Increasingly databases can get do-overs on bad plans. A micro-level example might be dataflow splitting particularly expensive tasks so as to better parallelize

Increasingly databases can get do-overs on bad plans. A micro-level example might be dataflow splitting particularly expensive tasks so as to better parallelize them: https://cloud.google.com/blog/products/gcp/no-shard-left-behind-dynamic-work-rebalancing-in-google-cloud-dataflow https://cloud.google.com/blog/products/gcp/no-shard-left-beh... Spark will re-run the query planner after doing the map stage of a map-reduce operation so as to leverage the information collected: https://databricks.com/blog/2020/05/29/adaptive-query-execution-speeding-up-spark-sql-at-runtime.html https://databricks.com/blog/2020/05/29/adaptive-query-execut... Both examples represent changing parameters about a lower level plan than simply join operators, but the industry is certainly moving in this direction.