As previously discussed, the CBO assumes the actual costs and overheads associated with all I/Os to be the same, regardless of the type of I/O, unless told otherwise via the optimizer_index_cost_adj parameter.
Another key assumption the CBO makes by default is that all I/Os will be physical I/Os (PIO) and so be relatively expensive to perform and “worthy” of being costed.
However, this of course is not always the case with many of the blocks being requested and accessed by Oracle already cached in the buffer cache. In specific scenarios, the CBO can take the likely caching characteristics of indexes into consideration and reduce the cost of an index related execution path accordingly. Note however this only applies for I/Os associated for index specific blocks in specific scenarios where the same index is repeatedly accessed.
For example, in the case of a nested loop join where the inner table is typically accessed via an index look-up, the same index may repeatedly access the table many times within the loop. Many of the blocks associated with this index are therefore quite likely to be cached as the index structure is being continually accessed. Same scenario for an index look-up process as a result of an IN list condition. For each element in the IN list, an index is often used to look-up the corresponding value in the table, thereby accessing the specific index again and again for each element in the IN list. As the index is continually being accessed, many of its associated blocks are likely to already be cached in memory.
The purpose of the optimizer_index_caching parameter is to tell the CBO what percentage of index related blocks are likely to already be cached in the buffer cache during these types of operations and so should not be considered in the overall costings associated with the index related execution path. The default is 0 which means by default Oracle doesn’t consider any index blocks to ever be cached and all I/Os associated with an index during an index access path need to treated as PIOs and costed accordingly. If however the optimizer_index_caching parameter is set to say 25, it means that the CBO will consider 25% of all I/Os associated directly with index blocks are likely to already be cached and will therefore reduce the overall cost of index block I/Os by 25%.
As discussed previously, the CBO I/O based costing formula is:
basic index range scan cost = index blevel + ceil(index selectivity x leaf blocks) + ceil(table selectivity x clustering factor)
The optimizer_index_caching parameter adjusts the formula in the following manner by reducing just the index accesses portion of the formula:
basic index range scan cost = ceil((index blevel + ceil(index selectivity x leaf blocks)) x (1- optimizer_index_caching)) + ceil(table selectivity x clustering factor)
but only for specific index scans such as nested loop joins and IN list conditions where an index is likely to be continually accessed within the same execution path.
So if we were to go back to the example I covered in the single predicate demo in the first CBO and Indexes Introduction post as shown below:
SQL> select * from bowie_stuff2 where id = 420;
2000 rows selected.
Execution Plan
———————————————————-
Plan hash value: 134336835
——————————————————————————
|Id| Operation | Name | Rows | Bytes | Cost |
——————————————————————————
|0| SELECT STATEMENT | | 2000 | 36000 | 18 |
|1| TABLE ACCESS BY INDEX ROWID| BOWIE_STUFF2 | 2000 | 36000 | 18 |
|*2| INDEX RANGE SCAN | BOWIE_STUFF2_I | 2000 | | 9 |
——————————————————————————
we notice that the cost of the execution plan is 18.
If we now change the optimizer_index_caching parameter to say 75, meaning that 75% of all index blocks are now likely to be cached and rerun the query:
SQL> alter system set optimizer_index_caching=75;
System altered.
SQL> select * from bowie_stuff2 where id = 420;
2000 rows selected.
Execution Plan
———————————————————-
Plan hash value: 134336835
——————————————————————————
|Id| Operation | Name | Rows | Bytes | Cost |
——————————————————————————
|0| SELECT STATEMENT | | 2000 | 36000 | 18 |
|1| TABLE ACCESS BY INDEX ROWID| BOWIE_STUFF2 | 2000 | 36000 | 18 |
|*2| INDEX RANGE SCAN | BOWIE_STUFF2_I | 2000 | | 9 |
——————————————————————————
we notice that the cost remains unchanged at 18 and parameter has had no effect, as the query was based on a single table equality predicate and did not have processing involving either a nest loop or IN list condition.
However, if we run the second IN list predicate demo involving an IN list condition as shown below (first resetting the optimizer_index_caching parameter back to 0):
SQL> alter system set optimizer_index_caching=0;
System altered.
SQL> SELECT * FROM bowie_stuff2 WHERE id in (20, 30, 420);
6000 rows selected.
Execution Plan
———————————————————-
Plan hash value: 2964430066
——————————————————————————-
|Id| Operation | Name | Rows | Bytes | Cost |
——————————————————————————-
|0| SELECT STATEMENT | | 6000 | 105K| 49 |
|1| INLIST ITERATOR | | | | |
|2| TABLE ACCESS BY INDEX ROWID| BOWIE_STUFF2 | 6000 | 105K| 49 |
|*3| INDEX RANGE SCAN | BOWIE_STUFF2_I | 6000 | | 23 |
——————————————————————————-
We note we had a cost of 49. Remember, the cost of 49 was calculated in the following manner as we have 3 elements in the IN list condition:
cost = index blevel + ceil(index selectivity x leaf blocks) + ceil(table selectivity x clustering factor)
= 2 + 3 x ceil(0.01 x 602) + ceil(0.03 x 852)
= 2 + 3×7 + 26
= 2 + 21 + 26
= 23 + 26 = 49
If we now alter the optimizer_index_caching parameter to 75 and rerun the same IN list query:
SQL> alter system set optimizer_index_caching=75;
System altered.
SQL> SELECT * FROM bowie_stuff2 WHERE id in (20, 30, 420);
6000 rows selected.
Execution Plan
———————————————————-
Plan hash value: 2964430066
——————————————————————————-
|Id| Operation | Name | Rows | Bytes | Cost |
——————————————————————————-
|0| SELECT STATEMENT | | 6000 | 105K| 32 |
|1| INLIST ITERATOR | | | | |
|2| TABLE ACCESS BY INDEX ROWID| BOWIE_STUFF2 | 6000 | 105K| 32 |
|*3| INDEX RANGE SCAN | BOWIE_STUFF2_I | 6000 | | 6 |
——————————————————————————-
we notice the cost has been reduced from 49 down to 32. How has the optimizer_index_caching set to 75 changed the costs:
basic index range scan cost = ceil((index blevel + ceil(index selectivity x leaf blocks)) x (1- optimizer_index_caching)) + ceil(table selectivity x clustering factor)
= ceil((2 + 3 x ceil(0.01 x 602)) x (1-0.75)) + ceil(0.03 x 852)
= ceil((2 + (3×7)) x 0.25) + 26
= ceil((2 + 21) x 0.25)+ 26
= 6 + 26 = 32
So whereas previously there were 23 index block I/Os, this has been reduced down to just 6. Note that the I/Os and associated costs with accessing the actual table blocks within the index scan remains unaltered.
So how to set this parameter in a database ? Well, there are a number of issues with it all.
Firstly, as with the optimizer_index_cost_adj parameter, there’s only the one “global” parameter (for the system or session) which means any value needs to be averaged out for all indexes and for all situations in which this parameter can have an effect. However, some indexes may for example be quite small and heavily accessed and as such quite likely to have most index blocks cached at any point in time (including leaf blocks) whereas other indexes may be quite huge and rarely and randomly accessed which means perhaps only the branch level blocks are likely to be cached even during a (say) IN list operation. As discussed previously, with all averages there will likely be examples where the value is appropriate, too high or too low depending the the characteristics of specific indexes.
Secondly, the poor table related blocks don’t have an equivalent parameter and so Oracle always assumes not only the table blocks within an index scan will be PIOs, but a FTS will only ever consist of PIOs, which conversely might not always be the case. So while we might make a reasonable guesstimate of the likelihood of an index block being cached (say via the buffer cache hit ratio, a study of the v$bh view, etc.), the CBO makes so such allowances for the possible caching characteristics of table related blocks. Yes, index blocks are more likely to be cached, especially during the specific scenarios in which the optimizer_index_caching parameter has an effect, but that doesn’t mean table blocks will always be PIOs. Therefore simply setting this parameter to what might appear a reasonable generalistic index caching value might still run the risk of favouring indexes unduly, even though it only impacts the index accessed blocks in the costing formula, as the CBO doesn’t make any such cost allowances for table blocks that might be cached in a FTS.
In the ideal world, we would have some idea of the caching characteristics of all individual indexes and tables and based on the segments being accessed and their associated caching characteristics, have the CBO make the necessary adjustments to it’s costing estimates in an execution path. Until we reach such an ideal world (which might not be that far away BTW), I basically recommend not to set this parameter at all and again simply ensure you use accurate system statistics and have accurate enough segment statistics.
I recommend setting this parameter if and when you find the CBO is commonly not choosing appropriate indexes for the above mentioned scenarios when perhaps it should and a slight “nudge” of costs in the right direction is sufficient to address the issues. The optimizer_index_caching parameter is not quite as overly “dangerous” if set incorrectly as the optimizer_index_cost_adj parameter can be, as it only impacts the “half” of the formula relating directly to index block I/Os and not the table block I/Os, which often constitute the greater proportion of overall I/Os in many index range scan operations (although as my example above shows, this depends as well).
However, with both of the optimizer_index parameters set, they can both have a hand in reducing the overall costs of an index related execution plan. The optimizer_index_caching parameter first impacts the cost of just the half of the formula relating to index block I/Os as shown above and then the optimizer_index_cost_adj parameter further impacts the overall resultant cost. So if we were to run the IN list query again, but this time also set the optimizer_index_cost_adj to say 25 as well as leaving the optimizer_index_caching to 75:
SQL> alter system set optimizer_index_cost_adj=25;
System altered.
SQL> SELECT * FROM bowie_stuff2 WHERE id in (20, 30, 420);
6000 rows selected.
Execution Plan
———————————————————-
Plan hash value: 2964430066
——————————————————————————-
|Id| Operation | Name | Rows | Bytes | Cost |
——————————————————————————-
|0| SELECT STATEMENT | | 6000 | 105K| 8 |
|1| INLIST ITERATOR | | | | |
|2| TABLE ACCESS BY INDEX ROWID| BOWIE_STUFF2 | 6000 | 105K| 8 |
|*3| INDEX RANGE SCAN | BOWIE_STUFF2_I | 6000 | | 2 |
——————————————————————————-
We note the the cost of the execution plan has further reduced down from 32 to just 8. Basically it’s just the previous cost of 32 x optimizer_index_cost_adj = 32 x 0.25 = 8.
However, rather than setting either of these parameters, I would simply recommend the appropriate use of system statistics and the CPU costing model as I’ll discuss later.
参考至:http://richardfoote.wordpress.com/2009/09/01/optimizer_index_caching-parameter/
如有错误,欢迎指正
邮箱:czmcj@163.com
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内容概要:本文详细介绍了光伏并网逆变器的全栈开发资料,涵盖了从硬件设计到控制算法的各个方面。首先,文章深入探讨了功率接口板的设计,包括IGBT缓冲电路、PCB布局以及EMI滤波器的具体参数和设计思路。接着,重点讲解了主控DSP板的核心控制算法,如MPPT算法的实现及其注意事项。此外,还详细描述了驱动扩展板的门极驱动电路设计,特别是光耦隔离和驱动电阻的选择。同时,文章提供了并联仿真的具体实现方法,展示了环流抑制策略的效果。最后,分享了许多宝贵的实战经验和调试技巧,如主变压器绕制、PWM输出滤波、电流探头使用等。 适合人群:从事电力电子、光伏系统设计的研发工程师和技术爱好者。 使用场景及目标:①帮助工程师理解和掌握光伏并网逆变器的硬件设计和控制算法;②提供详细的实战经验和调试技巧,提升产品的可靠性和性能;③适用于希望深入了解光伏并网逆变器全栈开发的技术人员。 其他说明:文中不仅提供了具体的电路设计和代码实现,还分享了许多宝贵的实际操作经验和常见问题的解决方案,有助于提高开发效率和产品质量。
内容概要:本文详细介绍了粒子群优化(PSO)算法与3-5-3多项式相结合的方法,在机器人轨迹规划中的应用。首先解释了粒子群算法的基本原理及其在优化轨迹参数方面的作用,随后阐述了3-5-3多项式的数学模型,特别是如何利用不同阶次的多项式确保轨迹的平滑过渡并满足边界条件。文中还提供了具体的Python代码实现,展示了如何通过粒子群算法优化时间分配,使3-5-3多项式生成的轨迹达到时间最优。此外,作者分享了一些实践经验,如加入惩罚项以避免超速,以及使用随机扰动帮助粒子跳出局部最优。 适合人群:对机器人运动规划感兴趣的科研人员、工程师和技术爱好者,尤其是有一定编程基础并对优化算法有初步了解的人士。 使用场景及目标:适用于需要精确控制机器人运动的应用场合,如工业自动化生产线、无人机导航等。主要目标是在保证轨迹平滑的前提下,尽可能缩短运动时间,提高工作效率。 其他说明:文中不仅给出了理论讲解,还有详细的代码示例和调试技巧,便于读者理解和实践。同时强调了实际应用中需要注意的问题,如系统的建模精度和安全性考量。
KUKA机器人相关资料
内容概要:本文详细探讨了光子晶体中的束缚态在连续谱中(BIC)及其与轨道角动量(OAM)激发的关系。首先介绍了光子晶体的基本概念和BIC的独特性质,随后展示了如何通过Python代码模拟二维光子晶体中的BIC,并解释了BIC在光学器件中的潜在应用。接着讨论了OAM激发与BIC之间的联系,特别是BIC如何增强OAM激发效率。文中还提供了使用有限差分时域(FDTD)方法计算OAM的具体步骤,并介绍了计算本征态和三维Q值的方法。此外,作者分享了一些实验中的有趣发现,如特定条件下BIC表现出OAM特征,以及不同参数设置对Q值的影响。 适合人群:对光子晶体、BIC和OAM感兴趣的科研人员和技术爱好者,尤其是从事微纳光子学研究的专业人士。 使用场景及目标:适用于希望通过代码模拟深入了解光子晶体中BIC和OAM激发机制的研究人员。目标是掌握BIC和OAM的基础理论,学会使用Python和其他工具进行模拟,并理解这些现象在实际应用中的潜力。 其他说明:文章不仅提供了详细的代码示例,还分享了许多实验心得和技巧,帮助读者避免常见错误,提高模拟精度。同时,强调了物理离散化方式对数值计算结果的重要影响。
内容概要:本文详细介绍了如何使用C#和Halcon 17.12构建一个功能全面的工业视觉项目。主要内容涵盖项目配置、Halcon脚本的选择与修改、相机调试、模板匹配、生产履历管理、历史图像保存以及与三菱FX5U PLC的以太网通讯。文中不仅提供了具体的代码示例,还讨论了实际项目中常见的挑战及其解决方案,如环境配置、相机控制、模板匹配参数调整、PLC通讯细节、生产数据管理和图像存储策略等。 适合人群:从事工业视觉领域的开发者和技术人员,尤其是那些希望深入了解C#与Halcon结合使用的专业人士。 使用场景及目标:适用于需要开发复杂视觉检测系统的工业应用场景,旨在提高检测精度、自动化程度和数据管理效率。具体目标包括但不限于:实现高效的视觉处理流程、确保相机与PLC的无缝协作、优化模板匹配算法、有效管理生产和检测数据。 其他说明:文中强调了框架整合的重要性,并提供了一些实用的技术提示,如避免不同版本之间的兼容性问题、处理实时图像流的最佳实践、确保线程安全的操作等。此外,还提到了一些常见错误及其规避方法,帮助开发者少走弯路。
内容概要:本文探讨了分布式电源(DG)接入对9节点配电网节点电压的影响。首先介绍了9节点配电网模型的搭建方法,包括定义节点和线路参数。然后,通过在特定节点接入分布式电源,利用Matlab进行潮流计算,模拟DG对接入点及其周围节点电压的影响。最后,通过绘制电压波形图,直观展示了不同DG容量和接入位置对配电网电压分布的具体影响。此外,还讨论了电压越限问题以及不同线路参数对电压波动的影响。 适合人群:电力系统研究人员、电气工程学生、从事智能电网和分布式能源研究的专业人士。 使用场景及目标:适用于研究分布式电源接入对配电网电压稳定性的影响,帮助优化分布式电源的规划和配置,确保电网安全稳定运行。 其他说明:文中提供的Matlab代码和图表有助于理解和验证理论分析,同时也为后续深入研究提供了有价值的参考资料。