[大数据]FlinkTable&Sql 中 OutputSelector升级到OutputTag 对数据分流（包含对比实例）

案例说明

利用flink source 功能实现一个自定义的实时数据源。
达到的效果是：将实时的商品数据进行分流，分成even 和 odd 两个流进行join。条件是名称相同，最后把join 的结果输出。

flink 1.10 版本 的outputSelector 实现

public class MyStreamingSource implements SourceFunction<Item> {

    private boolean isRunning = true;

    /**
     * 重写run方法产生一个源源不断的数据发送源
     * @param ctx
     * @throws Exception
     */
    public void run(SourceContext<Item> ctx) throws Exception {
        while(isRunning){
            Item item = generateItem();
            ctx.collect(item);

            //每秒产生一条数据
            Thread.sleep(1000);
        }
    }
    @Override
    public void cancel() {
        isRunning = false;
    }

    //随机产生一条商品数据
    private Item generateItem(){
        int i = new Random().nextInt(100);
        ArrayList<String> list = new ArrayList();
        list.add("HAT");
        list.add("TIE");
        list.add("SHOE");
        Item item = new Item();
        item.setName(list.get(new Random().nextInt(3)));
        item.setId(i);
        return item;
    }
}


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

        EnvironmentSettings bsSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
        StreamExecutionEnvironment bsEnv = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment bsTableEnv = StreamTableEnvironment.create(bsEnv, bsSettings);

        SingleOutputStreamOperator<Item> source = bsEnv.addSource(new MyStreamingSource()).map(new MapFunction<Item, Item>() {
            @Override
            public Item map(Item item) throws Exception {
                return item;
            }
        });

        DataStream<Item> evenSelect = source.split(new OutputSelector<Item>() {
            @Override
            public Iterable<String> select(Item value) {
                List<String> output = new ArrayList<>();
                if (value.getId() % 2 == 0) {
                    output.add("even");
                } else {
                    output.add("odd");
                }
                return output;
            }
        }).select("even");

        DataStream<Item> oddSelect = source.split(new OutputSelector<Item>() {
            @Override
            public Iterable<String> select(Item value) {
                List<String> output = new ArrayList<>();
                if (value.getId() % 2 == 0) {
                    output.add("even");
                } else {
                    output.add("odd");
                }
                return output;
            }
        }).select("odd");


        bsTableEnv.createTemporaryView("evenTable", evenSelect, "name,id");
        bsTableEnv.createTemporaryView("oddTable", oddSelect, "name,id");

        Table queryTable = bsTableEnv.sqlQuery("select a.id,a.name,b.id,b.name from evenTable as a join oddTable as b on a.name = b.name");

        queryTable.printSchema();

        bsTableEnv.toRetractStream(queryTable, TypeInformation.of(new TypeHint<Tuple4<Integer,String,Integer,String>>(){})).print();

        bsEnv.execute("streaming sql job");
    }

}

flink 1.12 版本 OutputTag 实现

public class MyStreamingSource implements SourceFunction<Item> {

    private boolean isRunning = true;

    /**
     * 重写run方法产生一个源源不断的数据发送源
     * @param ctx
     * @throws Exception
     */
    public void run(SourceContext<Item> ctx) throws Exception {
        while(isRunning){
            Item item = generateItem();
//            System.out.println(item);
            ctx.collect(item);

            //每秒产生一条数据
            Thread.sleep(1000);
        }
    }
    @Override
    public void cancel() {
        isRunning = false;
    }

    //随机产生一条商品数据
    private Item generateItem(){
        int i = new Random().nextInt(100);
        ArrayList<String> list = new ArrayList();
        list.add("HAT");
        list.add("TIE");
        list.add("SHOE");
        Item item = new Item();
        item.setName(list.get(new Random().nextInt(3)));
        item.setId(i);
        return item;
    }
}


class StreamingDemo {
    public static void main(String[] args) throws Exception {
        EnvironmentSettings bsSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
        StreamExecutionEnvironment bsEnv = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment bsTableEnv = StreamTableEnvironment.create(bsEnv, bsSettings);
        //首先定义两个sideoutput来保存切分出来的数据
        OutputTag<Item> outputTag1 = new OutputTag<Item>("even") {};//保存偶数
        OutputTag<Item> outputTag2 = new OutputTag<Item>("odd") {};//保存奇数
        SingleOutputStreamOperator<Item> source = bsEnv.addSource(new MyStreamingSource()).map(new MapFunction<Item, Item>() {
            @Override
            public Item map(Item item) throws Exception {
                return item;
            }
        });

        SingleOutputStreamOperator<Item> result= source.process(new ProcessFunction<Item,Item>() {
            @Override
            public void processElement(Item value, Context ctx, Collector<Item> out) throws Exception {
                if (value.getId() % 2 ==  0) {
                    ctx.output(outputTag1, value);
                } else {
                    ctx.output(outputTag2, value);
                }
            }
        });
        DataStream<Item> so1 = result.getSideOutput(outputTag1);
        DataStream<Item> so2 = result.getSideOutput(outputTag2);
        bsTableEnv.createTemporaryView("evenTable", so1);
        bsTableEnv.createTemporaryView("oddTable", so2);
        Table queryTable = bsTableEnv.sqlQuery("select a.id,a.name,b.id,b.name from evenTable as a join oddTable as b on a.name = b.name");
        bsTableEnv.toRetractStream(queryTable, TypeInformation.of(new TypeHint<Tuple4<Integer,String,Integer,String>>(){})).print();

        bsEnv.execute("streaming sql job");
    }

}