oracle 两表两列数据对比_Oracle中比对2张表之间数据是否一致的几种方法

注意以下几种数据比对方式适用的前提条件：

1. 所要比对的表的结构是一致的

2. 比对过程中源端和 目标端 表上的数据都是静态的，没有任何DML修改

方式1：

假设你所要进行数据比对的数据库其中有一个版本为11g且该表上有相应的主键索引(primary key index)或者唯一非空索引(unique key ¬ null)的话，那么恭喜你！　你可以借助11g 新引入的专门做数据对比的PL/SQL Package dbms_comparison来实现数据校验的目的，如以下演示：

Source 源端版本为11gR2 :

conn maclean/maclean

SQL> select * from v$version;

BANNER

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

Oracle Database 11g Enterprise Edition Release 11.2.0.3.0 - 64bit Production

PL/SQL Release 11.2.0.3.0 - Production

CORE 11.2.0.3.0 Production

TNS for Linux: Version 11.2.0.3.0 - Production

NLSRTL Version 11.2.0.3.0 - Production

SQL> select * from global_name;

GLOBAL_NAME

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

www.oracledatabase12g.com & www.askmaclean.com

drop table test1;

create table test1 tablespace users as select object_id t1,object_name t2 from dba_objects where object_id is not null;

alter table test1 add primary key(t1);

exec dbms_stats.gather_table_stats('MACLEAN','TEST1',cascade=>TRUE);

create database link maclean connect to maclean identified by maclean using 'G10R21';

Database link created.

以上源端数据库版本为11.2.0.3 ， 源表结构为test1(t1 number primary key,t2 varchar2(128)，透过dblink链接到版本为10.2.0.1的目标端

conn maclean/maclean

SQL> select * from v$version

BANNER

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

Oracle Database 10g Enterprise Edition Release 10.2.0.1.0 - 64bi

PL/SQL Release 10.2.0.1.0 - Production

CORE 10.2.0.1.0 Production

TNS for Linux: Version 10.2.0.1.0 - Production

NLSRTL Version 10.2.0.1.0 - Production

create table test2 tablespace users as select object_id t1,object_name t2

from dba_objects where object_id is not null;

alter table test2 add primary key(t1);

exec dbms_stats.gather_table_stats('MACLEAN','TEST2',cascade=>TRUE);

目标端版本为10.2.0.1 ， 表结构为test2(t1 number primary key,t2 varchar2(128))。

注意这里2张表上均必须有相同的主键索引或者伪主键索引(pseudoprimary key伪主键要求是唯一键且所有的成员列均是非空NOT NULL)。

实际创建comparison对象，并实施校验：

begin

dbms_comparison.create_comparison(comparison_name    => 'MACLEAN_TEST_COM',

schema_name        => 'MACLEAN',

object_name        => 'TEST1',

dblink_name        => 'MACLEAN',

remote_schema_name => 'MACLEAN',

remote_object_name => 'TEST2',

scan_mode          => dbms_comparison.CMP_SCAN_MODE_FULL);

end;

PL/SQL procedure successfully completed.

SQL> set linesize 80 pagesize 1400

SQL> select * from user_comparison where comparison_name='MACLEAN_TEST_COM';

COMPARISON_NAME COMPA SCHEMA_NAME

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

OBJECT_NAME OBJECT_TYPE REMOTE_SCHEMA_NAME

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

REMOTE_OBJECT_NAME REMOTE_OBJECT_TYP

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

DBLINK_NAME

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

SCAN_MODE SCAN_PERCENT

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

CYCLIC_INDEX_VALUE

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

NULL_VALUE

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

LOCAL_CONVERGE_TAG

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

REMOTE_CONVERGE_TAG

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

MAX_NUM_BUCKETS MIN_ROWS_IN_BUCKET

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

LAST_UPDATE_TIME

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

MACLEAN_TEST_COM TABLE MACLEAN

TEST1 TABLE MACLEAN

TEST2 TABLE

MACLEAN

FULL

ORA$STREAMS$NV

1000 10000

20-DEC-11 01.08.44.562092 PM

利用dbms_comparison.create_comparison创建comparison后，新建的comparison会出现在user_comparison视图中；

以上我们完成了comparison的创建，但实际的校验仍未发生我们利用10046事件监控这个数据对比过程：

conn maclean/maclean

set timing on;

alter system flush shared_pool;

alter session set events '10046 trace name context forever,level 8';

set serveroutput on

DECLARE

retval dbms_comparison.comparison_type;

BEGIN

IF dbms_comparison.compare('MACLEAN_TEST_COM', retval, perform_row_dif => TRUE) THEN

dbms_output.put_line('No Differences');

ELSE

dbms_output.put_line('Differences Found');

END IF;

END;

/

Differences Found =====> 返回结果为Differences Found，说明数据存在差异并不一致

PL/SQL procedure successfully completed.

Elapsed: 00:00:10.87

===========================10046 tkprof result =========================

SELECT MIN("T1"), MAX("T1")

FROM

"MACLEAN"."TEST1"

SELECT MIN("T1"), MAX("T1")

FROM

"MACLEAN"."TEST2"@MACLEAN

SELECT COUNT(1)

FROM

"MACLEAN"."TEST1" s WHERE ("T1" >= :scan_min AND "T1" <= :scan_max )

SELECT COUNT(1)

FROM

"MACLEAN"."TEST2"@MACLEAN s WHERE ("T1" >= :scan_min AND "T1" <= :scan_max )

SELECT q.wb1, min(q."T1") min_range1, max(q."T1") max_range1, count(*)

num_rows, sum(q.s_hash) sum_range_hash

FROM

(SELECT /*+ FULL(s) */  width_bucket(s."T1", :scan_min1, :scan_max_inc1,

:num_buckets) wb1, s."T1", ora_hash(NVL(to_char(s."T1"), 'ORA$STREAMS$NV'),

4294967295, ora_hash(NVL((s."T2"), 'ORA$STREAMS$NV'), 4294967295, 0))

s_hash FROM "MACLEAN"."TEST1" s WHERE (s."T1">=:scan_min1 AND s."T1"<=

:scan_max1) ) q GROUP BY q.wb1 ORDER BY q.wb1

SELECT /*+ REMOTE_MAPPED */ q.wb1, min(q."T1") min_range1, max(q."T1")

max_range1, count(*) num_rows, sum(q.s_hash) sum_range_hash

FROM

(SELECT /*+ FULL(s) REMOTE_MAPPED */  width_bucket(s."T1", :scan_min1,

:scan_max_inc1, :num_buckets) wb1, s."T1", ora_hash(NVL(to_char(s."T1"),

'ORA$STREAMS$NV'), 4294967295, ora_hash(NVL((s."T2"), 'ORA$STREAMS$NV'),

4294967295, 0)) s_hash FROM "MACLEAN"."TEST2"@MACLEAN s WHERE (s."T1">=

:scan_min1 AND s."T1"<=:scan_max1) ) q GROUP BY q.wb1 ORDER BY q.wb1

SELECT /*+ FULL(P) +*/ * FROM "MACLEAN"."TEST2" P

SELECT /*+ FULL ("A1") */

WIDTH_BUCKET("A1"."T1", :SCAN_MIN1, :SCAN_MAX_INC1, :NUM_BUCKETS),

MIN("A1"."T1"),

MAX("A1"."T1"),

COUNT(*),

SUM(ORA_HASH(NVL(TO_CHAR("A1"."T1"), 'ORA$STREAMS$NV'),

4294967295,

ORA_HASH(NVL("A1"."T2", 'ORA$STREAMS$NV'), 4294967295, 0)))

FROM "MACLEAN"."TEST2" "A1"

WHERE "A1"."T1" >= :SCAN_MIN1

AND "A1"."T1" <= :SCAN_MAX1

GROUP BY WIDTH_BUCKET("A1"."T1", :SCAN_MIN1, :SCAN_MAX_INC1, :NUM_BUCKETS)

ORDER BY WIDTH_BUCKET("A1"."T1", :SCAN_MIN1, :SCAN_MAX_INC1, :NUM_BUCKETS)

SELECT ROWID, "T1", "T2"

FROM "MACLEAN"."TEST2" "R"

WHERE "T1" >= :1

AND "T1" <= :2

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

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

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

|   0 | SELECT STATEMENT             |             |   126 |  3528 |     4   (0)| 00:00:01 |

|*  1 |  FILTER                      |             |       |       |            |          |

|   2 |   TABLE ACCESS BY INDEX ROWID| TEST2       |   126 |  3528 |     4   (0)| 00:00:01 |

|*  3 |    INDEX RANGE SCAN          | SYS_C006255 |   227 |       |     2   (0)| 00:00:01 |

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

Predicate Information (identified by operation id):

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

1 - filter(TO_NUMBER(:1)<=TO_NUMBER(:2))

3 - access("T1">=TO_NUMBER(:1) AND "T1"<=TO_NUMBER(:2))

SELECT ll.l_rowid, rr.r_rowid, NVL(ll."T1", rr."T1") idx_val

FROM

(SELECT l.rowid l_rowid, l."T1", ora_hash(NVL(to_char(l."T1"),

'ORA$STREAMS$NV'), 4294967295, ora_hash(NVL((l."T2"), 'ORA$STREAMS$NV'),

4294967295, 0)) l_hash  FROM "MACLEAN"."TEST1" l WHERE l."T1">=:scan_min1

AND l."T1"<=:scan_max1 ) ll FULL OUTER JOIN (SELECT /*+ NO_MERGE

REMOTE_MAPPED */ r.rowid r_rowid, r."T1", ora_hash(NVL(to_char(r."T1"),

'ORA$STREAMS$NV'), 4294967295, ora_hash(NVL((r."T2"), 'ORA$STREAMS$NV'),

4294967295, 0)) r_hash FROM "MACLEAN"."TEST2"@MACLEAN r WHERE r."T1">=

:scan_min1  AND r."T1"<=:scan_max1 ) rr ON  ll."T1"=rr."T1" WHERE ll.l_hash

IS NULL OR rr.r_hash IS NULL OR ll.l_hash <> rr.r_hash

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

| Id  | Operation                       | Name         | Rows  | Bytes | Cost (%CPU)| Time     | Inst   |IN-OUT|

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

|   0 | SELECT STATEMENT                |              |   190 |   754K|     9  (12)| 00:00:01 |        |      |

|*  1 |  VIEW                           | VW_FOJ_0     |   190 |   754K|     9  (12)| 00:00:01 |        |      |

|*  2 |   HASH JOIN FULL OUTER          |              |   190 |   754K|     9  (12)| 00:00:01 |        |      |

|   3 |    VIEW                         |              |   190 |  7220 |     4   (0)| 00:00:01 |        |      |

|*  4 |     FILTER                      |              |       |       |            |          |        |      |

|   5 |      TABLE ACCESS BY INDEX ROWID| TEST1        |   190 |  5510 |     4   (0)| 00:00:01 |        |      |

|*  6 |       INDEX RANGE SCAN          | SYS_C0013098 |   341 |       |     2   (0)| 00:00:01 |        |      |

|   7 |    VIEW                         |              |   126 |   495K|     4   (0)| 00:00:01 |        |      |

|   8 |     REMOTE                      | TEST2        |   126 |  3528 |     4   (0)| 00:00:01 | MACLE~ | R->S |

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

Predicate Information (identified by operation id):

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

1 - filter("LL"."L_HASH" IS NULL OR "RR"."R_HASH" IS NULL OR "LL"."L_HASH"<>"RR"."R_HASH")

2 - access("LL"."T1"="RR"."T1")

4 - filter(TO_NUMBER(:SCAN_MIN1)<=TO_NUMBER(:SCAN_MAX1))

6 - access("L"."T1">=TO_NUMBER(:SCAN_MIN1) AND "L"."T1"<=TO_NUMBER(:SCAN_MAX1))

Remote SQL Information (identified by operation id):

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

8 - SELECT ROWID,"T1","T2" FROM "MACLEAN"."TEST2" "R" WHERE "T1">=:1 AND "T1"<=:2 (accessing

'MACLEAN' )

可以看到以上过程中虽然没有避免对TEST1、TEST2表的全表扫描(FULL TABLE SCAN)， 但是好在实际参与HASH JOIN FULL OUTER 的仅是访问索引后获得的少量数据，所以效率还是挺高的。

此外可以通过user_comparison_row_dif了解实际那些row存在差异，如：

SQL> set linesize 80 pagesize 1400

SQL> select *

2 from user_comparison_row_dif

3 where comparison_name = 'MACLEAN_TEST_COM'

4 and rownum < 2;

COMPARISON_NAME SCAN_ID LOCAL_ROWID REMOTE_ROWID

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

INDEX_VALUE

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

STA LAST_UPDATE_TIME

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

MACLEAN_TEST_COM 42 AAATWGAAEAAANBrAAB AAANJrAAEAAB8AMAAd

46

DIF 20-DEC-11 01.18.08.917257 PM

以上利用dbms_comparison包完成了一次简单的数据比对，该方法适用于11g以上版本且要求表上有主键索引或非空唯一索引， 且不支持以下数据类型字段的比对

LONG

LONG RAW

ROWID

UROWID

CLOB

NCLOB

BLOB

BFILE

User-defined types (including object types, REFs, varrays, and nested tables)

Oracle-supplied types (including any types, XML types, spatial types, and media types)

若要比对存有以上类型字段的表，那么需要在create_comparison时指定column_list参数排除掉这些类型的字段。

方法1 dbms_comparison的优势在于可以提供详细的比较信息，且在有适当索引的前提下效率较高。

缺点在于有数据库版本的要求(at least 11gR1)， 且也不支持LONG 、CLOB等字段的比较。

方式2：

利用minus Query 对比数据

这可以说是操作上最简单的一种方法，如：

select * from test1 minus select * from test2@maclean;

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

| Id | Operation | Name | Rows | Bytes |TempSpc| Cost (%CPU)| Time | Inst |IN-OUT|

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

| 0 | SELECT STATEMENT | | 75816 | 3527K| | 1163 (40)| 00:00:14 | | |

| 1 | MINUS | | | | | | | | |

| 2 | SORT UNIQUE | | 75816 | 2147K| 2984K| 710 (1)| 00:00:09 | | |

| 3 | TABLE ACCESS FULL| TEST1 | 75816 | 2147K| | 104 (1)| 00:00:02 | | |

| 4 | SORT UNIQUE | | 50467 | 1379K| 1800K| 453 (1)| 00:00:06 | | |

| 5 | REMOTE | TEST2 | 50467 | 1379K| | 56 (0)| 00:00:01 | MACLE~ | R->S |

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

Remote SQL Information (identified by operation id):

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

5 - SELECT "T1","T2" FROM "TEST2" "TEST2" (accessing 'MACLEAN' )

Select *

from (select 'MACLEAN.TEST1' "Row Source", a.*

from (select /*+ FULL(Tbl1)  */

T1, T2

from MACLEAN.TEST1 Tbl1

minus

select /*+ FULL(Tbl2)  */

T1, T2

from MACLEAN.TEST2@"MACLEAN" Tbl2) A

union all

select 'MACLEAN.TEST2@"MACLEAN"', b.*

from (select /*+ FULL(Tbl2)  */

T1, T2

from MACLEAN.TEST2@"MACLEAN" Tbl2

minus

select /*+ FULL(Tbl1)  */

T1, T2

from MACLEAN.TEST1 Tbl1) B) Order by 1;

MINUS Clause会导致2张表均在本地被全表扫描(TABLE FULL SCAN)，且要求发生SORT排序。 若所对比的表上有大量的数据，那么排序的代价将会是非常大的， 因此这种方法的效率不高。

方式2 MINUS的优点在于操作简便，特别适合于小表之间的数据检验。

缺点在于 由于SORT排序可能导致在大数据量的情况下效率很低， 且同样不支持LOB 和 LONG 这样的大对象。

方式3：

使用not exists子句，如：

select *

from test1 a

where not exists (select 1

from test2 b

where a.t1 = b.t1

and a.t2 = b.t2);

no rows selected

Elapsed: 00:00:00.06

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

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

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

| 0 | SELECT STATEMENT | | 75816 | 7996K| | 691 (1)| 00:00:09 |

|* 1 | HASH JOIN ANTI | | 75816 | 7996K| 3040K| 691 (1)| 00:00:09 |

| 2 | TABLE ACCESS FULL| TEST1 | 75816 | 2147K| | 104 (1)| 00:00:02 |

| 3 | TABLE ACCESS FULL| TEST2 | 77512 | 5979K| | 104 (1)| 00:00:02 |

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

Predicate Information (identified by operation id):

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

1 - access("A"."T1"="B"."T1" AND "A"."T2"="B"."T2")

照理说在数据量较大的情况下not exists使用的HASH JOIN ANTI是在性能上是优于MINUS操作的， 但是当所要比较的表身处不同的2个数据库(distributed query)时将无法使用HASH JOIN ANTI，而会使用FILTER OPERATION这种效率极低的操作:

select *

from test1 a

where not exists (select 1

from test2@maclean b

where a.t1 = b.t1

and a.t2 = b.t2)

no rows selected

Elapsed: 00:01:05.76

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

| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | Inst |IN-OUT|

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

| 0 | SELECT STATEMENT | | 75816 | 2147K| 147K (1)| 00:29:31 | | |

|* 1 | FILTER | | | | | | | |

| 2 | TABLE ACCESS FULL| TEST1 | 75816 | 2147K| 104 (1)| 00:00:02 | | |

| 3 | REMOTE | TEST2 | 1 | 29 | 2 (0)| 00:00:01 | MACLE~ | R->S |

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

Predicate Information (identified by operation id):

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

1 - filter( NOT EXISTS (SELECT 0 FROM "B" WHERE "B"."T1"=:B1 AND "B"."T2"=:B2))

Remote SQL Information (identified by operation id):

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

3 - SELECT "T1","T2" FROM "TEST2" "B" WHERE "T1"=:1 AND "T2"=:2 (accessing

'MACLEAN' )

可以从以上执行计划看到FILTER 操作是十分昂贵的。

补充：

有网友反映可以通过增加 unnest hint 让CBO优化器在远程子查询有效的情况下整体考虑整个查询块，这样可以让执行计划用上HASH JOIN RIGHT ANTI， 这是我一开始没有考虑到的。

select *

from test1 a

where not exists (select /*+ unnset */

1

from test2@maclean b

where a.t1 = b.t1

and a.t2 = b.t2);

PLAN_TABLE_OUTPUT

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

Plan hash value: 1776635653

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

| Id  | Operation            | Name  | Rows  | Bytes |TempSpc| Cost (%CPU)| Time     | Inst   |IN-OUT|

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

|   0 | SELECT STATEMENT     |       | 79815 |    12M|       |   594   (1)| 00:00:08 |        |      |

|*  1 |  HASH JOIN RIGHT ANTI|       | 79815 |    12M|  1816K|   594   (1)| 00:00:08 |        |      |

|   2 |   REMOTE             | TEST2 | 20420 |  1575K|       |    56   (0)| 00:00:01 | MACLE~ | R->S |

|   3 |   TABLE ACCESS FULL  | TEST1 | 79815 |  6157K|       |   104   (1)| 00:00:02 |        |      |

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

Predicate Information (identified by operation id):

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

1 - access("A"."T1"="B"."T1" AND "A"."T2"="B"."T2")

Remote SQL Information (identified by operation id):

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

2 - SELECT "T1","T2" FROM "TEST2" "B" (accessing 'MACLEAN' )

在此基础上加入ordered hint 可以让执行计划使用HASH JOIN ANTI

select /*+ ordered */ *

from test1 a

where not exists (select /*+ unnset */

1

from test2@maclean b

where a.t1 = b.t1

and a.t2 = b.t2);

PLAN_TABLE_OUTPUT

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

Plan hash value: 3089912131

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

| Id  | Operation          | Name  | Rows  | Bytes |TempSpc| Cost (%CPU)| Time     | Inst   |IN-OUT|

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

|   0 | SELECT STATEMENT   |       | 79815 |    12M|       |   594   (1)| 00:00:08 |        |      |

|*  1 |  HASH JOIN ANTI    |       | 79815 |    12M|  7096K|   594   (1)| 00:00:08 |        |      |

|   2 |   TABLE ACCESS FULL| TEST1 | 79815 |  6157K|       |   104   (1)| 00:00:02 |        |      |

|   3 |   REMOTE           | TEST2 | 20420 |  1575K|       |    56   (0)| 00:00:01 | MACLE~ | R->S |

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

Predicate Information (identified by operation id):

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

1 - access("A"."T1"="B"."T1" AND "A"."T2"="B"."T2")

Remote SQL Information (identified by operation id):

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

3 - SELECT "T1","T2" FROM "TEST2" "B" (accessing 'MACLEAN' )

方式3 的优点在于操作简便， 且当需要对比的表位于同一数据库时效率要比MINUS方式高，但如果是distributed query分布式查询则效率可能会因FILTER操作而急剧下降，这时候需要我们手动添加unnest这样的SQL提示，以保证执行计划使用HASH JOIN ANTI操作，这样能够保证not exists方式的性能。not exists同样不支持CLOB等大对象。

方式4：

Toad、PL/SQL Developer等图形化工具都提供了compare table data的功能， 这里我们以Toad工具为例，介绍如何使用该工具校验数据：

打开Toad 链接数据库-> 点击Database-> Compare -> Data

分别在Source 1和Source 2对话框中输入源表和目标表的信息

因为Toad的底层实际上使用了MINUS操作，所以提高SORT_AREA_SIZE有助于提高compare的性能，若使用AUTO PGA则可以不设置。

选择所要比较的列

首先可以比较2张表的行数，点击Execute计算count

使用MINUS 找出其中一张表上有，而另一张没有的行

使用MINUS 找出所有的差别

Toad的compare data功能是基于MINUS实现的，所以效率上并没有优势。但是通过图形界面省去了写SQL语句的麻烦。这种方法同样不支持LOB、LONG等对象。

方式5：

这是一种别出心裁的做法。 将一行数据的上所有字段合并起来，并使用dbms_utility.get_hash_value对合并后的中间值取hash value，再将所有这些从各行所获得的hash值sum累加， 若2表的hash累加值相等则判定2表的数据一致。

简单来说，如下面这样：

create table hash_one as select object_id t1,object_name t2 from dba_objects;

select dbms_utility.get_hash_value(t1||t2,0,power(2,30)) from hash_one where rownum <3;

DBMS_UTILITY.GET_HASH_VALUE(T1||T2,0,POWER(2,30))

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

89209477

757190129

select sum(dbms_utility.get_hash_value(t1||t2,0,power(2,30))) from hash_one;

SUM(DBMS_UTILITY.GET_HASH_VALU

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

40683165992756

select sum(dbms_utility.get_hash_value(object_id||object_name,0,power(2,30))) from dba_objects;

SUM(DBMS_UTILITY.GET_HASH_VALU

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

40683165992756

对于列较多的表，手动去构造所有字段合并可能会比较麻烦，利用以下SQL可以快速构造出我们所需要的语句：

放到PL/SQL Developer等工具中运行，在sqlplus 中可能因ORA-00923: FROM keyword not found where expected出错

select 'select sum(dbms_utility.get_hash_value('||column_name_path||',0,power(2,30)) ) from '||owner||'.'||table_name||';'  from (select owner,table_name,column_name_path,row_number() over(partition by table_name order by table_name,curr_level desc) column_name_path_rank from (select owner,table_name,column_name,rank,level as curr_level,ltrim(sys_connect_by_path(column_name,'||''|''||'),'||''|''||') column_name_path from (select owner,table_name,column_name,row_number() over(partition by table_name order by table_name,column_name) rank from　dba_tab_columns where owner=UPPER('&OWNER')  and table_name=UPPER('&TABNAME')  order by table_name,column_name) connect by table_name = prior table_name and rank-1 = prior rank)) where column_name_path_rank=1;

使用示范：

SQL> @get_hash_col

Enter value for owner: SYS

Enter value for tabname: TAB$

'SELECTSUM(DBMS_UTILITY.GET_HASH_VALUE('||COLUMN_NAME_PATH||',0,POWER(2,30)))FROM

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

select sum(dbms_utility.get_hash_value(ANALYZETIME||'|'||AUDIT$||'|'||AVGRLN||'|

'||AVGSPC||'|'||AVGSPC_FLB||'|'||BLKCNT||'|'||BLOCK#||'|'||BOBJ#||'|'||CHNCNT||'

|'||CLUCOLS||'|'||COLS||'|'||DATAOBJ#||'|'||DEGREE||'|'||EMPCNT||'|'||FILE#||'|'

||FLAGS||'|'||FLBCNT||'|'||INITRANS||'|'||INSTANCES||'|'||INTCOLS||'|'||KERNELCO

LS||'|'||MAXTRANS||'|'||OBJ#||'|'||PCTFREE$||'|'||PCTUSED$||'|'||PROPERTY||'|'||

ROWCNT||'|'||SAMPLESIZE||'|'||SPARE1||'|'||SPARE2||'|'||SPARE3||'|'||SPARE4||'|'

||SPARE5||'|'||SPARE6||'|'||TAB#||'|'||TRIGFLAG||'|'||TS#,0,1073741824) ) from S

YS.TAB$;

利用以上生成的SQL 计算表的sum(hash)值

select sum(dbms_utility.get_hash_value(ANALYZETIME || '|' || AUDIT$ || '|' ||

AVGRLN || '|' || AVGSPC || '|' ||

AVGSPC_FLB || '|' || BLKCNT || '|' ||

BLOCK# || '|' || BOBJ# || '|' ||

CHNCNT || '|' || CLUCOLS || '|' || COLS || '|' ||

DATAOBJ# || '|' || DEGREE || '|' ||

EMPCNT || '|' || FILE# || '|' ||

FLAGS || '|' || FLBCNT || '|' ||

INITRANS || '|' || INSTANCES || '|' ||

INTCOLS || '|' || KERNELCOLS || '|' ||

MAXTRANS || '|' || OBJ# || '|' ||

PCTFREE$ || '|' || PCTUSED$ || '|' ||

PROPERTY || '|' || ROWCNT || '|' ||

SAMPLESIZE || '|' || SPARE1 || '|' ||

SPARE2 || '|' || SPARE3 || '|' ||

SPARE4 || '|' || SPARE5 || '|' ||

SPARE6 || '|' || TAB# || '|' ||

TRIGFLAG || '|' || TS#,

0,

1073741824))

from SYS.TAB$;

SUM(DBMS_UTILITY.GET_HASH_VALU

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

1646389632463

方式5 利用累加整行数据的hash来判定表上数据是否一致， 仅需要对2张表做全表扫描，效率上是这几种方法中最高的， 且能保证较高的准确率。

但是该hash方式存在以下几点不足：

1. 所有字段合并的整行数据可能超过4000字节，这时会出现ORA-1498错误。换而言之使用这种方式的前提是表中任一行的行长不能超过4000 bytes，当然常规情况下很少会有一行数据超过4000 bytes，也可以通过dba_tables.avg_row_len平均行长的统计信息来判定，若avg_row_len<<4000 那么一般不会有溢出的问题。

2. 该hash 方式仅能帮助判断 数据是否一致， 而无法提供更多有用的，例如是哪些行不一致等细节信息

3. 同样的该hash方式对于lob、long字段也无能为力

利用SQL可以快速构造产生sum(hash)值的SQL语句，也就是一个行转列的功能吧？用wmsys.wm_wm_concat函数可以大大简化SQL代码：