Flink入门到实战-阶段一(集群安装&使用)_flink 1.17.1

hello world

引入pom.xml

<build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <configuration>
                    <source>8</source>
                    <target>8</target>
                </configuration>
            </plugin>
        </plugins>
    </build>
 
    <properties>
        <flink.version>1.13.0</flink.version>
        <java.version>1.8</java.version>
        <scala.binary.version>2.12</scala.binary.version>
        <slf4j.version>1.7.30</slf4j.version>
    </properties>
    <dependencies>
        <!-- 引入 Flink 相关依赖-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
 
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-cep_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
 
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <!-- 引入日志管理相关依赖-->
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-api</artifactId>
            <version>${slf4j.version}</version>
        </dependency>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
            <version>${slf4j.version}</version>
        </dependency>
 
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-java-bridge_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner-blink_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-to-slf4j</artifactId>
            <version>2.14.0</version>
        </dependency>
    </dependencies>

log4j.properties

log4j.rootLogger=ERROR, stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d %p [%c] - %m%n
log4j.appender.logfile=org.apache.log4j.FileAppender
log4j.appender.logfile.File=target/spring.log
log4j.appender.logfile.layout=org.apache.log4j.PatternLayout
log4j.appender.logfile.layout.ConversionPattern=%d %p [%c] - %m%n

DataSet(早期的批处理)实现

数据准备

nihao nihao
jiushi nihao

处理程序 

public class FlinkSoctet {
    public static void main(String[] args) throws Exception {
        //得到执行环境对象
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
        DataSource<String> initData = env.readTextFile("input/wc.txt");
 
        //使用内部内的优点是不用考虑类型擦除的问题
        MapOperator<String, Tuple2<String, Integer>> mapValue = initData.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String textItem, Collector<String> out) throws Exception {
                String[] resItem = textItem.split(" ");
                for (String s : resItem) {
                    out.collect(s);
                }
            }
        }).map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String item) throws Exception {
                return Tuple2.of(item, 1);
            }
        });
 
        //mapValue得到的数据是(key,value)元组类型，0表示key的位置，1表示value的位置
        //下面就是用key进行分组，用value进行求和
        mapValue.groupBy(0).sum(1).print();
 
    }
}

得到的结果

(nihao,3)
(jiushi,1)

DataStream(流处理Api实现)

数据准备

nihao nihao
jiushi nihao

处理程序

public class FlinkSoctet {
    public static void main(String[] args) throws Exception {
        //得到执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStreamSource<String> initData = env.readTextFile("input/wc.txt");
 
        SingleOutputStreamOperator<Tuple2<String, Integer>> map = initData.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String item, Collector<String> out) throws Exception {
                String[] resItem = item.split(" ");
                for (String s : resItem) {
                    out.collect(s);
                }
            }
        }).map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String item) throws Exception {
                return Tuple2.of(item, 1);
            }
        });
 
        //对于得到的元组的流数据，进行分组聚合
        map.keyBy(new KeySelector<Tuple2<String, Integer>, String>() {
            @Override
            public String getKey(Tuple2<String, Integer> value) throws Exception {
                return value.f0;
            }
        }).sum(1).print();
 
        //由于是流处理程序，所以这里要不断的执行
        env.execute();
    }
}

得到的结果

前面的数字是分配到的Task的编号，可以看到key相同的数据到了一个Task里面执行，比如nihao都在1线程里面处理

2> (jiushi,1)
1> (nihao,1)
1> (nihao,2)
1> (nihao,3)

DataStream(流处理Api处理Socket)

在发送数据的linux上面执行

 nc -lk 9997

处理程序 

public class FlinkSoctet {
    public static void main(String[] args) throws Exception {
        //得到执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStreamSource<String> initData = env.socketTextStream("master",9997);
 
        SingleOutputStreamOperator<Tuple2<String, Integer>> map = initData.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String item, Collector<String> out) throws Exception {
                String[] resItem = item.split(" ");
                for (String s : resItem) {
                    out.collect(s);
                }
            }
        }).map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String item) throws Exception {
                return Tuple2.of(item, 1);
            }
        });
 
        //对于得到的元组的流数据，进行分组聚合
        map.keyBy(new KeySelector<Tuple2<String, Integer>, String>() {
            @Override
            public String getKey(Tuple2<String, Integer> value) throws Exception {
                return value.f0;
            }
        }).sum(1).print();
 
        //由于是流处理程序，所以这里要不断的执行
        env.execute();
    }
}

输入数据

q v d s fd a q

得到的结果

9> (d,1)
8> (fd,1)
4> (q,1)
9> (s,1)
4> (q,2)
11> (a,1)
3> (v,1)

集群搭建和使用

下载安装

Apache Flink: Downloads

集群规划

解压

tar -zxvf flink-1.13.2-bin-scala_2.12.tgz

修改配置文件

flink-conf.yaml

jobmanager.rpc.address: master

masters

master:8081

workers

master
node1
node2

分发到集群其他机器

./xsync /home/bigdata/congxueflink

启动集群(在bin目录里面)

./start-cluster.sh

访问http://master:8081/

 关闭

./stop-cluster.sh

WebUI提交任务

加入打包插件

    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <configuration>
                    <source>8</source>
                    <target>8</target>
                </configuration>
            </plugin>
 
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-assembly-plugin</artifactId>
                <version>3.0.0</version>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>

编写一个监听socket的程序

public class FlinkSoctet {
    public static void main(String[] args) throws Exception {
        //得到执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStreamSource<String> initData = env.socketTextStream("master",9997);
 
        SingleOutputStreamOperator<Tuple2<String, Integer>> map = initData.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String item, Collector<String> out) throws Exception {
                String[] resItem = item.split(" ");
                for (String s : resItem) {
                    out.collect(s);
                }
            }
        }).map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String item) throws Exception {
                return Tuple2.of(item, 1);
            }
        });
 
        //对于得到的元组的流数据，进行分组聚合
        map.keyBy(new KeySelector<Tuple2<String, Integer>, String>() {
            @Override
            public String getKey(Tuple2<String, Integer> value) throws Exception {
                return value.f0;
            }
        }).sum(1).print();
 
        //由于是流处理程序，所以这里要不断的执行
        env.execute();
    }
}

然后打包

提交成功并执行

 查看处理的数据

命令提交任务

 -m是jobmanager开放提交的地址

-c是提交jar的启动类

-p是并行度

最后的jar就是自己打包的jar

./flink run -m master:8081 -c com.chongxue.flink.FlinkSoctet -p 2 flink-1.0-SNAPSHOT.jar

停止任务(后面的是job的id)

./flink cancel d9e4f4dcb0516551d6611675c16113bb

部署模式介绍

• 会话模式:上面我们搭建的就是会话模式，集群的什么周期大于一切.适合任务小而多的场景
• 单作业模式:提交一个任务就启动一个集群
• 应用模式:和单作业模式不同的是，单作业模式一个应用里面每执行一个job提交，那么就启动一个集群，如果是应用模式那么就是只启动一个集群，还有应用模式提价job的jar在jobmanager上面，缓解了单作业模式由客户端提交到jobmanager的网络开销压力

Yarn模式部署

环境准备

配置环境变量

sudo vi  /etc/profile.d/my_env.sh

HADOOP_HOME=/home/bigdata/hadoop/hadoop
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
export HADOOP_CONF_DIR=${HADOOP_HOME}/etc/hadoop
export HADOOP_CLASSPATH=`hadoop classpath`

source /etc/profile.d/my_env.sh

可选配置：更具自己的需求配置启动的资源分配

 vim flink-conf.yaml

jobmanager.memory.process.size: 1600m
taskmanager.memory.process.size: 1728m
taskmanager.numberOfTaskSlots: 8
parallelism.default: 1

 yarn会话模式

./yarn-session.sh -nm test

 ./flink run -m node1:34982 -c com.chongxue.flink.FlinkSoctet -p 2 flink-1.0-SNAPSHOT.jar

./flink run -d -t yarn-per-job -c com.chongxue.flink.FlinkSoctet flink-1.0-SNAPSHOT.jar