循序渐进讲解Oracle数据库的Hash join

Oracle Hash join 是一种非常高效的join 算法，主要以CPU（hash计算）和内存空间（创建hash table）为代价获得最大的效率。Hash join一般用于大表和小表之间的连接，我们将小表构建到内存中，称为Hash cluster，大表称为PRobe表。

效率

Hash join具有较高效率的两个原因：

1.Hash 查询，根据映射关系来查询值，不需要遍历整个数据结构。

2.Mem 访问速度是Disk的万倍以上。

理想化的Hash join的效率是接近对大表的单表选择扫描的。

首先我们来比较一下，几种join之间的效率，首先 optimizer会自动选择使用hash join。

注意到Cost= 221

SQL> select * from vendition t,customer b WHERE t.customerid = b.customerid;

100000 rows selected.

Execution Plan

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

Plan hash value: 3402771356

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

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

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

| 0 | SELECT STATEMENT | | 106K| 22M| 221 (3)| 00:00:03 |

|* 1 | HASH JOIN | | 106K| 22M| 221 (3)| 00:00:03 |

| 2 | TABLE access FULL| CUSTOMER | 5000 | 424K| 9 (0)| 00:00:01 |

| 3 | TABLE ACCESS FULL| VENDITION | 106K| 14M| 210 (2)| 00:00:03 |

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

不使用hash，这时optimizer自动选择了merge join。。

注意到Cost=3507大大的增加了。

SQL> select /*+ USE_MERGE (t b) */* from vendition t,customer b WHERE t.customerid = b.customerid;

100000 rows selected.

Execution Plan

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

Plan hash value: 1076153206

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

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

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

| 0 | SELECT STATEMENT | | 106K| 22M| | 3507 (1)| 00:00:43 |

| 1 | MERGE JOIN | | 106K| 22M| | 3507 (1)| 00:00:43 |

| 2 | SORT JOIN | | 5000 | 424K| | 10 (10)| 00:00:01 |

| 3 | TABLE ACCESS FULL| CUSTOMER | 5000 | 424K| | 9 (0)| 00:00:01 |

|* 4 | SORT JOIN | | 106K| 14M| 31M| 3496 (1)| 00:00:42 |

| 5 | TABLE ACCESS FULL| VENDITION | 106K| 14M| | 210 (2)| 00:00:03 |

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

那么Nest loop呢，经过漫长的等待后，发现Cost达到了惊人的828K，同时伴随3814337 consistent gets（由于没有建索引），可见在这个测试中，Nest loop是最低效的。在给customerid建立唯一索引后，减低到106K，但仍然是内存join的上千倍。

SQL> select /*+ USE_NL(t b) */* from vendition t,customer b WHERE t.customerid = b.customerid;

100000 rows selected.

Execution Plan

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

Plan hash value: 2015764663

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

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

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

| 0 | SELECT STATEMENT | | 106K| 22M| 828K (2)| 02:45:41 |

| 1 | NESTED LOOPS | | 106K| 22M| 828K (2)| 02:45:41 |

| 2 | TABLE ACCESS FULL| VENDITION | 106K| 14M| 210 (2)| 00:00:03 |

|* 3 | TABLE ACCESS FULL| CUSTOMER | 1 | 87 | 8 (0)| 00:00:01 |

HASH的内部

HASH_AREA_SIZE在Oracle 9i 和以前，都是影响hash join性能的一个重要的参数。但是在10g发生了一些变化。Oracle不建议使用这个参数，除非你是在MTS模式下。Oracle建议采用自动PGA管理（设置PGA_AGGREGATE_TARGET和WORKAREA_SIZE_POLICY）来，替代使用这个参数。由于我的测试环境是mts环境，自动内存管理，所以我在这里只讨论mts下的hash join。

Mts的PGA中，只包含了一些栈空间信息，UGA则包含在large pool中，那么实际类似hash，sort，merge等操作都是有large pool来分配空间，large pool同时也是auto管理的，它和SGA_TARGET有关。所以在这种条件下，内存的分配是很灵活。

Hash连接根据内存分配的大小，可以有三种不同的效果：

1.optimal 内存完全足够

2.onepass 内存不能装载完小表

3.multipass workarea executions 内存严重不足

下面，分别测试小表为50行，500行和5000行，内存的分配情况（内存都能完全转载）。

Vendition表 10W条记录

Customer表 5000

Customer_small 500，去Customer表前500行建立

Customer_pity 50，取Customer表前50行建立

表的统计信息如下：

SQL> SELECT s.table_name,S.BLOCKS,S.AVG_SPACE,S.NUM_ROWS,S.AVG_ROW_LEN,S.EMPTY_BLOCKS FROM user_tables S WHERE table_name IN ('CUSTOMER','VENDITION','CUSTOMER_SMALL','CUSTOMER_PITY') ;

TABLE_NAME BLOCKS AVG_SPACE NUM_ROWS AVG_ROW_LEN EMPTY_BLOCKS

CUSTOMER 35 1167 5000 38 5

CUSTOMER_PITY 4 6096 50 37 4

CUSTOMER_SMALL 6 1719 500 36 2

VENDITION 936 1021 100000 64 88打开10104事件追踪：（hash 连接追踪）

ALTER SYSTEM SET EVENTS ‘ 10104 TRACE NAME CONTEXT,LEVEL 2’;

测试SQL

SELECT * FROM vendition a,customer b WHERE a.customerid = b.customerid;

SELECT * FROM vendition a,customer_small b WHERE a.customerid = b.customerid;

SELECT * FROM vendition a,customer_pity b WHERE a.customerid = b.customerid;

小表50行时候的trace分析：

*** 2008-03-23 18:17:49.467

*** session ID:(773.23969) 2008-03-23 18:17:49.467

kxhfInit(): enter

kxhfInit(): exit

*** RowSrcId: 1 HASH JOIN STATISTICS (INITIALIZATION) ***

Join Type: INNER join

Original hash-area size: 3883510

PS:hash area的大小，大约380k，本例中最大的表也不过250块左右，所以内存完全可以完全装载

Memory for slot table: 2826240

Calculated overhead for partitions and row/slot managers: 1057270

Hash-join fanout: 8

Number of partitions: 8

PS:hash 表数据连一个块都没装满，Oracle仍然对数据进行了分区，这里和以前在一些文档上看到的，当内存不足时才会对数据分区的说法，发生了变化。

Number of slots: 23

Multiblock IO: 15

Block size(KB): 8

Cluster (slot) size(KB): 120

PS:分区中全部行占有的cluster的size

Minimum number of bytes per block: 8160

Bit vector memory allocation(KB): 128

Per partition bit vector length(KB): 16

Maximum possible row length: 270

Estimated build size (KB): 0

Estimated Build Row Length (includes overhead): 45

# Immutable Flags:

Not BUFFER(execution) output of the join for PQ

Evaluate Left Input Row Vector

Evaluate Right Input Row Vector

# Mutable Flags:

IO sync

kxhfSetPhase: phase=BUILD

kxhfAddChunk: add chunk 0 (sz=32) to slot table

kxhfAddChunk: chunk 0 (lbs=0x2a97825c38, slotTab=0x2a97825e00) successfuly added

kxhfSetPhase: phase=PROBE_1

qerhjFetch: max build row length (mbl=44)

*** RowSrcId: 1 END OF HASH JOIN BUILD (PHASE 1) ***

Revised row length: 45

Revised build size: 2KB

kxhfResize(enter): resize to 12 slots (numAlloc=8, max=23)

kxhfResize(exit): resized to 12 slots (numAlloc=8, max=12)

Slot table resized: old=23 wanted=12 got=12 unload=0

*** RowSrcId: 1 HASH JOIN BUILD HASH TABLE (PHASE 1) ***

Total number of partitions: 8

Number of partitions which could fit in memory: 8

Number of partitions left in memory: 8

Total number of slots in in-memory partitions: 8

Total number of rows in in-memory partitions: 50

(used as preliminary number of buckets in hash table)

Estimated max # of build rows that can fit in avail memory: 66960

### Partition Distribution ###

Partition:0 rows:5 clusters:1 slots:1 kept=1

Partition:1 rows:6 clusters:1 slots:1 kept=1

Partition:2 rows:4 clusters:1 slots:1 kept=1

Partition:3 rows:9 clusters:1 slots:1 kept=1

Partition:4 rows:5 clusters:1 slots:1 kept=1

Partition:5 rows:9 clusters:1 slots:1 kept=1

Partition:6 rows:4 clusters:1 slots:1 kept=1

Partition:7 rows:8 clusters:1 slots:1 kept=1

PS:每个分区只有不到10行，这里有一个重要的参数Kept，1在内存中，0在磁盘

*** (continued) HASH JOIN BUILD HASH TABLE (PHASE 1) ***

PS:hash join的第一阶段，但是要观察更多的阶段，需提高trace的level，这里略过

Revised number of hash buckets (after flushing): 50

Allocating new hash table.

*** (continued) HASH JOIN BUILD HASH TABLE (PHASE 1) ***

Requested size of hash table: 16

Actual size of hash table: 16

Number of buckets: 128

Match bit vector allocated: FALSE

kxhfResize(enter): resize to 14 slots (numAlloc=8, max=12)

kxhfResize(exit): resized to 14 slots (numAlloc=8, max=14)

freeze work area size to: 2359K (14 slots)

*** (continued) HASH JOIN BUILD HASH TABLE (PHASE 1) ***

Total number of rows (may have changed): 50

Number of in-memory partitions (may have changed): 8

Final number of hash buckets: 128

Size (in bytes) of hash table: 1024

kxhfIterate(end_iterate): numAlloc=8, maxSlots=14

*** (continued) HASH JOIN BUILD HASH TABLE (PHASE 1) ***

### 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: 86

Number of buckets with 1 rows: 37

Number of buckets with 2 rows: 5

Number of buckets with 3 rows: 0

PS:桶里面的行数，最大的桶也只有2行，理论上，桶里面的行数越少，性能越佳。

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: 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

Nmber 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: 128 Empty buckets: 86 Non-empty buckets: 42

PS:创建了128个桶，Oracle 7开始的计算公式

Bucket数=0.8*hash_area_size/(hash_multiblock_io_count*db_block_size)

但是不准确，估计10g发生了变化。

Total number of rows: 50

Maximum number of rows in a bucket: 2

Average number of rows in non-empty buckets: 1.190476

小表500行时候的trace分析

Original hash-area size: 3925453

Memory for slot table: 2826240

。。。

Hash-join fanout: 8

Number of partitions: 8

。。。

### Partition Distribution ###

Partition:0 rows:52 clusters:1 slots:1 kept=1

Partition:1 rows:63 clusters:1 slots:1 kept=1

Partition:2 rows:55 clusters:1 slots:1 kept=1

Partition:3 rows:74 clusters:1 slots:1 kept=1

Partition:4 rows:66 clusters:1 slots:1 kept=1

Partition:5 rows:66 clusters:1 slots:1 kept=1

Partition:6 rows:54 clusters:1 slots:1 kept=1

Partition:7 rows:70 clusters:1 slots:1 kept=1

PS：每个partition的行数增加

。。。

Number of buckets with 0 rows: 622

Number of buckets with 1 rows: 319

Number of buckets with 2 rows: 71

Number of buckets with 3 rows: 10

Number of buckets with 4 rows: 2

Number of buckets with 5 rows: 0

。。。

### Hash table overall statistics ###

Total buckets: 1024 Empty buckets: 622 Non-empty buckets: 402

Total number of rows: 500

Maximum number of rows in a bucket: 4

Average number of rows in non-empty buckets: 1.243781

小表5000行时候的trace分析

Original hash-area size: 3809692

Memory for slot table: 2826240

。。。

Hash-join fanout: 8

Number of partitions: 8

Nuber of slots: 23

Multiblock IO: 15

Block size(KB): 8

Cluster (slot) size(KB): 120

Minimum number of bytes per block: 8160

Bit vector memory allocation(KB): 128

Per partition bit vector length(KB): 16

Maximum possible row length: 270

Estimated build size (KB): 0

。。。

### Partition Distribution ###

Partition:0 rows:588 clusters:1 slots:1 kept=1

Partition:1 rows:638 clusters:1 slots:1 kept=1

Partition:2 rows:621 clusters:1 slots:1 kept=1

Partiton:3 rows:651 clusters:1 slots:1 kept=1

Partition:4 rows:645 clusters:1 slots:1 kept=1

Partition:5 rows:611 clusters:1 slots:1 kept=1

Partitio:6 rows:590 clusters:1 slots:1 kept=1

Partition:7 rows:656 clusters:1 slots:1 kept=1

。。。

# than the true number.

Number of buckets with 0 rows: 4429

Number of buckets with 1 rows: 2762

Number of buckets with 2 rows: 794

Number of buckets with 3 rows: 182

Number of buckets with 4 rows: 23

Number of buckets with 5 rows: 2

Number of buckets with 6 rows: 0

。。。

### Hash table overall statistics ###

Total buckets: 8192 Empty buckets: 4429 Non-empty buckets: 3763

Total number of rows: 5000

Maximum number of rows in a bucket: 5

PS:当小表上升到5000行的时候，bucket的rows最大也不过5行。注意，如果bucket行数过多，遍历带来的开销会带来性能的严重下降。

Average number of rows in non-empty buckets: 1.328727

结论：

Oracle数据库10g中，内存问题并不是干扰Hash join的首要问题，现今硬件价格越来越便宜，内存2G，8G，64G的环境也很常见。大家在针对hash join调优的过程，更要偏重于partition和bucket的数据分配诊断。