案例一：找出最受欢迎的电影。

数据样本一览：

• <版本 A>
  popular-movies.py

from pyspark import SparkConf, SparkContext

conf = SparkConf().setMaster("local").setAppName("PopularMovies")
sc = SparkContext(conf=conf)

lines = sc.textFile("hdfs:///u.data")
movies = lines.map(lambda x: (int(x.split()[1]), 1))
movieCounts = movies.reduceByKey(lambda x, y: x + y)

print(movieCounts.collect())

flipped = movieCounts.map(lambda x: (x[1], x[0]))
sortedMovies = flipped.sortByKey()

results = sortedMovies.collect()

for result in results:
    print(result)

运行：$ spark-submit popular-movies.py
运行结果

• <版本 B> popular-movies-nicer.py

from pyspark import SparkConf, SparkContext


def loadMovieNames():
    movieNames = {}
    with open("/u.item") as f:
        for line in f:
            line = line.encode('utf-8').decode('utf-8-sig')
            fields = line.split('|')
            movieNames[int(fields[0])] = fields[1]
    return movieNames


conf = SparkConf().setMaster("local").setAppName("PopularMovies")
sc = SparkContext(conf=conf)

nameDict = sc.broadcast(loadMovieNames())

lines = sc.textFile("hdfs:///u.data")
movies = lines.map(lambda x: (int(x.split()[1]), 1))
movieCounts = movies.reduceByKey(lambda x, y: x + y)

flipped = movieCounts.map(lambda x: (x[1], x[0]))
sortedMovies = flipped.sortByKey()

sortedMoviesWithNames = sortedMovies.map(lambda x: (nameDict.value[x[1]], x[0]))

results = sortedMoviesWithNames.collect()

for result in results:
    print(result)

运行：$ spark-submit most-popular-movies-nicer.py
运行结果

• <版本 C> popular-movies-dataframe.py

from pyspark.sql import SparkSession
from pyspark.sql import Row
from pyspark.sql import functions


def loadMovieNames():
    movieNames = {}
    with open("/u.item") as f:
        for line in f:
            line = line.encode('utf-8').decode('utf-8-sig')
            fields = line.split('|')
            movieNames[int(fields[0])] = fields[1]
    return movieNames


spark = SparkSession.builder.config("spark.sql.warehouse.dir", "file:///C:/temp").appName("PopularMovies").getOrCreate()

nameDict = loadMovieNames()

lines = spark.SparkContext.textFile("hdfs:///u.data")

movies = lines.map(lambda x: Row(movieID=int(x.split()[1])))

movieDataset = spark.createDataFrame(movies)

topMoviesIDs = movieDataset.groupBy("movieID").count().orderBy("count", ascending=False).cache()

topMoviesIDs.show()

too10 = topMoviesIDs.take(10)

print('\n')

for result in too10:
    print("%s: %d" % (nameDict[result[0]], result[1]))

spark.stop()

运行：$spark-submit popular-movies-dataframe.py
运行结果

案例二：(A)找出社交图中最受欢迎的超级英雄；(B)超级之间的间隔

漫威数据集：

如果两个英雄出现在同一本漫画书里面，则将他们连接。因此可以创建一个超级英雄图。

• <问题 A> most-popular-superhero.py

from pyspark import SparkConf, SparkContext

conf = SparkConf().setMaster("local").setAppName("PopularHero")
sc = SparkContext(conf=conf)


def countCoOccurences(line):
    elements = line.split()
    return int(elements[0]), len(elements) - 1


def parseNames(line):
    fields = line.split('\"')
    return int(fields[0]), fields[1].encode("utf8")


names = sc.textFile("hdfs:///Marvel-names.txt")
namesRdd = names.map(parseNames)

lines = sc.textFile("hdfs:///Marvel-graph.txt")
pairings = lines.map(countCoOccurences)

totalFriendsByCharacter = pairings.reduceByKey(lambda x, y: x + y)

flipped = totalFriendsByCharacter.map(lambda x: (x[1], x[0]))

mostPopular = flipped.max()
mostPopularName = namesRdd.lookup(mostPopular[1])[0]

print(mostPopularName.decode() + " is the most popular superhero,with" + str(mostPopular[0]) + " co.appearances.")

运行: $ spark-submit most-popular-superhero.py
实验结果

• <版本 B> degrees-of-separate.py

from pyspark import SparkConf, SparkContext

conf = SparkConf().setMaster("local").setAppName("DegreesOfSeparation")
sc = SparkContext(conf=conf)

startCharacterID = 5306
targetCharacterID = 14

hitCounter = sc.accumulator(0)


def convertToBFS(line):
    fields = line.split()
    heroID = int(fields[0])
    connections = []
    for connection in fields[1:]:
        connections.append((int(connection)))

    color = 'WHITE'
    distance = 9999

    if heroID == startCharacterID:
        color = 'GRAY'
        distance = 0

    return heroID, (connections, distance, color)


def createStartingRdd():
    inputFile = sc.textFile("hdfs:///Marvel-graph.txt")
    return inputFile.map(convertToBFS)


def bfsMap(node):
    CharacterID = node[0]
    data = node[1]
    connections = data[0]
    distance = data[1]
    color = data[2]

    results = []

    if color == 'GRAY':
        for connection in connections:
            newCharacterID = connection
            newDistance = distance + 1
            newColor = 'GRAY'
            if targetCharacterID == connection:
                hitCounter.add(1)

            newEntry = (newCharacterID, ([], newDistance, newColor))
            results.append(newEntry)

        color = 'BLACK'

    results.append((CharacterID, (connections, distance, color)))

    return results


def bfsReduce(data1, data2):
    edges1 = data1[0]
    edges2 = data2[0]
    distance1 = data1[1]
    distance2 = data2[1]
    color1 = data1[2]
    color2 = data2[2]

    distance = 9999
    color = 'WHITE'
    edges = []

    if len(edges1) > 0:
        edges1.extend(edges1)
    if len(edges2) > 0:
        edges2.extend(edges2)

    if distance1 < distance:
        distance = distance1

    if distance2 < distance:
        distance = distance2

    if color1 == 'WHITE' and (color2 == 'GRAY' or color2 == 'BLACK'):
        color = color2

    if color1 == 'GRAY' and color2 == 'BLACK':
        color = color2

    if color2 == 'WHITE' and (color1 == 'GRAY' or color1 == 'BLACK'):
        color = color1

    if color2 == 'GRAY' and color1 == 'BLACK':
        color = color1

    return edges, distance, color


iterationRdd = createStartingRdd()

for iteration in range(0, 10):
    print("Running BFS iteration# " + str(iteration + 1))

    mapped = iterationRdd.flatMap(bfsMap)

    print("Processing" + str(mapped.count()) + " values.")

    if hitCounter.value > 0:
        print("Hit the target character ! From " + str(hitCounter.value)
              + " diferent direction(s).")
        break

    iterationRdd = mapped.reduceByKey(bfsReduce)

运行：$ spark-submit degrees-of-separate.py
实验结果