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SpringBoot操作spark處理hdfs文件的操作方法

更新時(shí)間：2025年01月09日 11:56:45 作者：念言-ny

本文介紹了如何使用Spring Boot操作Spark處理HDFS文件,包括導(dǎo)入依賴、配置Spark信息、編寫Controller和Service處理地鐵數(shù)據(jù)、運(yùn)行項(xiàng)目以及觀察Spark和HDFS的狀態(tài),感興趣的朋友跟隨小編一起看看吧

SpringBoot操作spark處理hdfs文件

1、導(dǎo)入依賴

<!--        spark依賴-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.12</artifactId>
            <version>3.2.2</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.12</artifactId>
            <version>3.2.2</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-mllib -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-mllib_2.12</artifactId>
            <version>3.2.2</version>
        </dependency>

2、配置spark信息

建立一個(gè)配置文件，配置spark信息

import org.apache.spark.SparkConf;
import org.apache.spark.sql.SparkSession;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
//將文件交于spring管理
@Configuration
public class SparkConfig {
    //使用yml中的配置
    @Value("${spark.master}")
    private String sparkMaster;
    @Value("${spark.appName}")
    private String sparkAppName;
    @Value("${hdfs.user}")
    private String hdfsUser;
    @Value("${hdfs.path}")
    private String hdfsPath;
    @Bean
    public SparkConf sparkConf() {
        SparkConf conf = new SparkConf();
        conf.setMaster(sparkMaster);
        conf.setAppName(sparkAppName);
        // 添加HDFS配置
        conf.set("fs.defaultFS", hdfsPath);
        conf.set("spark.hadoop.hdfs.user",hdfsUser);
        return conf;
    }
    @Bean
    public SparkSession sparkSession() {
        return SparkSession.builder()
                .config(sparkConf())
                .getOrCreate();
    }
}

3、controller和service

controller類

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;
import xyz.zzj.traffic_main_code.service.SparkService;
@RestController
@RequestMapping("/spark")
public class SparkController {
    @Autowired
    private SparkService sparkService;
    @GetMapping("/run")
    public String runSparkJob() {
        //讀取Hadoop HDFS文件
        String filePath = "hdfs://192.168.44.128:9000/subwayData.csv";
        sparkService.executeHadoopSparkJob(filePath);
        return "Spark job executed successfully!";
    }
}

處理地鐵數(shù)據(jù)的service

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Service;
import xyz.zzj.traffic_main_code.service.SparkReadHdfs;
import java.io.IOException;
import java.net.URI;
import static org.apache.spark.sql.functions.*;
@Service
public class SparkReadHdfsImpl implements SparkReadHdfs {
    private final SparkSession spark;
    @Value("${hdfs.user}")
    private String hdfsUser;
    @Value("${hdfs.path}")
    private String hdfsPath;
    @Autowired
    public SparkReadHdfsImpl(SparkSession spark) {
        this.spark = spark;
    }
    /**
     * 讀取HDFS上的CSV文件并上傳到HDFS
     * @param filePath
     */
    @Override
    public void sparkSubway(String filePath) {
        try {
            // 設(shè)置Hadoop配置
            JavaSparkContext jsc = JavaSparkContext.fromSparkContext(spark.sparkContext());
            Configuration hadoopConf = jsc.hadoopConfiguration();
            hadoopConf.set("fs.defaultFS", hdfsPath);
            hadoopConf.set("hadoop.user.name", hdfsUser);
            // 讀取HDFS上的文件
            Dataset<Row> df = spark.read()
                    .option("header", "true") // 指定第一行是列名
                    .option("inferSchema", "true") // 自動(dòng)推斷列的數(shù)據(jù)類型
                    .csv(filePath);
            // 顯示DataFrame的所有數(shù)據(jù)
//            df.show(Integer.MAX_VALUE, false);
            // 對(duì)DataFrame進(jìn)行清洗和轉(zhuǎn)換操作
            // 檢查缺失值
            df.select("number", "people", "dateTime").na().drop().show();
            // 對(duì)數(shù)據(jù)進(jìn)行類型轉(zhuǎn)換
            Dataset<Row> df2 = df.select(
                    col("number").cast(DataTypes.IntegerType),
                    col("people").cast(DataTypes.IntegerType),
                    to_date(col("dateTime"), "yyyy年MM月dd日").alias("dateTime")
            );
            // 去重
            Dataset<Row> df3 = df2.dropDuplicates();
            // 數(shù)據(jù)過(guò)濾，確保people列沒(méi)有負(fù)數(shù)
            Dataset<Row> df4 = df3.filter(col("people").geq(0));
//            df4.show();
            // 數(shù)據(jù)聚合，按dateTime分組，統(tǒng)計(jì)每天的總客流量
            Dataset<Row> df6 = df4.groupBy("dateTime").agg(sum("people").alias("total_people"));
//            df6.show();
            sparkForSubway(df6,"/time_subwayData.csv");
            //數(shù)據(jù)聚合，獲取每天人數(shù)最多的地鐵number
            Dataset<Row> df7 = df4.groupBy("dateTime").agg(max("people").alias("max_people"));
            sparkForSubway(df7,"/everyday_max_subwayData.csv");
            //數(shù)據(jù)聚合，計(jì)算每天的客流強(qiáng)度：每天總people除以632840
            Dataset<Row> df8 = df4.groupBy("dateTime").agg(sum("people").divide(632.84).alias("strength"));
            sparkForSubway(df8,"/everyday_strength_subwayData.csv");
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
    private static void sparkForSubway(Dataset<Row> df6, String hdfsPath) throws IOException {
        // 保存處理后的數(shù)據(jù)到HDFS
        df6.coalesce(1)
                .write().mode("overwrite")
                .option("header", "true")
                .csv("hdfs://192.168.44.128:9000/time_subwayData");
        // 創(chuàng)建Hadoop配置
        Configuration conf = new Configuration();
        // 獲取FileSystem實(shí)例
        FileSystem fs = FileSystem.get(URI.create("hdfs://192.168.44.128:9000"), conf);
        // 定義臨時(shí)目錄和目標(biāo)文件路徑
        Path tempDir = new Path("/time_subwayData");
        FileStatus[] files = fs.listStatus(tempDir);
        // 檢查目標(biāo)文件是否存在，如果存在則刪除
        Path targetFile1 = new Path(hdfsPath);
        if (fs.exists(targetFile1)) {
            fs.delete(targetFile1, true); // true 表示遞歸刪除
        }
        for (FileStatus file : files) {
            if (file.isFile() && file.getPath().getName().startsWith("part-")) {
                Path targetFile = new Path(hdfsPath);
                fs.rename(file.getPath(), targetFile);
            }
        }
        // 刪除臨時(shí)目錄
        fs.delete(tempDir, true);
    }
}

4、運(yùn)行

項(xiàng)目運(yùn)行完后，打開瀏覽器
- spark處理地鐵數(shù)據(jù)
http://localhost:8686/spark/dispose
觀察spark和hdfs
- http://192.168.44.128:8099/
- http://192.168.44.128:9870/explorer.html#/