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https://issues.apache.org/jira/browse/YARN-11582?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17771367#comment-17771367
]
ASF GitHub Bot commented on YARN-11582:
---------------------------------------
slfan1989 commented on code in PR #6139:
URL: https://github.com/apache/hadoop/pull/6139#discussion_r1343670977
##########
hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-resourcemanager/src/test/java/org/apache/hadoop/yarn/server/resourcemanager/scheduler/capacity/TestApplicationLimitsByPartition.java:
##########
@@ -1027,4 +1027,83 @@ public void testAMLimitByAllResources() throws Exception
{
rm.close();
}
+
+ @Test(timeout = 120000)
+ public void testDiagnosticWhenAMActivated() throws Exception {
+ /*
+ * Test Case:
+ * Verify AM resource limit per partition level and per queue level. So
+ * we use 2 queues to verify this case.
+ * Queue a1 supports labels (x,y). Configure am-resource-limit as 0.2 (x)
+ * Queue c1 supports default label. Configure am-resource-limit as 0.2
+ *
+ * Queue A1 for label X can only support 2Gb AM resource.
+ * Queue C1 (empty label) can support 2Gb AM resource.
+ *
+ * Verify atleast one AM is launched, and AM resources should not go more
+ * than 2GB in each queue.
+ */
+
+ simpleNodeLabelMappingToManager();
+ CapacitySchedulerConfiguration config = (CapacitySchedulerConfiguration)
+ TestUtils.getConfigurationWithQueueLabels(conf);
+
+ // After getting queue conf, configure AM resource percent for Queue A1
+ // as 0.2 (Label X) and for Queue C1 as 0.2 (Empty Label)
+ final String A1 = CapacitySchedulerConfiguration.ROOT + ".a" + ".a1";
+ final String C1 = CapacitySchedulerConfiguration.ROOT + ".c" + ".c1";
+ config.setMaximumAMResourcePercentPerPartition(A1, "x", 0.2f);
+ config.setMaximumApplicationMasterResourcePerQueuePercent(C1, 0.2f);
+
+ // Now inject node label manager with this updated config
+ MockRM rm1 = new MockRM(config) {
+ @Override
+ public RMNodeLabelsManager createNodeLabelManager() {
+ return mgr;
+ }
+ };
+
+ rm1.getRMContext().setNodeLabelManager(mgr);
+ rm1.start();
+ MockNM nm1 = rm1.registerNode("h1:1234", 10 * GB); // label = x
+ rm1.registerNode("h2:1234", 10 * GB); // label = y
+ MockNM nm3 = rm1.registerNode("h3:1234", 10 * GB); // label = <empty>
+
+ // Submit app1 with 1Gb AM resource to Queue A1 for label X
+ MockRMAppSubmissionData data5 =
+ MockRMAppSubmissionData.Builder.createWithMemory(GB, rm1)
+ .withAppName("app")
+ .withUser("user")
+ .withAcls(null)
+ .withQueue("a1")
+ .withAmLabel("x")
+ .build();
+ RMApp app1 = MockRMAppSubmitter.submit(rm1, data5);
+
+ // Submit app2 with 1Gb AM resource to Queue A1 for label X
+ MockRMAppSubmissionData data4 =
+ MockRMAppSubmissionData.Builder.createWithMemory(GB, rm1)
Review Comment:
In order to satisfy checkstyle, the indentation of the next line of code
should generally be maintained at 5 characters.
```
MockRMAppSubmissionData data4 =
MockRMAppSubmissionData.Builder.createWithMemory(GB, rm1)
```
> Improve WebUI diagnosticMessage to show AM Container resource request size
> --------------------------------------------------------------------------
>
> Key: YARN-11582
> URL: https://issues.apache.org/jira/browse/YARN-11582
> Project: Hadoop YARN
> Issue Type: Improvement
> Components: applications, resourcemanager
> Affects Versions: 3.3.4
> Reporter: xiaojunxiang
> Priority: Major
> Labels: pull-request-available
> Attachments: image-2023-10-02-00-05-34-337.png, success_ShowAMInfo.jpg
>
>
> When Yarn resources are insufficient, the newly submitted job AM may be in
> the state of "Application is Activated, waiting for resources to be assigned
> for AM". This is obviously because Yarn doesn't have enough resources to
> allocate another AM Container, so we want to know how large the AM Container
> is currently allocated. Unfortunately, the current diagnosticMessage on the
> Web page does not show this data. Therefore, it is necessary to add the
> resource size of the AM Container in the diagnosticMessage, which will be
> very useful for us to troubleshoise the production faults on line.
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