Help Center/ GaussDB/ Feature Guide(Centralized_8.x)/ Partitioned Table/ Partitioned Table Query Optimization/ Partition Pruning/ Dynamic Partition Pruning/ Dynamic Parameterized Path Pruning
Updated on 2024-06-07 GMT+08:00
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Dynamic Parameterized Path Pruning

Dynamic parameterized path pruning is supported in the following scenarios:
  1. Supported partitioning levels: level-1 partition and level-2 partition
  2. Supported partitioning types: range partitioning, interval partitioning, hash partitioning, and list partitioning.
  3. Supported operator types: index scan, index-only scan, and bitmap scan.
  4. Supported expression types: comparison expression (<, <=, =, >=, >) and logical expression.

Dynamic parameterized path pruning does not support subquery expressions, STABLE and VOLATILE functions, cross-QueryBlock parameterized paths, BitmapOr operator, or BitmapAnd operator.

  • Typical scenarios where dynamic parameterized path pruning is supported are as follows:
    1. Comparison expressions 
      gaussdb=#
-- Create partitioned tables and indexes.
gaussdb=# CREATE TABLE t1 (c1 INT, c2 INT)
PARTITION BY RANGE (c1)
(
    PARTITION p1 VALUES LESS THAN(10),
    PARTITION p2 VALUES LESS THAN(20),
    PARTITION p3 VALUES LESS THAN(MAXVALUE)
);
CREATE TABLE t2 (c1 INT, c2 INT)
PARTITION BY RANGE (c1)
(
    PARTITION p1 VALUES LESS THAN(10),
    PARTITION p2 VALUES LESS THAN(20),
    PARTITION p3 VALUES LESS THAN(MAXVALUE)
);
CREATE INDEX t1_c1 ON t1(c1) LOCAL;
CREATE INDEX t2_c1 ON t2(c1) LOCAL;
CREATE INDEX t1_c2 ON t1(c2) LOCAL;
CREATE INDEX t2_c2 ON t2(c2) LOCAL;

gaussdb=# EXPLAIN (VERBOSE ON, COSTS OFF) SELECT * FROM t2 JOIN t1 ON t1.c1 = t2.c2;
                         QUERY PLAN                          
-------------------------------------------------------------
 Hash Join
   Output: t2.c1, t2.c2, t1.c1, t1.c2
   Hash Cond: (t2.c2 = t1.c1)
   ->  Partition Iterator
         Output: t2.c1, t2.c2
         Iterations: 3
         ->  Partitioned Seq Scan on public.t2
               Output: t2.c1, t2.c2
               Selected Partitions:  1..3
   ->  Hash
         Output: t1.c1, t1.c2
         ->  Partition Iterator
               Output: t1.c1, t1.c2
               Iterations: 3
               ->  Partitioned Seq Scan on public.t1
                     Output: t1.c1, t1.c2
                     Selected Partitions:  1..3
(17 rows)

gaussdb=# EXPLAIN (VERBOSE ON, COSTS OFF) SELECT * FROM t2 JOIN t1 ON t1.c1 < t2.c2;
                         QUERY PLAN                          
-------------------------------------------------------------
 Nested Loop
   Output: t2.c1, t2.c2, t1.c1, t1.c2
   ->  Partition Iterator
         Output: t2.c1, t2.c2
         Iterations: 3
         ->  Partitioned Seq Scan on public.t2
               Output: t2.c1, t2.c2
               Selected Partitions:  1..3
   ->  Partition Iterator
         Output: t1.c1, t1.c2
         Iterations: PART
         ->  Partitioned Index Scan using t2_c1 on public.t1
               Output: t1.c1, t1.c2
               Index Cond: (t1.c1 < t2.c2)
               Selected Partitions:  1..3 (ppi-pruning)
(15 rows)

gaussdb=# EXPLAIN (VERBOSE ON, COSTS OFF) SELECT * FROM t2 JOIN t1 ON t1.c1 > t2.c2;
                         QUERY PLAN                          
-------------------------------------------------------------
 Nested Loop
   Output: t2.c1, t2.c2, t1.c1, t1.c2
   ->  Partition Iterator
         Output: t2.c1, t2.c2
         Iterations: 3
         ->  Partitioned Seq Scan on public.t2
               Output: t2.c1, t2.c2
               Selected Partitions:  1..3
   ->  Partition Iterator
         Output: t1.c1, t1.c2
         Iterations: PART
         ->  Partitioned Index Scan using t2_c1 on public.t1
               Output: t1.c1, t1.c2
               Index Cond: (t1.c1 > t2.c2)
               Selected Partitions:  1..3 (ppi-pruning)
(15 rows)
    2. Logical expressions 
      gaussdb=# EXPLAIN (VERBOSE ON, COSTS OFF) SELECT * FROM t2 JOIN t1 ON t1.c1 = t2.c2 AND t1.c2 = 2;
                         QUERY PLAN                          
-------------------------------------------------------------
 Hash Join
   Output: t2.c1, t2.c2, t1.c1, t1.c2
   Hash Cond: (t2.c2 = t1.c1)
   ->  Partition Iterator
         Output: t2.c1, t2.c2
         Iterations: 3
         ->  Partitioned Seq Scan on public.t2
               Output: t2.c1, t2.c2
               Selected Partitions:  1..3
   ->  Hash
         Output: t1.c1, t1.c2
         ->  Partition Iterator
               Output: t1.c1, t1.c2
               Iterations: 3
               ->  Partitioned Bitmap Heap Scan on public.t1
                     Output: t1.c1, t1.c2
                     Recheck Cond: (t1.c2 = 2)
                     Selected Partitions:  1..3
                     ->  Partitioned Bitmap Index Scan on t1_c2
                           Index Cond: (t1.c2 = 2)
(20 rows)
  • Typical scenarios where dynamic parameterized path pruning is not supported are as follows:
    1. BitmapOr and BitmapAnd operators 
      gaussdb=# set enable_seqscan=off;
gaussdb=# EXPLAIN (VERBOSE ON, COSTS OFF) SELECT * FROM t2 JOIN t1 ON t1.c1 = t2.c2 OR t1.c1 = 2;
                           QUERY PLAN                            
-----------------------------------------------------------------
 Nested Loop
   Output: t2.c1, t2.c2, t1.c1, t1.c2
   ->  Partition Iterator
         Output: t2.c1, t2.c2
         Iterations: 3
         ->  Partitioned Seq Scan on public.t2
               Output: t2.c1, t2.c2
               Selected Partitions:  1..3
   ->  Partition Iterator
         Output: t1.c1, t1.c2
         Iterations: 3
         ->  Partitioned Bitmap Heap Scan on public.t1
               Output: t1.c1, t1.c2
               Recheck Cond: ((t1.c1 = t2.c2) OR (t1.c1 = 2))
               Selected Partitions:  1..3
               ->  BitmapOr
                     ->  Partitioned Bitmap Index Scan on t1_c1
                           Index Cond: (t1.c1 = t2.c2)
                     ->  Partitioned Bitmap Index Scan on t1_c1
                           Index Cond: (t1.c1 = 2)
(20 rows)
    2. Implicit conversion 
      gaussdb=# CREATE TABLE t3(c1 TEXT, c2 INT);
CREATE TABLE
gaussdb=# EXPLAIN (VERBOSE ON, COSTS OFF) SELECT * FROM t1 JOIN t3 ON t1.c1 = t3.c1;
                         QUERY PLAN                          
-------------------------------------------------------------
 Nested Loop
   Output: t1.c1, t1.c2, t3.c1, t3.c2
   ->  Seq Scan on public.t3
         Output: t3.c1, t3.c2
   ->  Partition Iterator
         Output: t1.c1, t1.c2
         Iterations: 3
         ->  Partitioned Index Scan using t1_c1 on public.t1
               Output: t1.c1, t1.c2
               Index Cond: (t1.c1 = (t3.c1)::bigint)
               Selected Partitions:  1..3
(11 rows)
    3. Functions 
      gaussdb=# EXPLAIN (VERBOSE ON, COSTS OFF) SELECT * FROM t1 JOIN t3 ON t1.c1 = LENGTHB(t3.c1);
                         QUERY PLAN                          
-------------------------------------------------------------
 Nested Loop
   Output: t1.c1, t1.c2, t3.c1, t3.c2
   ->  Seq Scan on public.t3
         Output: t3.c1, t3.c2
   ->  Partition Iterator
         Output: t1.c1, t1.c2
         Iterations: 3
         ->  Partitioned Index Scan using t1_c1 on public.t1
               Output: t1.c1, t1.c2
               Index Cond: (t1.c1 = lengthb(t3.c1))
               Selected Partitions:  1..3
(11 rows)

gaussdb=# DROP TABLE t1;
gaussdb=# DROP TABLE t2;
gaussdb=# DROP TABLE t3;