feat: implement unop, binop, and func expressions #2
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Signed-off-by: Yuchen Liang <yuchenl3@andrew.cmu.edu>
yliang412
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Signed-off-by: Alex Chi <iskyzh@gmail.com>
skyzh
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yliang412
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Mar 11, 2024
Closes #65. This PR produces metadata in optimizer output. Currently, the only metadata kept is the group id. The group id information has the following benefits: - allow the consumer of the optd plan to recover reusable fragments in the DAG-shaped query plan. - implement adaptive physical collector outside `optd-core`. - display cardinality cost: #89 Looking forward to feedbacks! In the example below, we produce the same plan as before when adaptiveness is enabled. ```sql ❯ create table t1(t1v1 int, t1v2 int); 0 rows in set. Query took 0.009 seconds. Execution took 0.000 secs, Planning took 0.000 secs ❯ explain select * from t1 as a, t1 as b; +--------------------------------------------------+-------------------------------------------------------------------------------------------------+ | plan_type | plan | +--------------------------------------------------+-------------------------------------------------------------------------------------------------+ | logical_plan after datafusion | Projection: a.t1v1, a.t1v2, b.t1v1, b.t1v2 | | | CrossJoin: | | | SubqueryAlias: a | | | TableScan: t1 | | | SubqueryAlias: b | | | TableScan: t1 | | logical_plan after optd | LogicalProjection { exprs: [ #0, #1, #2, #3 ] } | | | └── LogicalJoin { join_type: Cross, cond: true } | | | ├── LogicalScan { table: t1 } | | | └── LogicalScan { table: t1 } | | physical_plan after optd | PhysicalProjection { exprs: [ #0, #1, #2, #3 ] } | | | └── PhysicalNestedLoopJoin { join_type: Cross, cond: true } | | | ├── PhysicalScan { table: t1 } | | | └── PhysicalScan { table: t1 } | | physical_plan after optd-join-order | (NLJ t1 t1) | | physical_plan after optd-all-join-orders | SAME TEXT AS ABOVE | | physical_plan after optd-all-logical-join-orders | (Join t1 t1) | | physical_plan | CollectorExec group_id=!17 | | | ProjectionExec: expr=[<expr>@0 as col0, <expr>@1 as col1, <expr>@2 as col2, <expr>@3 as col3] | | | CollectorExec group_id=!5 | | | CrossJoinExec | | | CollectorExec group_id=!1 | | | MemoryExec: partitions=1, partition_sizes=[0] | | | CollectorExec group_id=!1 | | | MemoryExec: partitions=1, partition_sizes=[0] | | | | +--------------------------------------------------+-------------------------------------------------------------------------------------------------+ ``` --------- Signed-off-by: Yuchen Liang <yuchenl3@andrew.cmu.edu>
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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