Flink简介
Apache Flink是由Apache软件基金会开发的开源流处理框架，其核心是用Java和Scala编写的分布式流数据流引擎。Flink以数据并行和流水线方式执行任意流数据程序，Flink的流水线运行时系统可以执行批处理和流处理程序。此外，Flink的运行时本身也支持迭代算法的执行。
前提条件
需要已经部署创建好CCE集群，集群下至少有一个可用节点，集群内节点已经绑定了弹性公网IP、kubectl命令行工具。
部署流程
1. Download
为了运行Flink，需要java8或11的环境，使用如下的指令确定java的版本。
java -version
下载软件包并且进入目录下。
$ wget https://www.apache.org/dyn/closer.lua/flink/flink-1.12.2/flink-1.12.2-src.tgz
$ cd flink-1.12.2
2. Start a Cluster
运行脚本完成flink在集群上的部署。
$ ./bin/start-cluster.sh
3. Submit a job
随后可以使用如下的指令提交作业。
$ ./bin/flink run examples/streaming/WordCount.jar
$ tail log/flink-*-taskexecutor-*.out
Flink on Volcano
1. 部署组件
Flink cluster的部署需要创建两个deploy、一个service和一个configmap。调度策略采用volcano。flink-configuration-configmap.yaml内容如下
apiVersion: v1
kind: ConfigMap
metadata:
name: flink-config
labels:
app: flink
data:
flink-conf.yaml: |+
jobmanager.rpc.address: flink-jobmanager
taskmanager.numberOfTaskSlots: 2
blob.server.port: 6124
jobmanager.rpc.port: 6123
taskmanager.rpc.port: 6122
queryable-state.proxy.ports: 6125
jobmanager.memory.process.size: 1600m
taskmanager.memory.process.size: 1728m
parallelism.default: 2
log4j-console.properties: |+
# This affects logging for both user code and Flink
rootLogger.level = INFO
rootLogger.appenderRef.console.ref = ConsoleAppender
rootLogger.appenderRef.rolling.ref = RollingFileAppender
# Uncomment this if you want to _only_ change Flink's logging
#logger.flink.name = org.apache.flink
#logger.flink.level = INFO
# The following lines keep the log level of common libraries/connectors on
# log level INFO. The root logger does not override this. You have to manually
# change the log levels here.
logger.akka.name = akka
logger.akka.level = INFO
logger.kafka.name= org.apache.kafka
logger.kafka.level = INFO
logger.hadoop.name = org.apache.hadoop
logger.hadoop.level = INFO
logger.zookeeper.name = org.apache.zookeeper
logger.zookeeper.level = INFO
# Log all infos to the console
appender.console.name = ConsoleAppender
appender.console.type = CONSOLE
appender.console.layout.type = PatternLayout
appender.console.layout.pattern = %d{yyyy-MM-dd HH:mm:ss,SSS} %-5p %-60c %x - %m%n
# Log all infos in the given rolling file
appender.rolling.name = RollingFileAppender
appender.rolling.type = RollingFile
appender.rolling.append = false
appender.rolling.fileName = ${sys:log.file}
appender.rolling.filePattern = ${sys:log.file}.%i
appender.rolling.layout.type = PatternLayout
appender.rolling.layout.pattern = %d{yyyy-MM-dd HH:mm:ss,SSS} %-5p %-60c %x - %m%n
appender.rolling.policies.type = Policies
appender.rolling.policies.size.type = SizeBasedTriggeringPolicy
appender.rolling.policies.size.size=100MB
appender.rolling.strategy.type = DefaultRolloverStrategy
appender.rolling.strategy.max = 10
# Suppress the irrelevant (wrong) warnings from the Netty channel handler
logger.netty.name = org.apache.flink.shaded.akka.org.jboss.netty.channel.DefaultChannelPipeline
logger.netty.level = OFF
service用来提供JobManager的REST和UI端口的服务，jobmanager-service.yaml内容如下
apiVersion: v1
kind: Service
metadata:
name: flink-jobmanager
spec:
type: ClusterIP
ports:
- name: rpc
port: 6123
- name: blob-server
port: 6124
- name: webui
port: 8081
selector:
app: flink
component: jobmanager
jobmanager-session-deployment.yaml内容如下
apiVersion: apps/v1
kind: Deployment
metadata:
name: flink-jobmanager
spec:
replicas: 1
selector:
matchLabels:
app: flink
component: jobmanager
template:
metadata:
labels:
app: flink
component: jobmanager
spec:
containers:
- name: jobmanager
image: flink:1.11.0-scala_2.11
args: ["jobmanager"]
ports:
- containerPort: 6123
name: rpc
- containerPort: 6124
name: blob-server
- containerPort: 8081
name: webui
livenessProbe:
tcpSocket:
port: 6123
initialDelaySeconds: 30
periodSeconds: 60
volumeMounts:
- name: flink-config-volume
mountPath: /opt/flink/conf
securityContext:
runAsUser: 9999 # refers to user _flink_ from official flink image, change if necessary
volumes:
- name: flink-config-volume
configMap:
name: flink-config
items:
- key: flink-conf.yaml
path: flink-conf.yaml
- key: log4j-console.properties
path: log4j-console.properties
taskmanager-session-deployment.yaml内容如下
apiVersion: apps/v1
kind: Deployment
metadata:
name: flink-taskmanager
spec:
replicas: 2
selector:
matchLabels:
app: flink
component: taskmanager
template:
metadata:
labels:
app: flink
component: taskmanager
spec:
containers:
- name: taskmanager
image: flink:1.11.0-scala_2.11
args: ["taskmanager"]
ports:
- containerPort: 6122
name: rpc
- containerPort: 6125
name: query-state
livenessProbe:
tcpSocket:
port: 6122
initialDelaySeconds: 30
periodSeconds: 60
volumeMounts:
- name: flink-config-volume
mountPath: /opt/flink/conf/
securityContext:
runAsUser: 9999 # refers to user _flink_ from official flink image, change if necessary
volumes:
- name: flink-config-volume
configMap:
name: flink-config
items:
- key: flink-conf.yaml
path: flink-conf.yaml
- key: log4j-console.properties
path: log4j-console.properties
在集群节点创建好上面四个yaml配置文件，使用如下指令进行部署。
kubectl create -f flink-configuration-configmap.yaml
kubectl create -f jobmanager-service.yaml
kubectl create -f jobmanager-session-deployment.yaml
kubectl create -f taskmanager-session-deployment.yaml
创建成功后查询：
kubectl get cm| grep flink
kubectl get svc | grep flink
kubectl get pod -owide | grep Flink
2. 对外发布服务
创建好flink负载之后，需要像外部发布服务。
- 若使用华为云CCE进行测试，进入CCE的”工作负载-无状态负载”页面。选择flink-jobmanager，单击”访问方式”。
- 点击“添加service”，选择节点访问，输入容器端口位8081。
- 点击CCE中的网络管理，能够看到刚才我们添加的service，访问对外发布的链接。
- 进入flink的Dashboard页面，点击submit new job提交任务官方的WordCount作业。目录为flink-1.12.2/examples/streaming/WordCount.jar。