Flink 如何分流数据

• 场景
• 分流方式
• 如何分流
  • 使用Filter分流
  • 使用Split分流
  • 使用Side Output分流

场景

获取流数据的时候,通常需要根据所需把流拆分出其他多个流,根据不同的流再去作相应的处理。

举个例子:创建一个商品实时流,商品有季节标签,需要对不同标签的商品做统计处理,这个时候就需要把商品数据流根据季节标签分流。

分流方式

• 使用Filter分流
• 使用Split分流
• 使用Side Output分流

如何分流

先模拟一个实时的数据流

import lombok.Data;
@Data
public class Product {
    public Integer id;
    public String seasonType;
}

自定义Source

import common.Product;
import org.apache.flink.streaming.api.functions.source.SourceFunction;

import java.util.ArrayList;
import java.util.Random;

public class ProductStremingSource implements SourceFunction {
    private boolean isRunning = true;

    @Override
    public void run(SourceContext ctx) throws Exception {
        while (isRunning){
            // 每一秒钟产生一条数据
            Product product = generateProduct();
            ctx.collect(product);
            Thread.sleep(1000);
        }
    }

    private Product generateProduct(){
        int i = new Random().nextInt(100);
        ArrayList list = new ArrayList();
        list.add("spring");
        list.add("summer");
        list.add("autumn");
        list.add("winter");
        Product product = new Product();
        product.setSeasonType(list.get(new Random().nextInt(4)));
        product.setId(i);
        return product;
    }
    @Override
    public void cancel() {

    }
}

输出:

使用Filter分流

使用 filter 算子根据数据的字段进行过滤。

import common.Product;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import source.ProductStremingSource;

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

        DataStreamSource source = env.addSource(new ProductStremingSource());

        // 使用Filter分流
        SingleOutputStreamOperator spring = source.filter(product -> "spring".equals(product.getSeasonType()));
        SingleOutputStreamOperator summer = source.filter(product -> "summer".equals(product.getSeasonType()));
        SingleOutputStreamOperator autumn  = source.filter(product -> "autumn".equals(product.getSeasonType()));
        SingleOutputStreamOperator winter  = source.filter(product -> "winter".equals(product.getSeasonType()));
        source.print();
        winter.printToErr();

        env.execute("output");
    }
}

结果输出(红色为季节标签是winter的分流输出):

使用Split分流

重写OutputSelector内部类的select()方法,根据数据所需要分流的类型反正不同的标签下,返回SplitStream,通过SplitStream的select()方法去选择相应的数据流。

只分流一次是没有问题的，但是不能使用它来做连续的分流。

SplitStream已经标记过时了

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

        DataStreamSource source = env.addSource(new ProductStremingSource());

        // 使用Split分流
        SplitStream dataSelect = source.split(new OutputSelector() {
            @Override
            public Iterable select(Product product) {
                List seasonTypes = new ArrayList<>();
                String seasonType = product.getSeasonType();
                switch (seasonType){
                    case "spring":
                        seasonTypes.add(seasonType);
                        break;
                    case "summer":
                        seasonTypes.add(seasonType);
                        break;
                    case "autumn":
                        seasonTypes.add(seasonType);
                        break;
                    case "winter":
                        seasonTypes.add(seasonType);
                        break;
                    default:
                        break;
                }
                return seasonTypes;
            }
        });
        DataStream spring = dataSelect.select("machine");
        DataStream summer = dataSelect.select("docker");
        DataStream autumn = dataSelect.select("application");
        DataStream winter = dataSelect.select("middleware");
        source.print();
        winter.printToErr();

        env.execute("output");
    }
}

使用Side Output分流

推荐使用这种方式

首先需要定义一个OutputTag用于标识不同流

可以使用下面的几种函数处理流发送到分流中:

• ProcessFunction
• KeyedProcessFunction
• CoProcessFunction
• KeyedCoProcessFunction
• ProcessWindowFunction
• ProcessAllWindowFunction

之后再用getSideOutput(OutputTag)选择流。

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

        DataStreamSource source = env.addSource(new ProductStremingSource());

        // 使用Side Output分流
        final OutputTag spring = new OutputTag("spring");
        final OutputTag summer = new OutputTag("summer");
        final OutputTag autumn = new OutputTag("autumn");
        final OutputTag winter = new OutputTag("winter");
        SingleOutputStreamOperator sideOutputData = source.process(new ProcessFunction() {
            @Override
            public void processElement(Product product, Context ctx, Collector out) throws Exception {
                String seasonType = product.getSeasonType();
                switch (seasonType){
                    case "spring":
                        ctx.output(spring,product);
                        break;
                    case "summer":
                        ctx.output(summer,product);
                        break;
                    case "autumn":
                        ctx.output(autumn,product);
                        break;
                    case "winter":
                        ctx.output(winter,product);
                        break;
                    default:
                        out.collect(product);
                }
            }
        });

        DataStream springStream = sideOutputData.getSideOutput(spring);
        DataStream summerStream = sideOutputData.getSideOutput(summer);
        DataStream autumnStream = sideOutputData.getSideOutput(autumn);
        DataStream winterStream = sideOutputData.getSideOutput(winter);

        // 输出标签为:winter 的数据流
        winterStream.print();

        env.execute("output");
    }
}

结果输出:

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