Merge pull request #841 from whosefriendA/main

translate profiling-karmada.md to Chinese

Author: karmada-bot

i18n/zh/docusaurus-plugin-content-docs/current/developers/profiling-karmada.md

@@ -1,24 +1,25 @@
 ---
-title: Profiling Karmada
+title: Karmada 性能分析
 ---
 
-## Enable profiling
+## 启用性能分析
 
-To profile Karmada components running inside a Kubernetes pod, set --enable-pprof flag to true in the yaml of Karmada components. 
-The default profiling address is 127.0.0.1:6060, and it can be configured via `--profiling-bind-address`.
-The components which are compiled by the Karmada source code support the flag above, including `Karmada-agent`, `Karmada-aggregated-apiserver`, `Karmada-controller-manager`, `Karmada-descheduler`, `Karmada-search`, `Karmada-scheduler`, `Karmada-scheduler-estimator`, `Karmada-webhook`.
 
-```
---enable-pprof                                                                                                                                                                
-                Enable profiling via web interface host:port/debug/pprof/.
---profiling-bind-address string                                                                                                                                               
-                The TCP address for serving profiling(e.g. 127.0.0.1:6060, :6060). This is only applicable if profiling is enabled. (default ":6060")
+若要对运行在 Kubernetes Pod 中的 Karmada 组件进行性能分析，需要在组件的 YAML 配置中将 `--enable-pprof` 参数设置为 `true`。
+ 默认的分析地址为 `127.0.0.1:6060`，也可以通过 `--profiling-bind-address` 参数进行配置。
+
+以下由 Karmada 源码构建的组件支持该功能：`Karmada-agent`, `Karmada-aggregated-apiserver`, `Karmada-controller-manager`, `Karmada-descheduler`, `Karmada-search`, `Karmada-scheduler`, `Karmada-scheduler-estimator`, `Karmada-webhook`.
 
 ```
+--enable-pprof                                                              
+                通过 Web 接口 host:port/debug/pprof/ 启用性能分析。
+--profiling-bind-address string
+                用于提供性能分析服务的 TCP 地址（如 127.0.0.1:6060、:6060）。仅在启用分析时有效，默认值为 ":6060"。
+```
 
-## Expose the endpoint at the local port
+## 通过本地端口暴露分析接口
 
-You can get at the application in the pod by port forwarding with kubectl, for example:
+你可以通过 `kubectl port-forward` 命令将 Pod 中的分析端口转发到本地，例如：
 
 ```shell
 $ kubectl -n karmada-system get pod
@@ -33,31 +34,35 @@ Forwarding from 127.0.0.1:6060 -> 6060
 Forwarding from [::1]:6060 -> 6060
 ```
 
-The HTTP endpoint will now be available as a local port.
+此时，HTTP 分析接口将可通过本地端口访问。
+
 
-## Generate the data
+## 生成分析数据
 
-You can then generate the file for the memory profile with curl and pipe the data to a file:
+你可以通过 `curl` 命令获取内存分析数据（heap profile）并保存到文件：
 
 ```shell
-curl http://localhost:6060/debug/pprof/heap  > heap.pprof
+curl http://localhost:6060/debug/pprof/heap > heap.pprof
 ```
 
-Generate the file for the CPU profile with curl and pipe the data to a file (7200 seconds is two hours):
+获取 CPU 分析数据并保存（以下示例持续时间为 7200 秒，即两小时）：
 
 ```shell
 curl "http://localhost:6060/debug/pprof/profile?seconds=7200" > cpu.pprof
 ```
 
-## Analyze the data
 
-To analyze the data:
+## 分析数据
+
+使用 Go 的 `pprof` 工具分析生成的数据文件：
 
 ```shell
+
 go tool pprof heap.pprof
 ```
 
-## Read more about profiling
 
-1. [Profiling Golang Programs on Kubernetes](https://danlimerick.wordpress.com/2017/01/24/profiling-golang-programs-on-kubernetes/)
-2. [Official Go blog](https://blog.golang.org/pprof)
+## 深入阅读性能分析相关内容
+
+1. [在 Kubernetes 上分析 Golang 程序性能](https://danlimerick.wordpress.com/2017/01/24/profiling-golang-programs-on-kubernetes/)
+2. [Go 官方博客](https://blog.golang.org/pprof)