Flutter与iOS和Android原生页面交互

文章目录

• 快速使用

快速使用

1. 打开 https://flink.apache.org/downloads/ 下载 flink

2. 解压

 tar -xzvf flink-1.11.2-bin-scala_2.11.tgz

3. 启动 flink

./bin/start-cluster.sh

4. 打开 flink web 页面 localhost:8081

5. 编写结合 Kafka 词频统计程序

package org.example;

import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.common.typeinfo.Types;
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.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.util.Collector;

import java.util.Properties;

public class WordCountKafkaInStdOut {

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

        // 设置Flink执行环境 
        StreamExecutionEnvironment env =
            StreamExecutionEnvironment.getExecutionEnvironment();

        // Kafka参数 
        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", "localhost:9092");
        properties.setProperty("group.id", "flink-group");
        String inputTopic = "Shakespeare";
        String outputTopic = "WordCount";

        // Source 
        FlinkKafkaConsumer<String> consumer =
            new FlinkKafkaConsumer<String>(inputTopic, new SimpleStringSchema(),
                properties);
        DataStream<String> stream = env.addSource(consumer);

        // Transformation 
        // 使用Flink  API对输入流的文本进行操作 
        // 按空格切词、计数、分区、设置时间窗口、聚合 
        DataStream<Tuple2<String, Integer>> wordCount = stream
            .flatMap((String line, Collector<Tuple2<String, Integer>> collector) -> {
                String[] tokens = line.split("\\s");
                // 输出结果  
                for (String token : tokens) {
                    if (token.length() > 0) {
                        collector.collect(new Tuple2<>(token, 1));
                    }
                }
            })
            .returns(Types.TUPLE(Types.STRING, Types.INT))
            .keyBy(0)
            .timeWindow(Time.seconds(5))
            .sum(1);

        // Sink 
        wordCount.print();

        // execute 
        env.execute("kafka streaming word count");

    }
} 

6. 打包应用(当然在这之前需要本地调试一下，至少得运行通吧😄)
7. 使用Flink提供的命令行工具flink，将打包好的作业提交到集群上。命令行的参数 --class 用来指定哪个主类作为入口。

./bin/flink run --class org.example.WordCountKafkaInStdOut xxtarget/flink_study-1.0-SNAPSHOT.jar

class 建议直接拷贝引用

8. web 页面查看作业提交成功

9. kafka 生产者随便发点消息

10. 查看作业日志,词频统计结果

11. 关闭 flink

./bin/stop-cluster.sh