From 31cf903a204e838ab1d0599052fc4631fb6426d5 Mon Sep 17 00:00:00 2001
From: wforget <643348094@qq.com>
Date: Tue, 24 Jun 2025 10:26:53 +0800
Subject: [PATCH 1/2] [SPARK-52560][BUILD] Bump ap-loader 4.0(v10) to support
 for async-profiler 4.0

---
 pom.xml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pom.xml b/pom.xml
index ec0b9db3ca2aa..2d7c0c0549b54 100644
--- a/pom.xml
+++ b/pom.xml
@@ -310,7 +310,7 @@
     <tomcat.annotations.api.version>6.0.53</tomcat.annotations.api.version>
 
     <!-- Version used in Profiler -->
-    <ap-loader.version>3.0-9</ap-loader.version>
+    <ap-loader.version>4.0-10</ap-loader.version>
 
     <CodeCacheSize>128m</CodeCacheSize>
     <!-- Needed for consistent times -->

From 0f898b5fd269ccdc314fecc0e0eedd96dc159872 Mon Sep 17 00:00:00 2001
From: wforget <643348094@qq.com>
Date: Tue, 24 Jun 2025 11:12:45 +0800
Subject: [PATCH 2/2] update doc

---
 connector/profiler/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/connector/profiler/README.md b/connector/profiler/README.md
index 9e0a58444c605..742b1a8bb5da3 100644
--- a/connector/profiler/README.md
+++ b/connector/profiler/README.md
@@ -16,7 +16,7 @@ or
 
 ## Executor Code Profiling
 
-The spark profiler module enables code profiling of executors in cluster mode based on the [async profiler](https://github.com/async-profiler/async-profiler/blob/v3.0/README.md), a low overhead sampling profiler. This allows a Spark application to capture CPU and memory profiles for application running on a cluster which can later be analyzed for performance issues. The profiler captures [Java Flight Recorder (jfr)](https://access.redhat.com/documentation/es-es/red_hat_build_of_openjdk/17/html/using_jdk_flight_recorder_with_red_hat_build_of_openjdk/openjdk-flight-recorded-overview) files for each executor; these can be read by many tools including Java Mission Control and Intellij.
+The spark profiler module enables code profiling of executors in cluster mode based on the [async profiler](https://github.com/async-profiler/async-profiler/blob/v4.0/README.md), a low overhead sampling profiler. This allows a Spark application to capture CPU and memory profiles for application running on a cluster which can later be analyzed for performance issues. The profiler captures [Java Flight Recorder (jfr)](https://access.redhat.com/documentation/es-es/red_hat_build_of_openjdk/17/html/using_jdk_flight_recorder_with_red_hat_build_of_openjdk/openjdk-flight-recorded-overview) files for each executor; these can be read by many tools including Java Mission Control and Intellij.
 
 The profiler writes the jfr files to the executor's working directory in the executor's local file system and the files can grow to be large, so it is advisable that the executor machines have adequate storage. The profiler can be configured to copy the jfr files to a hdfs location before the executor shuts down.
 
@@ -95,7 +95,7 @@ Then enable the profiling in the configuration.
   <td>event=wall,interval=10ms,alloc=2m,lock=10ms,chunktime=300s</td>
   <td>
       Arguments to pass to the Async Profiler. Detailed options are documented in the comments here:
-      <a href="https://github.com/async-profiler/async-profiler/blob/v3.0/src/arguments.cpp#L44">Profiler arguments</a>.  
+      <a href="https://github.com/async-profiler/async-profiler/blob/v4.0/docs/ProfilerOptions.md">Profiler options</a>.  
        Note that the arguments to start, stop, specify output format, and output file do not have to be specified.
   </td>
   <td>4.0.0</td>