flink-----实时项目---day06-------1. 获取窗口迟到的数据 2.双流join（inner join和left join（有点小问题）） 3 订单Join案例（订单数据接入到kafka，订单数据的join实现，订单数据和迟到数据join的实现）

1. 获取窗口迟到的数据

　　主要流程就是给迟到的数据打上标签，然后使用相应窗口流的实例调用sideOutputLateData(lateDataTag)，从而获得窗口迟到的数据，进而进行相关的计算，具体代码见下

WindowLateDataDemo

package cn._51doit.flink.day10;

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.TimeCharacteristic;

import org.apache.flink.streaming.api.datastream.*;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;

import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;

import org.apache.flink.streaming.api.windowing.time.Time;

import org.apache.flink.streaming.api.windowing.windows.TimeWindow;

import org.apache.flink.util.OutputTag;

public class WindowLateDataDemo {

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

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        DataStreamSource<String> lines = env.socketTextStream("feng05", 8888);

        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

        // 数据类型：时间戳,单词，获取时间戳

        SingleOutputStreamOperator<String> assignTimestampsAndWatermarks = lines.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<String>(Time.seconds(2)) {

            @Override

            public long extractTimestamp(String element) {

                String stampTime = element.split(",")[0];

                return Long.parseLong(stampTime);

            }

        });

        // 切分数据

        SingleOutputStreamOperator<Tuple2<String, Integer>> wordAndOne = assignTimestampsAndWatermarks.map(new MapFunction<String, Tuple2<String, Integer>>() {

            @Override

            public Tuple2<String, Integer> map(String line) throws Exception {

                String word = line.split(",")[1];

                return Tuple2.of(word, 1);

            }

        });

        // 按照key分组

        KeyedStream<Tuple2<String, Integer>, Tuple> keyed = wordAndOne.keyBy(0);

        OutputTag<Tuple2<String, Integer>> lateDataTag = new OutputTag<Tuple2<String, Integer>>("late-data"){};

        // 划分窗口并进行计算

        WindowedStream<Tuple2<String, Integer>, Tuple, TimeWindow> window = keyed.window(TumblingEventTimeWindows.of(Time.seconds(5)));

        SingleOutputStreamOperator<Tuple2<String, Integer>> summed = window.sideOutputLateData(lateDataTag).sum(1);

        // 获取迟到数据的测流

        DataStream<Tuple2<String, Integer>> lateDataStream = summed.getSideOutput(lateDataTag);

        summed.print("准时的数据: ");

        SingleOutputStreamOperator<Tuple2<String, Integer>> result = summed.union(lateDataStream).keyBy(0).sum(1);

        result.print();

        env.execute();

    }

}

2.双流join

　补充：

　　Join、CoGroup和CoFlatMap这三个运算符都能够将双数据流转换为单个数据流。Join和CoGroup会根据指定的条件进行数据配对操作，不同的是Join只输出匹配成功的数据对，CoGroup无论是否有匹配都会输出。CoFlatMap没有匹配操作，只是分别去接收两个流的输入。　

　　简单来说，就是两个数据流进行join操作，类似mysql中的join，其也包含inner join、left join、right join、full join等，以下是具体例子

inner join

　　此处案例是先获取两个数据流，然后分别设置水位线，在进行join操作，从而得到joinedStream实例，然后调用joinStream中的where、equalTo方法，即可join到key相同的数据（注意此处需要自己定义key选择器），接着就是划分窗口，处理窗口中的数据

注意：条件相同并且在同一个窗口中的数据才能被join上

知识点：

　　一个窗口会有很多分区，只有当每个分区中的时间都满足窗口的触发条件，窗口才会被触发，所以下面的测试为了方便都将并行度设置为1

StreamDataSourceA

package cn._51doit.flink.day10;

import org.apache.flink.api.java.tuple.Tuple3;

import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction;

public class StreamDataSourceA extends RichParallelSourceFunction<Tuple3<String, String, Long>> {

    private volatile boolean running = true;

    @Override

    public void run(SourceContext<Tuple3<String, String, Long>> ctx) throws InterruptedException {

        //事先准备好的数据

        Tuple3[] elements = new Tuple3[]{

                Tuple3.of("a", "1", 1000000050000L), //[50000 - 60000)

                Tuple3.of("a", "2", 1000000054000L), //[50000 - 60000)

                Tuple3.of("a", "3", 1000000079900L), //[70000 - 80000)

                Tuple3.of("a", "4", 1000000115000L), //[110000 - 120000)  // 115000 - 5001 = 109999 >= 109999

                Tuple3.of("b", "5", 1000000100000L), //[100000 - 110000)

                Tuple3.of("b", "6", 1000000108000L)  //[100000 - 110000)

        };

        int count = 0;

        while (running && count < elements.length) {

            //将数据发出去

            ctx.collect(new Tuple3<>((String) elements[count].f0, (String) elements[count].f1, (Long) elements[count].f2));

            count++;

            Thread.sleep(1000);

        }

        //Thread.sleep(100000000);

    }

    @Override

    public void cancel() {

        running = false;

    }

}

StreamDataSourceB

package cn._51doit.flink.day10;

import org.apache.flink.api.java.tuple.Tuple3;

import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction;

public class StreamDataSourceB extends RichParallelSourceFunction<Tuple3<String, String, Long>> {

    private volatile boolean running = true;

    @Override

    public void run(SourceContext<Tuple3<String, String, Long>> ctx) throws Exception {

        // a ,1 hangzhou

        Tuple3[] elements = new Tuple3[]{

                Tuple3.of("a", "hangzhou", 1000000059000L), //[50000, 60000)

                Tuple3.of("b", "beijing",  1000000105000L), //[100000, 110000)

        };

        int count = 0;

        while (running && count < elements.length) {

            //将数据发出去

            ctx.collect(new Tuple3<>((String) elements[count].f0, (String) elements[count].f1, (long) elements[count].f2));

            count++;

            Thread.sleep(1000);

        }

        //Thread.sleep(100000000);

    }

    @Override

    public void cancel() {

        running = false;

    }

}

FlinkTumblingWindowsInnerJoinDemo

package cn._51doit.flink.day10;

import org.apache.flink.api.common.functions.JoinFunction;

import org.apache.flink.api.java.functions.KeySelector;

import org.apache.flink.api.java.tuple.Tuple3;

import org.apache.flink.api.java.tuple.Tuple5;

import org.apache.flink.streaming.api.TimeCharacteristic;

import org.apache.flink.streaming.api.datastream.DataStream;

import org.apache.flink.streaming.api.datastream.JoinedStreams;

import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;

import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;

import org.apache.flink.streaming.api.windowing.time.Time;

public class FlinkTumblingWindowsInnerJoinDemo {

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

        int windowSize = 10;

        long delay = 5001L;

        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 设置时间类型

        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

        //通过Env设置并行度为1，即以后所有的DataStream的并行都是1

        env.setParallelism(1);

        // 设置数据源

        //第一个流（左流）

        DataStream<Tuple3<String, String, Long>> leftSource = env.addSource(new StreamDataSourceA()).name("Demo SourceA");

        //第二个流（右流）

        DataStream<Tuple3<String, String, Long>> rightSource = env.addSource(new StreamDataSourceB()).name("Demo SourceB");

        // 对leftSource设置水位线

        // （"a", "1", 1000）

        SingleOutputStreamOperator<Tuple3<String, String, Long>> leftStream = leftSource.assignTimestampsAndWatermarks(

                new BoundedOutOfOrdernessTimestampExtractor<Tuple3<String, String, Long>>(Time.milliseconds(delay)) {

            @Override

            public long extractTimestamp(Tuple3<String, String, Long> element) {

                return element.f2;

            }

        });

        // 对rightSource设置水位线

        // （"a", "hangzhou", 6000）

        SingleOutputStreamOperator<Tuple3<String, String, Long>> rightStream = rightSource.assignTimestampsAndWatermarks(

                new BoundedOutOfOrdernessTimestampExtractor<Tuple3<String, String, Long>>(Time.milliseconds(delay)) {

            @Override

            public long extractTimestamp(Tuple3<String, String, Long> element) {

                return element.f2;

            }

        });

        // join操作

        JoinedStreams<Tuple3<String, String, Long>, Tuple3<String, String, Long>> joinedStream = leftStream.join(rightStream);

        joinedStream

                .where(new LeftSelectKey())

                .equalTo(new RightSelectKey())

                .window(TumblingEventTimeWindows.of(Time.seconds(windowSize)))

                .apply(new JoinFunction<Tuple3<String, String, Long>, Tuple3<String, String, Long>, Tuple5<String, String, String, Long, Long>>() {

                    // 两个流的key的值相等，并且在同一个窗口内

                    @Override

                    public Tuple5<String, String, String, Long, Long> join(Tuple3<String, String, Long> first, Tuple3<String, String, Long> second) throws Exception {

                        // a, 1, "hangzhou", 1000001000, 1000006000

                        return new Tuple5<>(first.f0, first.f1, second.f1, first.f2, second.f2);

                    }

                }).print();

        env.execute();

    }

    public static class LeftSelectKey implements KeySelector<Tuple3<String, String, Long>,String>{

        @Override

        public String getKey(Tuple3<String, String, Long> w) throws Exception {

            return w.f0;

        }

    }

    public static class RightSelectKey implements KeySelector<Tuple3<String, String, Long>, String> {

        @Override

        public String getKey(Tuple3<String, String, Long> w) {

            return w.f0;

        }

    }

}

运行结果

为什么b没有被join上？

left join

FlinkTumblingWindowLeftJoinDemo

package cn._51doit.flink.day10;

import org.apache.flink.api.common.functions.CoGroupFunction;

import org.apache.flink.api.java.functions.KeySelector;

import org.apache.flink.api.java.tuple.Tuple3;

import org.apache.flink.api.java.tuple.Tuple5;

import org.apache.flink.streaming.api.TimeCharacteristic;

import org.apache.flink.streaming.api.datastream.DataStream;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;

import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;

import org.apache.flink.streaming.api.windowing.time.Time;

import org.apache.flink.util.Collector;

public class FlinkTumblingWindowsLeftJoinDemo {

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

        int windowSize = 10;

        long delay = 5000L;

        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

        env.setParallelism(1);

        // 设置数据源

        DataStream<Tuple3<String, String, Long>> leftSource = env.addSource(new StreamDataSourceA()).name("Demo SourceA");

        DataStream<Tuple3<String, String, Long>> rightSource = env.addSource(new StreamDataSourceB()).name("Demo SourceB");

        // 设置水位线

        DataStream<Tuple3<String, String, Long>> leftStream = leftSource.assignTimestampsAndWatermarks(

                new BoundedOutOfOrdernessTimestampExtractor<Tuple3<String, String, Long>>(Time.milliseconds(delay)) {

                    @Override

                    public long extractTimestamp(Tuple3<String, String, Long> element) {

                        return element.f2;

                    }

                }

        );

        DataStream<Tuple3<String, String, Long>> rightStream = rightSource.assignTimestampsAndWatermarks(

                new BoundedOutOfOrdernessTimestampExtractor<Tuple3<String, String, Long>>(Time.milliseconds(delay)) {

                    @Override

                    public long extractTimestamp(Tuple3<String, String, Long> element) {

                        return element.f2;

                    }

                }

        );

        int parallelism1 = leftStream.getParallelism();

        int parallelism2 = rightStream.getParallelism();

        // left join 操作

        leftStream.coGroup(rightStream)

                .where(new LeftSelectKey())

                .equalTo(new RightSelectKey())

                .window(TumblingEventTimeWindows.of(Time.seconds(windowSize)))

                .apply(new LeftJoin())

                .print();

        env.execute("TimeWindowDemo");

    }

    public static class LeftJoin implements CoGroupFunction<Tuple3<String, String, Long>, Tuple3<String, String, Long>, Tuple5<String, String, String, Long, Long>> {

        //coGroup左流的数据和有流的数据取出来，可以将key相同，并且在同一个窗口的数据取出来

        @Override

        public void coGroup(

                Iterable<Tuple3<String, String, Long>> leftElements, Iterable<Tuple3<String, String, Long>> rightElements, Collector<Tuple5<String, String, String, Long, Long>> out) {

            //leftElements是左流的数据

            for (Tuple3<String, String, Long> leftElem : leftElements) {

                boolean hadElements = false;

                //如果左边的流join上了右边的流rightElements就不为空

                for (Tuple3<String, String, Long> rightElem : rightElements) {

                    //将join上的数据输出

                    out.collect(new Tuple5<>(leftElem.f0, leftElem.f1, rightElem.f1, leftElem.f2, rightElem.f2));

                    hadElements = true;

                }

                if (!hadElements) {

                    //没join上，给右边的数据赋空值

                    out.collect(new Tuple5<>(leftElem.f0, leftElem.f1, "null", leftElem.f2, -1L));

                }

            }

        }

    }

    public static class LeftSelectKey implements KeySelector<Tuple3<String, String, Long>, String> {

        @Override

        public String getKey(Tuple3<String, String, Long> w) {

            return w.f0;

        }

    }

    public static class RightSelectKey implements KeySelector<Tuple3<String, String, Long>, String> {

        @Override

        public String getKey(Tuple3<String, String, Long> w) {

            return w.f0;

        }

    }

}

做法和join类似，只是此处使用coGroup算子

3. 订单Join案例

在各种各样的系统中，都订单数据表

    订单表：订单主表、订单明细表

    订单主表：

        订单id、订单状态、订单总金额、订单的时间、用户ID

    订单明细表：

        订单主表的ID、商品ID、商品的分类ID、商品的单价、商品的数量

统计某个分类的成交金额

    订单状态为成交的（主表中）

    商品的分类ID（明细表中）

    商品的金额即单价*数量（明细表中）

在京东或淘宝，下来一个单

    o1000，已支付，600,2020-06-29 19:42:00，feng

    p1, 100, 1, 食品

    p1, 200, 1, 食品

    p1, 300, 1, 食品

3.1 订单数据的join实现

读取kafka中topic的数据的工具类

package cn._51doit.flink.day10;

import org.apache.flink.api.common.restartstrategy.RestartStrategies;

import org.apache.flink.api.common.serialization.DeserializationSchema;

import org.apache.flink.api.common.time.Time;

import org.apache.flink.api.java.utils.ParameterTool;

import org.apache.flink.runtime.state.filesystem.FsStateBackend;

import org.apache.flink.streaming.api.CheckpointingMode;

import org.apache.flink.streaming.api.datastream.DataStream;

import org.apache.flink.streaming.api.environment.CheckpointConfig;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;

import java.util.Arrays;

import java.util.List;

import java.util.Properties;

public class FlinkUtilsV2 {

    private static StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

    public static <T> DataStream<T> createKafkaDataStream(ParameterTool parameters, Class<? extends DeserializationSchema<T>> clazz) throws Exception {

        String topics = parameters.getRequired("kafka.topics");

        String groupId = parameters.getRequired("group.id");

        return createKafkaDataStream(parameters, topics, groupId, clazz);

    }

    public static <T> DataStream<T> createKafkaDataStream(ParameterTool parameters, String topics, Class<? extends DeserializationSchema<T>> clazz) throws Exception {

        String groupId = parameters.getRequired("group.id");

        return createKafkaDataStream(parameters, topics, groupId, clazz);

    }

    public static <T> DataStream<T> createKafkaDataStream(ParameterTool parameters, String topics, String groupId, Class<? extends DeserializationSchema<T>> clazz) throws Exception {

        //将ParameterTool的参数设置成全局的参数

        env.getConfig().setGlobalJobParameters(parameters);

        //开启checkpoint

        env.enableCheckpointing(parameters.getLong("checkpoint.interval", 10000L), CheckpointingMode.EXACTLY_ONCE);

        //重启策略

        env.setRestartStrategy(RestartStrategies.fixedDelayRestart(parameters.getInt("restart.times", 10), Time.seconds(5)));

        //设置statebackend

        String path = parameters.get("state.backend.path");

        if(path != null) {

            //最好的方式将setStateBackend配置到Flink的全局配置文件中flink-conf.yaml

            env.setStateBackend(new FsStateBackend(path));

        }

        //设置cancel任务不用删除checkpoint

        env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);

        env.getCheckpointConfig().setMaxConcurrentCheckpoints(3);

        //String topics = parameters.getRequired("kafka.topics");

        List<String> topicList = Arrays.asList(topics.split(","));

        Properties properties = parameters.getProperties();

        properties.setProperty("group.id", groupId);

        //创建FlinkKafkaConsumer

        FlinkKafkaConsumer<T> kafkaConsumer = new FlinkKafkaConsumer<T>(

                topicList,

                clazz.newInstance(),

                properties

        );

        return env.addSource(kafkaConsumer);

    }

    public static StreamExecutionEnvironment getEnv() {

        return env;

    }

}

OrderDetail

package cn._51doit.flink.day10;

import java.util.Date;

public class OrderDetail {

    private Long id;

    private Long order_id;

    private int category_id;

    private String categoryName;

    private Long sku;

    private Double money;

    private int amount;

    private Date create_time;

    private Date update_time;

    //对数据库的操作类型：INSERT、UPDATE

    private String type;

    public Long getId() {

        return id;

    }

    public void setId(Long id) {

        this.id = id;

    }

    public Long getOrder_id() {

        return order_id;

    }

    public void setOrder_id(Long order_id) {

        this.order_id = order_id;

    }

    public int getCategory_id() {

        return category_id;

    }

    public void setCategory_id(int category_id) {

        this.category_id = category_id;

    }

    public Long getSku() {

        return sku;

    }

    public void setSku(Long sku) {

        this.sku = sku;

    }

    public Double getMoney() {

        return money;

    }

    public void setMoney(Double money) {

        this.money = money;

    }

    public int getAmount() {

        return amount;

    }

    public void setAmount(int amount) {

        this.amount = amount;

    }

    public Date getCreate_time() {

        return create_time;

    }

    public void setCreate_time(Date create_time) {

        this.create_time = create_time;

    }

    public Date getUpdate_time() {

        return update_time;

    }

    public void setUpdate_time(Date update_time) {

        this.update_time = update_time;

    }

    public String getType() {

        return type;

    }

    public void setType(String type) {

        this.type = type;

    }

    public String getCategoryName() {

        return categoryName;

    }

    public void setCategoryName(String categoryName) {

        this.categoryName = categoryName;

    }

    @Override

    public String toString() {

        return "OrderDetail{" +

                "id=" + id +

                ", order_id=" + order_id +

                ", category_id=" + category_id +

                ", categoryName='" + categoryName + '\'' +

                ", sku=" + sku +

                ", money=" + money +

                ", amount=" + amount +

                ", create_time=" + create_time +

                ", update_time=" + update_time +

                ", type='" + type + '\'' +

                '}';

    }

}

OrderMain

package cn._51doit.flink.day10;

import java.util.Date;

public class OrderMain {

    private Long oid;

    private Date create_time;

    private Double total_money;

    private int status;

    private Date update_time;

    private String province;

    private String city;

    //对数据库的操作类型：INSERT、UPDATE

    private String type;

    public Long getOid() {

        return oid;

    }

    public void setOid(Long oid) {

        this.oid = oid;

    }

    public Date getCreate_time() {

        return create_time;

    }

    public void setCreate_time(Date create_time) {

        this.create_time = create_time;

    }

    public Double getTotal_money() {

        return total_money;

    }

    public void setTotal_money(Double total_money) {

        this.total_money = total_money;

    }

    public int getStatus() {

        return status;

    }

    public void setStatus(int status) {

        this.status = status;

    }

    public Date getUpdate_time() {

        return update_time;

    }

    public void setUpdate_time(Date update_time) {

        this.update_time = update_time;

    }

    public String getProvince() {

        return province;

    }

    public void setProvince(String province) {

        this.province = province;

    }

    public String getCity() {

        return city;

    }

    public void setCity(String city) {

        this.city = city;

    }

    public String getType() {

        return type;

    }

    public void setType(String type) {

        this.type = type;

    }

    @Override

    public String toString() {

        return "OrderMain{" +

                "oid=" + oid +

                ", create_time=" + create_time +

                ", total_money=" + total_money +

                ", status=" + status +

                ", update_time=" + update_time +

                ", province='" + province + '\'' +

                ", city='" + city + '\'' +

                ", type='" + type + '\'' +

                '}';

    }

}

业务代码

OrderJoin

package cn._51doit.flink.day10;

import com.alibaba.fastjson.JSON;

import com.alibaba.fastjson.JSONArray;

import com.alibaba.fastjson.JSONObject;

import org.apache.flink.api.common.functions.CoGroupFunction;

import org.apache.flink.api.common.serialization.SimpleStringSchema;

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.TimeCharacteristic;

import org.apache.flink.streaming.api.datastream.DataStream;

import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;

import org.apache.flink.streaming.api.functions.ProcessFunction;

import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;

import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;

import org.apache.flink.streaming.api.windowing.time.Time;

import org.apache.flink.util.Collector;

public class OrderJoin {

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

        ParameterTool parameters = ParameterTool.fromPropertiesFile(args[0]);

        //使用EventTime作为时间标准

        FlinkUtilsV2.getEnv().setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

        DataStream<String> orderMainLinesDataStream = FlinkUtilsV2.createKafkaDataStream(parameters, "ordermain", "g1", SimpleStringSchema.class);

        DataStream<String> orderDetailLinesDataStream = FlinkUtilsV2.createKafkaDataStream(parameters, "orderdetail", "g1", SimpleStringSchema.class);

        //对数据进行解析

        SingleOutputStreamOperator<OrderMain> orderMainDataStream = orderMainLinesDataStream.process(new ProcessFunction<String, OrderMain>() {

            @Override

            public void processElement(String line, Context ctx, Collector<OrderMain> out) throws Exception {

                //flatMap+filter

                try {

                    JSONObject jsonObject = JSON.parseObject(line);

                    String type = jsonObject.getString("type");

                    if (type.equals("INSERT") || type.equals("UPDATE")) {

                        JSONArray jsonArray = jsonObject.getJSONArray("data");

                        for (int i = 0; i < jsonArray.size(); i++) {

                            OrderMain orderMain = jsonArray.getObject(i, OrderMain.class);

                            orderMain.setType(type); //设置操作类型

                            out.collect(orderMain);

                        }

                    }

                } catch (Exception e) {

                    //e.printStackTrace();

                    //记录错误的数据

                }

            }

        });

        //对数据进行解析

        SingleOutputStreamOperator<OrderDetail> orderDetailDataStream = orderDetailLinesDataStream.process(new ProcessFunction<String, OrderDetail>() {

            @Override

            public void processElement(String line, Context ctx, Collector<OrderDetail> out) throws Exception {

                //flatMap+filter

                try {

                    JSONObject jsonObject = JSON.parseObject(line);

                    String type = jsonObject.getString("type");

                    if (type.equals("INSERT") || type.equals("UPDATE")) {

                        JSONArray jsonArray = jsonObject.getJSONArray("data");

                        for (int i = 0; i < jsonArray.size(); i++) {

                            OrderDetail orderDetail = jsonArray.getObject(i, OrderDetail.class);

                            orderDetail.setType(type); //设置操作类型

                            out.collect(orderDetail);

                        }

                    }

                } catch (Exception e) {

                    //e.printStackTrace();

                    //记录错误的数据

                }

            }

        });

        int delaySeconds = 2;

        //提取EventTime生成WaterMark

        SingleOutputStreamOperator<OrderMain> orderMainStreamWithWaterMark = orderMainDataStream.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<OrderMain>(Time.seconds(delaySeconds)) {

            @Override

            public long extractTimestamp(OrderMain element) {

                return element.getCreate_time().getTime();

            }

        });

        SingleOutputStreamOperator<OrderDetail> orderDetailStreamWithWaterMark = orderDetailDataStream.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<OrderDetail>(Time.seconds(delaySeconds)) {

            @Override

            public long extractTimestamp(OrderDetail element) {

                return element.getCreate_time().getTime();

            }

        });

        //Left Out JOIN，并且将订单明细表作为左表

        DataStream<Tuple2<OrderDetail, OrderMain>> joined = orderDetailStreamWithWaterMark.coGroup(orderMainStreamWithWaterMark)

                .where(new KeySelector<OrderDetail, Long>() {

                    @Override

                    public Long getKey(OrderDetail value) throws Exception {

                        return value.getOrder_id();

                    }

                })

                .equalTo(new KeySelector<OrderMain, Long>() {

                    @Override

                    public Long getKey(OrderMain value) throws Exception {

                        return value.getOid();

                    }

                })

                .window(TumblingEventTimeWindows.of(Time.seconds(5)))

                .apply(new CoGroupFunction<OrderDetail, OrderMain, Tuple2<OrderDetail, OrderMain>>() {

                    @Override

                    public void coGroup(Iterable<OrderDetail> first, Iterable<OrderMain> second, Collector<Tuple2<OrderDetail, OrderMain>> out) throws Exception {

                        for (OrderDetail orderDetail : first) {

                            boolean isJoined = false;

                            for (OrderMain orderMain : second) {

                                out.collect(Tuple2.of(orderDetail, orderMain));

                                isJoined = true;

                            }

                            if (!isJoined) {

                                out.collect(Tuple2.of(orderDetail, null));

                            }

                        }

                    }

                });

        joined.print();

        FlinkUtilsV2.getEnv().execute();

    }

}

3.2 订单数据和迟到数据join的实现

OrderJoinAdv

package cn._51doit.flink.day10;

import com.alibaba.fastjson.JSON;

import com.alibaba.fastjson.JSONArray;

import com.alibaba.fastjson.JSONObject;

import org.apache.flink.api.common.functions.CoGroupFunction;

import org.apache.flink.api.common.functions.RichMapFunction;

import org.apache.flink.api.common.serialization.SimpleStringSchema;

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.configuration.Configuration;

import org.apache.flink.streaming.api.TimeCharacteristic;

import org.apache.flink.streaming.api.datastream.DataStream;

import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;

import org.apache.flink.streaming.api.functions.ProcessFunction;

import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;

import org.apache.flink.streaming.api.functions.windowing.AllWindowFunction;

import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;

import org.apache.flink.streaming.api.windowing.time.Time;

import org.apache.flink.streaming.api.windowing.windows.TimeWindow;

import org.apache.flink.util.Collector;

import org.apache.flink.util.OutputTag;

import java.sql.Connection;

public class OrderJoinAdv {

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

        ParameterTool parameters = ParameterTool.fromPropertiesFile(args[0]);

        //使用EventTime作为时间标准

        FlinkUtilsV2.getEnv().setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

        DataStream<String> orderMainLinesDataStream = FlinkUtilsV2.createKafkaDataStream(parameters, "ordermain", "g1", SimpleStringSchema.class);

        DataStream<String> orderDetailLinesDataStream = FlinkUtilsV2.createKafkaDataStream(parameters, "orderdetail", "g1", SimpleStringSchema.class);

        //对数据进行解析

        SingleOutputStreamOperator<OrderMain> orderMainDataStream = orderMainLinesDataStream.process(new ProcessFunction<String, OrderMain>() {

            @Override

            public void processElement(String line, Context ctx, Collector<OrderMain> out) throws Exception {

                //flatMap+filter

                try {

                    JSONObject jsonObject = JSON.parseObject(line);

                    String type = jsonObject.getString("type");

                    if (type.equals("INSERT") || type.equals("UPDATE")) {

                        JSONArray jsonArray = jsonObject.getJSONArray("data");

                        for (int i = 0; i < jsonArray.size(); i++) {

                            OrderMain orderMain = jsonArray.getObject(i, OrderMain.class);

                            orderMain.setType(type); //设置操作类型

                            out.collect(orderMain);

                        }

                    }

                } catch (Exception e) {

                    //e.printStackTrace();

                    //记录错误的数据

                }

            }

        });

        //对数据进行解析

        SingleOutputStreamOperator<OrderDetail> orderDetailDataStream = orderDetailLinesDataStream.process(new ProcessFunction<String, OrderDetail>() {

            @Override

            public void processElement(String line, Context ctx, Collector<OrderDetail> out) throws Exception {

                //flatMap+filter

                try {

                    JSONObject jsonObject = JSON.parseObject(line);

                    String type = jsonObject.getString("type");

                    if (type.equals("INSERT") || type.equals("UPDATE")) {

                        JSONArray jsonArray = jsonObject.getJSONArray("data");

                        for (int i = 0; i < jsonArray.size(); i++) {

                            OrderDetail orderDetail = jsonArray.getObject(i, OrderDetail.class);

                            orderDetail.setType(type); //设置操作类型

                            out.collect(orderDetail);

                        }

                    }

                } catch (Exception e) {

                    //e.printStackTrace();

                    //记录错误的数据

                }

            }

        });

        int delaySeconds = 2;

        int windowSize = 5;

        //提取EventTime生成WaterMark

        SingleOutputStreamOperator<OrderMain> orderMainStreamWithWaterMark = orderMainDataStream.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<OrderMain>(Time.seconds(delaySeconds)) {

            @Override

            public long extractTimestamp(OrderMain element) {

                return element.getCreate_time().getTime();

            }

        });

        SingleOutputStreamOperator<OrderDetail> orderDetailStreamWithWaterMark = orderDetailDataStream.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<OrderDetail>(Time.seconds(delaySeconds)) {

            @Override

            public long extractTimestamp(OrderDetail element) {

                return element.getCreate_time().getTime();

            }

        });

        //定义迟到侧流输出的Tag

        OutputTag<OrderDetail> lateTag = new OutputTag<OrderDetail>("late-date") {};

        //对左表进行单独划分窗口，窗口的长度与cogroup的窗口长度一样

        SingleOutputStreamOperator<OrderDetail> orderDetailWithWindow = orderDetailStreamWithWaterMark.windowAll(TumblingEventTimeWindows.of(Time.seconds(windowSize)))

                .sideOutputLateData(lateTag) //将迟到的数据打上Tag

                .apply(new AllWindowFunction<OrderDetail, OrderDetail, TimeWindow>() {

                    @Override

                    public void apply(TimeWindow window, Iterable<OrderDetail> values, Collector<OrderDetail> out) throws Exception {

                        for (OrderDetail value : values) {

                            //out.collect(value);

                        }

                    }

                });

        //获取迟到的数据

        DataStream<OrderDetail> lateOrderDetailStream = orderDetailWithWindow.getSideOutput(lateTag);

        //应为orderDetail表的数据迟到数据不是很多，没必要使用异步IO，直接使用RichMapFunction

        SingleOutputStreamOperator<Tuple2<OrderDetail, OrderMain>> lateOrderDetailAndOrderMain = lateOrderDetailStream.map(new RichMapFunction<OrderDetail, Tuple2<OrderDetail, OrderMain>>() {

            @Override

            public void open(Configuration parameters) throws Exception {

                //创建书库的连接

            }

            @Override

            public Tuple2<OrderDetail, OrderMain> map(OrderDetail value) throws Exception {

                return null;

            }

            @Override

            public void close() throws Exception {

                //关闭数据库的连接

            }

        });

        //Left Out JOIN，并且将订单明细表作为左表

        DataStream<Tuple2<OrderDetail, OrderMain>> joined = orderDetailWithWindow.coGroup(orderMainStreamWithWaterMark)

                .where(new KeySelector<OrderDetail, Long>() {

                    @Override

                    public Long getKey(OrderDetail value) throws Exception {

                        return value.getOrder_id();

                    }

                })

                .equalTo(new KeySelector<OrderMain, Long>() {

                    @Override

                    public Long getKey(OrderMain value) throws Exception {

                        return value.getOid();

                    }

                })

                .window(TumblingEventTimeWindows.of(Time.seconds(windowSize)))

                .apply(new CoGroupFunction<OrderDetail, OrderMain, Tuple2<OrderDetail, OrderMain>>() {

                    @Override

                    public void coGroup(Iterable<OrderDetail> first, Iterable<OrderMain> second, Collector<Tuple2<OrderDetail, OrderMain>> out) throws Exception {

                        for (OrderDetail orderDetail : first) {

                            boolean isJoined = false;

                            for (OrderMain orderMain : second) {

                                out.collect(Tuple2.of(orderDetail, orderMain));

                                isJoined = true;

                            }

                            if (!isJoined) {

                                out.collect(Tuple2.of(orderDetail, null));

                            }

                        }

                    }

                });

        //join后，有可orderMain没有join上

        SingleOutputStreamOperator<Tuple2<OrderDetail, OrderMain>> punctualOrderDetailAndOrderMain = joined.map(new RichMapFunction<Tuple2<OrderDetail, OrderMain>, Tuple2<OrderDetail, OrderMain>>() {

            private transient Connection connection;

            @Override

            public void open(Configuration parameters) throws Exception {

                //打开数据库连接

            }

            @Override

            public Tuple2<OrderDetail, OrderMain> map(Tuple2<OrderDetail, OrderMain> tp) throws Exception {

                //每个关联上订单主表的数据，就查询书库

                if (tp.f1 == null) {

                    OrderMain orderMain = queryOrderMainFromMySQL(tp.f0.getOrder_id(), connection);

                    tp.f1 = orderMain;

                }

                return tp;

            }

            @Override

            public void close() throws Exception {

                //关闭数据库连接

            }

        });

        //将准时的和迟到的UNION到一起

        DataStream<Tuple2<OrderDetail, OrderMain>> allOrderStream = punctualOrderDetailAndOrderMain.union(lateOrderDetailAndOrderMain);

        //根据具体的场景，写入到Kafka、Hbase、ES、ClickHouse

        allOrderStream.print();

        FlinkUtilsV2.getEnv().execute();

    }

    private static OrderMain queryOrderMainFromMySQL(Long order_id, Connection connection) {

        return null;

    }

}

flink-----实时项目---day06-------1. 获取窗口迟到的数据 2.双流join（inner join和left join（有点小问题）） 3 订单Join案例（订单数据接入到kafka，订单数据的join实现，订单数据和迟到数据join的实现）的更多相关文章

5.Flink实时项目之业务数据准备
1. 流程介绍在上一篇文章中,我们已经把客户端的页面日志,启动日志,曝光日志分别发送到kafka对应的主题中.在本文中,我们将把业务数据也发送到对应的kafka主题中. 通过maxwell采集业务数 ...
6.Flink实时项目之业务数据分流
在上一篇文章中,我们已经获取到了业务数据的输出流,分别是dim层维度数据的输出流,及dwd层事实数据的输出流,接下来我们要做的就是把这些输出流分别再流向对应的数据介质中,dim层流向hbase中,dw ...
4.Flink实时项目之数据拆分
1. 摘要我们前面采集的日志数据已经保存到 Kafka 中,作为日志数据的 ODS 层,从 kafka 的ODS 层读取的日志数据分为 3 类, 页面日志.启动日志和曝光日志.这三类数据虽然都是用户 ...
9.Flink实时项目之订单宽表
1.需求分析订单是统计分析的重要的对象,围绕订单有很多的维度统计需求,比如用户.地区.商品.品类.品牌等等.为了之后统计计算更加方便,减少大表之间的关联,所以在实时计算过程中将围绕订单的相关数据整合 ...
10.Flink实时项目之订单维度表关联
1. 维度查询在上一篇中,我们已经把订单和订单明细表join完,本文将关联订单的其他维度数据,维度关联实际上就是在流中查询存储在 hbase 中的数据表.但是即使通过主键的方式查询,hbase 速度 ...
11.Flink实时项目之支付宽表
支付宽表支付宽表的目的,最主要的原因是支付表没有到订单明细,支付金额没有细分到商品上, 没有办法统计商品级的支付状况. 所以本次宽表的核心就是要把支付表的信息与订单明细关联上. 解决方案有两个一个 ...
7.Flink实时项目之独立访客开发
1.架构说明在上6节当中,我们已经完成了从ods层到dwd层的转换,包括日志数据和业务数据,下面我们开始做dwm层的任务. DWM 层主要服务 DWS,因为部分需求直接从 DWD 层到DWS 层中间 ...
3.Flink实时项目之流程分析及环境搭建
1. 流程分析前面已经将日志数据(ods_base_log)及业务数据(ods_base_db_m)发送到kafka,作为ods层,接下来要做的就是通过flink消费kafka 的ods数据,进行简 ...
8.Flink实时项目之CEP计算访客跳出
1.访客跳出明细介绍首先要识别哪些是跳出行为,要把这些跳出的访客最后一个访问的页面识别出来.那么就要抓住几个特征: 该页面是用户近期访问的第一个页面,这个可以通过该页面是否有上一个页面(last_p ...
1.Flink实时项目前期准备
1.日志生成项目日志生成机器:hadoop101 jar包:mock-log-0.0.1-SNAPSHOT.jar gmall_mock |----mock_common |----mock ...

随机推荐

王爽汇编第十章,call和ret指令
目录王爽汇编第十章,call和ret指令 call和ret指令概述: ret和retf ret指令 retf指令 call 和 ret 的配合使用 call指令详解 call原理 call指令所有写 ...
telnet IP 端口的作用
测试远程服务器的端口是否开启
Typecho部署小破站
写在前面以前利用 Github Page + Hexo框架 + Next主题搭建过静态博客,没错就是那个黑白色系的网页!但是体验并不是很好,一来本身是静态网页,页面内容要修改都需要在本地修改完上传到 ...
Redis INFO CPU 信息详解
一.INFO CPU 通过INFO CPU命令可以查看Redis进程对于CPU的使用情况,如下: 这几个字段的含义如下所示: used_cpu_sys: System CPU consumed by ...
JavaScript数组方法大集合
JavaScript数组方法集合本文总结一下js数组处理用到的所有的方法.自己做个笔记. 数组方法 concat() 合并两个或多个数组 concat()能合并两个或者多个数组,不会更改当前数组,而 ...
coding game，边打游戏边学编程，是一种怎么样的体验？
前言 hello,大家好,我是bigsai,好久不见,甚是想念! 在日常生活中,很多人喜欢玩游戏,因为游戏中有着对抗博弈.控制的喜悦,用灵魂指法完成一波靓丽的操作. 但实际上,你的按键都是对应代码中一 ...
redis sentinel搭建
/usr/local/bin /usr/local/etc https://www.centos.bz/2017/08/redis-3-x-sentinel-ha-service/ https://w ...
JDK 之 Arrays.asList - 源码分析
Arrays工具类提供了一个方法asList, 使用该方法可以将一个变长参数或者数组转换成List . 其源代码如下: @SafeVarargs public static <T> Lis ...
CODING添加ssh提示格式错误的问题
不能去.shh文件夹打开id_rsa.pub文件查看解决方法: 进入.ssh文件夹,然后右键git bash here 输入代码 cat id_rsa.pub 回车即可
开源一个由.netcore/.net framework4.6开发的saas微商城+独立部署版本微小程序商城
一.项目介绍开源一款基于.NET4.6开发的一款完整的微信商城SAAS平台,前端支持小程序.h5,由前端商城,商户管理后台,平台管理后台三大块组成,sass功能完善,支持商户拖拽式零代码创建并提交上 ...

flink-----实时项目---day06-------1. 获取窗口迟到的数据 2.双流join（inner join和left join（有点小问题）） 3 订单Join案例（订单数据接入到kafka，订单数据的join实现，订单数据和迟到数据join的实现）

flink-----实时项目---day06-------1. 获取窗口迟到的数据 2.双流join（inner join和left join（有点小问题）） 3 订单Join案例（订单数据接入到kafka，订单数据的join实现，订单数据和迟到数据join的实现）的更多相关文章

随机推荐

热门专题