对Hash Join的一次优化之--10104 trace-bucket

博客原地址：http://www.itpub.net/thread-955209-1-1.html

前两天解决了一个优化SQL的case，SQL语句如下，big_table为150G大小，small_table很小，9000多条记录，不到1M大小

hash_area_size, sort_area_size均设置足够大，可以进行optimal hash join和memory sort

select /*+ leading(b) use_hash(a b) */ distinct a.ID

from
BIG_TABLE a, SMALL_TABLE b

where (a.category = b.from_cat or

a.category2 = b.from_cat) and

a.site_id = b.site_id and

a.sale_end >= sysdate;

[PHP]

--------------------------------------------------------------------------

| Id | Operation | Name | Rows | Bytes | Cost (%CPU)|

--------------------------------------------------------------------------

| 0 | SELECT STATEMENT | | 2 | 174 | 18 (17)|

| 1 | SORT UNIQUE | | 2 | 174 | 18 (17)|

|* 2 | HASH JOIN | | 2 | 174 | 17 (12)|

| 3 | TABLE ACCESS FULL | SMALL_TABLE | 1879 | 48854 | 14 (8)|

|* 4 | TABLE ACCESS FULL | BIG_TABLE | 4 | 244 | 3 (34)|

--------------------------------------------------------------------------

[/PHP]

粗略来看，PLAN非常的完美，SQL HINT写的也很到位，小表在内build hash table,大表在外进行probe操作，

根据经验来看，整个SQL执行的时间应该和FTS BIG_TABLE的时间差不多

但是FTS BIG_TABLE的时间大约是8分钟，而真个SQL执行的时间长达3~4小时

那么问题究竟出在哪里?

FTS时间应该不会有太大变化，那么问题应该在hash join，设置event来trace一下hash join的过程。

SQL> alter session set events '10104 trace name context forever, level 2';

Session altered.

select /*+ leading(b) use_hash(a b) */ distinct a.ID

from BIG_TABLE a, SMALL_TABLE b

where (a.category = b.from_cat or

a.category2 = b.from_cat) and

a.site_id = b.site_id and

a.sale_end >= sysdate;

从trace file中Hash Table中这一段找出了问题所在：

### Hash table ###

# NOTE: The calculated number of rows in non-empty buckets may be smaller

# than the true number.

Number of buckets with 0 rows: 16373

Number of buckets with 1 rows: 0

Number of buckets with 2 rows: 0

Number of buckets with 3 rows: 1

Number of buckets with 4 rows: 0

Number of buckets with 5 rows: 0

Number of buckets with 6 rows: 0

Number of buckets with 7 rows: 1

Number of buckets with 8 rows: 0

Number of buckets with 9 rows: 0

Number of buckets with between 10 and 19 rows: 1

Number of buckets with between 20 and 29 rows: 1

Number of buckets with between 30 and 39 rows: 3

Number of buckets with between 40 and 49 rows: 0

Number of buckets with between 50 and 59 rows: 0

Number of buckets with between 60 and 69 rows: 0

Number of buckets with between 70 and 79 rows: 0

Number of buckets with between 80 and 89 rows: 0

Number of buckets with between 90 and 99 rows: 0

Number of buckets with 100 or more rows: 4

### Hash table overall statistics ###

Total buckets: 16384 Empty buckets: 16373 Non-empty buckets: 11

Total number of rows: 9232

Maximum number of rows in a bucket: 2531

Average number of rows in non-empty buckets: 839.272705

仔细看，在一个bucket中最多的行数竟然有2531行，因为bucket中是一个链表的结构，所以这几千行都是串在一个链表上。

由这一点想到这个Hash Table所依赖的hash key的distinct value可能太少，重复值太多。否则不应该会有这么多行在同一个bucket里面。

因为Join条件里面有两个列from_cat和site_id,穷举法有三种情况

1. Build hash table based on (from_cat,site_id):

SQL> select site_id,from_cat,count(*) from SMALL_TABLE group by site_id,from_cat having count(*)>100;

no rows selected

2. Build hash table based on (from_cat):

SQL> select from_cat,count(*) from SMALL_TABLE group by from_cat having count(*)>100;

no rows selected

3. Build hash table based on (site_id):

SQL> select site_id,count(*) from SMALL_TABLE group by site_id having count(*)>100;

SITE_ID COUNT(*)

---------- ----------

0 2531

2 2527

146 1490

210 2526

到这里可以发现，基于site_id这种情况和trace file中这两行很相符：

Number of buckets with 100 or more rows: 4

Maximum number of rows in a bucket: 2531

所以推断这个hash table是基于site_id而建的，而Big_Table中大量的行site_id=0，都落在这个linked list最长的bucket中.

而大部分行都会扫描完整个链表而最后被丢弃掉,所以这个Hash Join的操作效率非常差，几乎变为了Nest Loop操作

找到了根本原因，问题也就迎刃而解了。

理想状况下，hash table应当建立于(site_id,from_cat)上，那么问题肯定出在这个OR上，把OR用UNION改写

select /*+ leading(b) use_hash(a b) */ distinct a.ID

from BIG_TABLE a, SMALL_TABLE b

where a.category = b.from_cat and

a.site_id = b.site_id and

a.sale_end >= sysdate

UNION

select /*+ leading(b) use_hash(a b) */ distinct a.ID

from BIG_TABLE a, SMALL_TABLE b

where a.category2 = b.from_cat and

a.site_id = b.site_id and

a.sale_end >= sysdate;

[PHP]

--------------------------------------------------------------------------

| Id | Operation | Name | Rows | Bytes | Cost (%CPU)|

--------------------------------------------------------------------------

| 0 | SELECT STATEMENT | | 2 | 148 | 36 (59)|

| 1 | SORT UNIQUE | | 2 | 148 | 36 (59)|

| 2 | UNION-ALL | | | | |

|* 3 | HASH JOIN | | 1 | 74 | 17 (12)|

| 4 | TABLE ACCESS FULL| SMALL_TABLE | 1879 | 48854 | 14 (8)|

|* 5 | TABLE ACCESS FULL| BIG_TABLE | 4 | 192 | 3 (34)|

|* 6 | HASH JOIN | | 1 | 74 | 17 (12)|

| 7 | TABLE ACCESS FULL| SMALL_TABLE | 1879 | 48854 | 14 (8)|

|* 8 | TABLE ACCESS FULL| BIG_TABLE | 4 | 192 | 3 (34)|

--------------------------------------------------------------------------

[/PHP]

初看这个PLAN好像不如第一个PLAN，因为执行了两次BIG_TABLE的FTS，但是让我们在来看看HASH TABLE的结构

### Hash table ###

# NOTE: The calculated number of rows in non-empty buckets may be smaller

# than the true number.

Number of buckets with 0 rows: 9306

Number of buckets with 1 rows: 5310

Number of buckets with 2 rows: 1436

Number of buckets with 3 rows: 285

Number of buckets with 4 rows: 43

Number of buckets with 5 rows: 4

Number of buckets with 6 rows: 0

Number of buckets with 7 rows: 0

Number of buckets with 8 rows: 0

Number of buckets with 9 rows: 0

Number of buckets with between 10 and 19 rows: 0

Number of buckets with between 20 and 29 rows: 0

Number of buckets with between 30 and 39 rows: 0

Number of buckets with between 40 and 49 rows: 0

Number of buckets with between 50 and 59 rows: 0

Number of buckets with between 60 and 69 rows: 0

Number of buckets with between 70 and 79 rows: 0

Number of buckets with between 80 and 89 rows: 0

Number of buckets with between 90 and 99 rows: 0

Number of buckets with 100 or more rows: 0

### Hash table overall statistics ###

Total buckets: 16384 Empty buckets: 9306 Non-empty buckets: 7078

Total number of rows: 9232

Maximum number of rows in a bucket: 5

Average number of rows in non-empty buckets: 1.304323

这就是我们所需要的Hash Table，最长的链表只有五行数据

整个SQL的执行时间从三四个小时缩短为16分钟，大大超出了developer的预期

这个SQL单纯从PLAN上很难看出问题所在，需要了解Hash Join的机制，进行更深一步的分析