refactor(basic_re): 重构电影评分数据处理逻辑

-移除了不必要的环境变量设置和测试代码
- 新增 data_prepare模块用于初始化 RDD
- 添加了计算电影平均评分和过滤高评分电影的功能
- 优化了代码结构，提高了可读性和可维护性

basic_re.py

@@ -1,68 +1,35 @@
 from pyspark import SparkContext, SparkConf
-import os
-os.environ['JAVA_HOME'] = "/opt/module/jdk1.8.0_171"
-os.environ["PYSPARK_PYTHON"]="/usr/bin/python3"
-os.environ["PYSPARK_DRIVER_PYTHON"]="/usr/bin/python3"
-def get_ratings_tuple(entry):
-    items = entry.split('::')
-    return int(items[0]), int(items[1]), float(items[2])
-
-def get_movie_tuple(entry):
-    items = entry.split('::')
-    return int(items[0]), items[1]
-
-def sortFunction(tuple):
-    key = str('%06.3f' % tuple[0])
-    value = tuple[1]
-    return (key + ' ' + value)
-
-def init_rdds(sc):
-    ratingsFilename = "hdfs://master:9000/user/root/als_movie/ratings.txt"
-    moviesFilename = "hdfs://master:9000/user/root/als_movie/movies.dat"
-    numPartitions = 2
-    rawRatings = sc.textFile(ratingsFilename).repartition(numPartitions)
-    rawMovies = sc.textFile(moviesFilename)
-    ratingsRDD = rawRatings.map(get_ratings_tuple).cache()
-    moviesRDD = rawMovies.map(get_movie_tuple).cache()
-    return ratingsRDD, moviesRDD
+import data_prepare
+from test_helper import Test
+def getCountsAndAverages(IDandRatingsTuple):
+    movie = IDandRatingsTuple[0]
+    ratings = IDandRatingsTuple[1]
+    return (movie, (len(ratings), float(sum(ratings)) / len(ratings)))
 
 if __name__ == "__main__":
     import sys, os
     os.environ["PYSPARK_PYTHON"] = "/usr/bin/python3"
     os.environ["PYSPARK_DRIVER_PYTHON"] = "/usr/bin/python3"
-
     conf = SparkConf().setMaster("spark://master:7077").setAppName("als_movie")
     sc = SparkContext.getOrCreate(conf)
     sc.setLogLevel("ERROR")
+    sc.addPyFile("data_prepare.py")
 
-    ratingsRDD, moviesRDD = init_rdds(sc)
+    ratingsRDD, moviesRDD = data_prepare.init_rdds(sc)
+    movieIDsWithRatingsRDD = (ratingsRDD
+                              .map(lambda x: (x[1], x[2]))
+                              .groupByKey())
+    print('movieIDsWithRatingsRDD: %s\n' % movieIDsWithRatingsRDD.take(3))
 
-    ratingsCount = ratingsRDD.count()
-    moviesCount = moviesRDD.count()
+    movieIDsWithAvgRatingsRDD = movieIDsWithRatingsRDD.map(lambda rec: getCountsAndAverages(rec))
+    print('movieIDsWithAvgRatingsRDD1: %s\n' % movieIDsWithAvgRatingsRDD.take(3))
 
-    print('There are %s ratings and %s movies in the datasets' % (ratingsCount, moviesCount))
-    print('Ratings: %s' % ratingsRDD.take(3))
-    print('Movies: %s' % moviesRDD.take(3))
+    movieNameWithAvgRatingsRDD = (moviesRDD.join(movieIDsWithAvgRatingsRDD)
+                                  .map(lambda movie: (movie[1][1][1], movie[1][0], movie[1][1][0])))
+    print('movieNameWithAvgRatingsRDD2: %s\n' % movieNameWithAvgRatingsRDD.take(3))
 
-    tmp1 = [(1, u'alpha'), (2, u'alpha'), (2, u'beta'), (3, u'alpha'), (1, u'epsilon'), (1, u'delta')]
-    tmp2 = [(1, u'delta'), (2, u'alpha'), (2, u'beta'), (3, u'alpha'), (1, u'epsilon'), (1, u'alpha')]
-
-    oneRDD = sc.parallelize(tmp1)
-    twoRDD = sc.parallelize(tmp2)
-    oneSorted = oneRDD.sortByKey(True).collect()
-    twoSorted = twoRDD.sortByKey(True).collect()
-    print(oneSorted)
-    print(twoSorted)
-    assert set(oneSorted) == set(twoSorted)
-    assert twoSorted[0][0] < twoSorted.pop()[0]
-    assert oneSorted[0:2] != twoSorted[0:2]
-
-    print(oneRDD.sortBy(sortFunction, True).collect())
-    print(twoRDD.sortBy(sortFunction, True).collect())
-
-    oneSorted1 = oneRDD.takeOrdered(oneRDD.count(), key=sortFunction)
-    twoSorted1 = twoRDD.takeOrdered(twoRDD.count(), key=sortFunction)
-    print('one is %s' % oneSorted1)
-    print('two is %s' % twoSorted1)
-    assert oneSorted1 == twoSorted1
+    movieLimitedAndSortedByRatingRDD = (movieNameWithAvgRatingsRDD
+                                        .filter(lambda movie: movie[2] > 500)
+                                        .sortBy(data_prepare.sortFunction, False))
+    print('Movies with highest ratings: %s' % movieLimitedAndSortedByRatingRDD.take(20))
     sc.stop()