- QueueingHint implementation for NodeAffinity is reverted because we found potential scenarios where events that make Pods schedulable could be missed. ([#122327](https://github.com/kubernetes/kubernetes/pull/122327), [@sanposhiho](https://github.com/sanposhiho)) [SIG Scheduling]

[ {
  "scenario" : "org.gradle.performance.experiment.declarativedsl.DeclarativeDslFirstUsePerformanceTest.clean checkout",
  "durations" : [ {
    "testProject" : "largeEmptyMultiProjectDeclarativeDsl",
    "linux" : 8
  } ]
}, {
  "scenario" : "org.gradle.performance.experiment.declarativedsl.DeclarativeDslFirstUsePerformanceTest.cold daemon",
  "durations" : [ {
    "testProject" : "largeEmptyMultiProjectDeclarativeDsl",
    "linux" : 8
  } ]
}, {

    @SuppressWarnings("unchecked")
    Scenario<T> starter = new Scenario<>((Ordering<T>) ordering, list, emptyArray);
    verifyScenario(starter, 0);
  }

  private static <T extends @Nullable Object> void verifyScenario(Scenario<T> scenario, int level) {
    scenario.testCompareTo();
    scenario.testIsOrdered();
    scenario.testMinAndMax();
    scenario.testBinarySearch();
    scenario.testSortedCopy();

    @SuppressWarnings("unchecked")
    Scenario<T> starter = new Scenario<>((Ordering<T>) ordering, list, emptyArray);
    verifyScenario(starter, 0);
  }

  private static <T extends @Nullable Object> void verifyScenario(Scenario<T> scenario, int level) {
    scenario.testCompareTo();
    scenario.testIsOrdered();
    scenario.testMinAndMax();
    scenario.testBinarySearch();
    scenario.testSortedCopy();

     * Tests that <code>a:2.0</code> wins in the scenario:
     * <pre>
     * a:1.0
     * a:2.0
     * </pre>
     */
    @Test
    void testEqual() {
        ResolutionNode a1n = createResolutionNode(a1);
        ResolutionNode a2n = createResolutionNode(a2);

        assertResolveConflict(a2n, a1n, a2n);
    }

    /**
     * Tests that <code>a:2.0</code> wins in the scenario:
     * <pre>
     * a:2.0
     * a:1.0

    deletion of the Node object, deletion of the instance in the cloud provider,
    or force deletion of the pod from the api-server).
    This helps prevent "split brain" scenarios in clustered applications by
    ensuring that unreachable pods will not be presumed dead unless some
    "fencing" operation has provided one of the above indications.

            performanceSubProject = "performance",
            bucketIndex = bucketIndex,
            extraParameters = "--profiler $profiler --tests \"${performanceScenario.scenario.className}.${performanceScenario.scenario.scenario}\"",

**Note**: You probably don't need `contextvars`, and you should probably avoid using them. But they are powerful and useful in some advanced scenarios, for example, migrating from code that used Flask's `g` semi-global variable.

### Concorrência é melhor que paralelismo?

Não! Essa não é a moral da história.

Concorrência é diferente de paralelismo. E é melhor em cenários **específicos** que envolvam um monte de espera. Por isso, geralmente é muito melhor do que paralelismo para desenvolvimento de aplicações web. Mas não para tudo.

Então, para equilibrar tudo, imagine a seguinte historinha:

abstract class AdHocPerformanceScenario(os: Os, arch: Arch = Arch.AMD64) : BuildType({
    val id = "Util_Performance_AdHocPerformanceScenario${os.asName()}${arch.asName()}"
    name = "AdHoc Performance Scenario - ${os.asName()} ${arch.asName()}"
    id(id)

    applyPerformanceTestSettings(os = os, arch = arch, timeout = 420)
    artifactRules = individualPerformanceTestArtifactRules

    params {
        text(