递归查询两种写法的性能差异
对于递归查询" title="递归查询">递归查询，KINGBASE用户可以选择使用connect by ，或者使用 with recursive 。下面，我们以例子来看下二者的差别。
一、构造数据
create table test_recursive(id integer,pid integer,name varchar,description text);insert into test_recursive(id,name,description) select generate_series(1,100000),"a"||generate_series(1,100000),repeat("desc",500);
update test_recursive set pid=1 where id between 2 and 10;
update test_recursive set pid=mod(id,9)+2 where id between 11 and 100;
update test_recursive set pid=mod(id,90)+11 where id between 101 and 1000;
update test_recursive set pid=mod(id,900)+101 where id between 1001 and 10000;
update test_recursive set pid=mod(id,9000)+1001 where id between 10001 and 100000;
create table test_recursive_random(id integer,pid integer,name varchar,description text);
insert into test_recursive_random select * from test_recursive order by random;
create index ind_test_recursive_random_id on test_recursive_random(id);
create index ind_test_recursive_random_pid on test_recursive_random(pid);
vacuum full test_recursive_random;
analyze test_recursive_random;
create index ind_test_recursive_id on test_recursive(id);
create index ind_test_recursive_pid on test_recursive(pid);
vacuum full test_recursive;
analyze test_recursive;
本例子构造了5层的数据，有排序与非排序两种数据。
二、使用connect by
connect by的查询性能：用时 746ms
test=# explain analyze select id,pid,name from test_recursive start with id=1 connect by prior id = pid ;QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------------------
Recursive Union (cost=0.29..422.37 rows=101 width=14) (actual time=0.038..728.281 rows=100000 loops=1)
-> Index Scan using ind_test_recursive_id on test_recursive (cost=0.29..8.31 rows=1 width=14) (actual time=0.015..0.017 rows=1 loops=1)
Index Cond: (id = 1)
-> Nested Loop (cost=0.42..41.30 rows=10 width=14) (actual time=0.002..0.003 rows=1 loops=100000)
-> WorkTable Scan on "connect" (cost=0.00..0.02 rows=1 width=4) (actual time=0.000..0.000 rows=1 loops=100000)
-> Index Scan using ind_test_recursive_pid on test_recursive (cost=0.42..41.18 rows=10 width=14) (actual time=0.002..0.002 rows=1 loops=100000)
Index Cond: (pid = (PRIOR test_recursive.id))
Planning Time: 0.185 ms
Execution Time: 746.102 ms
(9 rows)
三、Kingbase with recursive 查询
1、排序数据：用时302ms
explain analyze with recursive tmp1 as (select id,pid,name from test_recursive where id=1
union all
select a.id,a.pid,a.name from test_recursive a inner join tmp1 b on a.pid=b.id )
select * from tmp1;
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------------------------------------------
CTE Scan on tmp1 (cost=4013.94..4033.96 rows=1001 width=40) (actual time=0.020..297.856 rows=100000 loops=1)
CTE tmp1
-> Recursive Union (cost=0.29..4013.94 rows=1001 width=14) (actual time=0.018..257.298 rows=100000 loops=1)
-> Index Scan using ind_test_recursive_id on test_recursive (cost=0.29..8.31 rows=1 width=14) (actual time=0.016..0.018 rows=1 loops=1)
Index Cond: (id = 1)
-> Nested Loop (cost=0.42..398.56 rows=100 width=14) (actual time=20.529..38.777 rows=16666 loops=6)
-> WorkTable Scan on tmp1 b (cost=0.00..0.20 rows=10 width=4) (actual time=0.003..2.150 rows=16667 loops=6)
-> Index Scan using ind_test_recursive_pid on test_recursive a (cost=0.42..39.74 rows=10 width=14) (actual time=0.001..0.002 rows=1 loops=100000)
Index Cond: (pid = b.id)
Planning Time: 0.207 ms
Execution Time: 302.244 ms
(11 rows)
2、非排序数据：440ms
test=# explain analyze with recursive tmp1 as (test(# select id,pid,name from test_recursive_random where id=1
test(# union all
test(# select a.id,a.pid,a.name from test_recursive_random a inner join tmp1 b on a.pid=b.id )
test-# select * from tmp1;
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
CTE Scan on tmp1 (cost=4206.87..4226.89 rows=1001 width=40) (actual time=0.020..434.721 rows=100000 loops=1)
CTE tmp1
-> Recursive Union (cost=0.29..4206.87 rows=1001 width=14) (actual time=0.018..397.456 rows=100000 loops=1)
-> Index Scan using ind_test_recursive_random_id on test_recursive_random (cost=0.29..8.31 rows=1 width=14) (actual time=0.017..0.018 rows=1 loops=1)
Index Cond: (id = 1)
-> Nested Loop (cost=4.50..417.85 rows=100 width=14) (actual time=33.080..62.311 rows=16666 loops=6)
-> WorkTable Scan on tmp1 b (cost=0.00..0.20 rows=10 width=4) (actual time=0.007..2.412 rows=16667 loops=6)
-> Bitmap Heap Scan on test_recursive_random a (cost=4.50..41.67 rows=10 width=14) (actual time=0.002..0.003 rows=1 loops=100000)
Recheck Cond: (pid = b.id)
Heap Blocks: exact=99557
-> Bitmap Index Scan on ind_test_recursive_random_pid (cost=0.00..4.49 rows=10 width=0) (actual time=0.001..0.001 rows=1 loops=100000)
Index Cond: (pid = b.id)
Planning Time: 0.304 ms
Execution Time: 439.563 ms
(14 rows)
3、使用hash join：260ms
test=# set enable_nestloop=off;SET
test=# explain analyze with recursive tmp1 as (
test(# select id,pid,name from test_recursive where id=1
test(# union all
test(# select a.id,a.pid,a.name from test_recursive a inner join tmp1 b on a.pid=b.id )
test-# select * from tmp1;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------------
CTE Scan on tmp1 (cost=24101.58..24121.60 rows=1001 width=40) (actual time=0.018..255.766 rows=100000 loops=1)
CTE tmp1
-> Recursive Union (cost=0.29..24101.58 rows=1001 width=14) (actual time=0.016..218.427 rows=100000 loops=1)
-> Index Scan using ind_test_recursive_id on test_recursive (cost=0.29..8.31 rows=1 width=14) (actual time=0.015..0.017 rows=1 loops=1)
Index Cond: (id = 1)
-> Hash Join (cost=0.33..2407.32 rows=100 width=14) (actual time=13.828..32.571 rows=16666 loops=6)
Hash Cond: (a.pid = b.id)
-> Seq Scan on test_recursive a (cost=0.00..2031.00 rows=100000 width=14) (actual time=0.005..8.240 rows=100000 loops=6)
-> Hash (cost=0.20..0.20 rows=10 width=4) (actual time=5.114..5.114 rows=16667 loops=6)
Buckets: 131072 (originally 1024) Batches: 2 (originally 1) Memory Usage: 3073kB
-> WorkTable Scan on tmp1 b (cost=0.00..0.20 rows=10 width=4) (actual time=0.004..2.068 rows=16667 loops=6)
Planning Time: 0.196 ms
Execution Time: 260.360 ms
(13 rows)
四、执行计划差异分析
- connect by 查询执行逻辑：查询是通过 pid = prior id ，也就是将前条记录的 id 作为值，传给 pid 进行索引扫描。逻辑上可以看做是逐个分支查询，上个分支查询结束，再进行下个分支扫描。loop = 100000，就是表示针对每条记录，都要访问一次索引。
- with recursive 查询逻辑：是按层次查询，上层结果都返回后，再执行下层查询。每层可以根据所有ctid进行排序，也就是 Bitmap Index Scan，将所有ctid都返回，排序，再访问表，效率提高。另外，由于是每层数据返回后，再去关联查找下层数据，可以使用hash join，提升访问效率。 rows=16666 loop = 6，表示需要访问6个批次，每次平均 16666 条记录。
五、Oracle connect by 查询性能
以下是同样数据量的情况下，Oracle connect by 查询的性能：
SQL> select id,pid,name from test_recursive start with id=1 connect by prior id = pid ;100000 rows selected.
Elapsed: 00:00:00.98
Execution Plan
----------------------------------------------------------
Plan hash value: 2099392185
----------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
----------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 12 | 384 | 18 (12)| 00:00:01 |
|* 1 | CONNECT BY WITH FILTERING | | | | | |
| 2 | TABLE ACCESS BY INDEX ROWID BATCHED | TEST_RECURSIVE | 1 | 32 | 2 (0)| 00:00:01 |
|* 3 | INDEX RANGE SCAN | IND_TEST_RECURSIVE_ID | 1 | | 1 (0)| 00:00:01 |
| 4 | NESTED LOOPS | | 11 | 495 | 14 (0)| 00:00:01 |
| 5 | CONNECT BY PUMP | | | | | |
| 6 | TABLE ACCESS BY INDEX ROWID BATCHED| TEST_RECURSIVE | 11 | 352 | 12 (0)| 00:00:01 |
|* 7 | INDEX RANGE SCAN | IND_TEST_RECURSIVE_PID | 11 | | 1 (0)| 00:00:01 |
----------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - access("PID"=PRIOR "ID")
3 - access("ID"=1)
7 - access("connect$_by$_pump$_002"."prior id "="PID")
Note
-----
- dynamic statistics used: dynamic sampling (level=2)
- this is an adaptive plan
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
101983 consistent gets
0 physical reads
0 redo size
2337649 bytes sent via SQL*Net to client
73769 bytes received via SQL*Net from client
6668 SQL*Net roundtrips to/from client
8 sorts (memory)
0 sorts (disk)
100000 rows processed
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