteristics of cloud storage systems, NVMe arrays, or containerized environments. Features like prefetching, which can dramatically improve performance for certain access patterns, aren't always properly accounted for in optimizer cost calculations.
The testing showed that bitmap scans consistently outperformed index scans due to prefetching capabilities, yet optimizers often chose the slower index scan approach. These hardware-level optimizations can completely change the performance landscape in ways that traditional cost models don't capture.
Alternative Approaches and Workarounds
Some developers advocate for more deterministic query approaches, particularly for application-generated queries where predictability trumps theoretical optimization. Techniques like using Common Table Expressions (CTEs) as optimization barriers, adjusting session-level planner settings, or restructuring queries can provide more control over execution plans.
Higher-end database systems offer adaptive query processing, which allows the engine to adjust its approach during execution based on actual performance data. However, these features aren't universally available and add their own complexity to database management.
The fundamental challenge remains: query optimization is essentially a lossy compression problem. Optimizers must make fast decisions based on limited information, and perfect plans for all possible scenarios may be mathematically impossible to achieve within reasonable time and resource constraints.
Reference: How often is the query plan optimal?