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09、Flink深入:Flink的各种创建执行环境的方法

转载 作者:大佬之路 更新时间:2024-01-07 13:34:24 26 4
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1. 创建DataSet的执行环境以及WordCount程序

package com.ddkk.hello;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.operators.Order;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.UnsortedGrouping;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;

/**
 * Author ddkk.com  弟弟快看,程序员编程资料站
 * Desc
 * 需求:使用Flink完成WordCount-DataSet
 * 编码步骤
 * 1.准备环境-env
 * 2.准备数据-source
 * 3.处理数据-transformation
 * 4.输出结果-sink
 * 5.触发执行-execute//如果有print,DataSet不需要调用execute,DataStream需要调用execute
 */
public class WordCount1 {
    public static void main(String[] args) throws Exception {
        //老版本的批处理API如下,但已经不推荐使用了
        //1.准备环境-env
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
        //2.准备数据-source
        DataSet<String> lineDS = env.fromElements("itcast hadoop spark","itcast hadoop spark","itcast hadoop","itcast");
        //3.处理数据-transformation
        //3.1每一行数据按照空格切分成一个个的单词组成一个集合
        /*
        public interface FlatMapFunction<T, O> extends Function, Serializable {
            void flatMap(T value, Collector<O> out) throws Exception;
        }
         */
        DataSet<String> wordsDS = lineDS.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String value, Collector<String> out) throws Exception {
                //value就是一行行的数据
                String[] words = value.split(" ");
                for (String word : words) {
                    out.collect(word);//将切割处理的一个个的单词收集起来并返回
                }
            }
        });
        //3.2对集合中的每个单词记为1
        /*
        public interface MapFunction<T, O> extends Function, Serializable {
            O map(T value) throws Exception;
        }
         */
        DataSet<Tuple2<String, Integer>> wordAndOnesDS = wordsDS.map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String value) throws Exception {
                //value就是进来一个个的单词
                return Tuple2.of(value, 1);
            }
        });

        //3.3对数据按照单词(key)进行分组
        //0表示按照tuple中的索引为0的字段,也就是key(单词)进行分组
        UnsortedGrouping<Tuple2<String, Integer>> groupedDS = wordAndOnesDS.groupBy(0);

        //3.4对各个组内的数据按照数量(value)进行聚合就是求sum
        //1表示按照tuple中的索引为1的字段也就是按照数量进行聚合累加!
        DataSet<Tuple2<String, Integer>> aggResult = groupedDS.sum(1);

        //3.5排序
        DataSet<Tuple2<String, Integer>> result = aggResult.sortPartition(1, Order.DESCENDING).setParallelism(1);

        //4.输出结果-sink
        result.print();

        //5.触发执行-execute//如果有print,DataSet不需要调用execute,DataStream需要调用execute
        //env.execute();//'execute()', 'count()', 'collect()', or 'print()'.
    }
}

2. 创建DataStream的执行环境以及WordCount程序

package com.ddkk.hello;

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

/**
 * Author ddkk.com  弟弟快看,程序员编程资料站
 * Desc
 * 需求:使用Flink完成WordCount-DataStream
 * 编码步骤
 * 1.准备环境-env
 * 2.准备数据-source
 * 3.处理数据-transformation
 * 4.输出结果-sink
 * 5.触发执行-execute
 */
public class WordCount2 {
    public static void main(String[] args) throws Exception {
        //新版本的流批统一API,既支持流处理也支持批处理
        //1.准备环境-env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
        //env.setRuntimeMode(RuntimeExecutionMode.STREAMING);
        //env.setRuntimeMode(RuntimeExecutionMode.BATCH);

        //2.准备数据-source
        DataStream<String> linesDS = env.fromElements("itcast hadoop spark","itcast hadoop spark","itcast hadoop","itcast");

        //3.处理数据-transformation
        //3.1每一行数据按照空格切分成一个个的单词组成一个集合
        /*
        public interface FlatMapFunction<T, O> extends Function, Serializable {
            void flatMap(T value, Collector<O> out) throws Exception;
        }
         */
        DataStream<String> wordsDS = linesDS.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String value, Collector<String> out) throws Exception {
                //value就是一行行的数据
                String[] words = value.split(" ");
                for (String word : words) {
                    out.collect(word);//将切割处理的一个个的单词收集起来并返回
                }
            }
        });
        //3.2对集合中的每个单词记为1
        /*
        public interface MapFunction<T, O> extends Function, Serializable {
            O map(T value) throws Exception;
        }
         */
        DataStream<Tuple2<String, Integer>> wordAndOnesDS = wordsDS.map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String value) throws Exception {
                //value就是进来一个个的单词
                return Tuple2.of(value, 1);
            }
        });

        //3.3对数据按照单词(key)进行分组
        //0表示按照tuple中的索引为0的字段,也就是key(单词)进行分组
        //KeyedStream<Tuple2<String, Integer>, Tuple> groupedDS = wordAndOnesDS.keyBy(0);
KeyedStream<Tuple2<String, Integer>, String> groupedDS = wordAndOnesDS.keyBy(t -> t.f0);
        //3.4对各个组内的数据按照数量(value)进行聚合就是求sum
        //1表示按照tuple中的索引为1的字段也就是按照数量进行聚合累加!
        DataStream<Tuple2<String, Integer>> result = groupedDS.sum(1);

        //4.输出结果-sink
        result.print();

        //5.触发执行-execute
        env.execute();//DataStream需要调用execute
    }
}

3. Lambda版的WordCount程序

package com.ddkk.hello;

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

import java.util.Arrays;

/**
 * Author ddkk.com  弟弟快看,程序员编程资料站
 * Desc
 * 需求:使用Flink完成WordCount-DataStream--使用lambda表达式
 * 编码步骤
 * 1.准备环境-env
 * 2.准备数据-source
 * 3.处理数据-transformation
 * 4.输出结果-sink
 * 5.触发执行-execute
 */
public class WordCount3_Lambda {
    public static void main(String[] args) throws Exception {
        //1.准备环境-env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
        //env.setRuntimeMode(RuntimeExecutionMode.STREAMING);
        //env.setRuntimeMode(RuntimeExecutionMode.BATCH);

        //2.准备数据-source
        DataStream<String> linesDS = env.fromElements("itcast hadoop spark", "itcast hadoop spark", "itcast hadoop", "itcast");
        //3.处理数据-transformation
        //3.1每一行数据按照空格切分成一个个的单词组成一个集合
        /*
        public interface FlatMapFunction<T, O> extends Function, Serializable {
            void flatMap(T value, Collector<O> out) throws Exception;
        }
         */
        //lambda表达式的语法:
        // (参数)->{方法体/函数体}
        //lambda表达式就是一个函数,函数的本质就是对象
        DataStream<String> wordsDS = linesDS.flatMap(
                (String value, Collector<String> out) -> Arrays.stream(value.split(" ")).forEach(out::collect)
        ).returns(Types.STRING);

        //3.2对集合中的每个单词记为1
        /*
        public interface MapFunction<T, O> extends Function, Serializable {
            O map(T value) throws Exception;
        }
         */
        /*DataStream<Tuple2<String, Integer>> wordAndOnesDS = wordsDS.map(
                (String value) -> Tuple2.of(value, 1)
        ).returns(Types.TUPLE(Types.STRING, Types.INT));*/
        DataStream<Tuple2<String, Integer>> wordAndOnesDS = wordsDS.map(
                (String value) -> Tuple2.of(value, 1)
                , TypeInformation.of(new TypeHint<Tuple2<String, Integer>>() {})
        );

        //3.3对数据按照单词(key)进行分组
        //0表示按照tuple中的索引为0的字段,也就是key(单词)进行分组
        //KeyedStream<Tuple2<String, Integer>, Tuple> groupedDS = wordAndOnesDS.keyBy(0);
        //KeyedStream<Tuple2<String, Integer>, String> groupedDS = wordAndOnesDS.keyBy((KeySelector<Tuple2<String, Integer>, String>) t -> t.f0);
        KeyedStream<Tuple2<String, Integer>, String> groupedDS = wordAndOnesDS.keyBy(t -> t.f0);

        //3.4对各个组内的数据按照数量(value)进行聚合就是求sum
        //1表示按照tuple中的索引为1的字段也就是按照数量进行聚合累加!
        DataStream<Tuple2<String, Integer>> result = groupedDS.sum(1);

        //4.输出结果-sink
        result.print();

        //5.触发执行-execute
        env.execute();
    }
}

4. 在windos本地创建含有WebUI的Flink执行环境

4.1. 导入依赖

<!--flink的web包,可以在本地idea执行程序时显示web界面-->
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-runtime-web_2.12</artifactId>
    <version>${flink.version}</version>
</dependency>

4.2. 具体代码

import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;

import java.text.SimpleDateFormat;

/**
 * @date: 2021/7/1
 *  @Author ddkk.com  弟弟快看,程序员编程资料站
 * @desc: 在windos本地创建Flink的含有WebUI的执行环境
 */
public class WebUIForIDEA {

    public static void main(String[] args) throws Exception {

        // 使用流的执行环境类创建本地并含有WebUI的执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(new Configuration());

        env
                .addSource(new SourceFunction<String>() {

                    boolean flag = true;
                    SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");

                    @Override
                    public void run(SourceContext<String> sourceContext) throws Exception {
                        while (flag) {
                            sourceContext.collect(sdf.format(System.currentTimeMillis()));
                            Thread.sleep(1000);
                        }
                    }

                    @Override
                    public void cancel() {
                        flag = false;
                    }
                })
                .print("WebUIForIDEA >>>>>");

        // 启动执行
        env.execute("WebUIForIDEA");
    }
}

4.3. 执行效果

访问url:http://localhost:8081/#/job-manager/stdout

 

 

5. 在Yarn上运行Flink程序

步骤一:编写代码

package com.ddkk.hello;

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

import java.util.Arrays;

/**
 * Author ddkk.com  弟弟快看,程序员编程资料站
 * Desc
 * 需求:使用Flink完成WordCount-DataStream--使用lambda表达式--修改代码使适合在Yarn上运行
 * 编码步骤
 * 1.准备环境-env
 * 2.准备数据-source
 * 3.处理数据-transformation
 * 4.输出结果-sink
 * 5.触发执行-execute//批处理不需要调用!流处理需要
 */
public class WordCount4_Yarn {
    public static void main(String[] args) throws Exception {
        //获取参数
        ParameterTool params = ParameterTool.fromArgs(args);
        String output = null;
        if (params.has("output")) {
            output = params.get("output");
        } else {
            output = "hdfs://node1:8020/wordcount/output_" + System.currentTimeMillis();
        }

        //1.准备环境-env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
        //env.setRuntimeMode(RuntimeExecutionMode.STREAMING);
        //env.setRuntimeMode(RuntimeExecutionMode.BATCH);

        //2.准备数据-source
        DataStream<String> linesDS = env.fromElements("itcast hadoop spark", "itcast hadoop spark", "itcast hadoop", "itcast");
        //3.处理数据-transformation
        DataStream<Tuple2<String, Integer>> result = linesDS
                .flatMap(
                        (String value, Collector<String> out) -> Arrays.stream(value.split(" ")).forEach(out::collect)
                ).returns(Types.STRING)
                .map(
                        (String value) -> Tuple2.of(value, 1)
                ).returns(Types.TUPLE(Types.STRING, Types.INT))
                //.keyBy(0);
                .keyBy((KeySelector<Tuple2<String, Integer>, String>) t -> t.f0)
                .sum(1);

        //4.输出结果-sink
        result.print();

        //如果执行报hdfs权限相关错误,可以执行 hadoop fs -chmod -R 777  /
        System.setProperty("HADOOP_USER_NAME", "root");//设置用户名
        //result.writeAsText("hdfs://node1:8020/wordcount/output_"+System.currentTimeMillis()).setParallelism(1);
        result.writeAsText(output).setParallelism(1);

        //5.触发执行-execute
        env.execute();
    }
}

步骤二:打包上传

 

 

 

步骤三:提交执行

Apache Flink 1.12 Documentation: Execution Mode (Batch/Streaming)

 

执行命令:

/export/server/flink/bin/flink run -Dexecution.runtime-mode=BATCH -m yarn-cluster -yjm 1024 -ytm 1024 -c com.ddkk.hello.WordCount4_Yarn /root/wc.jar --output hdfs://node1:8020/wordcount/output_xx

步骤四:在web页面观察提交的程序

http://node1:8088/cluster

http://node1:50070/explorer.html#/

或者在Standalone模式下使用web界面提交

注意事项

写入HDFS如果存在权限问题进行如下设置:

hadoop fs -chmod -R 777 /

并在代码中添加:

System.setProperty("HADOOP_USER_NAME", "root")

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