-
env-准备环境
-
source-加载数据
-
transformation-数据处理转换
-
sink-数据输出
-
execute-执行
流程图：
DataStream API 开发
//nightlies.apache.org/flink/flink-docs-release-1.13/docs/dev/datastream/overview/
添加依赖
java
<properties>
<flink.version>1.13.6</flink.version>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java_2.11</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients_2.11</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-api-java-bridge_2.11</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-planner-blink_2.11</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-shaded-hadoop-2-uber</artifactId>
<version>2.7.5-10.0</version>
</dependency>
<dependency>
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
<version>1.2.17</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<version>1.18.24</version>
</dependency>
</dependencies>
<build>
<extensions>
<extension>
<groupId>org.apache.maven.wagon</groupId>
<artifactId>wagon-ssh</artifactId>
<version>2.8</version>
</extension>
</extensions>
<plugins>
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>wagon-maven-plugin</artifactId>
<version>1.0</version>
<configuration>
<!--上传的本地jar的位置-->
<fromFile>target/${project.build.finalName}.jar</fromFile>
<!--远程拷贝的地址-->
<url>scp://root:root@bigdata01:/opt/app</url>
</configuration>
</plugin>
</plugins>
</build>编写代码
java
package com.bigdata.day01;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
public class WordCount01 {
/**
* 1. env-准备环境
* 2. source-加载数据
* 3. transformation-数据处理转换
* 4. sink-数据输出
* 5. execute-执行
*/
public static void main(String[] args) throws Exception {
// 导入常用类时要注意 不管是在本地开发运行还是在集群上运行，都这么写，非常方便
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// 这个是 自动 ，根据流的性质，决定是批处理还是流处理
//env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
// 批处理流， 一口气把数据算出来
// env.setRuntimeMode(RuntimeExecutionMode.BATCH);
// 流处理，默认是这个 可以通过打印批和流的处理结果，体会流和批的含义
env.setRuntimeMode(RuntimeExecutionMode.STREAMING);
// 获取数据 多态的写法 DataStreamSource 它是 DataStream 的子类
DataStream<String> dataStream01 = env.fromElements("spark flink kafka", "spark sqoop flink", "kakfa hadoop flink");
DataStream<String> flatMapStream = dataStream01.flatMap(new FlatMapFunction<String, String>() {
@Override
public void flatMap(String line, Collector<String> collector) throws Exception {
String[] arr = line.split(" ");
for (String word : arr) {
// 循环遍历每一个切割完的数据，放入到收集器中，就可以形成一个新的DataStream
collector.collect(word);
}
}
});
//flatMapStream.print();
// Tuple2 指的是2元组
DataStream<Tuple2<String, Integer>> mapStream = flatMapStream.map(new MapFunction<String, Tuple2<String, Integer>>() {
@Override
public Tuple2<String, Integer> map(String word) throws Exception {
return Tuple2.of(word, 1); // ("hello",1)
}
});
DataStream<Tuple2<String, Integer>> sumResult = mapStream.keyBy(new KeySelector<Tuple2<String, Integer>, String>() {
@Override
public String getKey(Tuple2<String, Integer> tuple2) throws Exception {
return tuple2.f0;
}
// 此处的1 指的是元组的第二个元素，进行相加的意思
}).sum(1);
sumResult.print();
// 执行
env.execute();
}
}批处理结果：前面的序号代表分区
流处理结果：
也可以通过如下方式修改分区数量：
java
env.setParallelism(2);关于并行度的代码演示：
系统以及算子都可以设置并行度，或者获取并行度
java
package com.bigdata.day01;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
public class WordCount01 {
/**
* 1. env-准备环境
* 2. source-加载数据
* 3. transformation-数据处理转换
* 4. sink-数据输出
* 5. execute-执行
*/
public static void main(String[] args) throws Exception {
// 导入常用类时要注意 不管是在本地开发运行还是在集群上运行，都这么写，非常方便
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// 这个是 自动 ，根据流的性质，决定是批处理还是流处理
//env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
// 批处理流， 一口气把数据算出来
// env.setRuntimeMode(RuntimeExecutionMode.BATCH);
// 流处理，默认是这个 可以通过打印批和流的处理结果，体会流和批的含义
env.setRuntimeMode(RuntimeExecutionMode.STREAMING);
// 将任务的并行度设置为2
// env.setParallelism(2);
// 通过这个获取系统的并行度
int parallelism = env.getParallelism();
System.out.println(parallelism);
// 获取数据 多态的写法 DataStreamSource 它是 DataStream 的子类
DataStream<String> dataStream01 = env.fromElements("spark flink kafka", "spark sqoop flink", "kakfa hadoop flink");
DataStream<String> flatMapStream = dataStream01.flatMap(new FlatMapFunction<String, String>() {
@Override
public void flatMap(String line, Collector<String> collector) throws Exception {
String[] arr = line.split(" ");
for (String word : arr) {
// 循环遍历每一个切割完的数据，放入到收集器中，就可以形成一个新的DataStream
collector.collect(word);
}
}
});
// 每一个算子也有自己的并行度，一般跟系统保持一致
System.out.println("flatMap的并行度："+flatMapStream.getParallelism());
//flatMapStream.print();
// Tuple2 指的是2元组
DataStream<Tuple2<String, Integer>> mapStream = flatMapStream.map(new MapFunction<String, Tuple2<String, Integer>>() {
@Override
public Tuple2<String, Integer> map(String word) throws Exception {
return Tuple2.of(word, 1); // ("hello",1)
}
});
DataStream<Tuple2<String, Integer>> sumResult = mapStream.keyBy(new KeySelector<Tuple2<String, Integer>, String>() {
@Override
public String getKey(Tuple2<String, Integer> tuple2) throws Exception {
return tuple2.f0;
}
// 此处的1 指的是元组的第二个元组，进行相加的意思
}).sum(1);
sumResult.print();
// 执行
env.execute();
}
}- 打包、上传
文件夹需要提前准备好
提交我们自己开发打包的任务
java
flink run -c com.bigdata.day01.WordCount01 /opt/app/FlinkDemo-1.0-SNAPSHOT.jar
去界面中查看运行结果：
因为你这个是集群运行的，所以标准输出流中查看，假如第一台没有，去第二台查看，一直点。
