Zakelly commented on code in PR #24279: URL: https://github.com/apache/flink/pull/24279#discussion_r1497039627
########## docs/content.zh/docs/ops/debugging/flame_graphs.md: ########## @@ -27,36 +27,37 @@ under the License. # Flame Graphs Review Comment: 我建议就直接翻译成火焰图吧，也省去很多空格的烦恼。 ```suggestion # 火焰图 Flame Graphs ``` ########## docs/content.zh/docs/ops/debugging/flame_graphs.md: ########## @@ -27,36 +27,37 @@ under the License. # Flame Graphs -[Flame Graphs](http://www.brendangregg.com/flamegraphs.html) are a visualization that effectively surfaces answers to questions like: -- Which methods are currently consuming CPU resources? -- How does consumption by one method compare to the others? -- Which series of calls on the stack led to executing a particular method? +[Flame Graphs](http://www.brendangregg.com/flamegraphs.html) 是一种有效的可视化工具，可以回答以下问题： + +- 目前哪些方法正在消耗CPU资源？ +- 一个方法的消耗与其他方法相比如何？ +- 哪一系列的堆栈调用导致了特定方法的执行? {{< img src="/fig/flame_graph_on_cpu.png" class="img-fluid" width="90%" >}} {{% center %}} Flame Graph {{% /center %}} -Flame Graphs are constructed by sampling stack traces a number of times. Each method call is presented by a bar, where the length of the bar is proportional to the number of times it is present in the samples. +Flame Graphs是通过多次采样堆栈跟踪来构建的。每个方法调用都由一个条形图表示，其中条形图的长度与其在样本中出现的次数成比例。 -Starting with Flink 1.13, Flame Graphs are natively supported in Flink. In order to produce a Flame Graph, navigate to the job graph of a running job, select an operator of interest and in the menu to the right click on the Flame Graph tab: +从Flink 1.13版本开始，Flink原生支持Flame Graphs。要生成一个Flame Graph，请导航到正在运行的作业的作业图，选择感兴趣的算子，并在右侧菜单中点击“Flame Graph”选项卡： Review Comment: ```suggestion 从 Flink 1.13 版本开始，Flink 原生支持火焰图。要生成一个火焰图，请导航到正在运行的作业图，选择感兴趣的算子，并在右侧菜单中点击 "Flame Graph" 选项卡： ``` ########## docs/content.zh/docs/ops/debugging/flame_graphs.md: ########## @@ -65,26 +66,25 @@ The Off-CPU Flame Graph visualizes blocking calls found in the samples. A distin Off-CPU Flame Graph {{% /center %}} -Mixed mode Flame Graphs are constructed from stack traces of threads in all possible states. +混合模式的 Flame Graphs 是由处于所有可能状态的线程的堆栈跟踪构建而成。 {{< img src="/fig/flame_graph_mixed.png" class="img-fluid" width="90%" >}} {{% center %}} -Flame Graph in Mixed Mode +混合模式的 Flame Graph {{% /center %}} -## Sampling process +## 采样过程 -The collection of stack traces is done purely within the JVM, so only method calls within the Java runtime are visible (no system calls). +堆栈跟踪的收集纯粹在JVM内部进行，因此只能看到Java运行时内的方法调用（没有系统调用）。 -Flame Graph construction is performed at the level of an individual [operator]({{< ref "docs/concepts/glossary" >}}#operator) by default, -i.e. all [task]({{< ref "docs/concepts/glossary" >}}#task) threads of that operator are sampled in parallel and their stack traces are combined. -If a method call consumes 100% of the resources in one of the parallel tasks but none in the others, -the bottleneck might be obscured by being averaged out. +默认情况下，Flame Graph 的构建是在单个[operator]({{< ref "docs/concepts/glossary" >}}#operator)级别上进行的， +即该算子的所有[task]({{< ref "docs/concepts/glossary" >}}#task)线程并行采样，并将它们的堆栈跟踪合并起来。 +如果某个方法调用在其中一个并行任务中占用了100%的资源，但在其他任务中没有占用，则可能会被平均化而掩盖住瓶颈。 -Starting with Flink 1.17, Flame Graph provides "drill down" visualizations to the task level. -Select a subtask of interest, and you can see the flame graph of the corresponding subtask. +从Flink 1.17版本开始，Flame Graph 提供了 "drill down" 可视化到任务级别的功能。 Review Comment: ```suggestion Flink 从 1.17 版本开始提供了单并发级别火焰图可视化的功能。 ``` ########## docs/content.zh/docs/ops/debugging/flame_graphs.md: ########## @@ -27,36 +27,37 @@ under the License. # Flame Graphs -[Flame Graphs](http://www.brendangregg.com/flamegraphs.html) are a visualization that effectively surfaces answers to questions like: -- Which methods are currently consuming CPU resources? -- How does consumption by one method compare to the others? -- Which series of calls on the stack led to executing a particular method? +[Flame Graphs](http://www.brendangregg.com/flamegraphs.html) 是一种有效的可视化工具，可以回答以下问题： + +- 目前哪些方法正在消耗CPU资源？ +- 一个方法的消耗与其他方法相比如何？ +- 哪一系列的堆栈调用导致了特定方法的执行? {{< img src="/fig/flame_graph_on_cpu.png" class="img-fluid" width="90%" >}} {{% center %}} Flame Graph {{% /center %}} -Flame Graphs are constructed by sampling stack traces a number of times. Each method call is presented by a bar, where the length of the bar is proportional to the number of times it is present in the samples. +Flame Graphs是通过多次采样堆栈跟踪来构建的。每个方法调用都由一个条形图表示，其中条形图的长度与其在样本中出现的次数成比例。 -Starting with Flink 1.13, Flame Graphs are natively supported in Flink. In order to produce a Flame Graph, navigate to the job graph of a running job, select an operator of interest and in the menu to the right click on the Flame Graph tab: +从Flink 1.13版本开始，Flink原生支持Flame Graphs。要生成一个Flame Graph，请导航到正在运行的作业的作业图，选择感兴趣的算子，并在右侧菜单中点击“Flame Graph”选项卡： {{< img src="/fig/flame_graph_operator.png" class="img-fluid" width="90%" >}} {{% center %}} -Operator's On-CPU Flame Graph +算子级别的 On-CPU Flame Graph Review Comment: ```suggestion 算子级别的 On-CPU 火焰图 ``` ########## docs/content.zh/docs/ops/debugging/flame_graphs.md: ########## @@ -65,26 +66,25 @@ The Off-CPU Flame Graph visualizes blocking calls found in the samples. A distin Off-CPU Flame Graph {{% /center %}} -Mixed mode Flame Graphs are constructed from stack traces of threads in all possible states. +混合模式的 Flame Graphs 是由处于所有可能状态的线程的堆栈跟踪构建而成。 {{< img src="/fig/flame_graph_mixed.png" class="img-fluid" width="90%" >}} {{% center %}} -Flame Graph in Mixed Mode +混合模式的 Flame Graph {{% /center %}} -## Sampling process +## 采样过程 -The collection of stack traces is done purely within the JVM, so only method calls within the Java runtime are visible (no system calls). +堆栈跟踪的收集纯粹在JVM内部进行，因此只能看到Java运行时内的方法调用（没有系统调用）。 Review Comment: ```suggestion 堆栈跟踪的收集纯粹在 JVM 内部进行，因此只能看到 Java 运行时内的方法调用（看不到系统调用）。 ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
