Flink 广播流连接示例：使用 BroadcastProcessFunction 进行逻辑处理

以下是使用 Flink 连接正常流和广播流的示例，并使用 BroadcastProcessFunction 进行逻辑处理。

import org.apache.flink.api.common.state.BroadcastState;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.BroadcastStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.co.BroadcastProcessFunction;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.util.Collector;

import java.util.HashMap;
import java.util.Map;

public class BroadcastProcessExample {

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 创建正常流
        SourceFunction<Tuple2<String, Integer>> normalSource = new NormalSource();
        env.addSource(normalSource)
                .connect(getBroadcastStream(env))
                .process(new MyBroadcastProcessFunction())
                .print();

        env.execute('Broadcast Process Example');
    }

    // 创建广播流
    private static BroadcastStream<Tuple2<String, Integer>> getBroadcastStream(StreamExecutionEnvironment env) {
        SourceFunction<Tuple2<String, Integer>> broadcastSource = new BroadcastSource();
        MapStateDescriptor<Void, Tuple2<String, Integer>> broadcastStateDescriptor =
                new MapStateDescriptor('broadcast-state',
                        TypeInformation.of(new TypeHint<Void>() {}),
                        TypeInformation.of(new TypeHint<Tuple2<String, Integer>>() {}));
        return env.addSource(broadcastSource)
                .broadcast(broadcastStateDescriptor);
    }

    // 正常流数据源
    private static class NormalSource implements SourceFunction<Tuple2<String, Integer>> {
        private volatile boolean isRunning = true;

        @Override
        public void run(SourceContext<Tuple2<String, Integer>> ctx) throws Exception {
            while (isRunning) {
                // 生成一些数据
                Tuple2<String, Integer> data = new Tuple2('key', 1);
                ctx.collect(data);
                Thread.sleep(1000);
            }
        }

        @Override
        public void cancel() {
            isRunning = false;
        }
    }

    // 广播流数据源
    private static class BroadcastSource implements SourceFunction<Tuple2<String, Integer>> {
        private volatile boolean isRunning = true;

        @Override
        public void run(SourceContext<Tuple2<String, Integer>> ctx) throws Exception {
            while (isRunning) {
                // 生成一些数据
                Tuple2<String, Integer> data = new Tuple2('key', 100);
                ctx.collect(data);
                Thread.sleep(5000);
            }
        }

        @Override
        public void cancel() {
            isRunning = false;
        }
    }

    // 广播处理函数
    private static class MyBroadcastProcessFunction extends BroadcastProcessFunction<Tuple2<String, Integer>, Tuple2<String, Integer>, String> {
        private transient MapStateDescriptor<Void, Tuple2<String, Integer>> broadcastStateDescriptor;

        @Override
        public void open(org.apache.flink.configuration.Configuration parameters) throws Exception {
            broadcastStateDescriptor = new MapStateDescriptor('broadcast-state',
                    TypeInformation.of(new TypeHint<Void>() {}),
                    TypeInformation.of(new TypeHint<Tuple2<String, Integer>>() {}));
        }

        @Override
        public void processBroadcastElement(Tuple2<String, Integer> value, Context ctx, Collector<String> out) throws Exception {
            BroadcastState<Void, Tuple2<String, Integer>> broadcastState = ctx.getBroadcastState(broadcastStateDescriptor);
            broadcastState.put(null, value);
        }

        @Override
        public void processElement(Tuple2<String, Integer> value, ReadOnlyContext ctx, Collector<String> out) throws Exception {
            BroadcastState<Void, Tuple2<String, Integer>> broadcastState = ctx.getBroadcastState(broadcastStateDescriptor);
            Tuple2<String, Integer> broadcastValue = broadcastState.get(null);

            if (broadcastValue != null && value.f1 > broadcastValue.f1) {
                out.collect('Value ' + value.f1 + ' is greater than broadcast value ' + broadcastValue.f1);
            }
        }
    }
}

代码中，首先定义了两个数据源，NormalSource 用于生成正常流数据，BroadcastSource 用于生成广播流数据。然后，getBroadcastStream 方法使用 BroadcastStream 将广播流连接到正常流中，并将广播流的数据广播到所有并行任务中。

接下来定义了 BroadcastProcessFunction，用于处理正常流数据。processBroadcastElement 方法将广播流数据放入广播状态，processElement 方法从广播状态获取广播流数据并与正常流数据比较，根据逻辑进行处理。最后，将正常流的处理结果打印出来。

在执行程序时，将不断生成正常流和广播流数据，并进行处理。

该示例演示了 Flink 中使用 BroadcastProcessFunction 连接正常流和广播流的基本方法，并进行逻辑处理。在实际应用中，可以根据具体需求修改代码，例如自定义数据源和处理逻辑。