Search & Ranking Systems • Query Parsing & OptimizationHard⏱️ ~2 min
Runtime Adaptivity and Plan Invalidation: Handling Estimation Errors
Static query plans assume cardinality estimates are accurate, but misestimates of 10 to 1000x are common due to data skew, stale statistics, or correlated predicates. Runtime adaptivity allows the execution engine to detect estimation errors during execution and adjust the plan dynamically, switching join strategies, enabling dynamic filtering with Bloom filters, or repartitioning skewed keys. This prevents catastrophic plan failures without requiring full reoptimization.
Modern systems track actual versus estimated cardinalities at operator boundaries. If observed rows exceed thresholds (for example, 10x over estimate), the engine can switch from nested loop to hash join mid execution, apply runtime filters to downstream operators, or trigger adaptive repartitioning to mitigate skew and avoid straggler tasks. Google BigQuery and Amazon Athena use runtime Bloom filters generated from the build side of a join and pushed to the probe side to filter irrelevant partitions or rows, reducing shuffle volume by 50% or more when selectivity is high.
Plan invalidation and recompilation occur when schema changes, statistics updates, or parameter distributions shift significantly. Amazon Aurora and Google Spanner invalidate cached plans on Data Definition Language (DDL) operations and statistics refresh to avoid stale plans. The trade off is recompilation overhead: frequent invalidations cause hard parse storms and latch contention. Systems mitigate by tying plans to selectivity ranges (plan baselines) or using parameter sniffing guards that force recompile only when parameter histograms cross thresholds, balancing stability with accuracy.
💡 Key Takeaways
•Runtime adaptivity detects cardinality misestimates during execution (observed rows exceed estimated by 10x or more) and switches join strategies or enables dynamic filters without full reoptimization
•Bloom filter pushdown generated from join build side and pushed to probe side can reduce shuffle volume by 50% or more by filtering partitions early, commonly used in BigQuery and Athena
•Plan invalidation triggers on schema changes or statistics updates to avoid stale plans. Trade off: frequent invalidations cause hard parse storms (2 to 10 milliseconds (ms) per query) and metadata latch contention under high Query Per Second (QPS)
•Adaptive repartitioning detects skewed keys at runtime and splits heavy keys across multiple tasks to avoid straggler tasks that can extend query latency by 10 to 100x
•Parameter sniffing mitigation: tie plans to selectivity classes or force recompile when parameter histograms cross thresholds, avoiding locked in bad plans for bimodal distributions
📌 Examples
Adaptive join switching: Query estimates 1000 rows for nested loop join, observes 500,000 rows after 10% complete. Engine aborts nested loop, builds hash table, and switches to hash join, reducing total time from projected 60 seconds to 8 seconds.
Google BigQuery Bloom filter: Build side of dimension join (100k rows) generates Bloom filter (1 megabyte (MB)), pushes to fact table scan. Fact scan tests 1 billion rows, filters 95% before shuffle, reducing shuffle from 50 gigabytes (GB) to 2 GB and cutting query time from 45 seconds to 12 seconds.
Amazon Aurora plan invalidation: Statistics update after bulk insert increases table row count from 1 million to 10 million. Next query execution detects version mismatch, invalidates cached plan, and reoptimizes, switching from index scan to parallel full scan and dropping latency from 5 seconds to under 1 second.