一、环境

1.1getExecutionEnvironment

创建一个执行环境，表示当前执行程序的上下文。如果程序是独立调用的，则此方法返回本地执行环境；如果从命令行客户端调用程序以提交到集群，则此方法返回此集群的执行环境，也就是说，getExecutionEnvironment会根据查询运行的方式决定返回什么样的运行环境，是最常用的一种创建执行环境的方式。

#批处理环境
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

#流处理环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

设置并行度:如果没有设置并行度，会以flink-conf.yaml中的配置为准，默认为1

 //设置并行度为8
 env.setParallelism(8);

1.2createLocalEnvironment

返回本地执行环境，需要在调用时指定默认的并行度

LocalStreamEnvironment env = StreamExecutionEnvironment.createLocalEnvironment(1);

1.3createRemoteEnvironment

返回集群执行环境，将Jar提交到远程服务器。需要在调用时指定JobManager的IP和端口号，并指定要在集群中运行的Jar包

StreamExecutionEnvironment env = StreamExecutionEnvironment.createRemoteEnvironment("IP",端口号,jar包路径)

二、从集合中读取数据


import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import java.util.Arrays;

/**
 * @author : Ashiamd email: [email protected]
 * @date : 2021/1/31 5:13 PM
 * 测试Flink从集合中获取数据
 */
public class SourceTest1_Collection {
    public static void main(String[] args) throws Exception {
        // 创建执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 设置env并行度1，使得整个任务抢占同一个线程执行
        env.setParallelism(1);

        // Source: 从集合Collection中获取数据
        DataStream<SensorReading> dataStream = env.fromCollection(
                Arrays.asList(
                        new SensorReading("sensor_1", 1547718199L, 35.8),
                        new SensorReading("sensor_6", 1547718201L, 15.4),
                        new SensorReading("sensor_7", 1547718202L, 6.7),
                        new SensorReading("sensor_10", 1547718205L, 38.1)
                )
        );

        DataStream<Integer> intStream = env.fromElements(1,2,3,4,5,6,7,8,9);

        // 打印输出
        dataStream.print("SENSOR");
        intStream.print("INT");

        // 执行
        env.execute("JobName");

    }

}

三、从文件中读取数据

文件由自己创建一个txt文件

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

/**
 * @author : Ashiamd email: [email protected]
 * @date : 2021/1/31 5:26 PM
 * Flink从文件中获取数据
 */
public class SourceTest2_File {
    public static void main(String[] args) throws Exception {
        // 创建执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 使得任务抢占同一个线程
        env.setParallelism(1);

        // 从文件中获取数据输出
        DataStream<String> dataStream = env.readTextFile("/tmp/Flink_Tutorial/src/main/resources/sensor.txt");

        dataStream.print();

        env.execute();
    }
}

四、从KafKa中读取数据

1.导入依赖

<dependencies>
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.11</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-java</artifactId>
      <version>1.10.1</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-streaming-java_2.12</artifactId>
      <version>1.10.1</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-clients_2.12</artifactId>
      <version>1.10.1</version>
    </dependency>
    <!-- kafka -->
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-connector-kafka_2.11</artifactId>
      <version>1.12.1</version>
    </dependency>

  </dependencies>

2.启动KafKa

启动Zookeeper

./bin/zookeeper-server-start.sh [config/zookeeper.properties]

启动KafKa服务

./bin/kafka-server-start.sh -daemon ./config/server.properties

启动KafKa生产者

./bin/kafka-console-producer.sh --broker-list localhost:9092  --topic sensor

3.java代码

import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import java.util.Properties;

/**
 * @author : Ashiamd email: [email protected]
 * @date : 2021/1/31 5:44 PM
 */
public class SourceTest3_Kafka {

    public static void main(String[] args) throws Exception {
        // 创建执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 设置并行度1
        env.setParallelism(1);

        Properties properties = new Properties();
        //监听的kafka端口
        properties.setProperty("bootstrap.servers", "localhost:9092");
        // 下面这些次要参数
        properties.setProperty("group.id", "consumer-group");
        properties.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        properties.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        properties.setProperty("auto.offset.reset", "latest");

        // flink添加外部数据源
        DataStream<String> dataStream = env.addSource(new FlinkKafkaConsumer<String>("sensor", new SimpleStringSchema(),properties));

        // 打印输出
        dataStream.print();

        env.execute();
    }
}

Flink 流处理API