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https://issues.apache.org/jira/browse/HADOOP-19737?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=18040326#comment-18040326
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ASF GitHub Bot commented on HADOOP-19737:
-----------------------------------------
anmolanmol1234 commented on code in PR #8056:
URL: https://github.com/apache/hadoop/pull/8056#discussion_r2556290012
##########
hadoop-tools/hadoop-azure/src/main/java/org/apache/hadoop/fs/azurebfs/services/ReadBufferManagerV2.java:
##########
@@ -1097,4 +1222,210 @@ private void incrementActiveBufferCount() {
private void decrementActiveBufferCount() {
numberOfActiveBuffers.getAndDecrement();
}
+
+ /**
+ * Returns the process ID (PID) of the currently running JVM.
+ * This method uses {@link ProcessHandle#current()} to obtain the ID of the
+ * Java process.
+ *
+ * @return the PID of the current JVM process
+ */
+ public long getJvmProcessId() {
+ return ProcessHandle.current().pid();
+ }
+
+
+ /**
+ * Represents current statistics of the read thread pool and system.
+ */
+ public static class ReadThreadPoolStats {
Review Comment:
taken
> ABFS: Add metrics to identify improvements with read and write aggressiveness
> -----------------------------------------------------------------------------
>
> Key: HADOOP-19737
> URL: https://issues.apache.org/jira/browse/HADOOP-19737
> Project: Hadoop Common
> Issue Type: Sub-task
> Components: fs/azure
> Affects Versions: 3.5.0, 3.4.2
> Reporter: Anmol Asrani
> Assignee: Anmol Asrani
> Priority: Major
> Labels: pull-request-available
>
> Introduces new performance metrics in the ABFS driver to monitor and evaluate
> the effectiveness of read and write aggressiveness tuning. These metrics help
> in understanding how thread pool behavior, CPU utilization, and heap
> availability impact overall I/O throughput and latency. By capturing detailed
> statistics such as active thread count, pool size, and system resource
> utilization, this enhancement enables data-driven analysis of optimizations
> made to improve ABFS read and write performance under varying workloads.
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