Report distributed tasks in remote benchmarks#462
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… be the same (#427) This is one PR from the following stack of PRs: - #427 <- you are here - #469 - #461 - #462 - #463 - #464 - #432 This is a preparatory step towards: - #377 This is an optimization that allows workers to communicate in-memory avoiding network calls and serialization in case it needs to communicate to itself. This optimization shows good improvements if using a small number of workers, and fades away as more workers are used. Even if this shows improvements today, it will become very meaningful in adaptative query execution: there will be times when two consecutive stages are assigned 1 task each, and as that was done dynamically, we cannot eagerly do the optimization that collapses those two stages into one, so instead, we assigned both stages to the same worker, and the optimization in this PR kicks in, executing the plan fully locally.
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This is one PR from the following stack of PRs: - #427 - #469 <- you are here - #461 - #462 - #463 - #464 - #432 This PR factors out common recursion patterns used in the distributed planner into reusable helpers, reducing complexity in critical parts of the codebase. By centralizing recursion logic, we improve code maintainability and reduce the risk of subtle bugs in recursive tree traversals.
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This is one PR from the following stack of PRs:
This PR adds reporting of distributed task counts in remote benchmarks, providing visibility into how tasks are being distributed across workers during benchmark runs. This allows us to better understand the impact of the distributed planner and task distribution strategies on performance.