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new f467ae7929 Update Async Profiler doc (#12842)
f467ae7929 is described below
commit f467ae7929832b0ac25888a8f1bbc65ec6eecb73
Author: zhengziyi0117 <[email protected]>
AuthorDate: Mon Dec 9 16:53:49 2024 +0800
Update Async Profiler doc (#12842)
---
docs/en/concepts-and-designs/profiling.md | 22 ++++++-----
...c-profiler.md => backend-java-app-profiling.md} | 43 ++++++++++++++--------
docs/menu.yml | 4 +-
3 files changed, 42 insertions(+), 27 deletions(-)
diff --git a/docs/en/concepts-and-designs/profiling.md
b/docs/en/concepts-and-designs/profiling.md
index 52101addda..5c1ae91e96 100644
--- a/docs/en/concepts-and-designs/profiling.md
+++ b/docs/en/concepts-and-designs/profiling.md
@@ -19,9 +19,6 @@ In the SkyWalking landscape, we provided three ways to
support profiling within
In-process profiling is primarily provided by auto-instrument agents in the
VM-based runtime.
### Tracing Profiling
-Learn more tech details from the post, [**Use Profiling to Fix the Blind Spot
of Distributed
-Tracing**](sdk-profiling.md).
-
This feature resolves the issue <1> through capture the snapshot of the thread
stacks periodically.
The OAP would aggregate the thread stack per RPC request, and provide a
hierarchy graph to indicate the slow methods
based
@@ -30,20 +27,25 @@ on continuous snapshot.
The period is usually every 10-100 milliseconds, which is not recommended to
be less, due to this capture would usually
cause classical stop-the-world for the VM, which would impact the whole
process performance.
+Learn more tech details from the post, [**Use Profiling to Fix the Blind Spot
of Distributed
+Tracing**](sdk-profiling.md).
+
For now, Java and Python agents support this.
-### Async Profiler
+### Java App Profiling
+
+Java App Profiling uses the
[AsyncProfiler](https://github.com/async-profiler/async-profiler) for sampling
Async Profiler is a low overhead sampling profiler for Java that does not
suffer from Safepoint bias problem. It features HotSpot-specific APIs to
collect stack traces and to track memory allocations. The profiler works with
OpenJDK and other Java runtimes based on the HotSpot JVM.
-async-profiler can trace the following kinds of events:
+Async Profiler can trace the following kinds of events:
-CPU cycles
-Hardware and Software performance counters like cache misses, branch misses,
page faults, context switches etc.
-Allocations in Java Heap
-Contented lock attempts, including both Java object monitors and ReentrantLocks
+- CPU cycles
+- Allocations in Java Heap
+- Contented lock attempts, including both Java object monitors and
ReentrantLocks
+- and
[more](https://github.com/async-profiler/async-profiler/blob/master/docs/ProfilingModes.md)
-For now, Java agent support this.
+Only Java agent support this.
## Out-of-process profiling
diff --git a/docs/en/setup/backend/backend-async-profiler.md
b/docs/en/setup/backend/backend-java-app-profiling.md
similarity index 64%
rename from docs/en/setup/backend/backend-async-profiler.md
rename to docs/en/setup/backend/backend-java-app-profiling.md
index db20a40874..b76c1cc4cd 100644
--- a/docs/en/setup/backend/backend-async-profiler.md
+++ b/docs/en/setup/backend/backend-java-app-profiling.md
@@ -1,19 +1,32 @@
-# Async Profiler
+# Java App Profiling
+
+Java App Profiling uses the AsyncProfiler for sampling
Async Profiler is bound within the auto-instrument agent and corresponds to
[In-Process
Profiling](../../concepts-and-designs/profiling.md#in-process-profiling).
-It is passed to the proxy in the form of a task, allowing it to be enabled or
disabled dynamically.
-When service encounters performance issues (cpu usage, memory allocation,
locks), async-profiler task can be created.
-When the proxy receives a task, it enables Async Profiler for sampling.
-After sampling is completed, a flame graph will be generated for performance
analysis to determine the specific business code line that caused the
performance problem.
+It is delivered to the agent in the form of a task, allowing it to be enabled
or disabled dynamically.
+When service encounters performance issues (cpu usage, memory allocation,
locks), Async Profiler task can be created.
+When the agent receives a task, it enables Async Profiler for sampling.
+After sampling is completed, the sampling results are analyzed by requesting
the server to render a flame graph for performance
+analysis to determine the specific business code lines that cause performance
problems.
-## Activate async profiler in the OAP
+## Activate Async Profiler in the OAP
OAP and the agent use a brand-new protocol to exchange Async Profiler data, so
it is necessary to start OAP with the following configuration:
```yaml
receiver-async-profiler:
- selector: ${SW_RECEIVER_ASYNC_PROFILER:default}
- default:
+ selector: ${SW_RECEIVER_ASYNC_PROFILER:default}
+ default:
+ # Used to manage the maximum size of the jfr file that can be received,
the unit is Byte, default is 30M
+ jfrMaxSize: ${SW_RECEIVER_ASYNC_PROFILER_JFR_MAX_SIZE:31457280}
+ # Used to determine whether to receive jfr in memory file or physical
file mode
+ #
+ # The memory file mode have fewer local file system limitations, so they
are by default. But it costs more memory.
+ #
+ # The physical file mode will use less memory when parsing and is more
friendly to parsing large files.
+ # However, if the storage of the tmp directory in the container is
insufficient, the oap server instance may crash.
+ # It is recommended to use physical file mode when volume mounting is
used or the tmp directory has sufficient storage.
+ memoryParserEnabled:
${SW_RECEIVER_ASYNC_PROFILER_MEMORY_PARSER_ENABLED:true}
```
## Async Profiler Task with Analysis
@@ -45,18 +58,18 @@ When the Agent receives a Async Profiler task from OAP, it
automatically generat
### Wait the agent to collect data and upload
-At this point, async-profiler will trace the following kinds of events:
+At this point, Async Profiler will trace the events you selected when you
created the task:
-1. CPU cycles
-2. Hardware and Software performance counters like cache misses, branch
misses, page faults, context switches etc.
-3. Allocations in Java Heap
-4. Contented lock attempts, including both Java object monitors and
ReentrantLocks
+1. CPU,WALL,ITIMER,CTIMER: CPU cycles
+2. ALLOC: Allocations in Java Heap
+3. LOCK: Contented lock attempts, including both Java object monitors and
ReentrantLocks
-Finally, java agent will upload the jfr file produced by async-profiler to the
oap server for online performance analysis.
+Finally, the agent will upload the jfr file produced by Async Profiler to the
oap server for online performance analysis.
### Query the profiling task progresses
-Wait for async-profiler to complete data collection and upload successfully，We
can query the execution log of the async-profiler task and the successful and
failed instances,which includes the following information:
+Wait for Async Profiler to complete data collection and upload successfully.
+We can query the execution logs of the Async Profiler task and the task
status, which includes the following information:
1. **successInstanceIds**: SuccessInstanceIds gives instances that have
executed the task successfully.
2. **errorInstanceIds**: ErrorInstanceIds gives instances that failed to
execute the task.
diff --git a/docs/menu.yml b/docs/menu.yml
index 3a5e7492d4..39b0d7d052 100644
--- a/docs/menu.yml
+++ b/docs/menu.yml
@@ -260,8 +260,8 @@ catalog:
path: "/en/setup/backend/backend-ebpf-profiling"
- name: "Continuous Profiling"
path: "/en/setup/backend/backend-continuous-profiling"
- - name: "Async Profiler"
- path: "/en/setup/backend/backend-async-profiler"
+ - name: "Java App Profiling"
+ path: "/en/setup/backend/backend-java-app-profiling"
- name: "Event"
path: "/en/concepts-and-designs/event/"
- name: "Extension"
