[flink 实时流基础] flink 源算子

学习笔记

Flink可以从各种来源获取数据，然后构建DataStream进行转换处理。一般将数据的输入来源称为数据源（data source），而读取数据的算子就是源算子（source operator）。所以，source就是我们整个处理程序的输入端。

在Flink1.12以前，旧的添加source的方式，是调用执行环境的addSource()方法：
DataStream stream = env.addSource(…);
方法传入的参数是一个“源函数”（source function），需要实现SourceFunction接口。
从Flink1.12开始，主要使用流批统一的新Source架构：
DataStreamSource stream = env.fromSource(…)
Flink直接提供了很多预实现的接口，此外还有很多外部连接工具也帮我们实现了对应的Source，通常情况下足以应对我们的实际需求。

1. 从集合读

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

        // 1. 从集合读
//        DataStreamSource<Integer> source = env.fromCollection(Arrays.asList(1, 2, 3));

        // 2. 直接填元素
        DataStreamSource<Integer> source = env.fromElements(1, 2, 3, 4);

        source.print();

        env.execute();
    }

2. 从文件读取

		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-connector-files</artifactId>
			<version>${flink.version}</version>
		</dependency>

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

        FileSource<String> source = FileSource.forRecordStreamFormat(
            new TextLineInputFormat(),
            new Path("input/world.txt"))
            .build();

        env
            .fromSource(source, WatermarkStrategy.noWatermarks(), "fileSource")
            .print();


        env.execute();
    }

3. 从 socket 读取

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

        DataStreamSource<String> source = env.socketTextStream("localhost", 7777);
        source.print();


        env.execute();
    }

可以使用 nc -l 7777创建一个监听链接的 tcp

4. 从 kafka 读取

		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-connector-kafka</artifactId>
			<version>${flink.version}</version>
		</dependency>

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

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        KafkaSource<String> kafkaSource = KafkaSource.<String>builder()
            .setBootstrapServers("hadoop102:9092")
            .setTopics("topic_1")
            .setGroupId("atguigu")
            .setStartingOffsets(OffsetsInitializer.latest())
            .setValueOnlyDeserializer(new SimpleStringSchema()) 
            .build();

        DataStreamSource<String> stream = env.fromSource(kafkaSource, WatermarkStrategy.noWatermarks(), "kafka-source");

        stream.print("Kafka");

        env.execute();
    }

5. 从数据生成器读取数据

		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-connector-datagen</artifactId>
			<version>${flink.version}</version>
		</dependency>

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

        env.setParallelism(1);

        DataGeneratorSource<String> dataGeneratorSource = new DataGeneratorSource<>(new GeneratorFunction<Long, String>() {
            @Override
            public String map(Long value) throws Exception {
                return "Number:" + value;
            }
        }, 10, // 自动生成的数字序列
            RateLimiterStrategy.perSecond(10), // 限速策略，每秒生成10条
            Types.STRING // 返回类型
        );


        env.fromSource(dataGeneratorSource, WatermarkStrategy.noWatermarks(), "datagenerator").print();


        env.execute();


    }