How SingleStore Learns: Feedback Reoptimization Explained

SingleStore’s Feedback Reoptimization (FR) addresses slow analytical queries caused by stale statistics, inaccurate cardinality estimates, skewed data, and difficult multi-table join estimates that can lead optimizers to choose inefficient join orders, hash sides, or distribution strategies. Rather than relying only on pre-execution statistics, FR selectively profiles expensive queries, compares estimated and actual runtime behavior, identifies significant mismatches, produces an alternative plan using observed row counts and resource use, and adopts that plan only if testing shows improvement in CPU, network time, or memory without meaningful regressions elsewhere. Benchmark results showed the greatest benefits on skewed JOB data, where 73% of queries improved by an average of 25%, while TPC-H saw 42% improve by an average of 15%; rejected candidate plans leave the current plan intact while preserving feedback for future optimization. Available by default in SingleStore 9.0 and later, FR can work automatically or be triggered for individual cached plans, while version 9.1 adds idle-cluster reoptimization for background tuning. FR is intended for estimate-related planning errors rather than missing indexes, poor shard keys, inherently costly query designs, or parameter-sensitive workloads that need different plans for different inputs.

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