Flink中Transform怎么用

小编给大家分享一下Flink中Transform怎么用，相信大部分人都还不怎么了解，因此分享这篇文章给大家参考一下，希望大家阅读完这篇文章后大有收获，下面让我们一起去了解一下吧！

分组聚合

  String path = "E:\\GIT\\flink-learn\\flink-learn\\telemetering.txt";        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();        TupleTypeInfo> typeInfo = new TupleTypeInfo<>(Types.STRING, Types.DOUBLE, Types.LONG);        TupleCsvInputFormat> tupleCsvInputFormat =                new TupleCsvInputFormat<>(new Path(path), typeInfo);        DataStreamSource> dataStreamSource = env.createInput(tupleCsvInputFormat, typeInfo);        //或   DataStreamSource> dataStreamSource = env.readFile(tupleCsvInputFormat, path);        SingleOutputStreamOperator> operator = dataStreamSource                .filter(Objects::nonNull)//                .map()//                .flatMap()//                .keyBy(0)                .keyBy(tuple -> tuple.f0)                .minBy(1);//                .min()//                .max(1);//                .maxBy(1, false);//                .sum(1);//                .reduce();//                .process();        operator.print().setParallelism(1);        env.execute();

分流/合流

String path = "E:\\GIT\\flink-learn\\flink-learn\\telemetering.txt";        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();        PojoTypeInfo typeInfo = (PojoTypeInfo) Types.POJO(TelemeterDTO.class);        PojoCsvInputFormat inputFormat = new PojoCsvInputFormat<>(new Path(path), typeInfo, new String[]{"code", "value", "timestamp"});        DataStreamSource dataStreamSource = env.createInput(inputFormat, typeInfo);        //分流        SplitStream splitStream = dataStreamSource                .split(item -> {                    if (item.getValue() > 100) {                        return Collections.singletonList("high");                    }                    return Collections.singletonList("low");                });        DataStream highStream = splitStream.select("high");        DataStream lowStream = splitStream.select("low");        //合流        ConnectedStreams connectedStreams = lowStream.connect(highStream);//        DataStream unionDataStream = lowStream.union(highStream); //需要类型一致        SingleOutputStreamOperator> operator = connectedStreams                .map(new CoMapFunction>() {                    @Override                    public Tuple3 map1(TelemeterDTO value) {                        return Tuple3.of(value.getCode(), value.getValue(), value.getTimestamp());                    }                    @Override                    public Tuple3 map2(TelemeterDTO value) {                        return Tuple3.of(value.getCode(), value.getValue(), value.getTimestamp());                    }                });        operator.print();        env.execute();

UDF函数，提供底层支持

• MapFunction

• FilterFunction

• ReduceFunction

• ProcessFunction

• SourceFunction

• SinkFunction

富函数

富函数 包含了生命周期，及上下文相关信息，如

• open() 可以在算子创建之初建立数据库连接

• close() 在在算子生命结束之前关闭资源

以上是"Flink中Transform怎么用"这篇文章的所有内容，感谢各位的阅读！相信大家都有了一定的了解，希望分享的内容对大家有所帮助，如果还想学习更多知识，欢迎关注行业资讯频道！