Kubernetes 下部署 Jmeter 集群
【摘要】 Kubernetes 下部署 Jmeter 集群
前提条件
Kubernetes > 1.16
部署拓扑
可以从 master 节点启动测试，master 节点把对应的测试脚本发送到对应的 slaves 节点，slave 节点的 pod/nodes 主要作用即发压。
部署文件清单：
jmeter_cluster_create.sh— 此脚本将要求一个唯一的 namespace，然后它将继续创建命名空间和所有组件（jmeter master，slaves，influxdb 和 grafana）。- 注意：在启动前，请在
jmeter_slaves_deploy.yaml文件中设置要用于 slaves 服务器的副本数，通常副本数应与拥有的 worker nodes 相匹配。 jmeter_master_configmap.yaml— Jmeter master 的应用配置。jmeter_master_deployment.yaml— Jmeter master 的部署清单。jmeter_slaves_deploy.yaml— Jmeter slave 的部署清单。jmeter_slave_svc.yaml— jmeter slave 的服务清单。使用 headless service，这使我们能够直接获取 jmeter slave 的 POD IP 地址，而我们不需要 DNS 或轮询。创建此文件是为了使 slave Pod IP 地址更容易直接发送到 jmeter master。jmeter_influxdb_configmap.yaml— influxdb 部署的应用配置。如果要在默认的 influxdb 端口之外使用 graphite 存储方法，这会将 influxdb 配置为暴露端口 2003，以便支持 graphite 。因此，可以使用 influxdb 部署来支持jmeter 后置监听器方法（graphite 和 influxdb）。jmeter_influxdb_deploy.yaml— Influxdb 的部署清单jmeter_influxdb_svc.yaml— Influxdb 的服务清单。jmeter_grafana_deploy.yaml— grafana 部署清单。jmeter_grafana_svc.yaml— grafana 部署的服务清单，默认情况下使用 NodePort，如果公有云中运行它，则可以将其更改为 LoadBalancer（并且可以设置 CNAME 以使用 FQDN 缩短名称）。dashboard.sh— 该脚本用于自动创建以下内容：- （1）influxdb pod 中的一个 influxdb 数据库（Jmeter）
- （2）grafana 中的数据源（jmeterdb）
start_test.sh—此脚本用于自动运行 Jmeter 测试脚本，而无需手动登录 Jmeter 主 shell，它将询问 Jmeter 测试脚本的位置，然后将其复制到 Jmeter master pod 并启动自动对 Jmeter slave 进行测试。jmeter_stop.sh- 停止测试GrafanaJMeterTemplate.json— 预先构建的 Jmeter grafana 仪表板。Dockerfile-base- 构建 Jmeter 基础镜像Dockerfile-master- 构建 Jmeter master 镜像Dockerfile-slave- 构建 Jmeter slave 镜像Dockerimages.sh- 批量构建 docker 镜像
docker 镜像
构建 docker 镜像
执行脚本，构建镜像：
./dockerimages.sh
查看镜像：
$ docker images
将镜像推送到 Registry：
$ sudo docker login --username=xxxx registry.cn-beijing.aliyuncs.com
$ sudo docker tag [ImageId] registry.cn-beijing.aliyuncs.com/7d/jmeter-base:[镜像版本号]
$ sudo docker push registry.cn-beijing.aliyuncs.com/7d/jmeter-base:[镜像版本号]
部署清单
Dockerfile-base （构建 Jmeter 基础镜像）：
FROM alpine:latest
LABEL MAINTAINER 7DGroup
ARG JMETER_VERSION=5.2.1
#定义时区参数
ENV TZ=Asia/Shanghai
RUN apk update && \
apk upgrade && \
apk add --update openjdk8-jre wget tar bash && \
mkdir /jmeter && cd /jmeter/ && \
wget https://mirrors.tuna.tsinghua.edu.cn/apache/jmeter/binaries/apache-jmeter-${JMETER_VERSION}.tgz && \
tar -xzf apache-jmeter-$JMETER_VERSION.tgz && rm apache-jmeter-$JMETER_VERSION.tgz && \
cd /jmeter/apache-jmeter-$JMETER_VERSION/ && \
wget -q -O /tmp/JMeterPlugins-Standard-1.4.0.zip https://jmeter-plugins.org/downloads/file/JMeterPlugins-Standard-1.4.0.zip && unzip -n /tmp/JMeterPlugins-Standard-1.4.0.zip && rm /tmp/JMeterPlugins-Standard-1.4.0.zip && \
wget -q -O /jmeter/apache-jmeter-$JMETER_VERSION/lib/ext/pepper-box-1.0.jar https://github.com/raladev/load/blob/master/JARs/pepper-box-1.0.jar?raw=true && \
cd /jmeter/apache-jmeter-$JMETER_VERSION/ && \
wget -q -O /tmp/bzm-parallel-0.7.zip https://jmeter-plugins.org/files/packages/bzm-parallel-0.7.zip && \unzip -n /tmp/bzm-parallel-0.7.zip && rm /tmp/bzm-parallel-0.7.zip && \
ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo "$TZ" > /etc/timezone
ENV JMETER_HOME /jmeter/apache-jmeter-$JMETER_VERSION/
ENV PATH $JMETER_HOME/bin:$PATH
#JMeter 主配置文件
ADD jmeter.properties $JMETER_HOME/bin/jmeter.properties
Dockerfile-master（构建 Jmeter master 镜像）：
FROM registry.cn-beijing.aliyuncs.com/7d/jmeter-base:latest
MAINTAINER 7DGroup
EXPOSE 60000
Dockerfile-slave（构建 Jmeter slave 镜像）：
Dockerfile-slave：
FROM registry.cn-beijing.aliyuncs.com/7d/jmeter-base:latest
MAINTAINER 7DGroup
EXPOSE 1099 50000
ENTRYPOINT $JMETER_HOME/bin/jmeter-server \
-Dserver.rmi.localport=50000 \
-Dserver_port=1099 \
-Jserver.rmi.ssl.disable=true
Dockerimages.sh（批量构建 docker 镜像）：
#!/bin/bash -e
docker build --tag="registry.cn-beijing.aliyuncs.com/7d/jmeter-base:latest" -f Dockerfile-base .
docker build --tag="registry.cn-beijing.aliyuncs.com/7d/jmeter-master:latest" -f Dockerfile-master .
docker build --tag="registry.cn-beijing.aliyuncs.com/7d/jmeter-slave:latest" -f Dockerfile-slave .
Kubernetes部署
部署组件
执行jmeter_cluster_create.sh,并输入一个唯一的 namespace
./jmeter_cluster_create.sh
等待一会，查看pods安装情况：
$ kubectl get pods -n 7dgroup
NAME READY STATUS RESTARTS AGE
influxdb-jmeter-584cf69759-j5m85 1/1 Running 2 5m
jmeter-grafana-6d5b75b7f6-57dxj 1/1 Running 1 5m
jmeter-master-84bfd5d96d-kthzm 1/1 Running 0 5m
jmeter-slaves-b5b75757-dxkxz 1/1 Running 0 5m
jmeter-slaves-b5b75757-n58jw 1/1 Running 0 5m
部署清单
主执行脚本
jmeter_cluster_create.sh（创建命名空间和所有组件（jmeter master，slaves，influxdb 和 grafana））:
#!/usr/bin/env bash
#Create multiple Jmeter namespaces on an existing kuberntes cluster
#Started On January 23, 2018
working_dir=`pwd`
echo "checking if kubectl is present"
if ! hash kubectl 2>/dev/null
then
echo "'kubectl' was not found in PATH"
echo "Kindly ensure that you can acces an existing kubernetes cluster via kubectl"
exit
fi
kubectl version --short
echo "Current list of namespaces on the kubernetes cluster:"
echo
kubectl get namespaces | grep -v NAME | awk '{print $1}'
echo
echo "Enter the name of the new tenant unique name, this will be used to create the namespace"
read tenant
echo
#Check If namespace exists
kubectl get namespace $tenant > /dev/null 2>&1
if [ $? -eq 0 ]
then
echo "Namespace $tenant already exists, please select a unique name"
echo "Current list of namespaces on the kubernetes cluster"
sleep 2
kubectl get namespaces | grep -v NAME | awk '{print $1}'
exit 1
fi
echo
echo "Creating Namespace: $tenant"
kubectl create namespace $tenant
echo "Namspace $tenant has been created"
echo
echo "Creating Jmeter slave nodes"
nodes=`kubectl get no | egrep -v "master|NAME" | wc -l`
echo
echo "Number of worker nodes on this cluster is " $nodes
echo
#echo "Creating $nodes Jmeter slave replicas and service"
echo
kubectl create -n $tenant -f $working_dir/jmeter_slaves_deploy.yaml
kubectl create -n $tenant -f $working_dir/jmeter_slaves_svc.yaml
echo "Creating Jmeter Master"
kubectl create -n $tenant -f $working_dir/jmeter_master_configmap.yaml
kubectl create -n $tenant -f $working_dir/jmeter_master_deploy.yaml
echo "Creating Influxdb and the service"
kubectl create -n $tenant -f $working_dir/jmeter_influxdb_configmap.yaml
kubectl create -n $tenant -f $working_dir/jmeter_influxdb_deploy.yaml
kubectl create -n $tenant -f $working_dir/jmeter_influxdb_svc.yaml
echo "Creating Grafana Deployment"
kubectl create -n $tenant -f $working_dir/jmeter_grafana_deploy.yaml
kubectl create -n $tenant -f $working_dir/jmeter_grafana_svc.yaml
echo "Printout Of the $tenant Objects"
echo
kubectl get -n $tenant all
echo namespace = $tenant > $working_dir/tenant_export
jmeter_slaves
jmeter_slaves_deploy.yaml（Jmeter slave 的部署清单）：
apiVersion: apps/v1
kind: Deployment
metadata:
name: jmeter-slaves
labels:
jmeter_mode: slave
spec:
replicas: 2
selector:
matchLabels:
jmeter_mode: slave
template:
metadata:
labels:
jmeter_mode: slave
spec:
containers:
- name: jmslave
image: registry.cn-beijing.aliyuncs.com/7d/jmeter-slave:latest
imagePullPolicy: IfNotPresent
ports:
- containerPort: 1099
- containerPort: 50000
resources:
limits:
cpu: 4000m
memory: 4Gi
requests:
cpu: 500m
memory: 512Mi
jmeter_slaves_svc.yaml（ Jmeter slave 的服务清单）：
apiVersion: v1
kind: Service
metadata:
name: jmeter-slaves-svc
labels:
jmeter_mode: slave
spec:
clusterIP: None
ports:
- port: 1099
name: first
targetPort: 1099
- port: 50000
name: second
targetPort: 50000
jmeter_master
jmeter_master_configmap.yaml（jmeter_master 应用配置）：
apiVersion: v1
kind: ConfigMap
metadata:
name: jmeter-load-test
labels:
app: influxdb-jmeter
data:
load_test: |
#!/bin/bash
#Script created to invoke jmeter test script with the slave POD IP addresses
#Script should be run like: ./load_test "path to the test script in jmx format"
/jmeter/apache-jmeter-*/bin/jmeter -n -t $1 `getent ahostsv4 jmeter-slaves-svc | cut -d' ' -f1 | sort -u | awk -v ORS=, '{print $1}' | sed 's/,$//'`
jmeter_master_deploy.yaml（jmeter_master 部署清单）：
apiVersion: apps/v1 # for versions before 1.9.0 use apps/v1beta2
kind: Deployment
metadata:
name: jmeter-master
labels:
jmeter_mode: master
spec:
replicas: 1
selector:
matchLabels:
jmeter_mode: master
template:
metadata:
labels:
jmeter_mode: master
spec:
containers:
- name: jmmaster
image: registry.cn-beijing.aliyuncs.com/7d/jmeter-master:latest
imagePullPolicy: IfNotPresent
command: [ "/bin/bash", "-c", "--" ]
args: [ "while true; do sleep 30; done;" ]
volumeMounts:
- name: loadtest
mountPath: /load_test
subPath: "load_test"
ports:
- containerPort: 60000
resources:
limits:
cpu: 4000m
memory: 4Gi
requests:
cpu: 500m
memory: 512Mi
volumes:
- name: loadtest
configMap:
name: jmeter-load-test
influxdb
jmeter_influxdb_configmap.yaml（influxdb 的应用配置）：
apiVersion: v1
kind: ConfigMap
metadata:
name: influxdb-config
labels:
app: influxdb-jmeter
data:
influxdb.conf: |
[meta]
dir = "/var/lib/influxdb/meta"
[data]
dir = "/var/lib/influxdb/data"
engine = "tsm1"
wal-dir = "/var/lib/influxdb/wal"
# Configure the graphite api
[[graphite]]
enabled = true
bind-address = ":2003" # If not set, is actually set to bind-address.
database = "jmeter" # store graphite data in this database
jmeter_influxdb_deploy.yaml（influxdb 部署清单）：
apiVersion: apps/v1
kind: Deployment
metadata:
name: influxdb-jmeter
labels:
app: influxdb-jmeter
spec:
replicas: 1
selector:
matchLabels:
app: influxdb-jmeter
template:
metadata:
labels:
app: influxdb-jmeter
spec:
containers:
- image: influxdb
imagePullPolicy: IfNotPresent
name: influxdb
volumeMounts:
- name: config-volume
mountPath: /etc/influxdb
ports:
- containerPort: 8083
name: influx
- containerPort: 8086
name: api
- containerPort: 2003
name: graphite
volumes:
- name: config-volume
configMap:
name: influxdb-config
jmeter_influxdb_svc.yaml（influxdb 部署服务清单）：
apiVersion: v1
kind: Service
metadata:
name: jmeter-influxdb
labels:
app: influxdb-jmeter
spec:
ports:
- port: 8083
name: http
targetPort: 8083
- port: 8086
name: api
targetPort: 8086
- port: 2003
name: graphite
targetPort: 2003
selector:
app: influxdb-jmeter
grafana
jmeter_grafana_deploy.yaml（grafana 部署清单）：
apiVersion: apps/v1
kind: Deployment
metadata:
name: jmeter-grafana
labels:
app: jmeter-grafana
spec:
replicas: 1
selector:
matchLabels:
app: jmeter-grafana
template:
metadata:
labels:
app: jmeter-grafana
spec:
containers:
- name: grafana
image: grafana/grafana:5.2.0
imagePullPolicy: IfNotPresent
ports:
- containerPort: 3000
protocol: TCP
env:
- name: GF_AUTH_BASIC_ENABLED
value: "true"
- name: GF_USERS_ALLOW_ORG_CREATE
value: "true"
- name: GF_AUTH_ANONYMOUS_ENABLED
value: "true"
- name: GF_AUTH_ANONYMOUS_ORG_ROLE
value: Admin
- name: GF_SERVER_ROOT_URL
# If you're only using the API Server proxy, set this value instead:
# value: /api/v1/namespaces/kube-system/services/monitoring-grafana/proxy
value: /
jmeter_grafana_svc.yaml（grafana 部署服务清单）：
apiVersion: v1
kind: Service
metadata:
name: jmeter-grafana
labels:
app: jmeter-grafana
spec:
ports:
- port: 3000
targetPort: 3000
selector:
app: jmeter-grafana
type: NodePort
---
apiVersion: extensions/v1beta1
kind: Ingress
metadata:
annotations:
nginx.ingress.kubernetes.io/service-weight: 'jmeter-grafana: 100'
name: jmeter-grafana-ingress
spec:
rules:
# 配置七层域名
- host: grafana-jmeter.7d.com
http:
paths:
# 配置Context Path
- path: /
backend:
serviceName: jmeter-grafana
servicePort: 3000
初始化 dashboard
启动 dashboard 脚本
$ ./dashboard.sh
检查 service 部署情况：
$ kubectl get svc -n 7dgroup
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
jmeter-grafana NodePort 10.96.6.201 <none> 3000:31801/TCP 10m
jmeter-influxdb ClusterIP 10.96.111.60 <none> 8083/TCP,8086/TCP,2003/TCP 10m
jmeter-slaves-svc ClusterIP None <none> 1099/TCP,50000/TCP 10m
我们可以通过 http://任意 node_ip:31801/ 访问 grafana
最后，我们在 grafana 导入 dashborad 模版:
如果你不喜欢这个模版，也可以导入热门模版：5496
部署清单
dashboard.sh 该脚本用于自动创建以下内容：
- （1）influxdb pod 中的一个 influxdb 数据库（Jmeter）
- （2）grafana 中的数据源（jmeterdb）
#!/usr/bin/env bash
working_dir=`pwd`
#Get namesapce variable
tenant=`awk '{print $NF}' $working_dir/tenant_export`
## Create jmeter database automatically in Influxdb
echo "Creating Influxdb jmeter Database"
##Wait until Influxdb Deployment is up and running
##influxdb_status=`kubectl get po -n $tenant | grep influxdb-jmeter | awk '{print $2}' | grep Running
influxdb_pod=`kubectl get po -n $tenant | grep influxdb-jmeter | awk '{print $1}'`
kubectl exec -ti -n $tenant $influxdb_pod -- influx -execute 'CREATE DATABASE jmeter'
## Create the influxdb datasource in Grafana
echo "Creating the Influxdb data source"
grafana_pod=`kubectl get po -n $tenant | grep jmeter-grafana | awk '{print $1}'`
## Make load test script in Jmeter master pod executable
#Get Master pod details
master_pod=`kubectl get po -n $tenant | grep jmeter-master | awk '{print $1}'`
kubectl exec -ti -n $tenant $master_pod -- cp -r /load_test /![]()jmeter/load_test
kubectl exec -ti -n $tenant $master_pod -- chmod 755 /jmeter/load_test
##kubectl cp $working_dir/influxdb-jmeter-datasource.json -n $tenant $grafana_pod:/influxdb-jmeter-datasource.json
kubectl exec -ti -n $tenant $grafana_pod -- curl 'http://admin:admin@127.0.0.1:3000/api/datasources' -X POST -H 'Content-Type: application/json;charset=UTF-8' --data-binary '{"name":"jmeterdb","type":"influxdb","url":"http://jmeter-influxdb:8086","access":"proxy","isDefault":true,"database":"jmeter","user":"admin","password":"admin"}'
启动测试
执行脚本
$ ./start_test.sh
需要一个测试脚本，本例为：web-test.jmx
$ ./start_test.sh
Enter path to the jmx file web-test.jmx
''SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/jmeter/apache-jmeter-5.0/lib/log4j-slf4j-impl-2.11.0.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/jmeter/apache-jmeter-5.0/lib/ext/pepper-box-1.0.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]
Jul 25, 2020 11:30:58 AM java.util.prefs.FileSystemPreferences$1 run
INFO: Created user preferences directory.
Creating summariser <summary>
Created the tree successfully using web-test.jmx
Configuring remote engine: 10.100.113.31
Configuring remote engine: 10.100.167.173
Starting remote engines
Starting the test @ Sat Jul 25 11:30:59 UTC 2020 (1595676659540)
Remote engines have been started
Waiting for possible Shutdown/StopTestNow/Heapdump message on port 4445
summary + 803 in 00:00:29 = 27.5/s Avg: 350 Min: 172 Max: 1477 Err: 0 (0.00%) Active: 40 Started: 40 Finished: 0
summary + 1300 in 00:00:29 = 45.3/s Avg: 367 Min: 172 Max: 2729 Err: 0 (0.00%) Active: 40 Started: 40 Finished: 0
summary = 2103 in 00:00:58 = 36.4/s Avg: 361 Min: 172 Max: 2729 Err: 0 (0.00%)
summary + 1400 in 00:00:31 = 45.4/s Avg: 342 Min: 160 Max: 2145 Err: 0 (0.00%) Active: 40 Started: 40 Finished: 0
summary = 3503 in 00:01:29 = 39.5/s Avg: 353 Min: 160 Max: 2729 Err: 0 (0.00%)
summary + 1400 in 00:00:31 = 45.2/s Avg: 352 Min: 169 Max: 2398 Err: 0 (0.00%) Active: 40 Started: 40 Finished: 0
summary = 4903 in 00:02:00 = 41.0/s Avg: 353 Min: 160 Max: 2729 Err: 0 (0.00%)
summary + 1400 in 00:00:30 = 46.8/s Avg: 344 Min: 151 Max: 1475 Err: 0 (0.00%) Active: 40 Started: 40 Finished: 0
summary = 6303 in 00:02:30 = 42.1/s Avg: 351 Min: 151 Max: 2729 Err: 0 (0.00%)
summary + 1200 in 00:00:28 = 43.5/s Avg: 354 Min: 163 Max: 2018 Err: 0 (0.00%) Active: 40 Started: 40 Finished: 0
summary = 7503 in 00:02:57 = 42.3/s Avg: 351 Min: 151 Max: 2729 Err: 0 (0.00%)
summary + 1300 in 00:00:30 = 43.7/s Avg: 456 Min: 173 Max: 2401 Err: 0 (0.00%) Active: 40 Started: 40 Finished: 0
summary = 8803 in 00:03:27 = 42.5/s Avg: 367 Min: 151 Max: 2729 Err: 0 (0.00%)
summary + 1400 in 00:00:31 = 44.9/s Avg: 349 Min: 158 Max: 2128 Err: 0 (0.00%) Active: 40 Started: 40 Finished: 0
summary = 10203 in 00:03:58 = 42.8/s Avg: 364 Min: 151 Max: 2729 Err: 0 (0.00%)
summary + 1400 in 00:00:32 = 44.3/s Avg: 351 Min: 166 Max: 1494 Err: 0 (0.00%) Active: 40 Started: 40 Finished: 0
summary = 11603 in 00:04:30 = 43.0/s Avg: 363 Min: 151 Max: 2729 Err: 0 (0.00%)
summary + 1400 in 00:00:30 = 46.9/s Avg: 344 Min: 165 Max: 2075 Err: 0 (0.00%) Active: 40 Started: 40 Finished: 0
summary = 13003 in 00:05:00 = 43.4/s Avg: 361 Min: 151 Max: 2729 Err: 0 (0.00%)
summary + 1300 in 00:00:28 = 46.0/s Avg: 352 Min: 159 Max: 1486 Err: 0 (0.00%) Active: 40 Started: 40 Finished: 0
summary = 14303 in 00:05:28 = 43.6/s Avg: 360 Min: 151 Max: 2729 Err: 0 (0.00%)
summary + 1400 in 00:00:31 = 45.6/s Avg: 339 Min: 163 Max: 2042 Err: 0 (0.00%) Active: 40 Started: 40 Finished: 0
summary = 15703 in 00:05:58 = 43.8/s Avg: 358 Min: 151 Max: 2729 Err: 0 (0.00%)
summary + 494 in 00:00:07 = 69.0/s Avg: 350 Min: 171 Max: 1499 Err: 0 (0.00%) Active: 0 Started: 40 Finished: 40
summary = 16197 in 00:06:06 = 44.3/s Avg: 358 Min: 151 Max: 2729 Err: 0 (0.00%)
Tidying up remote @ Sat Jul 25 11:37:09 UTC 2020 (1595677029361)
... end of run
查看测试数据：
部署清单
start_test.sh（此脚本用于自动运行 Jmeter 测试脚本，而无需手动登录 Jmeter 主 shell，它将询问 Jmeter 测试脚本的位置，然后将其复制到 Jmeter master pod 并启动自动对 Jmeter slave 进行测试）:
#!/usr/bin/env bash
#Script created to launch Jmeter tests directly from the current terminal without accessing the jmeter master pod.
#It requires that you supply the path to the jmx file
#After execution, test script jmx file may be deleted from the pod itself but not locally.
#直接从当前终端启动 Jmeter 测试而创建的脚本，无需访问 Jmeter master pod。
#要求提供 jmx 文件的路径
#执行后，测试脚本 jmx 文件可能会从 pod 本身删除，但不会在本地删除。
working_dir="`pwd`"
# 获取 namesapce 变量
tenant=`awk '{print $NF}' "$working_dir/tenant_export"`
jmx="$1"
[ -n "$jmx" ] || read -p 'Enter path to the jmx file ' jmx
if [ ! -f "$jmx" ];
then
echo "Test script file was not found in PATH"
echo "Kindly check and input the correct file path"
exit
fi
test_name="$(basename "$jmx")"
# 获取 master pod 详细信息
master_pod=`kubectl get po -n $tenant | grep jmeter-master | awk '{print $1}'`
kubectl cp "$jmx" -n $tenant "$master_pod:/$test_name"
## 启动 Jmeter 压测
kubectl exec -ti -n $tenant $master_pod -- /bin/bash /load_test "$test_name"
kubectl exec -ti -n $tenant $master_pod -- /bin/bash /load_test "$test_name"
jmeter_stop.sh（停止测试）:
#!/usr/bin/env bash
#Script writtent to stop a running jmeter master test
#Kindly ensure you have the necessary kubeconfig
#编写脚本来停止运行的 jmeter master 测试
#请确保你有必要的 kubeconfig
working_dir=`pwd`
#获取 namesapce 变量
tenant=`awk '{print $NF}' $working_dir/tenant_export`
master_pod=`kubectl get po -n $tenant | grep jmeter-master | awk '{print $1}'`
kubectl -n $tenant exec -it $master_pod -- bash -c "./jmeter/apache-jmeter-5.0/bin/stoptest.sh"
小结
传统 Jmeter 存在的问题：
- 并发数超过单节点承载能力时，多节点环境配置、维护复杂；
- 默认配置下无法并行运行多个测试，需要更改配置启动额外进程；
- 难以支持云环境下测试资源的弹性伸缩需求。
Kubernetes-Jmeter 带来的改变：
- 压测执行节点一键安装；
- 多个项目、多个测试可并行使用同一个测试资源池（最大并发数允许情况下， Kubernetes 也提供了 RBAC、namespace 等管理能力，支持多用户共享一个集群，并实现资源限制），提高资源利用率；
- 对接 Kubernetes HPA 根据并发数自动启动、释放压测执行节点。
源码地址：
参考资料：
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