Oracle硬解析的几个例子

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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%';


作者 陈字文（热衷于PM\ORACLE\JAVA等，欢迎同行交流)EMAIL:ziwen#163.com 扣扣:4零9零2零1零零








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。