大约是2 个星期前做一个夜班的时候，开发人员需要比对shareplex 数据同步复制软件在 源端和目标端的2 张表上的数据是否一致，实际上后来想了下shareplex 本身应当具有这种数据校验功能， 但是还是希望从数据库的角度得出几种可用的同表结构下的数据比对方法。
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。
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 分布式查询则效率会因FILTER 操作而急剧下降。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