Welcome 微信登录

首页 / 数据库 / MySQL / Oracle硬解析的几个例子

为了验证SQL硬解析的场景,设置了下面六个测试用的例子:1、没有绑定变量下的普通查询
2、测试绑定变量下的查询
3、测试绑定变量下sql有变化的查询
4、测试DML非绑定变量的解析
5、测试在过程中执行插入的时候非绑定变量的SQL解析
6、使用了绑定变量之后的,过程中的SQL解析情况/**
测试例子1:
 没有绑定变量下的普通查询
**/
drop table foo purge;CREATE TABLE foo AS
SELECT LEVEL AS x,100000-LEVEL AS y FROM dual  CONNECT BY LEVEL<=100000;ALTER SYSTEM FLUSH SHARED_POOL;SELECT * FROM foo WHERE x = 100;
SELECT * FROM foo WHERE x =999;
SELECT * FROM foo WHERE x=10000;SELECT T.SQL_TEXT, T.SQL_ID, T.EXECUTIONS, T.PARSE_CALLS
  FROM V$SQL T
 WHERE UPPER(T.SQL_TEXT) LIKE "%FOO%";/**
测试例子2:
 测试绑定变量下的查询
**/
drop table foo purge;CREATE TABLE foo AS
SELECT LEVEL AS x,100000-LEVEL AS y FROM dual  CONNECT BY LEVEL<=100000;ALTER SYSTEM FLUSH SHARED_POOL;VARIABLE temp NUMBER;exec :temp :=99;
SELECT * FROM foo WHERE X = :temp;exec :temp :=100;
SELECT * FROM foo WHERE X = :temp;exec :temp :=101;
SELECT * FROM foo WHERE X = :temp;SELECT T.SQL_TEXT, T.SQL_ID, T.EXECUTIONS, T.PARSE_CALLS
  FROM V$SQL T
 WHERE UPPER(T.SQL_TEXT) LIKE "%FOO%";/**测试例子3:
 测试绑定变量下sql有变化的查询
**/drop table foo purge;CREATE TABLE foo AS
SELECT LEVEL AS x,100000-LEVEL AS y FROM dual  CONNECT BY LEVEL<=100000;ALTER SYSTEM FLUSH SHARED_POOL;VARIABLE temp NUMBER;exec :temp :=99;
SELECT * FROM foo WHERE X = :temp;exec :temp :=100;
SELECT * FROM FOO WHERE X = :temp;exec :temp :=101;
SELECT * FROM foo WHERE X = :temp;SELECT T.SQL_TEXT, T.SQL_ID, T.EXECUTIONS, T.PARSE_CALLS
  FROM V$SQL T
 WHERE UPPER(T.SQL_TEXT) LIKE "%FOO%";/**
测试例子4:
 测试DML非绑定变量的解析
**/
 
drop table foo purge;CREATE TABLE foo AS
SELECT LEVEL AS x,100000-LEVEL AS y FROM dual  CONNECT BY LEVEL<=100000;ALTER SYSTEM FLUSH SHARED_POOL;INSERT INTO FOO VALUES(100,200);
INSERT INTO FOO VALUES(101,201);
INSERT INTO FOO VALUES(103,203);SELECT T.SQL_TEXT, T.SQL_ID, T.EXECUTIONS, T.PARSE_CALLS
  FROM V$SQL T
 WHERE UPPER(T.SQL_TEXT) LIKE "%FOO%";/**
测试例子5:
 测试在过程中执行插入的时候的SQL解析
**/drop table foo purge;CREATE TABLE foo AS
SELECT LEVEL AS x,100000-LEVEL AS y FROM dual  CONNECT BY LEVEL<=100000;ALTER SYSTEM FLUSH SHARED_POOL;BEGIN
  FOR I IN 1..3 LOOP
      IF I=1 THEN
       INSERT INTO FOO VALUES(1,1);
      ELSIF I=2 THEN
        INSERT INTO FOO VALUES(2,2);
      ELSIF I=3 THEN
        INSERT INTO FOO VALUES(3,3);
      END IF; 
  END LOOP;
END;
/
SELECT T.SQL_TEXT, T.SQL_ID, T.EXECUTIONS, T.PARSE_CALLS
  FROM V$SQL T
 WHERE UPPER(T.SQL_TEXT) LIKE "%FOO%";/**
测试例子6:
 使用了绑定变量之后的,过程中的SQL解析情况
**/drop table foo purge;CREATE TABLE foo AS
SELECT LEVEL AS x,100000-LEVEL AS y FROM dual  CONNECT BY LEVEL<=100000;ALTER SYSTEM FLUSH SHARED_POOL;BEGIN
  FOR I IN 1..200 LOOP
    INSERT INTO FOO VALUES(I,100000-I);
  END LOOP;
END;
/
SELECT T.SQL_TEXT, T.SQL_ID, T.EXECUTIONS, T.PARSE_CALLS
  FROM V$SQL T
 WHERE UPPER(T.SQL_TEXT) LIKE "%FOO%";通过上述六个情况的试验,我们最终可以得到如下结论:Oracle进行软解析的SQL必须是完全相同的,所谓相同的SQL必须是大小写一致(测试例子3),甚至是不能多一个或者少一个空格,这个结论可以通过修改测试例子3增加一个空格得到,结果就得到了不同的SQL_ID。只有完全一致的SQL,才可以得到相应的HASH值,从而才可以进行软解析。对于在SQL池中,我们需要分析在SQL池中出现的只有参数部分不同的SQL,如果出现了很多次,我们就有必要对其进行相应的变量绑定,从而降低硬解析成本,提高性能。更多Oracle相关信息见Oracle 专题页面 http://www.linuxidc.com/topicnews.aspx?tid=12了解Oracle物理体系必须知道的几个命令MySQL权限体系介绍相关资讯      Oracle硬解析  本文评论 查看全部评论 (0)
表情: 姓名: 字数


评论声明