0
点赞
收藏
分享

微信扫一扫

【Flink-API】之复习一系列Transformation/Sink操作

WikongGuan 2022-10-28 阅读 155
ide字段实体类Linux系统/运维


一、Map

1.1 介绍

1.DataStream->DataStream 数据集转换。
2.数据集合中的元素一一映射的关系。

1.2 MapFunction

public class Map01 {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // source
        DataStream<Integer> nums = env.fromElements(1, 2, 3, 4, 5);

        // 实现一：map 方法做映射，输入输出一直的2倍
        SingleOutputStreamOperator<Integer> result1 = nums.map(new MapFunction<Integer, Integer>() {
            @Override
            public Integer map(Integer value) throws Exception {
                return value * 2;
            }
        });

        // 实现二：lambada 表达式的应用
        SingleOutputStreamOperator<Integer> result2 = nums.map(i -> i * 2);

        // sink
        result1.print();
        result2.print();
        env.execute();
    }
}

结果如下：

【Flink-API】之复习一系列Transformation/Sink操作_实体类

1.3 RichMapFunction

1.open()方法 在构造方法之后，map方法之前，只执行一次，初始化方法 使用数据库
2.Configuration为全局的配置
3.close()方法 销毁之前执行一次，通常为资源额的释放
程序如下：

// RichMapFunction
        nums.map(new RichMapFunction<Integer, Integer>() {

            // open 在构造方法之后，map方法之前，只执行一次，初始化方法 使用数据库
            // Configuration为全局的配置
            @Override
            public void open(Configuration parameters) throws Exception {
                super.open(parameters);
            }

            @Override
            public Integer map(Integer value) throws Exception {
                return value * 10;
            }

            // close 销毁之前执行一次，通常为资源额的释放
            @Override
            public void close() throws Exception {
                super.close();
            }
        });

二、FlatMap

2.1 介绍

输入一个元素，会被切分成多个元素。一对多。

2.2 FlatMapFunction

public class FlatMap01 {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStream<String> lines = env.fromElements("GW001001 GW002002 GW003003", "GW001001 GW002002 GW003003", "GW001001 GW002002 GW003003");

        SingleOutputStreamOperator<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String line, Collector<String> out) throws Exception {
                
                //实现一：jdk8的流式处理 lambada表达式【推荐】
                //Arrays.stream(line.split(" ")).forEach(out::collect);
                
                //实现二：
                //Arrays.asList(line.split(" ")).forEach(w -> out.collect(w));

                //实现三：最原始的方式
                String[] words = line.split(" ");
                for (String word : words) {
                    out.collect(word);
                }
            }
        });

        words.print();
        env.execute();
    }
}

结果如下：

【Flink-API】之复习一系列Transformation/Sink操作_ide_02

2.3 RichFlatMapFunction

雷同方法 open() close()方法。

三、Filter

3.1 介绍

1.ture是留下，false过滤
2.实现对输入的数据进行逻辑判断，判断是否是奇数？

3.2 FilterFunction

public class Filter01 {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStream<Integer> nums = env.fromElements(1, 2, 3, 4, 5, 6, 7, 8, 9);

        //实现一：ture是留下，false时过滤
        SingleOutputStreamOperator<Integer> filter1 = nums.filter(new FilterFunction<Integer>() {
            @Override
            public boolean filter(Integer value) throws Exception {
                return value % 2 != 0;
            }
        });

        //实现二：lambada表达式：filter2
        //SingleOutputStreamOperator<Integer> filter2 = nums.filter(i -> i >= 5);
        SingleOutputStreamOperator<Integer> filter2 = nums.filter(i -> {
            //换行要有return
            return i >= 5;
        });

        filter1.print();
        filter2.print();

        env.execute();
    }
}

计算结果：

【Flink-API】之复习一系列Transformation/Sink操作_ide_03

四、KeyBy

实时计算的算子

4.1 lambda实现

1.实现输入一个，返回一个，使用lambada表达式 代替new Function，利用虚拟机开设一个socket端口号，实现实时聚合计算。
2.虚拟机centos中：nc -lk 8888
3.元组也是一个特殊的集合，角标 0 开始 最大Tuple25
4.代码实现：

public class KeyBy01 {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStream<String> lines = env.socketTextStream("192.168.52.200", 8888);

        //使用lambada表达式 代替new Function
        //输入一个返回一个
        SingleOutputStreamOperator<Tuple2<String, Integer>> wordAndOne = lines.map(w -> Tuple2.of(w, 1))
                .returns(Types.TUPLE(Types.STRING, Types.INT));

        //元组也是一个特殊的集合，角标 0 开始 最大Tuple25
        KeyedStream<Tuple2<String, Integer>, Tuple> keyed = wordAndOne.keyBy(0);

        //聚合
        keyed.print();

        env.execute("KeyBy01");
    }
}

运行结果：

【Flink-API】之复习一系列Transformation/Sink操作_字段_04

4.2 KeyBy自定义实体类

1.实体类WordAndCount

public class WordAndCount {
    private String word;
    private Long counts;
}

2.keyby sum

public class KeyBy02 {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStream<String> lines = env.socketTextStream("192.168.52.200", 8808);

        //输入一个返回一个
        SingleOutputStreamOperator<WordAndCount> wordAndOne = lines.map(new MapFunction<String, WordAndCount>() {
            @Override
            public WordAndCount map(String value) throws Exception {
                return Turbine.of(value,1L);
            }
        });

        //根据实体类的字段进行聚合
        KeyedStream<WordAndCount, Tuple> keyed = wordAndOne.keyBy("word");

        //聚合
        SingleOutputStreamOperator<WordAndCount> sumed = keyed.sum("counts");

        keyed.print();
        sumed.print();


        env.execute();
    }
}

运行结果：

【Flink-API】之复习一系列Transformation/Sink操作_实体类_05

4.3 keyBy多字段进行分组

代码：

public class KeyBy03 {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStream<String> lines = env.socketTextStream("192.168.52.200", 8828);

        //山东,烟台,2000
        //山东,烟台,2000
        //山东,烟台,2000
        SingleOutputStreamOperator<Tuple3<String, String, Double>> provinceCityMoney = lines.map(new MapFunction<String, Tuple3<String, String, Double>>() {
            @Override
            public Tuple3<String, String, Double> map(String line) throws Exception {
                //切分
                String[] fields = line.split(",");
                String province = fields[0];
                String city = fields[1];
                double money = Double.parseDouble(fields[2]);
                return Tuple3.of(province, city, money);
            }
        });

        //按照省份，城市分组  多个字段进行分组,最后一个字段进行聚合
        /**
         * 如果是自己定义的bean实体类，可以进行将字段写进去
         */
        SingleOutputStreamOperator<Tuple3<String, String, Double>> summed = provinceCityMoney.keyBy(0, 1).sum(2);
        summed.print();
        env.execute();
    }
}

运行结果：

【Flink-API】之复习一系列Transformation/Sink操作_ide_06

五、Reduce

聚合，sum只可以加法，而reduce可以乘法，可以自定义算子。

public class Reduce01 {
    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStream<String> lines = env.socketTextStream("192.168.52.200", 8828);

        //使用lambada表达式 代替new Function
        //输入一个返回一个
        SingleOutputStreamOperator<Tuple2<String, Integer>> wordAndOne = lines.map(w -> Tuple2.of(w, 1))
                .returns(Types.TUPLE(Types.STRING, Types.INT));

        //元组也是一个特殊的集合，角标 0 开始 最大Tuple25
        KeyedStream<Tuple2<String, Integer>, Tuple> keyed = wordAndOne.keyBy(0);

        //聚合
        SingleOutputStreamOperator<Tuple2<String, Integer>> reduced = keyed.reduce(new ReduceFunction<Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> reduce(Tuple2<String, Integer> value1, Tuple2<String, Integer> value2) throws Exception {

                String key = value1.f0;
                Integer count1 = value1.f1;
                Integer count2 = value2.f1;

                Integer counts = count1 + count2;

                return Tuple2.of(key, counts);
            }
        });

        reduced.print();
        env.execute();
    }
}

运行结果：

【Flink-API】之复习一系列Transformation/Sink操作_ide_07

六、Max

例如：输入
spark，10
spark，20
hadoop，10
和历史数据比较，求最大次数的，最大的留下，最小的丢弃

public class Max01 {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStream<String> lines = env.socketTextStream("192.168.52.200", 8888);

        SingleOutputStreamOperator<Tuple2<String, Integer>> wordAndOne = lines.map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String line) throws Exception {
                String[] fields = line.split(",");
                String words = fields[0];
                int num = Integer.parseInt(fields[1]);
                return Tuple2.of(words, num);
            }
        });

        //按照单词进行分组
        KeyedStream<Tuple2<String, Integer>, Tuple> keyed = wordAndOne.keyBy(0);

        //求最大次数的，最大的留下，最小的丢弃
        SingleOutputStreamOperator<Tuple2<String, Integer>> max = keyed.max(1);

        max.print();
        env.execute();
    }
}

运行结果：

【Flink-API】之复习一系列Transformation/Sink操作_ide_08

七、Sink

7.1 print()

打印到控制台也是sink.

7.2 CSV

public class AddSink01 {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStreamSource<String> lines = env.socketTextStream("192.168.52.200", 8888);

        SingleOutputStreamOperator<Tuple2<String, Integer>> wordAndOne = lines.map(new MapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> map(String line) throws Exception {
                String[] fields = line.split(",");
                String words = fields[0];
                int num = Integer.parseInt(fields[1]);

                return Tuple2.of(words, num);
            }
        });

        //按照单词进行分组
        KeyedStream<Tuple2<String, Integer>, Tuple> keyed = wordAndOne.keyBy(0);

        SingleOutputStreamOperator<Tuple2<String, Integer>> max = keyed.max(1);

        /**
         * 自定义sink,比如 写入数据库，磁盘等等
         * 不需要有返回就可以
         */
        max.addSink(new SinkFunction<Tuple2<String, Integer>>() {
            @Override
            public void invoke(Tuple2<String, Integer> value, Context context) throws Exception {

                System.out.println(value);

            }
        });
        /**
         * 写入磁盘
         *  如果是写入scv文件 必须时tuple格式
         */
        max.writeAsCsv("F:\\out222", FileSystem.WriteMode.OVERWRITE);
        max.print();
        env.execute();

    }
}

7.3 RedisSink

public class MyRedisSink extends RichSinkFunction<Turbine> {
    //初始化redis连接
    private transient Jedis jedis;

    @Override
    public void open(Configuration parameters) throws Exception {
        super.open(parameters);
        ParameterTool params = (ParameterTool) getRuntimeContext().getExecutionConfig().getGlobalJobParameters();
        String host = params.getRequired("redis.host");
        //String password = params.getRequired("redis.pwd");
        int db = params.getInt("redis.db", 0);
        jedis = new Jedis(host, 6379, 5000);
        //jedis.auth(password);
        jedis.select(db);
    }

    @Override
    public void invoke(Turbine value, Context context) throws Exception {
        if (!jedis.isConnected()) {
            jedis.connect();
        }
        //写入redis
        jedis.hset(value.word, value.province, String.valueOf(value.counts));
    }

    @Override
    public void close() throws Exception {
        super.close();
        jedis.close();
    }
}

7.4 MySqlSink

public class MySqlSink extends RichSinkFunction<GW200001> {

    //最好连接不参与序列化
    private transient Connection conn = null;

    @Override
    public void open(Configuration parameters) throws Exception {
        super.open(parameters);
        //创建mysql连接
        conn = DriverManager.getConnection("jdbc:mysql://localhost:3306/turbine?characterEncoding=UTF-8", "root", "123456");
        System.out.println("拿到连接了");
    }

    @Override
    public void invoke(GW200001 gw200001, Context context) throws Exception {
        //更新，插入，统计业务
        PreparedStatement pstm = null;
        try {
            pstm = conn.prepareStatement("insert into gw200001(wt_number, wt_date_time) values(?,?)");
            pstm.setString(1, gw200001.wt_number);
            pstm.setString(2, gw200001.wt_date_time);

            //执行sql  executeUpdate() executeQuery()
            System.out.println("执行sql");
            pstm.execute();
        } finally {
            if (pstm != null) {
                pstm.close();
                System.out.println("正常关闭");
            }
        }
    }

    @Override
    public void close() throws Exception {
        super.close();
        conn.close();
        System.out.println("正常关闭了！");
    }
}


举报

相关推荐

【Flink-API】之复习窗口Window

数据javaide系统/运维

docker安装一系列镜像

docker容器运维

【源码】“@Value 注入失败”引发的一系列骚操作

技术分享spring ...Java

linux提供了一系列的函数来操作GPIO

引脚寄存器io操作编程语言

Linux写个脚本执行一系列命令

python开发语言

PWA引发的一系列问题

webpackjavascriptreact.js

48、Flink DataStream API 编程指南（2）- DataStream的source、transformation、sink、调试

flinkflink hiveflink kafkaflink 编程指南flink datastream数据仓库大数据yyds干货盘点

谷歌宣布一系列新的 Android 功能

ChromeAndroidGooglePython后端开发

xcode14.3更新一系列问题

docker容器运维

Matlab中导入或导出一系列文件

matlab
0 条评论