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Unverified Commit ec0f8b84 authored by Kubernetes Prow Robot's avatar Kubernetes Prow Robot Committed by GitHub
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Merge pull request #77913 from s-ito-ts/golint_test_e2e_autoscaling

Fix golint failures of test/e2e/autoscaling
parents d8c33485 c101f40e
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Showing with 59 additions and 57 deletions
hack/.golint_failures
View file @ ec0f8b84
......@@ -590,7 +590,6 @@ staging/src/k8s.io/sample-apiserver/pkg/apis/wardle
staging/src/k8s.io/sample-apiserver/pkg/apis/wardle/v1alpha1
staging/src/k8s.io/sample-apiserver/pkg/registry/wardle/fischer
staging/src/k8s.io/sample-apiserver/pkg/registry/wardle/flunder
test/e2e/autoscaling
test/e2e/chaosmonkey
test/e2e/common
test/e2e/lifecycle/bootstrap
......
test/e2e/autoscaling/autoscaling_timer.go
View file @ ec0f8b84
......@@ -25,15 +25,15 @@ import (
"k8s.io/kubernetes/test/e2e/common"
"k8s.io/kubernetes/test/e2e/framework"
. "github.com/onsi/ginkgo"
. "github.com/onsi/gomega"
"github.com/onsi/ginkgo"
"github.com/onsi/gomega"
)
var _ = SIGDescribe("[Feature:ClusterSizeAutoscalingScaleUp] [Slow] Autoscaling", func() {
f := framework.NewDefaultFramework("autoscaling")
SIGDescribe("Autoscaling a service", func() {
BeforeEach(func() {
ginkgo.BeforeEach(func() {
// Check if Cloud Autoscaler is enabled by trying to get its ConfigMap.
_, err := f.ClientSet.CoreV1().ConfigMaps("kube-system").Get("cluster-autoscaler-status", metav1.GetOptions{})
if err != nil {
......@@ -41,12 +41,12 @@ var _ = SIGDescribe("[Feature:ClusterSizeAutoscalingScaleUp] [Slow] Autoscaling"
}
})
Context("from 1 pod and 3 nodes to 8 pods and >=4 nodes", func() {
ginkgo.Context("from 1 pod and 3 nodes to 8 pods and >=4 nodes", func() {
const nodesNum = 3 // Expect there to be 3 nodes before and after the test.
var nodeGroupName string // Set by BeforeEach, used by AfterEach to scale this node group down after the test.
var nodes *v1.NodeList // Set by BeforeEach, used by Measure to calculate CPU request based on node's sizes.
BeforeEach(func() {
ginkgo.BeforeEach(func() {
// Make sure there is only 1 node group, otherwise this test becomes useless.
nodeGroups := strings.Split(framework.TestContext.CloudConfig.NodeInstanceGroup, ",")
if len(nodeGroups) != 1 {
......@@ -64,10 +64,10 @@ var _ = SIGDescribe("[Feature:ClusterSizeAutoscalingScaleUp] [Slow] Autoscaling"
// Make sure all nodes are schedulable, otherwise we are in some kind of a problem state.
nodes = framework.GetReadySchedulableNodesOrDie(f.ClientSet)
schedulableCount := len(nodes.Items)
Expect(schedulableCount).To(Equal(nodeGroupSize), "not all nodes are schedulable")
gomega.Expect(schedulableCount).To(gomega.Equal(nodeGroupSize), "not all nodes are schedulable")
})
AfterEach(func() {
ginkgo.AfterEach(func() {
// Attempt cleanup only if a node group was targeted for scale up.
// Otherwise the test was probably skipped and we'll get a gcloud error due to invalid parameters.
if len(nodeGroupName) > 0 {
......@@ -77,7 +77,7 @@ var _ = SIGDescribe("[Feature:ClusterSizeAutoscalingScaleUp] [Slow] Autoscaling"
}
})
Measure("takes less than 15 minutes", func(b Benchmarker) {
ginkgo.Measure("takes less than 15 minutes", func(b ginkgo.Benchmarker) {
// Measured over multiple samples, scaling takes 10 +/- 2 minutes, so 15 minutes should be fully sufficient.
const timeToWait = 15 * time.Minute
......@@ -85,8 +85,8 @@ var _ = SIGDescribe("[Feature:ClusterSizeAutoscalingScaleUp] [Slow] Autoscaling"
// This test expects that 8 pods will not fit in 'nodesNum' nodes, but will fit in >='nodesNum'+1 nodes.
// Make it so that 'nodesNum' pods fit perfectly per node.
nodeCpus := nodes.Items[0].Status.Allocatable[v1.ResourceCPU]
nodeCpuMillis := (&nodeCpus).MilliValue()
cpuRequestMillis := int64(nodeCpuMillis / nodesNum)
nodeCPUMillis := (&nodeCpus).MilliValue()
cpuRequestMillis := int64(nodeCPUMillis / nodesNum)
// Start the service we want to scale and wait for it to be up and running.
nodeMemoryBytes := nodes.Items[0].Status.Allocatable[v1.ResourceMemory]
......@@ -99,10 +99,10 @@ var _ = SIGDescribe("[Feature:ClusterSizeAutoscalingScaleUp] [Slow] Autoscaling"
// Enable Horizontal Pod Autoscaler with 50% target utilization and
// scale up the CPU usage to trigger autoscaling to 8 pods for target to be satisfied.
targetCpuUtilizationPercent := int32(50)
hpa := common.CreateCPUHorizontalPodAutoscaler(resourceConsumer, targetCpuUtilizationPercent, 1, 10)
targetCPUUtilizationPercent := int32(50)
hpa := common.CreateCPUHorizontalPodAutoscaler(resourceConsumer, targetCPUUtilizationPercent, 1, 10)
defer common.DeleteHorizontalPodAutoscaler(resourceConsumer, hpa.Name)
cpuLoad := 8 * cpuRequestMillis * int64(targetCpuUtilizationPercent) / 100 // 8 pods utilized to the target level
cpuLoad := 8 * cpuRequestMillis * int64(targetCPUUtilizationPercent) / 100 // 8 pods utilized to the target level
resourceConsumer.ConsumeCPU(int(cpuLoad))
// Measure the time it takes for the service to scale to 8 pods with 50% CPU utilization each.
......
test/e2e/autoscaling/cluster_autoscaler_scalability.go
View file @ ec0f8b84
......@@ -33,8 +33,8 @@ import (
testutils "k8s.io/kubernetes/test/utils"
imageutils "k8s.io/kubernetes/test/utils/image"
. "github.com/onsi/ginkgo"
. "github.com/onsi/gomega"
"github.com/onsi/ginkgo"
"github.com/onsi/gomega"
"k8s.io/klog"
)
......@@ -65,7 +65,7 @@ var _ = framework.KubeDescribe("Cluster size autoscaler scalability [Slow]", fun
var originalSizes map[string]int
var sum int
BeforeEach(func() {
ginkgo.BeforeEach(func() {
framework.SkipUnlessProviderIs("gce", "gke", "kubemark")
// Check if Cloud Autoscaler is enabled by trying to get its ConfigMap.
......@@ -81,7 +81,7 @@ var _ = framework.KubeDescribe("Cluster size autoscaler scalability [Slow]", fun
for _, mig := range strings.Split(framework.TestContext.CloudConfig.NodeInstanceGroup, ",") {
size, err := framework.GroupSize(mig)
framework.ExpectNoError(err)
By(fmt.Sprintf("Initial size of %s: %d", mig, size))
ginkgo.By(fmt.Sprintf("Initial size of %s: %d", mig, size))
originalSizes[mig] = size
sum += size
}
......@@ -91,13 +91,13 @@ var _ = framework.KubeDescribe("Cluster size autoscaler scalability [Slow]", fun
nodes := framework.GetReadySchedulableNodesOrDie(f.ClientSet)
nodeCount = len(nodes.Items)
Expect(nodeCount).NotTo(BeZero())
gomega.Expect(nodeCount).NotTo(gomega.BeZero())
cpu := nodes.Items[0].Status.Capacity[v1.ResourceCPU]
mem := nodes.Items[0].Status.Capacity[v1.ResourceMemory]
coresPerNode = int((&cpu).MilliValue() / 1000)
memCapacityMb = int((&mem).Value() / 1024 / 1024)
Expect(nodeCount).Should(Equal(sum))
gomega.Expect(nodeCount).Should(gomega.Equal(sum))
if framework.ProviderIs("gke") {
val, err := isAutoscalerEnabled(3)
......@@ -109,8 +109,8 @@ var _ = framework.KubeDescribe("Cluster size autoscaler scalability [Slow]", fun
}
})
AfterEach(func() {
By(fmt.Sprintf("Restoring initial size of the cluster"))
ginkgo.AfterEach(func() {
ginkgo.By(fmt.Sprintf("Restoring initial size of the cluster"))
setMigSizes(originalSizes)
framework.ExpectNoError(framework.WaitForReadyNodes(c, nodeCount, scaleDownTimeout))
nodes, err := c.CoreV1().Nodes().List(metav1.ListOptions{})
......@@ -132,7 +132,7 @@ var _ = framework.KubeDescribe("Cluster size autoscaler scalability [Slow]", fun
klog.Infof("Made nodes schedulable again in %v", time.Since(s).String())
})
It("should scale up at all [Feature:ClusterAutoscalerScalability1]", func() {
ginkgo.It("should scale up at all [Feature:ClusterAutoscalerScalability1]", func() {
perNodeReservation := int(float64(memCapacityMb) * 0.95)
replicasPerNode := 10
......@@ -155,7 +155,7 @@ var _ = framework.KubeDescribe("Cluster size autoscaler scalability [Slow]", fun
defer testCleanup()
})
It("should scale up twice [Feature:ClusterAutoscalerScalability2]", func() {
ginkgo.It("should scale up twice [Feature:ClusterAutoscalerScalability2]", func() {
perNodeReservation := int(float64(memCapacityMb) * 0.95)
replicasPerNode := 10
additionalNodes1 := int(math.Ceil(0.7 * maxNodes))
......@@ -204,7 +204,7 @@ var _ = framework.KubeDescribe("Cluster size autoscaler scalability [Slow]", fun
klog.Infof("Scaled up twice")
})
It("should scale down empty nodes [Feature:ClusterAutoscalerScalability3]", func() {
ginkgo.It("should scale down empty nodes [Feature:ClusterAutoscalerScalability3]", func() {
perNodeReservation := int(float64(memCapacityMb) * 0.7)
replicas := int(math.Ceil(maxNodes * 0.7))
totalNodes := maxNodes
......@@ -232,7 +232,7 @@ var _ = framework.KubeDescribe("Cluster size autoscaler scalability [Slow]", fun
}, scaleDownTimeout))
})
It("should scale down underutilized nodes [Feature:ClusterAutoscalerScalability4]", func() {
ginkgo.It("should scale down underutilized nodes [Feature:ClusterAutoscalerScalability4]", func() {
perPodReservation := int(float64(memCapacityMb) * 0.01)
// underutilizedNodes are 10% full
underutilizedPerNodeReplicas := 10
......@@ -291,7 +291,7 @@ var _ = framework.KubeDescribe("Cluster size autoscaler scalability [Slow]", fun
}, timeout))
})
It("shouldn't scale down with underutilized nodes due to host port conflicts [Feature:ClusterAutoscalerScalability5]", func() {
ginkgo.It("shouldn't scale down with underutilized nodes due to host port conflicts [Feature:ClusterAutoscalerScalability5]", func() {
fullReservation := int(float64(memCapacityMb) * 0.9)
hostPortPodReservation := int(float64(memCapacityMb) * 0.3)
totalNodes := maxNodes
......@@ -307,28 +307,28 @@ var _ = framework.KubeDescribe("Cluster size autoscaler scalability [Slow]", fun
fullNodesCount := divider
underutilizedNodesCount := totalNodes - fullNodesCount
By("Reserving full nodes")
ginkgo.By("Reserving full nodes")
// run RC1 w/o host port
cleanup := ReserveMemory(f, "filling-pod", fullNodesCount, fullNodesCount*fullReservation, true, largeScaleUpTimeout*2)
defer cleanup()
By("Reserving host ports on remaining nodes")
ginkgo.By("Reserving host ports on remaining nodes")
// run RC2 w/ host port
cleanup2 := createHostPortPodsWithMemory(f, "underutilizing-host-port-pod", underutilizedNodesCount, reservedPort, underutilizedNodesCount*hostPortPodReservation, largeScaleUpTimeout)
defer cleanup2()
waitForAllCaPodsReadyInNamespace(f, c)
// wait and check scale down doesn't occur
By(fmt.Sprintf("Sleeping %v minutes...", scaleDownTimeout.Minutes()))
ginkgo.By(fmt.Sprintf("Sleeping %v minutes...", scaleDownTimeout.Minutes()))
time.Sleep(scaleDownTimeout)
By("Checking if the number of nodes is as expected")
ginkgo.By("Checking if the number of nodes is as expected")
nodes := framework.GetReadySchedulableNodesOrDie(f.ClientSet)
klog.Infof("Nodes: %v, expected: %v", len(nodes.Items), totalNodes)
Expect(len(nodes.Items)).Should(Equal(totalNodes))
gomega.Expect(len(nodes.Items)).Should(gomega.Equal(totalNodes))
})
Specify("CA ignores unschedulable pods while scheduling schedulable pods [Feature:ClusterAutoscalerScalability6]", func() {
ginkgo.Specify("CA ignores unschedulable pods while scheduling schedulable pods [Feature:ClusterAutoscalerScalability6]", func() {
// Start a number of pods saturating existing nodes.
perNodeReservation := int(float64(memCapacityMb) * 0.80)
replicasPerNode := 10
......@@ -348,7 +348,7 @@ var _ = framework.KubeDescribe("Cluster size autoscaler scalability [Slow]", fun
defer framework.DeleteRCAndWaitForGC(f.ClientSet, f.Namespace.Name, podsConfig.Name)
// Ensure that no new nodes have been added so far.
Expect(framework.NumberOfReadyNodes(f.ClientSet)).To(Equal(nodeCount))
gomega.Expect(framework.NumberOfReadyNodes(f.ClientSet)).To(gomega.Equal(nodeCount))
// Start a number of schedulable pods to ensure CA reacts.
additionalNodes := maxNodes - nodeCount
......@@ -375,7 +375,7 @@ func anyKey(input map[string]int) string {
func simpleScaleUpTestWithTolerance(f *framework.Framework, config *scaleUpTestConfig, tolerateMissingNodeCount int, tolerateMissingPodCount int) func() error {
// resize cluster to start size
// run rc based on config
By(fmt.Sprintf("Running RC %v from config", config.extraPods.Name))
ginkgo.By(fmt.Sprintf("Running RC %v from config", config.extraPods.Name))
start := time.Now()
framework.ExpectNoError(framework.RunRC(*config.extraPods))
// check results
......@@ -461,7 +461,7 @@ func addAnnotation(f *framework.Framework, nodes []v1.Node, key, value string) e
}
func createHostPortPodsWithMemory(f *framework.Framework, id string, replicas, port, megabytes int, timeout time.Duration) func() error {
By(fmt.Sprintf("Running RC which reserves host port and memory"))
ginkgo.By(fmt.Sprintf("Running RC which reserves host port and memory"))
request := int64(1024 * 1024 * megabytes / replicas)
config := &testutils.RCConfig{
Client: f.ClientSet,
......
test/e2e/autoscaling/custom_metrics_stackdriver_autoscaling.go
View file @ ec0f8b84
......@@ -33,7 +33,7 @@ import (
e2elog "k8s.io/kubernetes/test/e2e/framework/log"
"k8s.io/kubernetes/test/e2e/instrumentation/monitoring"
. "github.com/onsi/ginkgo"
"github.com/onsi/ginkgo"
"golang.org/x/oauth2/google"
)
......@@ -45,13 +45,13 @@ const (
)
var _ = SIGDescribe("[HPA] Horizontal pod autoscaling (scale resource: Custom Metrics from Stackdriver)", func() {
BeforeEach(func() {
ginkgo.BeforeEach(func() {
framework.SkipUnlessProviderIs("gce", "gke")
})
f := framework.NewDefaultFramework("horizontal-pod-autoscaling")
It("should scale down with Custom Metric of type Pod from Stackdriver [Feature:CustomMetricsAutoscaling]", func() {
ginkgo.It("should scale down with Custom Metric of type Pod from Stackdriver [Feature:CustomMetricsAutoscaling]", func() {
initialReplicas := 2
// metric should cause scale down
metricValue := int64(100)
......@@ -66,7 +66,7 @@ var _ = SIGDescribe("[HPA] Horizontal pod autoscaling (scale resource: Custom Me
tc.Run()
})
It("should scale down with Custom Metric of type Object from Stackdriver [Feature:CustomMetricsAutoscaling]", func() {
ginkgo.It("should scale down with Custom Metric of type Object from Stackdriver [Feature:CustomMetricsAutoscaling]", func() {
initialReplicas := 2
// metric should cause scale down
metricValue := int64(100)
......@@ -83,7 +83,7 @@ var _ = SIGDescribe("[HPA] Horizontal pod autoscaling (scale resource: Custom Me
tc.Run()
})
It("should scale down with External Metric with target value from Stackdriver [Feature:CustomMetricsAutoscaling]", func() {
ginkgo.It("should scale down with External Metric with target value from Stackdriver [Feature:CustomMetricsAutoscaling]", func() {
initialReplicas := 2
// metric should cause scale down
metricValue := externalMetricValue
......@@ -106,7 +106,7 @@ var _ = SIGDescribe("[HPA] Horizontal pod autoscaling (scale resource: Custom Me
tc.Run()
})
It("should scale down with External Metric with target average value from Stackdriver [Feature:CustomMetricsAutoscaling]", func() {
ginkgo.It("should scale down with External Metric with target average value from Stackdriver [Feature:CustomMetricsAutoscaling]", func() {
initialReplicas := 2
// metric should cause scale down
metricValue := externalMetricValue
......@@ -129,7 +129,7 @@ var _ = SIGDescribe("[HPA] Horizontal pod autoscaling (scale resource: Custom Me
tc.Run()
})
It("should scale down with Custom Metric of type Pod from Stackdriver with Prometheus [Feature:CustomMetricsAutoscaling]", func() {
ginkgo.It("should scale down with Custom Metric of type Pod from Stackdriver with Prometheus [Feature:CustomMetricsAutoscaling]", func() {
initialReplicas := 2
// metric should cause scale down
metricValue := int64(100)
......@@ -144,7 +144,7 @@ var _ = SIGDescribe("[HPA] Horizontal pod autoscaling (scale resource: Custom Me
tc.Run()
})
It("should scale up with two metrics of type Pod from Stackdriver [Feature:CustomMetricsAutoscaling]", func() {
ginkgo.It("should scale up with two metrics of type Pod from Stackdriver [Feature:CustomMetricsAutoscaling]", func() {
initialReplicas := 1
// metric 1 would cause a scale down, if not for metric 2
metric1Value := int64(100)
......@@ -175,7 +175,7 @@ var _ = SIGDescribe("[HPA] Horizontal pod autoscaling (scale resource: Custom Me
tc.Run()
})
It("should scale up with two External metrics from Stackdriver [Feature:CustomMetricsAutoscaling]", func() {
ginkgo.It("should scale up with two External metrics from Stackdriver [Feature:CustomMetricsAutoscaling]", func() {
initialReplicas := 1
// metric 1 would cause a scale down, if not for metric 2
metric1Value := externalMetricValue
......@@ -216,6 +216,7 @@ var _ = SIGDescribe("[HPA] Horizontal pod autoscaling (scale resource: Custom Me
})
})
// CustomMetricTestCase is a struct for test cases.
type CustomMetricTestCase struct {
framework *framework.Framework
hpa *as.HorizontalPodAutoscaler
......@@ -226,8 +227,9 @@ type CustomMetricTestCase struct {
scaledReplicas int
}
// Run starts test case.
func (tc *CustomMetricTestCase) Run() {
projectId := framework.TestContext.CloudConfig.ProjectID
projectID := framework.TestContext.CloudConfig.ProjectID
ctx := context.Background()
client, err := google.DefaultClient(ctx, gcm.CloudPlatformScope)
......@@ -251,11 +253,11 @@ func (tc *CustomMetricTestCase) Run() {
}
// Set up a cluster: create a custom metric and set up k8s-sd adapter
err = monitoring.CreateDescriptors(gcmService, projectId)
err = monitoring.CreateDescriptors(gcmService, projectID)
if err != nil {
framework.Failf("Failed to create metric descriptor: %v", err)
}
defer monitoring.CleanupDescriptors(gcmService, projectId)
defer monitoring.CleanupDescriptors(gcmService, projectID)
err = monitoring.CreateAdapter(monitoring.AdapterDefault)
if err != nil {
......
test/e2e/autoscaling/framework.go
View file @ ec0f8b84
......@@ -18,6 +18,7 @@ package autoscaling
import "github.com/onsi/ginkgo"
// SIGDescribe annotates the test with the SIG label.
func SIGDescribe(text string, body func()) bool {
return ginkgo.Describe("[sig-autoscaling] "+text, body)
}
test/e2e/autoscaling/horizontal_pod_autoscaling.go
View file @ ec0f8b84
......@@ -23,7 +23,7 @@ import (
"k8s.io/kubernetes/test/e2e/common"
"k8s.io/kubernetes/test/e2e/framework"
. "github.com/onsi/ginkgo"
"github.com/onsi/ginkgo"
)
// These tests don't seem to be running properly in parallel: issue: #20338.
......@@ -37,20 +37,20 @@ var _ = SIGDescribe("[HPA] Horizontal pod autoscaling (scale resource: CPU)", fu
SIGDescribe("[Serial] [Slow] Deployment", func() {
// CPU tests via deployments
It(titleUp, func() {
ginkgo.It(titleUp, func() {
scaleUp("test-deployment", common.KindDeployment, false, rc, f)
})
It(titleDown, func() {
ginkgo.It(titleDown, func() {
scaleDown("test-deployment", common.KindDeployment, false, rc, f)
})
})
SIGDescribe("[Serial] [Slow] ReplicaSet", func() {
// CPU tests via ReplicaSets
It(titleUp, func() {
ginkgo.It(titleUp, func() {
scaleUp("rs", common.KindReplicaSet, false, rc, f)
})
It(titleDown, func() {
ginkgo.It(titleDown, func() {
scaleDown("rs", common.KindReplicaSet, false, rc, f)
})
})
......@@ -58,16 +58,16 @@ var _ = SIGDescribe("[HPA] Horizontal pod autoscaling (scale resource: CPU)", fu
// These tests take ~20 minutes each.
SIGDescribe("[Serial] [Slow] ReplicationController", func() {
// CPU tests via replication controllers
It(titleUp+" and verify decision stability", func() {
ginkgo.It(titleUp+" and verify decision stability", func() {
scaleUp("rc", common.KindRC, true, rc, f)
})
It(titleDown+" and verify decision stability", func() {
ginkgo.It(titleDown+" and verify decision stability", func() {
scaleDown("rc", common.KindRC, true, rc, f)
})
})
SIGDescribe("ReplicationController light", func() {
It("Should scale from 1 pod to 2 pods", func() {
ginkgo.It("Should scale from 1 pod to 2 pods", func() {
scaleTest := &HPAScaleTest{
initPods: 1,
totalInitialCPUUsage: 150,
......@@ -79,7 +79,7 @@ var _ = SIGDescribe("[HPA] Horizontal pod autoscaling (scale resource: CPU)", fu
}
scaleTest.run("rc-light", common.KindRC, rc, f)
})
It("Should scale from 2 pods to 1 pod", func() {
ginkgo.It("Should scale from 2 pods to 1 pod", func() {
scaleTest := &HPAScaleTest{
initPods: 2,
totalInitialCPUUsage: 50,
......
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