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Kubernetes网络和集群性能测试

准备

测试环境

在以下几种环境下进行测试：

  • Kubernetes集群node节点上通过Cluster IP方式访问
  • Kubernetes集群内部通过service访问
  • Kubernetes集群外部通过traefik ingress暴露的地址访问

测试地址

Cluster IP: 10.254.149.31

Service Port：8000

Ingress Host：traefik.sample-webapp.io

测试工具

测试说明

通过向sample-webapp发送curl请求获取响应时间，直接curl后的结果为：

$ curl "http://10.254.149.31:8000/"
Welcome to the "Distributed Load Testing Using Kubernetes" sample web app

网络延迟测试

场景一、 Kubernetes集群node节点上通过Cluster IP访问

测试命令

curl -o /dev/null -s -w '%{time_connect} %{time_starttransfer} %{time_total}' "http://10.254.149.31:8000/"

10组测试结果

No time_connect time_starttransfer time_total
1 0.000 0.003 0.003
2 0.000 0.002 0.002
3 0.000 0.002 0.002
4 0.000 0.002 0.002
5 0.000 0.002 0.002
6 0.000 0.002 0.002
7 0.000 0.002 0.002
8 0.000 0.002 0.002
9 0.000 0.002 0.002
10 0.000 0.002 0.002

平均响应时间：2ms

时间指标说明

单位：秒

time_connect：建立到服务器的 TCP 连接所用的时间

time_starttransfer：在发出请求之后，Web 服务器返回数据的第一个字节所用的时间

time_total：完成请求所用的时间

场景二、Kubernetes集群内部通过service访问

测试命令

curl -o /dev/null -s -w '%{time_connect} %{time_starttransfer} %{time_total}' "http://sample-webapp:8000/"

10组测试结果

No time_connect time_starttransfer time_total
1 0.004 0.006 0.006
2 0.004 0.006 0.006
3 0.004 0.006 0.006
4 0.004 0.006 0.006
5 0.004 0.006 0.006
6 0.004 0.006 0.006
7 0.004 0.006 0.006
8 0.004 0.006 0.006
9 0.004 0.006 0.006
10 0.004 0.006 0.006

平均响应时间：6ms

场景三、在公网上通过traefik ingress访问

测试命令

curl -o /dev/null -s -w '%{time_connect} %{time_starttransfer} %{time_total}' "http://traefik.sample-webapp.io" >>result

10组测试结果

No time_connect time_starttransfer time_total
1 0.043 0.085 0.085
2 0.052 0.093 0.093
3 0.043 0.082 0.082
4 0.051 0.093 0.093
5 0.068 0.188 0.188
6 0.049 0.089 0.089
7 0.051 0.113 0.113
8 0.055 0.120 0.120
9 0.065 0.126 0.127
10 0.050 0.111 0.111

平均响应时间：110ms

测试结果

在这三种场景下的响应时间测试结果如下：

  • Kubernetes集群node节点上通过Cluster IP方式访问：2ms
  • Kubernetes集群内部通过service访问：6ms
  • Kubernetes集群外部通过traefik ingress暴露的地址访问：110ms

注意：执行测试的node节点/Pod与serivce所在的pod的距离（是否在同一台主机上），对前两个场景可以能会有一定影响。

网络性能测试

网络使用flannel的vxlan模式。

使用iperf进行测试。

服务端命令：

iperf -s -p 12345 -i 1 -M

客户端命令：

iperf -c ${server-ip} -p 12345 -i 1 -t 10 -w 20K

场景一、主机之间

[ ID] Interval       Transfer     Bandwidth
[  3]  0.0- 1.0 sec   598 MBytes  5.02 Gbits/sec
[  3]  1.0- 2.0 sec   637 MBytes  5.35 Gbits/sec
[  3]  2.0- 3.0 sec   664 MBytes  5.57 Gbits/sec
[  3]  3.0- 4.0 sec   657 MBytes  5.51 Gbits/sec
[  3]  4.0- 5.0 sec   641 MBytes  5.38 Gbits/sec
[  3]  5.0- 6.0 sec   639 MBytes  5.36 Gbits/sec
[  3]  6.0- 7.0 sec   628 MBytes  5.26 Gbits/sec
[  3]  7.0- 8.0 sec   649 MBytes  5.44 Gbits/sec
[  3]  8.0- 9.0 sec   638 MBytes  5.35 Gbits/sec
[  3]  9.0-10.0 sec   652 MBytes  5.47 Gbits/sec
[  3]  0.0-10.0 sec  6.25 GBytes  5.37 Gbits/sec

场景二、不同主机的Pod之间(使用flannel的vxlan模式)

[ ID] Interval       Transfer     Bandwidth
[  3]  0.0- 1.0 sec   372 MBytes  3.12 Gbits/sec
[  3]  1.0- 2.0 sec   345 MBytes  2.89 Gbits/sec
[  3]  2.0- 3.0 sec   361 MBytes  3.03 Gbits/sec
[  3]  3.0- 4.0 sec   397 MBytes  3.33 Gbits/sec
[  3]  4.0- 5.0 sec   405 MBytes  3.40 Gbits/sec
[  3]  5.0- 6.0 sec   410 MBytes  3.44 Gbits/sec
[  3]  6.0- 7.0 sec   404 MBytes  3.39 Gbits/sec
[  3]  7.0- 8.0 sec   408 MBytes  3.42 Gbits/sec
[  3]  8.0- 9.0 sec   451 MBytes  3.78 Gbits/sec
[  3]  9.0-10.0 sec   387 MBytes  3.25 Gbits/sec
[  3]  0.0-10.0 sec  3.85 GBytes  3.30 Gbits/sec

场景三、Node与非同主机的Pod之间（使用flannel的vxlan模式）

[ ID] Interval       Transfer     Bandwidth
[  3]  0.0- 1.0 sec   372 MBytes  3.12 Gbits/sec
[  3]  1.0- 2.0 sec   420 MBytes  3.53 Gbits/sec
[  3]  2.0- 3.0 sec   434 MBytes  3.64 Gbits/sec
[  3]  3.0- 4.0 sec   409 MBytes  3.43 Gbits/sec
[  3]  4.0- 5.0 sec   382 MBytes  3.21 Gbits/sec
[  3]  5.0- 6.0 sec   408 MBytes  3.42 Gbits/sec
[  3]  6.0- 7.0 sec   403 MBytes  3.38 Gbits/sec
[  3]  7.0- 8.0 sec   423 MBytes  3.55 Gbits/sec
[  3]  8.0- 9.0 sec   376 MBytes  3.15 Gbits/sec
[  3]  9.0-10.0 sec   451 MBytes  3.78 Gbits/sec
[  3]  0.0-10.0 sec  3.98 GBytes  3.42 Gbits/sec

场景四、不同主机的Pod之间（使用flannel的host-gw模式）

[ ID] Interval       Transfer     Bandwidth
[  5]  0.0- 1.0 sec   530 MBytes  4.45 Gbits/sec
[  5]  1.0- 2.0 sec   576 MBytes  4.84 Gbits/sec
[  5]  2.0- 3.0 sec   631 MBytes  5.29 Gbits/sec
[  5]  3.0- 4.0 sec   580 MBytes  4.87 Gbits/sec
[  5]  4.0- 5.0 sec   627 MBytes  5.26 Gbits/sec
[  5]  5.0- 6.0 sec   578 MBytes  4.85 Gbits/sec
[  5]  6.0- 7.0 sec   584 MBytes  4.90 Gbits/sec
[  5]  7.0- 8.0 sec   571 MBytes  4.79 Gbits/sec
[  5]  8.0- 9.0 sec   564 MBytes  4.73 Gbits/sec
[  5]  9.0-10.0 sec   572 MBytes  4.80 Gbits/sec
[  5]  0.0-10.0 sec  5.68 GBytes  4.88 Gbits/sec

场景五、Node与非同主机的Pod之间（使用flannel的host-gw模式）

[ ID] Interval       Transfer     Bandwidth
[  3]  0.0- 1.0 sec   570 MBytes  4.78 Gbits/sec
[  3]  1.0- 2.0 sec   552 MBytes  4.63 Gbits/sec
[  3]  2.0- 3.0 sec   598 MBytes  5.02 Gbits/sec
[  3]  3.0- 4.0 sec   580 MBytes  4.87 Gbits/sec
[  3]  4.0- 5.0 sec   590 MBytes  4.95 Gbits/sec
[  3]  5.0- 6.0 sec   594 MBytes  4.98 Gbits/sec
[  3]  6.0- 7.0 sec   598 MBytes  5.02 Gbits/sec
[  3]  7.0- 8.0 sec   606 MBytes  5.08 Gbits/sec
[  3]  8.0- 9.0 sec   596 MBytes  5.00 Gbits/sec
[  3]  9.0-10.0 sec   604 MBytes  5.07 Gbits/sec
[  3]  0.0-10.0 sec  5.75 GBytes  4.94 Gbits/sec

网络性能对比综述

使用Flannel的vxlan模式实现每个pod一个IP的方式，会比宿主机直接互联的网络性能损耗30%～40%，符合网上流传的测试结论。而flannel的host-gw模式比起宿主机互连的网络性能损耗大约是10%。

Vxlan会有一个封包解包的过程，所以会对网络性能造成较大的损耗，而host-gw模式是直接使用路由信息，网络损耗小，关于host-gw的架构请访问Flannel host-gw architecture。

Kubernete的性能测试

参考Kubernetes集群性能测试中的步骤，对kubernetes的性能进行测试。

我的集群版本是Kubernetes1.6.0，首先克隆代码，将kubernetes目录复制到$GOPATH/src/k8s.io/下然后执行：

$ ./hack/generate-bindata.sh
/usr/local/src/k8s.io/kubernetes /usr/local/src/k8s.io/kubernetes
Generated bindata file : test/e2e/generated/bindata.go has 13498 test/e2e/generated/bindata.go lines of lovely automated artifacts
No changes in generated bindata file: pkg/generated/bindata.go
/usr/local/src/k8s.io/kubernetes
$ make WHAT="test/e2e/e2e.test"
...
+++ [0425 17:01:34] Generating bindata:
    test/e2e/generated/gobindata_util.go
/usr/local/src/k8s.io/kubernetes /usr/local/src/k8s.io/kubernetes/test/e2e/generated
/usr/local/src/k8s.io/kubernetes/test/e2e/generated
+++ [0425 17:01:34] Building go targets for linux/amd64:
    test/e2e/e2e.test
$ make ginkgo
+++ [0425 17:05:57] Building the toolchain targets:
    k8s.io/kubernetes/hack/cmd/teststale
    k8s.io/kubernetes/vendor/github.com/jteeuwen/go-bindata/go-bindata
+++ [0425 17:05:57] Generating bindata:
    test/e2e/generated/gobindata_util.go
/usr/local/src/k8s.io/kubernetes /usr/local/src/k8s.io/kubernetes/test/e2e/generated
/usr/local/src/k8s.io/kubernetes/test/e2e/generated
+++ [0425 17:05:58] Building go targets for linux/amd64:
    vendor/github.com/onsi/ginkgo/ginkgo

$ export KUBERNETES_PROVIDER=local
$ export KUBECTL_PATH=/usr/bin/kubectl
$ go run hack/e2e.go -v -test  --test_args="--host=http://172.20.0.113:8080 --ginkgo.focus=\[Feature:Performance\]" >>log.txt

测试结果

Apr 25 18:27:31.461: INFO: API calls latencies: {
  "apicalls": [
    {
      "resource": "pods",
      "verb": "POST",
      "latency": {
        "Perc50": 2148000,
        "Perc90": 13772000,
        "Perc99": 14436000,
        "Perc100": 0
      }
    },
    {
      "resource": "services",
      "verb": "DELETE",
      "latency": {
        "Perc50": 9843000,
        "Perc90": 11226000,
        "Perc99": 12391000,
        "Perc100": 0
      }
    },
    ...
Apr 25 18:27:31.461: INFO: [Result:Performance] {
  "version": "v1",
  "dataItems": [
    {
      "data": {
        "Perc50": 2.148,
        "Perc90": 13.772,
        "Perc99": 14.436
      },
      "unit": "ms",
      "labels": {
        "Resource": "pods",
        "Verb": "POST"
      }
    },
...
2.857: INFO: Running AfterSuite actions on all node
Apr 26 10:35:32.857: INFO: Running AfterSuite actions on node 1

Ran 2 of 606 Specs in 268.371 seconds
SUCCESS! -- 2 Passed | 0 Failed | 0 Pending | 604 Skipped PASS

Ginkgo ran 1 suite in 4m28.667870101s
Test Suite Passed

从kubemark输出的日志中可以看到API calls latencies和Performance。

日志里显示，创建90个pod用时40秒以内，平均创建每个pod耗时0.44秒。

不同type的资源类型API请求耗时分布

Resource Verb 50% 90% 99%
services DELETE 8.472ms 9.841ms 38.226ms
endpoints PUT 1.641ms 3.161ms 30.715ms
endpoints GET 931µs 10.412ms 27.97ms
nodes PATCH 4.245ms 11.117ms 18.63ms
pods PUT 2.193ms 2.619ms 17.285ms

从log.txt日志中还可以看到更多详细请求的测试指标。

kubernetes-dashboard

注意事项

测试过程中需要用到docker镜像存储在GCE中，需要翻墙下载，我没看到哪里配置这个镜像的地址。该镜像副本已上传时速云：

用到的镜像有如下两个：

  • gcr.io/google_containers/pause-amd64:3.0
  • gcr.io/google_containers/serve_hostname:v1.4

时速云镜像地址：

  • index.tenxcloud.com/jimmy/pause-amd64:3.0
  • index.tenxcloud.com/jimmy/serve_hostname:v1.4

将镜像pull到本地后重新打tag。

Locust测试

请求统计

Method Name # requests # failures Median response time Average response time Min response time Max response time Average Content Size Requests/s
POST /login 5070 78 59000 80551 11218 202140 54 1.17
POST /metrics 5114232 85879 63000 82280 29518 331330 94 1178.77
None Total 5119302 85957 63000 82279 11218 331330 94 1179.94

响应时间分布

Name # requests 50% 66% 75% 80% 90% 95% 98% 99% 100%
POST /login 5070 59000 125000 140000 148000 160000 166000 174000 176000 202140
POST /metrics 5114993 63000 127000 142000 149000 160000 166000 172000 176000 331330
None Total 5120063 63000 127000 142000 149000 160000 166000 172000 176000 331330

以上两个表格都是瞬时值。请求失败率在2%左右。

Sample-webapp起了48个pod。

Locust模拟10万用户，每秒增长100个。

locust-test

关于Locust的使用请参考Github：https://github.com/rootsongjc/distributed-load-testing-using-kubernetes

参考

基于 Python 的性能测试工具 locust (与 LR 的简单对比)

Locust docs

python用户负载测试工具：locust

Kubernetes集群性能测试

CoreOS是如何将Kubernetes的性能提高10倍的

Kubernetes 1.3 的性能和弹性 —— 2000 节点，60,0000 Pod 的集群

运用Kubernetes进行分布式负载测试

Kubemark User Guide

Flannel host-gw architecture