Flink在Ubuntu上的数据流处理示例有哪些

Apache Flink 是一个分布式流处理框架，用于实时处理无界和有界数据流

1. 简单的流处理：

import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;  public class SimpleStreamProcessing {     public static void main(String[] args) throws Exception {         StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();          DataStream<String> source = env.fromElements("Hello", "Flink", "on", "Ubuntu");          DataStream<String> processed = source.map(s -> s.toUpperCase());          processed.print();          env.execute("Simple Stream Processing Example");     } } 

2. 计算单词频率：

import org.apache.flink.api.common.functions.FlatMapFunction; import org.apache.flink.api.java.tuple.Tuple2; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.util.Collector;  public class WordCountExample {     public static void main(String[] args) throws Exception {         StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();          DataStream<String> source = env.fromElements("Hello", "Flink", "on", "Ubuntu", "is", "awesome");          DataStream<Tuple2<String, Integer>> wordCounts = source                 .flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {                     @Override                     public void flatMap(String value, Collector<Tuple2<String, Integer>> out) {                         String[] words = value.split("\\s+");                         for (String word : words) {                             out.collect(new Tuple2<>(word, 1));                         }                     }                 })                 .keyBy(0)                 .sum(1);          wordCounts.print();          env.execute("Word Count Example");     } } 

3. 使用窗口函数计算滚动平均值：

import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.api.windowing.time.Time; import org.apache.flink.streaming.api.windowing.windows.TimeWindow;  public class RollingAverageExample {     public static void main(String[] args) throws Exception {         StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();          DataStream<Double> source = env.fromElements(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0);          DataStream<Double> rollingAverage = source                 .timeWindowAll(Time.seconds(3))                 .reduce((value1, value2) -> value1 + value2)                 .map(sum -> sum / 3);          rollingAverage.print();          env.execute("Rolling Average Example");     } } 

要运行这些示例，请确保已安装 Java 开发工具包（JDK）并正确配置了 Flink。然后，将示例代码保存为 Java 文件（例如 SimpleStreamProcessing.java），并使用以下命令编译和运行：

javac -cp /path/to/flink/lib/*: SimpleStreamProcessing.java java -cp /path/to/flink/lib/*: SimpleStreamProcessing.class SimpleStreamProcessing 

请注意，您需要根据实际情况替换 /path/to/flink/lib/ 为 Flink 安装目录中 lib 文件夹的路径。