如果表几乎相同,则比较数据块的哈希会更快,然后只比较有差异的块的所有数据。
我敢打赌,大部分运行时间都花在了传输和转换数据上。从 Employee 表中读取 100,000 行在数据库中可能只需要几秒钟。使用函数DBMS_SQLHASH.GETHASH,Oracle 可以快速为大量数据生成哈希。 (您可能需要让 DBA 运行 grant execute on sys.dbms_sqlhash to your_user;)
例如,想象一下这两个表(实际上它们要大得多,并且位于不同的数据库中):
create table EmployeeTable1 as
select 1 a, 2 b, 3 c, 'abcdefg' EmplName from dual union all
select 1 a, 2 b, 3 c, 'bcdefg' EmplName from dual union all
select 1 a, 2 b, 3 c, 'cdefg' EmplName from dual;
create table EmployeeTable2 as
select 1 a, 2 b, 3 c, 'abcdefg' EmplName from dual union all
select 1 a, 2 b, 3 c, 'bcdefg' EmplName from dual union all
select 9 a, 9 b, 9 c, 'cdefg' EmplName from dual;
为员工姓名的每个首字母生成一个哈希值。
--Table 1 hashes:
select 'a', dbms_sqlhash.gethash('select a,b,c,EmplName from EmployeeTable1 where EmplName like ''a%'' order by 1,2,3', 3) from dual union all
select 'b', dbms_sqlhash.gethash('select a,b,c,EmplName from EmployeeTable1 where EmplName like ''b%'' order by 1,2,3', 3) from dual union all
select 'c', dbms_sqlhash.gethash('select a,b,c,EmplName from EmployeeTable1 where EmplName like ''c%'' order by 1,2,3', 3) from dual;
a 923920839BFE25A44303718523CBFE1CEBB11053
b 355CB0FFAEBB60ECE2E81F3C9502F2F58A23F8BC
c F2D94D7CC0C82329E576CD867CDC52D933C37C2C <-- DIFFERENT
--Table 2 hashes:
select 'a', dbms_sqlhash.gethash('select a,b,c,EmplName from EmployeeTable2 where EmplName like ''a%'' order by 1,2,3', 3) from dual union all
select 'b', dbms_sqlhash.gethash('select a,b,c,EmplName from EmployeeTable2 where EmplName like ''b%'' order by 1,2,3', 3) from dual union all
select 'c', dbms_sqlhash.gethash('select a,b,c,EmplName from EmployeeTable2 where EmplName like ''c%'' order by 1,2,3', 3) from dual;
a 923920839BFE25A44303718523CBFE1CEBB11053
b 355CB0FFAEBB60ECE2E81F3C9502F2F58A23F8BC
c 6B7B1D374568B353E9A37EB35B4508B6AE665F8A <-- DIFFERENT
Python程序只需比较哈希值,就能很快发现“a”和“b”是相同的,区别在于以“c”开头的员工。然后程序只需比较所有细节以获得较小的结果集。
不幸的是,这个解决方案需要更多的编码,因为您必须在 Python 中构建循环,并构建多个 SQL 语句。如果您的表完全不同,解决方案将更慢,并且您需要处理数据以找到正确的块大小。