# 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** - [Locust](http://locust.io)：一个简单易用的用户负载测试工具，用来测试web或其他系统能够同时处理的并发用户数。 - curl - [kubemark](https://github.com/kubernetes/kubernetes/tree/master/test/e2e) - 测试程序：sample-webapp，源码见Github [kubernetes的分布式负载测试](https://github.com/rootsongjc/distributed-load-testing-using-kubernetes) **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： ```Bash $ 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** ```bash 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** ```bash 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** ```bash 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： ```bash iperf -s -p 12345 -i 1 -M ``` 客户端命令： ```bash 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](https://docs.openshift.com/container-platform/3.4/architecture/additional_concepts/flannel.html)。 ## Kubernete的性能测试 参考[Kubernetes集群性能测试](https://supereagle.github.io/2017/03/09/kubemark/)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： ```bash $ ./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** ```bash 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。  ### 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。  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)](https://testerhome.com/topics/4839) - [Locust docs](http://docs.locust.io/en/latest/what-is-locust.html) - [Kubernetes集群性能测试](https://supereagle.github.io/2017/03/09/kubemark/) - [CoreOS是如何将Kubernetes的性能提高10倍的](http://dockone.io/article/1050) - [运用Kubernetes进行分布式负载测试](http://www.csdn.net/article/2015-07-07/2825155) - [Kubemark User Guide](https://github.com/kubernetes/community/blob/master/contributors/devel/kubemark-guide.md) - [Flannel host-gw architecture](https://docs.openshift.com/container-platform/3.4/architecture/additional_concepts/flannel.html)