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avamingliavamingli
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fix unstable cte_prune
1 parent e520e30 commit 108422d

3 files changed

Lines changed: 99 additions & 93 deletions

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‎src/test/regress/expected/cte_prune.out‎

Lines changed: 45 additions & 46 deletions
Original file line numberDiff line numberDiff line change
@@ -1192,6 +1192,8 @@ HINT: The 'DISTRIBUTED BY' clause determines the distribution of data. Make sur
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CREATE TABLE t4 AS SELECT i as c, i+1 as d from generate_series(1,10)i;
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NOTICE: Table doesn't have 'DISTRIBUTED BY' clause -- Using column(s) named 'c' as the Apache Cloudberry data distribution key for this table.
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HINT: The 'DISTRIBUTED BY' clause determines the distribution of data. Make sure column(s) chosen are the optimal data distribution key to minimize skew.
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analyze t3;
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analyze t4;
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-- Additional filtering conditions are added to the consumer.
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-- This is caused by `PexprInferPredicates` in the ORCA preprocessor.
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explain verbose WITH t(a,b,d) AS
@@ -1206,91 +1208,88 @@ t WHERE cup.e < 10
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GROUP BY cup.c,cup.d, cup.e ,t.d, t.b
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ORDER BY 1,2,3,4
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LIMIT 10;
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QUERY PLAN
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----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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Limit (cost=102679372549802016.00..102679372549802016.00 rows=10 width=56)
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QUERY PLAN
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----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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Limit (cost=20000000031.26..20000000031.40 rows=10 width=56)
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Output: cup.c, cup.d, cup.e, (sum(t.d) OVER (?)), t.d, t.b
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-> Gather Motion 3:1 (slice1; segments: 3) (cost=102679372549802016.00..102679372549802016.00 rows=30 width=56)
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-> Gather Motion 3:1 (slice1; segments: 3) (cost=20000000031.26..20000000031.68 rows=30 width=56)
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Output: cup.c, cup.d, cup.e, (sum(t.d) OVER (?)), t.d, t.b
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Merge Key: cup.c, cup.d, cup.e, (sum(t.d) OVER (?))
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-> Limit (cost=102679372549802016.00..102679372549802016.00 rows=10 width=56)
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-> Limit (cost=20000000031.26..20000000031.28 rows=10 width=56)
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Output: cup.c, cup.d, cup.e, (sum(t.d) OVER (?)), t.d, t.b
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-> Sort (cost=102679372549802016.00..102679990317302016.00 rows=247107000000000 width=56)
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-> Sort (cost=20000000031.26..20000000031.54 rows=111 width=56)
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Output: cup.c, cup.d, cup.e, (sum(t.d) OVER (?)), t.d, t.b
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Sort Key: cup.c, cup.d, cup.e, (sum(t.d) OVER (?))
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-> WindowAgg (cost=102669708283873296.00..102674032656373296.00 rows=247107000000000 width=56)
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-> WindowAgg (cost=20000000026.91..20000000028.86 rows=111 width=56)
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Output: cup.c, cup.d, cup.e, sum(t.d) OVER (?), t.d, t.b
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Partition By: t.b
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-> Sort (cost=102669708283873296.00..102670326051373296.00 rows=247107000000000 width=48)
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-> Sort (cost=20000000026.91..20000000027.19 rows=111 width=48)
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Output: cup.c, cup.d, cup.e, t.d, t.b
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Sort Key: t.b
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-> Redistribute Motion 3:3 (slice2; segments: 3) (cost=102580688686565152.00..102601132929768272.00 rows=247107000000000 width=48)
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-> Redistribute Motion 3:3 (slice2; segments: 3) (cost=20000000018.14..20000000023.14 rows=111 width=48)
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Output: cup.c, cup.d, cup.e, t.d, t.b
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Hash Key: t.b
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-> HashAggregate (cost=102580688686565152.00..102596190789768272.00 rows=247107000000000 width=48)
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-> GroupAggregate (cost=20000000018.14..20000000020.92 rows=111 width=48)
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Output: cup.c, cup.d, cup.e, t.d, t.b
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Group Key: cup.c, cup.d, cup.e, t.d, t.b
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Planned Partitions: 512
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-> Redistribute Motion 3:3 (slice3; segments: 3) (cost=93196181903903024.00..102459992361252656.00 rows=741321000000000 width=48)
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-> Sort (cost=20000000018.14..20000000018.42 rows=111 width=48)
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Output: cup.c, cup.d, cup.e, t.d, t.b
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Hash Key: cup.c, cup.d, cup.e, t.d, t.b
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-> Streaming HashAggregate (cost=93196181903903024.00..102445165941252656.00 rows=741321000000000 width=48)
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Sort Key: cup.c, cup.d, cup.e, t.d, t.b
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-> Redistribute Motion 3:3 (slice3; segments: 3) (cost=20000000008.36..20000000014.36 rows=111 width=48)
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Output: cup.c, cup.d, cup.e, t.d, t.b
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Group Key: cup.c, cup.d, cup.e, t.d, t.b
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Planned Partitions: 512
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-> Nested Loop (cost=67221234716.02..7598699218584692.00 rows=525742695955889984 width=48)
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Hash Key: cup.c, cup.d, cup.e, t.d, t.b
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-> Nested Loop (cost=20000000008.36..20000000012.14 rows=111 width=48)
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Output: cup.c, cup.d, cup.e, t.d, t.b
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-> Broadcast Motion 3:3 (slice4; segments: 3) (cost=679.75..228161.25 rows=7413210 width=8)
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-> Broadcast Motion 3:3 (slice4; segments: 3) (cost=1.08..2.35 rows=10 width=8)
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Output: t.d, t.b
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-> Subquery Scan on t (cost=679.75..129318.45 rows=2471070 width=8)
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-> Subquery Scan on t (cost=1.08..2.22 rows=3 width=8)
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Output: t.d, t.b
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-> Hash Join (cost=679.75..129318.45 rows=2471070 width=12)
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-> Hash Join (cost=1.08..2.22 rows=3 width=12)
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Output: t3.a, t3.b, t4.d
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Hash Cond: (t4.d = t3.a)
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-> Redistribute Motion 3:3 (slice5; segments: 3) (cost=0.00..895.00 rows=28700 width=4)
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-> Redistribute Motion 3:3 (slice5; segments: 3) (cost=0.00..1.10 rows=3 width=4)
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Output: t4.d
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Hash Key: t4.d
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-> Seq Scan on cte_prune.t4 (cost=0.00..321.00 rows=28700 width=4)
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-> Seq Scan on cte_prune.t4 (cost=0.00..1.03 rows=3 width=4)
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Output: t4.d
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-> Hash (cost=321.00..321.00 rows=28700 width=8)
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-> Hash (cost=1.03..1.03 rows=3 width=8)
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Output: t3.a, t3.b
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-> Seq Scan on cte_prune.t3 (cost=0.00..321.00 rows=28700 width=8)
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-> Seq Scan on cte_prune.t3 (cost=0.00..1.03 rows=3 width=8)
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Output: t3.a, t3.b
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-> Materialize (cost=57221234036.27..64629019265.77 rows=70919709000 width=40)
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-> Materialize (cost=10000000007.29..10000000008.43 rows=11 width=40)
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Output: cup.c, cup.d, cup.e
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-> Subquery Scan on cup (cost=57221234036.27..64135905663.77 rows=70919709000 width=40)
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-> Subquery Scan on cup (cost=10000000007.29..10000000008.37 rows=11 width=40)
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Output: cup.c, cup.d, cup.e
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Filter: (cup.e < '10'::numeric)
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-> WindowAgg (cost=57221234036.27..61476416576.27 rows=212759127000 width=48)
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-> WindowAgg (cost=10000000007.29..10000000007.95 rows=33 width=48)
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Output: t4_1.c, t4_1.d, avg(t3_1.b) OVER (?), t3_1.b, t3_1.a
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Partition By: t3_1.a
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Order By: t3_1.b
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-> Sort (cost=57221234036.27..57753131853.77 rows=212759127000 width=16)
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-> Sort (cost=10000000007.29..10000000007.37 rows=33 width=16)
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Output: t3_1.b, t3_1.a, t4_1.c, t4_1.d
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Sort Key: t3_1.a, t3_1.b DESC
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-> Nested Loop (cost=10000000679.75..14463131602.75 rows=212759127000 width=16)
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-> Nested Loop (cost=10000000001.08..10000000006.44 rows=33 width=16)
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Output: t3_1.b, t3_1.a, t4_1.c, t4_1.d
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-> Broadcast Motion 3:3 (slice6; segments: 3) (cost=0.00..1469.00 rows=86100 width=8)
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Output: t4_1.c, t4_1.d
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-> Seq Scan on cte_prune.t4 t4_1 (cost=0.00..321.00 rows=28700 width=8)
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Output: t4_1.c, t4_1.d
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-> Materialize (cost=679.75..141673.80 rows=2471070 width=8)
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-> Hash Join (cost=1.08..2.22 rows=3 width=12)
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Output: t3_1.a, t3_1.b, t4_2.d
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-> Hash Join (cost=679.75..129318.45 rows=2471070 width=12)
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Output: t3_1.a, t3_1.b, t4_2.d
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Hash Cond: (t4_2.d = t3_1.a)
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-> Redistribute Motion 3:3 (slice7; segments: 3) (cost=0.00..895.00 rows=28700 width=4)
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Hash Cond: (t4_2.d = t3_1.a)
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-> Redistribute Motion 3:3 (slice6; segments: 3) (cost=0.00..1.10 rows=3 width=4)
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Output: t4_2.d
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Hash Key: t4_2.d
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-> Seq Scan on cte_prune.t4 t4_2 (cost=0.00..1.03 rows=3 width=4)
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Output: t4_2.d
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Hash Key: t4_2.d
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-> Seq Scan on cte_prune.t4 t4_2 (cost=0.00..321.00 rows=28700 width=4)
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Output: t4_2.d
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-> Hash (cost=321.00..321.00 rows=28700 width=8)
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-> Hash (cost=1.03..1.03 rows=3 width=8)
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Output: t3_1.a, t3_1.b
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-> Seq Scan on cte_prune.t3 t3_1 (cost=0.00..1.03 rows=3 width=8)
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Output: t3_1.a, t3_1.b
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-> Seq Scan on cte_prune.t3 t3_1 (cost=0.00..321.00 rows=28700 width=8)
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Output: t3_1.a, t3_1.b
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Settings: enable_parallel = 'off', optimizer = 'off'
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-> Materialize (cost=0.00..1.22 rows=10 width=8)
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Output: t4_1.c, t4_1.d
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-> Broadcast Motion 3:3 (slice7; segments: 3) (cost=0.00..1.17 rows=10 width=8)
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Output: t4_1.c, t4_1.d
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-> Seq Scan on cte_prune.t4 t4_1 (cost=0.00..1.03 rows=3 width=8)
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Output: t4_1.c, t4_1.d
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Optimizer: Postgres query optimizer
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(82 rows)
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(80 rows)
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WITH t(a,b,d) AS
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(

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