为什么需要SQL Profile
Why oracle need SQL Profiles,how it work and what are SQL Profiles...
使用DBMS_XPLAN.DISPLAY分析SQL执行计划，通常会看到Note中有类似下面这样的提示；
Note
----- - SQL profile "SYS_SQLPROF_0158283a9b920000" used for this statement
SQL profile由人为手工创建或在Automatic SQL Tunning阶段由SQL tuning advisor创建，它看起来有如下的意思:
- 在优化器评估SQL时使用了额外的对象帮助完成评估；
- 对象改变了优化器原先的评估计划；
当看到这些信息，比较关心的是这个对象(SLQ profile)是什么?它做了什么?是否真的需要它?带着这些疑问学习和探索，最终决解了遇到的问题。
SQL> @i
USERNAME INST_NAME HOST_NAME SID SERIAL# VERSION STARTED SPID OPID CPID SADDR PADDR
-------------------- -------------------- ------------------------- ----- -------- ---------- -------- ---------- ----- --------------- ---------------- ----------------
OPS$SYWU sydb sywu.com 288 22197 11.2.0.4.0 20160421 13736 46 3392:1312 0000000071FE0DA0 0000000072149F40
遇到的问题
假设有这样一张类似订单的表orders；
create table orders(order_no,order_date)
as
select
level,cast(sysdate-level/24 as date)
from
dual
connect by level<=5E5;
SQL> @desc orders
Name Null? Type
------------------------------- -------- ----------------------------
1 ORDER_NO NUMBER
2 ORDER_DATE DATE
保存订单信息，order_date上创建了索引。
create index idx_orders_dt on orders(order_date);
在交易中可能经常遇到某些原因导致交易延期的情况，为了测试这个问题，开发人员添加了未来某一天这样的日期值测试；这里用一个清晰的时间来代替未来的日期；
INSERT INTO ORDERS VALUES (-1, DATE '9999-01-01');
和正常使用的一样，该表定期收集了统计信息；
exec dbms_stats.gather_table_stats(user,'orders', cascade => true);
当系统查询当天的交易记录时发现优化器使用全表扫描，并非索引扫描；
-------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows |E-Bytes| Cost (%CPU)| E-Time | A-Rows | A-Time | Buffers | Reads |
-------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | | 130 (100)| | 10 |00:00:00.23 | 329 | 323 |
|* 1 | TABLE ACCESS FULL| ORDERS | 1 | 496K| 6302K| 130 (26)| 00:00:02 | 10 |00:00:00.23 | 329 | 323 |
-------------------------------------------------------------------------------------------------------------------------------
Query Block Name / Object Alias (identified by operation id):
-------------------------------------------------------------
1 - SEL$1 / ORDERS@SEL$1
Outline Data
-------------
/*+
BEGIN_OUTLINE_DATA
IGNORE_OPTIM_EMBEDDED_HINTS
OPTIMIZER_FEATURES_ENABLE('11.2.0.4')
DB_VERSION('11.2.0.4')
ALL_ROWS
OUTLINE_LEAF(@"SEL$1")
FULL(@"SEL$1" "ORDERS"@"SEL$1")
END_OUTLINE_DATA
*/
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter("ORDER_DATE">=TRUNC(SYSDATE@!,'fmdd'))
Column Projection Information (identified by operation id):
-----------------------------------------------------------
1 - "ORDERS"."ORDER_NO"[NUMBER,22], "ORDER_DATE"[DATE,7]
显然对于这样一张交易记录表，实际当天的记录数据只占全表数据量的4.1%左右，使用索引扫描的方式开销小于全表扫描，但优化器对范围评估错误。接着使用DBMS_SQLTUNE分析SQL；
var task_name varchar2(30)
BEGIN
:task_name := DBMS_SQLTUNE.CREATE_TUNING_TASK(
sql_text => 'select * from orders where order_date>=trunc(sysdate,''DD'')',
user_name => user,
scope => 'COMPREHENSIVE',
time_limit => 60,
task_name => 'orders_tuning_task');
END;
/
执行分析；
alter session set events '10046 trace name context forever,level 12';
exec DBMS_SQLTUNE.execute_tuning_task(task_name => :task_name);
分析结果；
col REPORT_TUNING format a200
select
dbms_sqltune.report_tuning_task(:task_name) REPORT_TUNING
from
dual;
REPORT_TUNING
----------------------------------------------------------------------------------------------------------------------
GENERAL INFORMATION SECTION
-------------------------------------------------------------------------------
Tuning Task Name : orders_tuning_task
Tuning Task Owner : OPS$SYWU
Workload Type : Single SQL Statement
Scope : COMPREHENSIVE
Time Limit(seconds): 60
Completion Status : COMPLETED
Started at : 11/07/2016 21:43:25
Completed at : 11/07/2016 21:43:27
-------------------------------------------------------------------------------
Schema Name: OPS$SYWU
SQL ID : 9ybj4xdc5hsrb
SQL Text : select * from orders where order_date>=trunc(sysdate,'DD')
-------------------------------------------------------------------------------
FINDINGS SECTION (1 finding)
-------------------------------------------------------------------------------
1- SQL Profile Finding (see explain plans section below)
--------------------------------------------------------
A potentially better execution plan was found for this statement.
Recommendation (estimated benefit: 98.78%)
------------------------------------------
- Consider accepting the recommended SQL profile.
execute dbms_sqltune.accept_sql_profile(task_name =>
'orders_tuning_task', task_owner => 'OPS$SYWU', replace => TRUE);
Validation results
------------------
The SQL profile was tested by executing both its plan and the original plan
and measuring their respective execution statistics. A plan may have been
only partially executed if the other could be run to completion in less time.
Original Plan With SQL Profile % Improved
------------- ---------------- ----------
Completion Status: COMPLETE COMPLETE
Elapsed Time (s): .041546 .000132 99.68 %
CPU Time (s): .029895 .0001 99.66 %
User I/O Time (s): .015204 .000032 99.78 %
Buffer Gets: 328 4 98.78 %
Physical Read Requests: 45 0 100 %
Physical Write Requests: 0 0
Physical Read Bytes: 10682368 9830 99.9 %
Physical Write Bytes: 0 0
Rows Processed: 10 10
Fetches: 10 10
Executions: 1 1
Notes
-----
1. Statistics for the original plan were averaged over 10 executions.
2. Statistics for the SQL profile plan were averaged over 10 executions.
-------------------------------------------------------------------------------
EXPLAIN PLANS SECTION
-------------------------------------------------------------------------------
1- Original With Adjusted Cost
------------------------------
Plan hash value: 1275100350
----------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
----------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 18 | 234 | 130 (26)| 00:00:02 |
|* 1 | TABLE ACCESS FULL| ORDERS | 18 | 234 | 130 (26)| 00:00:02 |
----------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter("ORDER_DATE">=TRUNC(SYSDATE@!,'fmdd'))
2- Using SQL Profile
--------------------
Plan hash value: 3364688013
---------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 18 | 234 | 3 (0)| 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID| ORDERS | 18 | 234 | 3 (0)| 00:00:01 |
|* 2 | INDEX RANGE SCAN | IDX_ORDERS_DT | 10 | | 2 (0)| 00:00:01 |
---------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - access("ORDER_DATE">=TRUNC(SYSDATE@!,'fmdd'))
-------------------------------------------------------------------------------
oracle通过分析发现了问题，产生了新的执行计划，并对比两个执行计划，新的执行计划改善90%+的性能，并且改善性能问题只需要同意使用SQL Profile即可；然后允许数据库使用SQL Profile。
exec dbms_sqltune.ACCEPT_SQL_PROFILE(TASK_NAME=>:task_name);
再次执行SQL时，优化器使用了SQL Profile和新的执行计划。
select * from orders where order_date>=trunc(sysdate,'DD'); SQL_ID 3zcvw1pxfcypm, child number 0
-------------------------------------
select * from orders where order_date>=trunc(sysdate,'DD') Plan hash value: 3364688013 ---------------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows |E-Bytes| Cost (%CPU)| E-Time | A-Rows | A-Time | Buffers |
---------------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | | 3 (100)| | 10 |00:00:00.01 | 6 |
| 1 | TABLE ACCESS BY INDEX ROWID| ORDERS | 1 | 18 | 234 | 3 (0)| 00:00:01 | 10 |00:00:00.01 | 6 |
|* 2 | INDEX RANGE SCAN | IDX_ORDERS_DT | 1 | 10 | | 2 (0)| 00:00:01 | 10 |00:00:00.01 | 3 |
--------------------------------------------------------------------------------------------------------------------------------------- Query Block Name / Object Alias (identified by operation id):
------------------------------------------------------------- 1 - SEL$1 / ORDERS@SEL$1
2 - SEL$1 / ORDERS@SEL$1 Outline Data
------------- /*+
BEGIN_OUTLINE_DATA
IGNORE_OPTIM_EMBEDDED_HINTS
OPTIMIZER_FEATURES_ENABLE('11.2.0.4')
DB_VERSION('11.2.0.4')
ALL_ROWS
OUTLINE_LEAF(@"SEL$1")
INDEX_RS_ASC(@"SEL$1" "ORDERS"@"SEL$1" ("ORDERS"."ORDER_DATE"))
END_OUTLINE_DATA
*/ Predicate Information (identified by operation id):
--------------------------------------------------- 2 - access("ORDER_DATE">=TRUNC(SYSDATE@!,'fmdd')) Column Projection Information (identified by operation id):
----------------------------------------------------------- 1 - "ORDERS"."ORDER_NO"[NUMBER,22], "ORDER_DATE"[DATE,7]
2 - "ORDERS".ROWID[ROWID,10], "ORDER_DATE"[DATE,7] Note
----- - SQL profile SYS_SQLPROF_01582d15092f0001 used for this statement SQL> @sql 2061925043
Show SQL text, child cursors and execution stats for SQL hash value 2061925043 child OPS$SYWU@sydb_111 report HASH_VALUE CH# PLAN_HASH SQL_TEXT SQL_PROFILE
---------- ----- ---------- -------------------------------------------------------------------------------------------------------------- ------------------------------
2061925043 0 3364688013 select * from orders where order_date>=trunc(sysdate,'DD') SYS_SQLPROF_01582d15092f0001 CH# PARENT_HANDLE OBJECT_HANDLE PARSES H_PARSES EXECUTIONS FETCHES ROWS_PROCESSED LIOS PIOS SORTS CPU_MS ELA_MS USERS_EXECUTING
----- ---------------- ---------------- ---------- ---------- ---------- ---------- -------------- ---------- ---------- ---------- ---------- ---------- ---------------
0 0000000064618858 0000000063A03108 1 3 1 2 10 16 6 0 7.999 8.621 0
Oracle 分析背后做了什么
很惊奇，为什么分析后优化器就能找出问题所在，此时焦点都集中在trace文件了；分析trace文件，发现如下信息；
SELECT /* DS_SVC */ /*+ dynamic_sampling(0) no_sql_tune no_monitoring
optimizer_features_enable(default) opt_param('parallel_execution_enabled',
'false') result_cache */ COUNT(C1)
FROM
(SELECT /*+ qb_name("innerQuery") NO_INDEX_FFS( "ORDERS") */ 1 AS C1 FROM
"ORDERS" SAMPLE BLOCK(56.8182, 2) SEED(1) "ORDERS") innerQuery
SELECT /* DS_SVC */ /*+ dynamic_sampling(0) no_sql_tune no_monitoring
optimizer_features_enable(default) opt_param('parallel_execution_enabled',
'false') result_cache */ COUNT(C1)
FROM
(SELECT /*+ qb_name("innerQuery") INDEX_FFS( "ORDERS" "IDX_ORDERS_DT") */ 1
AS C1 FROM "ORDERS" SAMPLE BLOCK(59.5238, 2) SEED(1) "ORDERS" WHERE (
"ORDERS".ORDER_DATE IS NOT NULL)) innerQuery
SELECT /* DS_SVC */ /*+ dynamic_sampling(0) no_sql_tune no_monitoring
optimizer_features_enable(default) opt_param('parallel_execution_enabled',
'false') result_cache */ COUNT(C1)
FROM
(SELECT /*+ qb_name("innerQuery") NO_INDEX_FFS( "ORDERS") */ 1 AS C1 FROM
"ORDERS" SAMPLE BLOCK(56.8182, 2) SEED(1) "ORDERS" WHERE
("ORDERS"."ORDER_DATE">=TRUNC(SYSDATE@!,'fmdd'))) innerQuery
SELECT /* DS_SVC */ /*+ dynamic_sampling(0) no_sql_tune no_monitoring
optimizer_features_enable(default) opt_param('parallel_execution_enabled',
'false') result_cache OPT_ESTIMATE(@"innerQuery", TABLE, "ORDERS",
SCALE_ROWS=3.545138895e-05) */ C1, C2, C3
FROM
(SELECT /*+ qb_name("innerQuery") INDEX( "ORDERS" "IDX_ORDERS_DT") */
COUNT(*) AS C1, 4294967295 AS C2, COUNT(*) AS C3 FROM "ORDERS" "ORDERS"
WHERE ("ORDERS"."ORDER_DATE">=TRUNC(SYSDATE@!,'fmdd'))) innerQuery
/* SQL Analyze(156,0) */ select * from orders where order_date>=trunc(sysdate,'DD')
从trace文件分析得出，oracle做了如下的操作:
- 动态采样分析全表数据(无索引)；
- 动态采样分析表中非空数据(无索引)；
- 用全表扫描的方式动态采样分析相关的数据；
- 用索引扫描的方式动态采样分析相关的数据；
- 对比旧的和新的执行计划；
Oracle对比旧的和新的执行计划后，将消耗小的执行计划信息保存到SQL profile中。
优化器的问题
通常优化器相信:
- 数据值分布均匀(比如假设：表列数据中，数值2比数值5一样使用频繁)；
- 数据行分布均匀(比如假设：没有物理cluster或者数据排序)；
- 对于范围数据是连续的，没有漏缺。
优化器信任收集的统计信息，这些信息包括表行记录数,distinct value,max/min value,直方图信息；换一种通俗的说法，统计信息捕获了表整体形状数据，但有些低级别的信息丢失了。这种分析对于大多数数据来说往往工作得很好，但实际情况中，不可避免的有违反规则的例外，比如对于一张大表，98%的数据可能以随机的方式分配在整个段中(segment)，剩余的2%的数据可能只集中在几个数据块中；不幸的是收集统计信息时没有记录这些细节；这就引发一个问题，已经有的统计信息不能完全有效的帮助优化器生成正确的执行计划，所以到了这里问题转变为什么可以弥补或纠正这些信息，让优化器面对这种特定的SQL时可以评估正确，生成好的执行计划。
什么是SQL Profile
通过上面的实验大体将SQL Profile定义为：
- 为特定SQL创建和保存执行计划信息；
- 使用实际运行时的数据来帮助优化器为特定的SQL评估和生成更好的执行计划；
首先通过dbms_sqltune.create_tunning_task创建任务告诉数据库存在问题的可以改善的SQL，这个操作在11G或以后的版本中可以通过Automatic SQL Tuning在对"most active" SQL 分析时创建；然后运行dbms_sqltune.execute_tuning_task评估，这个过程包括三个主要步骤:
- 动态采样分析表数据，获取到真实的实数信息(最重要的比如，Cardinality)
- 提供这些真实的实数信息给优化器，让优化器重新评估；
- 如果优化器评估出新的执行计划，重复多次运行旧的执行计划和新的执行计划，最后对比性能；
如果优化器试运行得出的结果为:
- 优化器评估后产生了新的执行计划；
- 新的执行计划比旧的执行计划性能消耗更小，大幅提升性能；
则表明优化器证明旧的评估对于特定的SQL是错误的，一些低级的数据被分析出，进一步，oracle会将这些信息保存供以后使用。但是如何保存这些信息呢?不可能通过定期性的更新统计信息，因为统计信息不包括这些信息。所以，数据库使用一个独立的对象(SQL Profile)保存SQL和这些(cardinality)信息。oracle 以opt_estimate hints的格式保存cardinality信息；
/+ opt_estimate(table, orders, scale_rows=10) */
或者
/+ opt_estimate(index_scan, orders, IDX_ORDERS_DT, scale_rows=0.001) */
所以如果使用了SQL Profile，评估时默认的cardinality将乘以这些数字，优化器会更真实的查看到表中的数据信息，然后做出评估。