How to know whether a query is using index or not?

本文介绍了一种在数据库中监控索引使用情况的方法。通过设置索引的 MONITORING USAGE 属性,可以查询 v$object_usage 视图来检查索引是否被应用程序使用。文章通过示例展示了如何创建和修改索引来启用或禁用此监控功能。
You can use the index monitoring feature to check if indexes are used by an application or not. If u set the MONITORING USAGE property for an index, you can query the v$object_usage to see if the index is being used or not. Here is an example:


sys@BBK> CREATE INDEX t1_idx ON t1(c1);

Index created.

sys@BBK> ALTER INDEX t1_idx MONITORING USAGE;

Index altered.

sys@BBK> SELECT table_name, index_name, monitoring, used FROM v$object_usage;

TABLE_NAME INDEX_NAME MON USE
------------------------------ ------------------------------ --- ---
T1 T1_IDX YES NO

sys@BBK> SELECT * FROM t1 WHERE c1 = 1;

no rows selected

sys@BBK> SELECT table_name, index_name, monitoring, used FROM v$object_usage;

TABLE_NAME INDEX_NAME MON USE
------------------------------ ------------------------------ --- ---
T1 T1_IDX YES YES

sys@BBK> ALTER INDEX t1_idx NOMONITORING USAGE;

Index altered.

sys@BBK> SELECT table_name, index_name, monitoring, used FROM v$object_usage;

TABLE_NAME INDEX_NAME MON USE
------------------------------ ------------------------------ --- ---
T1 T1_IDX NO YES

sys@BBK>

OR use the following way;

sql> set autot on exp
sql> SELECT * FROM t1 WHERE c1 = 1;
C - Brackets Stack Query / Time Limit: 3 sec / Memory Limit: 1024 MiB Score : 300 points Problem Statement A string T is called a good bracket sequence if and only if it satisfies the following condition: T can be made into an empty string by repeating the following operation zero or more times: Choose () contained in T as a (contiguous) substring and remove it. For example, (), (()()), and the empty string are good bracket sequences, but )()( and ))) are not good bracket sequences. There is a string S. Initially, S is an empty string. Process Q queries in the order they are given. After each query, determine whether S is a good bracket sequence. There are two types of queries: 1 c: A character c is given. c is either ( or ). Append c to the end of S. 2: Remove the last character of S. It is guaranteed that S is not an empty string at this time. Constraints 1≤Q≤8×10 5 c in queries of the first type is ( or ). It is guaranteed that S is not empty when a query of the second type is given. Q is an integer. Input The input is given from Standard Input in the following format, where query i ​ denotes the i-th query. Q query 1 ​ query 2 ​ ⋮ query Q ​ Each query is given in one of the following two formats: 1 c 2 Output Output Q lines. The i-th line should contain Yes if the string S immediately after processing the i-th query is a good bracket sequence, and No otherwise. Sample Input 1 Copy 8 1 ( 2 1 ( 1 ) 2 1 ( 1 ) 1 ) Sample Output 1 Copy No Yes No Yes No No No Yes S immediately after processing the 1st query is (, which is not a good bracket sequence. S immediately after processing the 2nd query is an empty string, which is a good bracket sequence. S immediately after processing the 3rd query is (, which is not a good bracket sequence. S immediately after processing the 4th query is (), which is a good bracket sequence. S immediately after processing the 5th query is (, which is not a good bracket sequence. S immediately after processing the 6th query is ((, which is not a good bracket sequence. S immediately after processing the 7th query is ((), which is not a good bracket sequence. S immediately after processing the 8th query is (()), which is a good bracket sequence.c++
10-20
【无人机】基于改进粒子群算法的无人机路径规划研究[和遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性和寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度和全局寻优方面的优势。; 适合人群:具备一定Matlab编程基础和优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型和改进思路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比思路,拓展到其他智能优化算法的研究与改进中。
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