process算子:处理每个keyBy(分区)输入到窗口的批量数据流(为KeyedStream类型数据流)
示例环境
java.version: 1.8.x flink.version: 1.11.1
示例数据源 (项目码云下载)
Flink 系例 之 搭建开发环境与数据
Process.java
import com.flink.examples.DataSource;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction;
import org.apache.flink.streaming.api.windowing.windows.GlobalWindow;
import org.apache.flink.util.Collector;
import java.util.Iterator;
import java.util.List;
public class Process {
public static void main(String[] args) throws Exception {
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
List> tuple3List = DataSource.getTuple3ToList();
DataStream dataStream = env.fromCollection(tuple3List)
.keyBy((KeySelector, String>) k -> k.f1)
//按数量窗口滚动,每3个输入数据流,计算一次
.countWindow(3)
//处理每keyBy后的窗口数据流,process方法通常应用于KeyedStream类型的数据流处理
.process(new ProcessWindowFunction, String, String, GlobalWindow>() {
@Override
public void process(String s, Context context, Iterable> input, Collector out) throws Exception {
Iterator> iterator = input.iterator();
int total = 0;
int i = 0;
while (iterator.hasNext()){
Tuple3 tuple3 = iterator.next();
total += tuple3.f2;
i ++ ;
}
out.collect(s + "共:"+i+"人,平均年龄:" + total/i);
}
});
dataStream.print();
env.execute("flink Process job");
}
}
打印结果
4> girl共:3人,平均年龄:24 2> man共:3人,平均年龄:26



