项目实战--Spring Boot 3整合Flink实现大数据文件处理

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.time.Time;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
 
public class FlinkProcessBigData {
 
    public static void main(String[] args) throws Exception {
        // 创建流执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3, Time.seconds(5)));
 
        // 创建表执行环境
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
 
        // 读取数据
        DataStream<String> text = env.readTextFile("path/to/your/input/file");
 
        // 转换数据
        DataStream<Tuple2<String, Integer>> wordCount = text
            .flatMap(new WordCount.Tokenizer())
            .keyBy(0)
            .sum(1);
 
        // 创建表
        tableEnv.createTemporaryView("WordCountTable", wordCount, "word", "count");
 
        // 执行SQL查询
        Table resultTable = tableEnv.sqlQuery("SELECT word, SUM(count) AS total FROM WordCountTable GROUP BY word");
 
        // 转换回DataStream
        DataStream<Row> result = tableEnv.toChangelogStream(resultTable);
 
        // 输出结果
        result.print();
 
        env.execute("Flink Word Count Example");
    }
 
    public static class Tokenizer implements MapFunction<String, Tuple2<String, Integer>> {
        @Override
        public Tuple2<String, Integer> map(String value) {
            return new Tuple2<>(value.toLowerCase(), 1);