在開發(fā)過(guò)程中，很多人經(jīng)常會(huì)使用到Hash Map或者Hash Set這種數(shù)據(jù)結(jié)構(gòu)，這種數(shù)據(jù)結(jié)構(gòu)的特點(diǎn)就是插入和訪問速度快。當(dāng)向集合中加入一個(gè)對(duì)象時(shí)，會(huì)調(diào)用hash算法來(lái)獲得hash code，然后根據(jù)hash code分配存放位置。訪問的時(shí)，根據(jù)hashcode直接找到存放位置。

Oracle Hash join 是一種非常高效的join 算法，主要以CPU（hash計(jì)算）和內(nèi)存空間（創(chuàng)建hash table）為代價(jià)獲得最大的效率。Hash join一般用于大表和小表之間的連接，我們將小表構(gòu)建到內(nèi)存中，稱為Hash cluster，大表稱為probe表。

效率

Hash join具有較高效率的兩個(gè)原因：

1.Hash 查詢，根據(jù)映射關(guān)系來(lái)查詢值，不需要遍歷整個(gè)數(shù)據(jù)結(jié)構(gòu)。

2.Mem 訪問速度是Disk的萬(wàn)倍以上。

理想化的Hash join的效率是接近對(duì)大表的單表選擇掃描的。

首先我們來(lái)比較一下，幾種join之間的效率，首先 optimizer會(huì)自動(dòng)選擇使用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，這時(shí)optimizer自動(dòng)選擇了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，經(jīng)過(guò)漫長(zhǎng)的等待后，發(fā)現(xiàn)Cost達(dá)到了驚人的828K，同時(shí)伴隨3814337 consistent gets（由于沒有建索引），可見在這個(gè)測(cè)試中，Nest loop是最低效的。在給customerid建立唯一索引后，減低到106K，但仍然是內(nèi)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的內(nèi)部

HASH_AREA_SIZE在Oracle 9i 和以前，都是影響hash join性能的一個(gè)重要的參數(shù)。但是在10g發(fā)生了一些變化。Oracle不建議使用這個(gè)參數(shù)，除非你是在MTS模式下。Oracle建議采用自動(dòng)PGA管理（設(shè)置PGA_AGGREGATE_TARGET和WORKAREA_SIZE_POLICY）來(lái)，替代使用這個(gè)參數(shù)。由于我的測(cè)試環(huán)境是mts環(huán)境，自動(dòng)內(nèi)存管理，所以我在這里只討論mts下的hash join。

Mts的PGA中，只包含了一些棧空間信息，UGA則包含在large pool中，那么實(shí)際類似hash，sort，merge等操作都是有l(wèi)arge pool來(lái)分配空間，large pool同時(shí)也是auto管理的，它和SGA_TARGET有關(guān)。所以在這種條件下，內(nèi)存的分配是很靈活。

Hash連接根據(jù)內(nèi)存分配的大小，可以有三種不同的效果：

1.optimal 內(nèi)存完全足夠

2.onepass 內(nèi)存不能裝載完小表

3.multipass workarea executions 內(nèi)存嚴(yán)重不足

下面，分別測(cè)試小表為50行，500行和5000行，內(nèi)存的分配情況（內(nèi)存都能完全轉(zhuǎn)載）。

Vendition表 10W條記錄

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

表的統(tǒng)計(jì)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打開10104事件追蹤：（hash 連接追蹤）

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

測(cè)試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行時(shí)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，大約380k，本例中最大的表也不過(guò)250塊左右，所以內(nèi)存完全可以完全裝載

Memory for slot table: 2826240

Calculated overhead for partitions and row/slot managers: 1057270

Hash-join fanout: 8

Number of partitions: 8

PS:hash 表數(shù)據(jù)連一個(gè)塊都沒裝滿，Oracle仍然對(duì)數(shù)據(jù)進(jìn)行了分區(qū)，這里和以前在一些文檔上看到的，當(dāng)內(nèi)存不足時(shí)才會(huì)對(duì)數(shù)據(jù)分區(qū)的說(shuō)法，發(fā)生了變化。

Number of slots: 23

Multiblock IO: 15

Block size(KB): 8

Cluster (slot) size(KB): 120

PS:分區(qū)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:每個(gè)分區(qū)只有不到10行，這里有一個(gè)重要的參數(shù)Kept，1在內(nèi)存中，0在磁盤

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

PS:hash join的第一階段，但是要觀察更多的階段，需提高trace的level，這里略過(guò)

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:桶里面的行數(shù)，最大的桶也只有2行，理論上，桶里面的行數(shù)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:創(chuàng)建了128個(gè)桶，Oracle 7開始的計(jì)算公式

Bucket數(shù)=0.8*hash_area_size/(hash_multiblock_io_count*db_block_size)

但是不準(zhǔn)確，估計(jì)10g發(fā)生了變化。

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行時(shí)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：每個(gè)partition的行數(shù)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行時(shí)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:當(dāng)小表上升到5000行的時(shí)候，bucket的rows最大也不過(guò)5行。注意，如果bucket行數(shù)過(guò)多，遍歷帶來(lái)的開銷會(huì)帶來(lái)性能的嚴(yán)重下降。

Average number of rows in non-empty buckets: 1.328727

結(jié)論：

Oracle數(shù)據(jù)庫(kù)10g中，內(nèi)存問題并不是干擾Hash join的首要問題，現(xiàn)今硬件價(jià)格越來(lái)越便宜，內(nèi)存2G，8G，64G的環(huán)境也很常見。大家在針對(duì)hash join調(diào)優(yōu)的過(guò)程，更要偏重于partition和bucket的數(shù)據(jù)分配診斷。