What SQL statements are consuming the most CPU, sort, and execution times?
哪些SQL语句消耗的CPU、排序和执行时间最多？
You could try the seemingly clever approach of measuring execution times until they converge.
一种看似聪明的方法是不断度量执行时间，直到结果值收敛。
To pinpoint performance problems in the stored procedures by examining execution times of the logging statements.
通过检查日志语句的执行时间来查明存储过程的性能问题。
Following are DB2BATCH run sample summaries illustrating query execution times before and after row compression has been enabled.
以下是DB2BATCH运行样例摘要，说明启动行压缩之前和之后的查询执行时间。
For this particular code, the first three runs measured execution times of 10.81, 10.79, and 10.80 seconds on my configuration.
在我的配置上，这段代码前三次运行的执行时间是10.81、10.79和10.80秒。
The top-level task Details screen displays all the tasks along with their groups, description, next execution times and frequency.
TaskDetails屏幕的顶层显示所有任务以及它们的组、描述、下一次执行时间和频率。
This approach might not be any more reliable than measuring the execution times until they converge, but it is simpler to implement.
这种方法可能并不比度量执行时间直至收敛的方法更可靠，但是更容易实现。
This execution block was then placed within a for loop and the sum of the execution times was recorded as the total execution time for a given test scenario.
然后将该执行块放在一个for循环中，并且将执行时间的总和作为给定测试方案的总执行时间记录下来。
Because JUnit itself decorates all test cases with a setUp and a tearDown method, execution times must be considered within the overall context of a test scenario.
由于JUnit本身用一个setUp和一个tearDown方法装饰所有测试样例，所以要在测试场景的整个上下文中考虑执行时间。
It includes job name, job execution times, number of rows processed, error or success status, time started, and time completed, which are all described in sequence.
它包括作业名称、作业执行次数、处理的行数、错误或成功状态、开始时间及完成时间，这些都是按顺序描述的。
Next, we created and refreshed the MQTs in the federated database and we ran the workload again to measure the execution times for the queries that are routed to the MQTs.
接下来，我们在联邦数据库中创建并刷新了MQT，我们再次运行工作负载以度量将路由到 MQT的查询的执行时间。
This API adds powerful options to dynamic code execution, but also imposes severe performance penalties in terms of method execution times in exchange for this flexibility.
此api为动态代码执行增加了功能强大的选项，但就方法执行时间而言，获得这种灵活性会导致严重的性能损失。
The time prediction method resolves the problem of uncertain execution times and provides a reference solution for real-time scheduling systems in uncertain environments.
采用时间预测的方法，可以较好地解决任务执行时间不确定性所带来的影响，为不确定环境下的实时调度系统提供一种很好的参考解决方案。
However, if literals are important to get optimal access plans, the shorter execution times of the SQL statements are more important than the elegancy of the application's source code.
但是，如果文本对于获取最优访问计划很重要，那么更短的sql语句执行时间比应用程序源代码的优雅性更重要。
Deploy efficient SQL by examining new SQL metrics, such as, number of executions, aggregated elapsed time, and minimum and maximum execution times, to help identify hot spots in the application.
通过检查新的SQL指标(比如执行次数、总的间隔时间、最大和最小执行时间)部署有效的SQL，帮助识别应用程序中的热点。
Deploy efficient SQL by examining new SQL metrics, such as, number of executions, aggregated elapsed time, and minimum and maximum execution times, to help identify hot spots in the application.
通过检查新的SQL指标(比如执行次数、总的间隔时间、最大和最小执行时间)部署有效的SQL，帮助识别应用程序中的热点。
应用推荐