搭建单机版K8S运行Flink集群
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环境要求 操作系统:
CentOS 7.x 64位
Kubernetes版本:v1.16.2
Docker版本:19.03.13-ce
Flink版本:1.14.3
使用中国YUM及镜像源
1.安装Kubernetes:
1.1 创建文件:/etc/yum.repos.d/kubernetes.repo,内容如下:
[kubernetes]
name=Kubernetes
baseurl=https://mirrors.aliyun.com/kubernetes/yum/repos/kubernetes-el7-x86_64/
enabled=1
gpgcheck=1
repo_gpgcheck=1
gpgkey=https://mirrors.aliyun.com/kubernetes/yum/doc/yum-key.gpg https://mirrors.aliyun.com/kubernetes/yum/doc/rpm-package-key.gpg
1.2 执行安装命令:
yum install -y kubelet-1.16.2 kubeadm-1.16.2 kubectl-1.16.2
1.3 启动kubelet服务并设置开机自启:
systemctl daemon-reload
systemctl start kubelet
systemctl enable kubelet
2.安装Docker:
2.1 创建文件:/etc/yum.repos.d/docker-ce.repo,内容如下:
[docker-ce-stable]
name=Docker CE Stable - $basearch
baseurl=https://mirrors.aliyun.com/docker-ce/linux/centos/7/$basearch/stable
enabled=1
gpgcheck=1
gpgkey=https://mirrors.aliyun.com/docker-ce/linux/centos/gpg
2.2 执行安装命令:
yum install -y docker-ce-19.03.13 docker-ce-cli-19.03.13 containerd.io
2.3 启动Docker服务并设置开机自启:
sudo systemctl start docker
sudo systemctl enable docker
2.4 验证Docker是否安装成功:
sudo docker run hello-world
如果输出“Hello from Docker!”则说明安装成功。
3.配置Kubernetes集群
3.1 初始化Kubernetes集群
kubeadm init --kubernetes-version=v1.16.2 --apiserver-advertise-address=192.168.143.135 --image-repository registry.aliyuncs.com/google_containers --pod-network-cidr=192.168.0.0/16
注意: 10.244.0.0是中--pod-network-cidr参数指定的Pod网络的地址段
3.2 设置kubectl命令行工具的上下文环境
mkdir -p $HOME/.kube
sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
sudo chown $(id -u):$(id -g) $HOME/.kube/config
4.配置Flannel网络插件
4.1下载Flannel的配置yaml文件
kubectl apply -f https://raw.githubusercontent.com/coreos/flannel/master/Documentation/kube-flannel.yml
4.2 确保所有Pod都正常运行
kubectl get pods --all-namespaces
问题:
Kubeadm初始化报错:[ERROR CRI]: container runtime is not running_架构师小冯的博客-CSDN博客 k8s端口被占用:[ERROR FileAvailable--etc-kubernetes-manifests-kub、[ERROR Port-10250]: Port 10250 is in use_k8s端口被etcd占用_Tz.的博客-CSDN博客
Remove imagefailed: rpc error: code = Unknown desc = Error response from daemon_技术奔跑把的博客-CSDN博客
基于Docker快速搭建Hadoop集群和Flink运行环境
前言
本文主要讲,基于Docker在本地快速搭建一个Hadoop 2.7.2集群和Flink 1.11.2运行环境,用于日常Flink任务运行测试。
前任栽树,后人乘凉,我们直接用Docker Hadoop镜像kiwenlau/hadoop-cluster-docker来搭建,这个镜像内已经配置部署好了Hadoop 2.7.2,感谢前辈们造好轮子。
该Docker Hadoop镜像优点:基于Docker快速搭建多节点Hadoop集群
我们要搭建一个3节点的Hadoop集群,集群架构如下图,一个主节点hadoop-master,两个数据节点hadoop-slave1和hadoop-slave2。每个Hadoop节点运行在一个Docker容器中,容器之间互相连通,构成一个Hadoop集群。
还不熟悉Docker的可以参见:菜鸟教程-Docker教程
搭建过程部分搬运自镜像作者教程:基于Docker搭建Hadoop集群之升级版
搭建集群
1.下载Docker镜像
sudo docker pull kiwenlau/hadoop:1.0
2.下载GitHub仓库
git clone https://github.com/kiwenlau/hadoop-cluster-docker
3.创建Hadoop网络
sudo docker network create --driver=bridge hadoop
4.运行Docker容器
cd hadoop-cluster-docker
./start-container.sh
运行结果
start hadoop-master container...
start hadoop-slave1 container...
start hadoop-slave2 container...
root@hadoop-master:~#
启动了3个容器,1个master, 2个slave
运行后就进入了hadoop-master容器的/root目录,我们在目录下新建一个自己的文件夹shadow
这时候不要着急启动Hadoop集群,我们先升级一下环境配置
环境升级
1.更新包
apt-get update
apt-get install vim
2.升级JDK
将JDK 1.7升级到JDK 1.8,先去官网下载一个JDK 1.8:jdk-8u261-linux-x64.tar.gz
从本地拷贝JDK 1.8到Docker容器hadoop-master
docker cp jdk-8u261-linux-x64.tar.gz hadoop-master:/root/shadow
解压升级
tar -zxvf jdk-8u261-linux-x64.tar.gz
sudo update-alternatives --install /usr/bin/java java /root/shadow/jdk1.8.0_261/bin/java 300
sudo update-alternatives --config java
sudo update-alternatives --install /usr/bin/javac javac /root/shadow/jdk1.8.0_261/bin/javac 300
sudo update-alternatives --config javac
java -version
javac -version
卸载JDK1.7:删除JDK1.7的目录即可
3.配置环境变量
vi ~/.bashrc
export HADOOP_HOME=/usr/local/hadoop
export HADOOP_CLASSPATH=$($HADOOP_HOME/bin/hadoop classpath)
export JAVA_HOME=/root/shadow/jdk1.8.0_261
export JAVA=/root/shadow/jdk1.8.0_261/bin/java
export PATH=$JAVA_HOME/bin:$PATH
export CLASS_PATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar:$CLASS_PATH:$HADOOP_CLASSPATH
source ~/.bashrc
4.修改集群启动脚本
vi start-hadoop.sh
关闭Hadoop安全模式,末尾加上:hadoop dfsadmin -safemode leave
配置Hadoop
修改Hadoop配置,Hadoop配置路径:/usr/local/hadoop/etc/hadoop
core-site.xml
<?xml version="1.0"?>
<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://hadoop-master:9000/</value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>/usr/local/hadoop/tmp</value>
</property>
<property>
<name>dfs.journalnode.edits.dir</name>
<value>/usr/local/hadoop/journal</value>
</property>
</configuration>
yarn-site.xml
<?xml version="1.0"?>
<configuration>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
<property>
<name>yarn.nodemanager.aux-services.mapreduce_shuffle.class</name>
<value>org.apache.hadoop.mapred.ShuffleHandler</value>
</property>
<property>
<name>yarn.resourcemanager.hostname</name>
<value>hadoop-master</value>
</property>
<property>
<name>yarn.nodemanager.resource.memory-mb</name>
<value>1024</value>
</property>
<property>
<name>yarn.scheduler.minimum-allocation-mb</name>
<value>1024</value>
</property>
<property>
<name>yarn.scheduler.maximum-allocation-mb</name>
<value>1024</value>
</property>
<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
</property>
<property>
<name>yarn.log-aggregation-enable</name>
<value>true</value>
</property>
<property>
<name>yarn.nodemanager.log-aggregation.roll-monitoring-interval-seconds</name>
<value>3600</value>
</property>
<property>
<name>yarn.nodemanager.remote-app-log-dir</name>
<value>/tmp/logs</value>
</property>
</configuration>
hdfs-site.xml
<?xml version="1.0"?>
<configuration>
<property>
<name>dfs.namenode.name.dir</name>
<value>file:///root/hdfs/namenode</value>
<description>NameNode directory for namespace and transaction logs storage.</description>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>file:///root/hdfs/datanode</value>
<description>DataNode directory</description>
</property>
<property>
<name>dfs.replication</name>
<value>2</value>
</property>
<property>
<name>dfs.permissions</name>
<value>false</value>
</property>
<property>
<name>dfs.safemode.threshold.pct</name>
<value>1</value>
</property>
<property>
<name>dfs.client.use.datanode.hostname</name>
<value>true</value>
</property>
<property>
<name>dfs.datanode.use.datanode.hostname</name>
<value>true</value>
</property>
</configuration>
配置Flink
1.Flink官网下载:Flink 1.11.2
2.从本地拷贝JDK 1.8到Docker容器hadoop-master
docker cp flink-1.11.2-bin-scala_2.11.tgz hadoop-master:/root/shadow
3.修改Flink配置
tar -zxvf flink-1.11.2-bin-scala_2.11.tgz
cd flink-1.11.2/conf/
vi flink-conf.yaml
flink-conf.yaml
jobmanager.rpc.address: hadoop-master
jobmanager.memory.process.size: 1024m
taskmanager.memory.process.size: 1024m
taskmanager.numberOfTaskSlots: 2
parallelism.default: 2
打包镜像
1.将刚刚配置好的容器hadoop-master打包成新的镜像
docker commit -m="Hadoop&Flink" -a="shadow" fd5163c5baac kiwenlau/hadoop:1.1
2.删除正在运行的容器
cd hadoop-cluster-docker
./rm-container.sh
3.修改启动脚本,将镜像版本改为1.1
vi start-container.sh
start-container.sh
#!/bin/bash
# the default node number is 3
N=$1:-3
# start hadoop master container
sudo docker rm -f hadoop-master &> /dev/null
echo "start hadoop-master container..."
sudo docker run -itd \\
--net=hadoop \\
-p 50070:50070 \\
-p 8088:8088 \\
-p 8032:8032 \\
-p 9000:9000 \\
--name hadoop-master \\
--hostname hadoop-master \\
kiwenlau/hadoop:1.1 &> /dev/null
# start hadoop slave container
i=1
while [ $i -lt $N ]
do
sudo docker rm -f hadoop-slave$i &> /dev/null
echo "start hadoop-slave$i container..."
sudo docker run -itd \\
--net=hadoop \\
--name hadoop-slave$i \\
--hostname hadoop-slave$i \\
kiwenlau/hadoop:1.1 &> /dev/null
i=$(( $i + 1 ))
done
# get into hadoop master container
sudo docker exec -it hadoop-master bash
启动集群
1.运行Docker容器
./start-container.sh
运行后就进入了hadoop-master容器的/root目录
2.启动Hadoop集群
./start-hadoop.sh
打开本机浏览器,查看已经启动的Hadoop集群:Hadoop集群
查看集群概况:集群概况
然后就可以愉快的在Docker Hadoop集群中测试Flink任务了!
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