feat(data): 添加数据准备脚本

- 新增 data_prepare.py 文件，用于初始化和处理电影评分数据
- 实现了从 HDFS 读取 ratings 和 movies 数据的功能
- 提供了数据解析和缓存的逻辑，为后续处理做准备

data_prepare.py

@@ -0,0 +1,40 @@
+from pyspark import SparkContext, SparkConf
+import os
+
+# 设置 Java 环境变量
+os.environ['JAVA_HOME'] = '/opt/module/jdk1.8.0_171'
+
+# 解析 ratings 行为 (userID, movieID, rating)
+def get_ratings_tuple(entry):
+    user, movie, rating, _ = entry.split('::')
+    return int(user), int(movie), float(rating)
+
+# 解析 movies 行为 (movieID, title)
+def get_movie_tuple(entry):
+    mid, title, _ = entry.split('::')
+    return int(mid), title
+
+# 用于排序时生成确定性键
+def sort_key(rec):
+    score, name = rec
+    return f"{score:06.3f} {name}"
+
+# 初始化并返回 ratingsRDD, moviesRDD
+def init_rdds(sc, hdfs_base='hdfs://master:9000/user/root/als_movie'):
+    ratings_path = f"{hdfs_base}/ratings.txt"
+    movies_path = f"{hdfs_base}/movies.dat"
+    raw_r = sc.textFile(ratings_path).repartition(2)
+    raw_m = sc.textFile(movies_path)
+    ratings_rdd = raw_r.map(get_ratings_tuple).cache()
+    movies_rdd = raw_m.map(get_movie_tuple).cache()
+    return ratings_rdd, movies_rdd
+
+if __name__ == '__main__':
+    conf = SparkConf().setMaster('spark://master:7077').setAppName('als_movie')
+    sc = SparkContext.getOrCreate(conf)
+    sc.setLogLevel('ERROR')
+
+    rdd_ratings, rdd_movies = init_rdds(sc)
+    print(f"Ratings count: {rdd_ratings.count()}")
+    print(f"Movies count: {rdd_movies.count()}")
+    sc.stop()