split plan node representation into a crate #15
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Signed-off-by: Alex Chi <iskyzh@gmail.com>
Gun9niR
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Mar 20, 2024
Generate the statistics in perftest and put them into `BaseCostModel` in
`DatafusionOptimizer`. Below is the comparison before & after stats are
added. You can check `PhysicalScan`, where the cost has changed.
The final cardinality remains the same because when stats on a column is
missing, we use a very small magic number `INVALID_SELECTIVITY` (0.001)
that just sets cardinality to 1.
### Todos in Future PRs
- Support generating stats on `Utf8`.
- Set a better magic number.
- Generate MCV.
### Before
```
plan space size budget used, not applying logical rules any more. current plan space: 1094
explain: PhysicalSort
├── exprs:SortOrder { order: Desc }
│ └── #1
├── cost: weighted=185.17,row_cnt=1.00,compute=179.17,io=6.00
└── PhysicalProjection { exprs: [ #0, #1 ], cost: weighted=182.12,row_cnt=1.00,compute=176.12,io=6.00 }
└── PhysicalAgg
├── aggrs:Agg(Sum)
│ └── Mul
│ ├── #0
│ └── Sub
│ ├── 1
│ └── #1
├── groups: [ #2 ]
├── cost: weighted=182.02,row_cnt=1.00,compute=176.02,io=6.00
└── PhysicalProjection { exprs: [ #0, #1, #2 ], cost: weighted=64.90,row_cnt=1.00,compute=58.90,io=6.00 }
└── PhysicalProjection { exprs: [ #0, #1, #4, #5, #6 ], cost: weighted=64.76,row_cnt=1.00,compute=58.76,io=6.00 }
└── PhysicalProjection { exprs: [ #2, #3, #5, #6, #7, #8, #9 ], cost: weighted=64.54,row_cnt=1.00,compute=58.54,io=6.00 }
└── PhysicalProjection { exprs: [ #0, #3, #4, #5, #6, #7, #8, #9, #10, #11 ], cost: weighted=64.24,row_cnt=1.00,compute=58.24,io=6.00 }
└── PhysicalProjection { exprs: [ #1, #2, #4, #5, #6, #7, #8, #9, #10, #11, #12, #13 ], cost: weighted=63.82,row_cnt=1.00,compute=57.82,io=6.00 }
└── PhysicalProjection { exprs: [ #0, #3, #8, #9, #10, #11, #12, #13, #14, #15, #16, #17, #18, #19 ], cost: weighted=63.32,row_cnt=1.00,compute=57.32,io=6.00 }
└── PhysicalNestedLoopJoin
├── join_type: Inner
├── cond:And
│ ├── Eq
│ │ ├── #11
│ │ └── #14
│ └── Eq
│ ├── #3
│ └── #15
├── cost: weighted=62.74,row_cnt=1.00,compute=56.74,io=6.00
├── PhysicalHashJoin { join_type: Inner, left_keys: [ #0 ], right_keys: [ #1 ], cost: weighted=35.70,row_cnt=1.00,compute=32.70,io=3.00 }
│ ├── PhysicalScan { table: customer, cost: weighted=1.00,row_cnt=1.00,compute=0.00,io=1.00 }
│ └── PhysicalHashJoin { join_type: Inner, left_keys: [ #0 ], right_keys: [ #0 ], cost: weighted=31.64,row_cnt=1.00,compute=29.64,io=2.00 }
│ ├── PhysicalProjection { exprs: [ #0, #1 ], cost: weighted=27.40,row_cnt=1.00,compute=26.40,io=1.00 }
│ │ └── PhysicalFilter
│ │ ├── cond:And
│ │ │ ├── Geq
│ │ │ │ ├── #2
│ │ │ │ └── 9131
│ │ │ └── Lt
│ │ │ ├── #2
│ │ │ └── 9496
│ │ ├── cost: weighted=27.30,row_cnt=1.00,compute=26.30,io=1.00
│ │ └── PhysicalProjection { exprs: [ #0, #1, #4 ], cost: weighted=1.14,row_cnt=1.00,compute=0.14,io=1.00 }
│ │ └── PhysicalScan { table: orders, cost: weighted=1.00,row_cnt=1.00,compute=0.00,io=1.00 }
│ └── PhysicalProjection { exprs: [ #0, #2, #5, #6 ], cost: weighted=1.18,row_cnt=1.00,compute=0.18,io=1.00 }
│ └── PhysicalScan { table: lineitem, cost: weighted=1.00,row_cnt=1.00,compute=0.00,io=1.00 }
└── PhysicalProjection { exprs: [ #0, #3, #7, #8, #9, #10 ], cost: weighted=15.72,row_cnt=1.00,compute=12.72,io=3.00 }
└── PhysicalHashJoin { join_type: Inner, left_keys: [ #3 ], right_keys: [ #0 ], cost: weighted=15.46,row_cnt=1.00,compute=12.46,io=3.00 }
├── PhysicalScan { table: supplier, cost: weighted=1.00,row_cnt=1.00,compute=0.00,io=1.00 }
└── PhysicalHashJoin { join_type: Inner, left_keys: [ #2 ], right_keys: [ #0 ], cost: weighted=11.40,row_cnt=1.00,compute=9.40,io=2.00 }
├── PhysicalProjection { exprs: [ #0, #1, #2 ], cost: weighted=1.14,row_cnt=1.00,compute=0.14,io=1.00 }
│ └── PhysicalScan { table: nation, cost: weighted=1.00,row_cnt=1.00,compute=0.00,io=1.00 }
└── PhysicalProjection { exprs: [ #0 ], cost: weighted=7.20,row_cnt=1.00,compute=6.20,io=1.00 }
└── PhysicalFilter
├── cond:Eq
│ ├── #1
│ └── "AMERICA"
├── cost: weighted=7.14,row_cnt=1.00,compute=6.14,io=1.00
└── PhysicalProjection { exprs: [ #0, #1 ], cost: weighted=1.10,row_cnt=1.00,compute=0.10,io=1.00 }
└── PhysicalScan { table: region, cost: weighted=1.00,row_cnt=1.00,compute=0.00,io=1.00 }
plan space size budget used, not applying logical rules any more. current plan space: 1094
qerrors: {"DataFusion": [5.0]}
```
### After
```
plan space size budget used, not applying logical rules any more. current plan space: 1094
explain: PhysicalSort
├── exprs:SortOrder { order: Desc }
│ └── #1
├── cost: weighted=336032.32,row_cnt=1.00,compute=259227.32,io=76805.00
└── PhysicalProjection { exprs: [ #0, #1 ], cost: weighted=336029.27,row_cnt=1.00,compute=259224.27,io=76805.00 }
└── PhysicalAgg
├── aggrs:Agg(Sum)
│ └── Mul
│ ├── #0
│ └── Sub
│ ├── 1
│ └── #1
├── groups: [ #2 ]
├── cost: weighted=336029.17,row_cnt=1.00,compute=259224.17,io=76805.00
└── PhysicalProjection { exprs: [ #0, #1, #2 ], cost: weighted=335912.05,row_cnt=1.00,compute=259107.05,io=76805.00 }
└── PhysicalProjection { exprs: [ #0, #1, #4, #5, #6 ], cost: weighted=335911.91,row_cnt=1.00,compute=259106.91,io=76805.00 }
└── PhysicalProjection { exprs: [ #2, #3, #5, #6, #7, #8, #9 ], cost: weighted=335911.69,row_cnt=1.00,compute=259106.69,io=76805.00 }
└── PhysicalProjection { exprs: [ #0, #3, #4, #5, #6, #7, #8, #9, #10, #11 ], cost: weighted=335911.39,row_cnt=1.00,compute=259106.39,io=76805.00 }
└── PhysicalProjection { exprs: [ #1, #2, #4, #5, #6, #7, #8, #9, #10, #11, #12, #13 ], cost: weighted=335910.97,row_cnt=1.00,compute=259105.97,io=76805.00 }
└── PhysicalProjection { exprs: [ #0, #3, #8, #9, #10, #11, #12, #13, #14, #15, #16, #17, #18, #19 ], cost: weighted=335910.47,row_cnt=1.00,compute=259105.47,io=76805.00 }
└── PhysicalNestedLoopJoin
├── join_type: Inner
├── cond:And
│ ├── Eq
│ │ ├── #11
│ │ └── #14
│ └── Eq
│ ├── #3
│ └── #15
├── cost: weighted=335909.89,row_cnt=1.00,compute=259104.89,io=76805.00
├── PhysicalProjection { exprs: [ #6, #7, #8, #9, #10, #11, #12, #13, #0, #1, #2, #3, #4, #5 ], cost: weighted=335619.21,row_cnt=1.00,compute=258944.21,io=76675.00 }
│ └── PhysicalHashJoin { join_type: Inner, left_keys: [ #1 ], right_keys: [ #0 ], cost: weighted=335618.63,row_cnt=1.00,compute=258943.63,io=76675.00 }
│ ├── PhysicalProjection { exprs: [ #4, #5, #0, #1, #2, #3 ], cost: weighted=332616.57,row_cnt=1.00,compute=257441.57,io=75175.00 }
│ │ └── PhysicalProjection { exprs: [ #0, #2, #5, #6, #16, #17 ], cost: weighted=332616.31,row_cnt=1.00,compute=257441.31,io=75175.00 }
│ │ └── PhysicalProjection { exprs: [ #2, #3, #4, #5, #6, #7, #8, #9, #10, #11, #12, #13, #14, #15, #16, #17, #0, #1 ], cost: weighted=332616.05,row_cnt=1.00,compute=257441.05,io=75175.00 }
│ │ └── PhysicalHashJoin { join_type: Inner, left_keys: [ #0 ], right_keys: [ #0 ], cost: weighted=332615.31,row_cnt=1.00,compute=257440.31,io=75175.00 }
│ │ ├── PhysicalProjection { exprs: [ #0, #1 ], cost: weighted=212263.25,row_cnt=1.00,compute=197263.25,io=15000.00 }
│ │ │ └── PhysicalFilter
│ │ │ ├── cond:And
│ │ │ │ ├── Geq
│ │ │ │ │ ├── #2
│ │ │ │ │ └── 9131
│ │ │ │ └── Lt
│ │ │ │ ├── #2
│ │ │ │ └── 9496
│ │ │ ├── cost: weighted=212263.15,row_cnt=1.00,compute=197263.15,io=15000.00
│ │ │ └── PhysicalProjection { exprs: [ #0, #1, #4 ], cost: weighted=16050.07,row_cnt=15000.00,compute=1050.07,io=15000.00 }
│ │ │ └── PhysicalScan { table: orders, cost: weighted=15000.00,row_cnt=15000.00,compute=0.00,io=15000.00 }
│ │ └── PhysicalScan { table: lineitem, cost: weighted=60175.00,row_cnt=60175.00,compute=0.00,io=60175.00 }
│ └── PhysicalScan { table: customer, cost: weighted=1500.00,row_cnt=1500.00,compute=0.00,io=1500.00 }
└── PhysicalProjection { exprs: [ #0, #3, #7, #8, #9, #10 ], cost: weighted=279.36,row_cnt=1.00,compute=149.36,io=130.00 }
└── PhysicalProjection { exprs: [ #4, #5, #6, #7, #8, #9, #10, #0, #1, #2, #3 ], cost: weighted=279.10,row_cnt=1.00,compute=149.10,io=130.00 }
└── PhysicalProjection { exprs: [ #1, #2, #3, #0, #4, #5, #6, #7, #8, #9, #10 ], cost: weighted=278.64,row_cnt=1.00,compute=148.64,io=130.00 }
└── PhysicalHashJoin { join_type: Inner, left_keys: [ #1 ], right_keys: [ #3 ], cost: weighted=278.18,row_cnt=1.00,compute=148.18,io=130.00 }
├── PhysicalProjection { exprs: [ #3, #0, #1, #2 ], cost: weighted=76.12,row_cnt=1.00,compute=46.12,io=30.00 }
│ └── PhysicalProjection { exprs: [ #0, #1, #2, #4 ], cost: weighted=75.94,row_cnt=1.00,compute=45.94,io=30.00 }
│ └── PhysicalProjection { exprs: [ #1, #2, #3, #4, #0 ], cost: weighted=75.76,row_cnt=1.00,compute=45.76,io=30.00 }
│ └── PhysicalHashJoin { join_type: Inner, left_keys: [ #0 ], right_keys: [ #2 ], cost: weighted=75.54,row_cnt=1.00,compute=45.54,io=30.00 }
│ ├── PhysicalProjection { exprs: [ #0 ], cost: weighted=23.48,row_cnt=1.00,compute=18.48,io=5.00 }
│ │ └── PhysicalFilter
│ │ ├── cond:Eq
│ │ │ ├── #1
│ │ │ └── "AMERICA"
│ │ ├── cost: weighted=23.42,row_cnt=1.00,compute=18.42,io=5.00
│ │ └── PhysicalProjection { exprs: [ #0, #1 ], cost: weighted=5.30,row_cnt=5.00,compute=0.30,io=5.00 }
│ │ └── PhysicalScan { table: region, cost: weighted=5.00,row_cnt=5.00,compute=0.00,io=5.00 }
│ └── PhysicalScan { table: nation, cost: weighted=25.00,row_cnt=25.00,compute=0.00,io=25.00 }
└── PhysicalScan { table: supplier, cost: weighted=100.00,row_cnt=100.00,compute=0.00,io=100.00 }
plan space size budget used, not applying logical rules any more. current plan space: 1094
qerrors: {"DataFusion": [5.0]}
```
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part of #8
We want a clean separation of the optimizer implementation and the plan node representations, so that we can ensure optd is extensible and can be used on different systems. Now, we have split all Datafusion plan node representations and the corresponding optimizer rules into
optd-datafusion-reprcrate.