Flink DataStream iterate算子的简单使用

逃离我推掉我的手 2021-12-21 08:35 272阅读 0赞

### Flink DataStream iterate算子的简单使用 ###

由于DataStream程序可能永远不会完成，因此没有最大迭代次数。相反你需要指定流的哪个部分反馈到迭代，哪个部分使用split转换或转发到下游filter。

下面看示例：

public class IterateOperator {
        public static void main(String[] args) throws Exception {
    
            StreamExecutionEnvironment sEnv = StreamExecutionEnvironment.getExecutionEnvironment();
            sEnv.setParallelism(1);
    
            Properties p = new Properties();
            p.setProperty("bootstrap.servers", "localhost:9092");
    
            DataStreamSource<String> source = sEnv.addSource(new FlinkKafkaConsumer010<String>("people", new SimpleStringSchema(), p));
    
            IterativeStream<People> iterate = source.map(new MapFunction<String, People>() {
                @Override
                public People map(String value) throws Exception {
                    return new Gson().fromJson(value, People.class);
                }
            }).iterate();
    
    
            SingleOutputStreamOperator<People> feedback = iterate.filter(new FilterFunction<People>() {
                @Override
                public boolean filter(People value) throws Exception {
                    return "caocao".equals(value.name());
                }
            });
    
            // 如果有符合feedback过滤条件的数据，比如：name为caocao的，会持续不断的循环输出
            feedback.print("feedback:");
    
            iterate.closeWith(feedback);
    
            SingleOutputStreamOperator<People> result = iterate.filter(new FilterFunction<People>() {
                @Override
                public boolean filter(People value) throws Exception {
                    return !"caocao".equals(value.name());
                }
            });
    
            result.print("result:");
    
    
            // split
            SplitStream<People> split = iterate.split(new OutputSelector<People>() {
                @Override
                public Iterable<String> select(People value) {
                    ArrayList<String> list = new ArrayList<>();
                    if ("male".equals(value.sex())) {
                        list.add("male");
                    } else {
                        list.add("female");
                    }
                    return list;
                }
            });
    
            DataStream<People> male = split.select("male");
            male.print("male:");
    
            iterate.closeWith(male);
    
            sEnv.execute("IterateOperator");
        }
    }

在kafka product端，输入\{“name”:“caocao”,“age”:18,“sex”:“male”\}，会发现"feedback:"的流和"male:"在持续不断的迭代输出。