duan

system/start/src/main/java/org/jeecg/modules/duan/java/controller/DashboardController.java

@@ -1,11 +1,12 @@
-package org.jeecg.duan.java.controller;
+package org.jeecg.modules.duan.java.controller;
 
 import io.swagger.annotations.Api;
 import lombok.extern.slf4j.Slf4j;
 import net.sf.json.JSONArray;
 import org.jeecg.common.api.vo.Result;
 
-import org.jeecg.duan.java.service.MusicService;
+import org.jeecg.modules.duan.java.service.MusicService;
+import org.jeecg.modules.duan.java.service.MusicService;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.web.bind.annotation.GetMapping;
 import org.springframework.web.bind.annotation.RequestMapping;

system/start/src/main/java/org/jeecg/modules/duan/java/entity/Ranking.java

@@ -1,4 +1,4 @@
-package org.jeecg.duan.java.entity;
+package org.jeecg.modules.duan.java.entity;
 
 import com.baomidou.mybatisplus.annotation.IdType;
 import com.baomidou.mybatisplus.annotation.TableId;
@@ -26,4 +26,25 @@ public class Ranking {
 
     @ApiModelProperty(value = "评论数")
     private Integer comment;
+
+    public Ranking(String name, String singer, Integer comment) {
+        this.name = name;
+        this.singer = singer;
+        this.comment = comment;
+    }
+
+    public Integer getComment() {
+        return this.comment;
+    }
+
+    public String getName() {
+        return this.name;
+    }
+
+    public String getSinger() {
+        return this.singer;
+    }
+
+    public Ranking() {
+    }
 }

system/start/src/main/java/org/jeecg/modules/duan/java/mapper/MusicMapper.java

@@ -1,8 +1,8 @@
-package org.jeecg.duan.java.mapper;
+package org.jeecg.modules.duan.java.mapper;
 
 
 import com.baomidou.mybatisplus.core.mapper.BaseMapper;
-import org.jeecg.duan.java.entity.Ranking;
+import org.jeecg.modules.duan.java.entity.Ranking;
 
 public interface MusicMapper extends BaseMapper<Ranking> {
 

system/start/src/main/java/org/jeecg/modules/duan/java/service/MusicService.java

@@ -1,4 +1,4 @@
-package org.jeecg.duan.java.service;
+package org.jeecg.modules.duan.java.service;
 
 import net.sf.json.JSONArray;
 import org.jeecg.common.api.vo.Result;

system/start/src/main/java/org/jeecg/modules/duan/java/service/impl/MusicServiceImpl.java

@@ -1,4 +1,4 @@
-package org.jeecg.duan.java.service.impl;
+package org.jeecg.modules.duan.java.service.impl;
 
 import com.baomidou.mybatisplus.core.conditions.update.UpdateWrapper;
 import com.baomidou.mybatisplus.extension.service.impl.ServiceImpl;
@@ -6,9 +6,11 @@ import net.sf.json.JSONArray;
 import net.sf.json.JSONObject;
 import org.jeecg.common.api.vo.Result;
 
-import org.jeecg.duan.java.entity.Ranking;
+import org.jeecg.modules.duan.java.entity.Ranking;
-import org.jeecg.duan.java.mapper.MusicMapper;
+import org.jeecg.modules.duan.java.mapper.MusicMapper;
-import org.jeecg.duan.java.service.MusicService;
+import org.jeecg.modules.duan.java.service.MusicService;
+import org.jeecg.modules.duan.java.mapper.MusicMapper;
+import org.jeecg.modules.duan.java.service.MusicService;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.stereotype.Service;
 

system/start/src/main/java/org/jeecg/modules/duan/scala/Analysis.scala

@@ -1,35 +1,37 @@
-package org.jeecg.duan.scala
+package org.jeecg.modules.duan.scala
 
 import com.alibaba.fastjson.{JSONArray, JSONObject}
 import com.baomidou.mybatisplus.core.conditions.update.UpdateWrapper
-import org.jeecg.duan.java.entity.Ranking
-import org.jeecg.duan.java.mapper.MusicMapper
 import org.jeecg.modules.drag.config.common.Result
-import org.springframework.beans.factory.annotation.Autowired
+import org.jeecg.modules.duan.java.entity.Ranking
-
+import org.jeecg.modules.duan.java.mapper.MusicMapper
 
+import java.lang
 import java.util.{Collections, Comparator}
+import scala.jdk.CollectionConverters.{IterableHasAsJava, IterableHasAsScala, ListHasAsScala}
 
 /**
  * 这个对象提供了排行榜分析的功能。
  */
 object Analysis {
 
-  @Autowired
+  private var musicMapper: MusicMapper = _ // Define the field for MusicMapper
-  private val musicMapper: MusicMapper = null
 
   /**
    * 检索排行榜并根据评论进行排序。
    *
    * @return Result 包含根据评论排序的排行榜的 JSON 数组。
    */
+
   def getRanking: Result[JSONArray] = {
-    val comparatorList: ComparatorList = new ComparatorList
     val rankings: List[Ranking] = null // 从数据库或其他来源获取排行榜
     val result: JSONArray = new JSONArray
     System.out.println(rankings) // 用于调试的日志排行榜
-    Collections sort(rankings, comparatorList) // 对排行榜进行排序
+
-    for (ranking <- rankings) {
+    // Sort the rankings using Scala's sorting methods
+    val sortedRankings = rankings.sortBy(_.getComment)
+
+    for (ranking <- sortedRankings) {
       System.out.println("\n\n\n\n\n" + ranking) // 打印每个排名信息，用于调试
       val `object`: JSONObject = new JSONObject
       `object`.put("comment", ranking.getComment) // 将评论信息添加到 JSON 对象中
@@ -37,23 +39,25 @@ object Analysis {
       `object`.put("songName", ranking.getName) // 将歌曲名称添加到 JSON 对象中
       result.add(`object`) // 将 JSON 对象添加到结果数组中
     }
+
     val updateWrapper: UpdateWrapper[Ranking] = new UpdateWrapper[Ranking]
     updateWrapper.setSql("comment=comment- FLOOR(   RAND()*100  )") // 随机减少评论数量
     musicMapper.update(null, updateWrapper) // 更新数据库中的排行榜评论数量
-    return Result.OK(result) // 返回带有排序后排行榜信息的成功结果
 
+    Result.OK(result) // 返回带有排序后排行榜信息的成功结果
   }
 
 
+  // Define a method to set the MusicMapper field
+  def setMusicMapper(musicMapper: MusicMapper): Unit = {
+    this.musicMapper = musicMapper
+  }
+
+  // Define a class for ComparatorList implementing Comparator[Ranking]
   private class ComparatorList extends Comparator[Ranking] {
     override def compare(o1: Ranking, o2: Ranking): Int = {
-      if (o1.getComment > o2.getComment) {
+      if (o1.getComment > o2.getComment) 1
-        1
+      else -1
-      }
-      else {
-        -(1)
-      }
     }
   }
-
 }

system/start/src/main/java/org/jeecg/modules/duan/scala/App.scala

@@ -1,4 +1,4 @@
-package org.jeecg.duan
+package org.jeecg.modules.duan.scala
 
 import org.apache.log4j.{Level, Logger}
 import org.apache.spark.{SparkConf, SparkContext}

system/start/src/main/java/org/jeecg/sy/java/HDFSUtils.java

@@ -0,0 +1,50 @@
+package org.jeecg.sy.java;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.fs.FSDataInputStream;
+import org.apache.hadoop.fs.FSDataOutputStream;
+import java.io.IOException;
+
+public class HDFSUtils {
+    private static FileSystem fileSystem;
+
+    // 初始化HDFS文件系统
+    public static void initFileSystem(String hdfsUri) throws IOException {
+        Configuration conf = new Configuration();
+        conf.set("fs.defaultFS", hdfsUri);
+        fileSystem = FileSystem.get(conf);
+    }
+
+    // 写入数据到HDFS文件
+    public static void writeToHDFS(String path, String data) throws IOException {
+        Path filePath = new Path(path);
+        FSDataOutputStream outputStream = fileSystem.create(filePath, true);
+        outputStream.writeUTF(data);
+        outputStream.close();
+    }
+
+    // 从HDFS文件读取数据
+    public static String readFromHDFS(String path) throws IOException {
+        Path filePath = new Path(path);
+        FSDataInputStream inputStream = fileSystem.open(filePath);
+        String data = inputStream.readUTF();
+        inputStream.close();
+        return data;
+    }
+
+    // 删除HDFS文件
+    public static boolean deleteFromHDFS(String path) throws IOException {
+        Path filePath = new Path(path);
+        return fileSystem.delete(filePath, true);
+    }
+
+    // 关闭文件系统连接
+    public static void closeFileSystem() throws IOException {
+        if (fileSystem != null) {
+            fileSystem.close();
+        }
+    }
+}
+

system/start/src/main/java/org/jeecg/sy/java/MainClass.java

@@ -0,0 +1,30 @@
+package org.jeecg.sy.java;
+
+import java.io.IOException;
+
+public class MainClass {
+    public static void main(String[] args) {
+        try {
+            // 初始化HDFS连接
+            HDFSUtils.initFileSystem("hdfs://localhost:9000");
+
+            // 写入数据到HDFS
+            HDFSUtils.writeToHDFS("/user/hadoop/testfile.txt", "Hello HDFS!");
+
+            // 从HDFS读取数据
+            String data = HDFSUtils.readFromHDFS("/user/hadoop/testfile.txt");
+            System.out.println("Read from HDFS: " + data);
+
+            // 删除HDFS文件
+            HDFSUtils.deleteFromHDFS("/user/hadoop/testfile.txt");
+
+            // 关闭HDFS连接
+            HDFSUtils.closeFileSystem();
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+    }
+
+
+}
+

system/start/src/main/java/org/jeecg/sy/java/MusicSpark.java

@@ -0,0 +1,56 @@
+package org.jeecg.sy.java;
+
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.functions;
+
+public class MusicSpark {
+    public static void main(String[] args) {
+        SparkSession spark = SparkSession.builder()
+                .appName("Music Shot Analysis")
+                .master("local[2]")
+                .getOrCreate();
+
+        // 读取数据
+        Dataset<Row> data = spark.read()
+                .format("csv")
+                .option("header", "true")
+                .load("path_to_your_csv_file");
+
+        // 投篮成功率分析
+        Dataset<Row> shotTypeSuccess = data
+                .groupBy("shot_type")
+                .agg(
+                        functions.avg(data.col("result").cast("integer")).alias("success_rate")
+                );
+
+        shotTypeSuccess.show();
+
+        // 每个球赛节次的得分情况
+        Dataset<Row> scoringByQuarter = data
+                .groupBy("qtr")
+                .agg(
+                        functions.sum(data.col("lebron_team_score")).alias("total_points")
+                );
+
+        scoringByQuarter.show();
+
+        // 根据投篮距离计算得分分布
+        Dataset<Row> scoreDistribution = data
+                .withColumn("distance_range", functions.when(data.col("distance_ft").$less(10), "0-10")
+                        .otherwise(functions.when(data.col("distance_ft").$less(20), "10-20")
+                                .otherwise("20+")))
+                .groupBy("distance_range")
+                .agg(
+                        functions.count("shot_type").alias("shots_made"),
+                        functions.sum(data.col("result").cast("integer")).alias("shots_scored")
+                );
+
+        scoreDistribution.show();
+
+        // 关闭Spark会话
+        spark.stop();
+    }
+}
+

system/start/src/main/java/org/jeecg/sy/java/ShotPrediction.java

@@ -0,0 +1,62 @@
+package org.jeecg.sy.java;
+
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.apache.spark.ml.classification.LogisticRegression;
+import org.apache.spark.ml.feature.VectorAssembler;
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+
+public class ShotPrediction {
+
+    public static void main(String[] args) {
+        SparkSession spark = SparkSession.builder()
+                .appName("Music Shot Prediction")
+                .master("local[2]")
+                .getOrCreate();
+
+        // 加载数据
+        Dataset<Row> data = spark.read()
+                .format("csv")
+                .option("header", "true")
+                .option("inferSchema", "true")
+                .load("path_to_your_csv_file");
+
+        // 数据预处理
+        data = data.withColumn("label", data.col("result").cast("integer"))
+                .withColumn("shot_type_num", data.col("shot_type").cast("integer"))
+                .withColumn("distance_ft_num", data.col("distance_ft").cast("integer"))
+                .withColumn("lead_num", data.col("lead").cast("integer"));
+
+        // 特征向量化
+        VectorAssembler assembler = new VectorAssembler()
+                .setInputCols(new String[]{"shot_type_num", "distance_ft_num", "lead_num"})
+                .setOutputCol("features");
+
+        // 创建逻辑回归模型
+        LogisticRegression lr = new LogisticRegression();
+
+        // 创建管道
+        Pipeline pipeline = new Pipeline()
+                .setStages(new PipelineStage[]{assembler, lr});
+
+        // 划分数据集
+        Dataset<Row>[] splits = data.randomSplit(new double[]{0.7, 0.3}, 42);
+        Dataset<Row> trainingData = splits[0];
+        Dataset<Row> testData = splits[1];
+
+        // 训练模型
+        PipelineModel model = pipeline.fit(trainingData);
+
+        // 模型评估
+        Dataset<Row> predictions = model.transform(testData);
+        predictions.select("features", "label", "prediction").show();
+
+        // 关闭Spark会话
+        spark.stop();
+    }
+}
+
+

system/start/src/main/java/org/jeecg/sy/java/SparkConn.java

@@ -0,0 +1,19 @@
+package org.jeecg.sy.java;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.sql.SparkSession;
+
+public class SparkConn {
+    public static void main(String[] args) {
+        // 初始化Spark
+        SparkSession session = SparkUtils.initSpark("Spark Java Application");
+
+        // 使用SparkUtils的方法处理数据
+        JavaRDD<Integer> doubledNumbers = SparkUtils.doubleNumbers();
+        SparkUtils.printRDD(doubledNumbers);
+
+        // 关闭Spark
+        SparkUtils.stopSpark();
+    }
+}
+

system/start/src/main/java/org/jeecg/sy/java/SparkUtils.java

@@ -0,0 +1,42 @@
+package org.jeecg.sy.java;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.SparkConf;
+import java.util.Arrays;
+import java.util.List;
+
+public class SparkUtils {
+    private static SparkSession sparkSession = null;
+
+    // 初始化SparkSession
+    public static SparkSession initSpark(String appName) {
+        if (sparkSession == null) {
+            SparkConf conf = new SparkConf().setAppName(appName).setMaster("local[2]");
+            sparkSession = SparkSession.builder().config(conf).getOrCreate();
+        }
+        return sparkSession;
+    }
+
+    // 关闭SparkSession
+    public static void stopSpark() {
+        if (sparkSession != null) {
+            sparkSession.stop();
+            sparkSession = null;
+        }
+    }
+
+    // 示例方法：创建一个整数RDD并将每个元素乘以2
+    public static JavaRDD<Integer> doubleNumbers() {
+        JavaSparkContext sc = new JavaSparkContext(sparkSession.sparkContext());
+        JavaRDD<Integer> numbersRDD = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5));
+        return numbersRDD.map(number -> number * 2);
+    }
+
+    // 示例方法：打印RDD的内容
+    public static void printRDD(JavaRDD<?> rdd) {
+        List<?> result = rdd.collect();
+        result.forEach(System.out::println);
+    }
+}