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feat(core/df-repr): better upper bound pruning to reduce plan space#265
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feat(core/df-repr): better upper bound pruning to reduce plan space#265
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Signed-off-by: Alex Chi Z <iskyzh@gmail.com> tune plan space budget Signed-off-by: Alex Chi Z <iskyzh@gmail.com>
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Reviewed:
- cross join to inner join changes
- main upper bound pruning logic in
optimize_inputs_inner
I need another look at task2.rs and potentially doc more aggressively like https://github.com/cmu-db/optd/pull/234. I actually like this version of the task graph a lot (no state machine logic).
Is there any other part of the logic I need pay special attention to?
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I think part of this PR is tuning to ensure the plan is what we want (i.e., filter selectivity and behavior on reaching the budget). The tasks2.rs logic is also something that we should fully understand as we are going to bring more logic into it (phys prop, etc.) |
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Needs to doc the task graph a little bit more but LGTM
Signed-off-by: Alex Chi Z <iskyzh@gmail.com>
Signed-off-by: Alex Chi Z <iskyzh@gmail.com> tune plan space budget Signed-off-by: Alex Chi Z <iskyzh@gmail.com>
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