Spark中groupByKey、reduceByKey与sortByKey

groupByKey把相同的key的数据分组到一个集合序列当中：

[("hello",1), ("world",1), ("hello",1), ("fly",1), ("hello",1), ("world",1)] --> [("hello",(1,1,1)),("word",(1,1)),("fly",(1))]

reduceByKey把相同的key的数据聚合到一起并进行相应的计算：

[("hello",1), ("world",1), ("hello",1), ("fly",1), ("hello",1), ("world",1)]  add--> [("hello",3）,("word",2),("fly",1)]

sortByKey按key的大小排序，默认为升序排序：

[(3,"hello"）,(2,"word"),(1,"fly")]  -->   [(1,"fly")，(2,"word")，(3,"hello")]

groupByKey、reduceByKey及sortByKey的比较：

from pyspark import SparkConf, SparkContext
from operator import add

conf = SparkConf()
sc = SparkContext(conf=conf)

def func_by_key():
    data = [
        "hello world", "hello fly", "hello world",
        "hello fly", "hello fly", "hello fly"
    ]
    data_rdd = sc.parallelize(data)
    word_rdd = data_rdd.flatMap(lambda s: s.split(" ")).map(lambda x: (x, 1))
    group_by_key_rdd = word_rdd.groupByKey()
    print("groupByKey:{}".format(group_by_key_rdd.mapValues(list).collect()))
    print("groupByKey mapValues(len):{}".format(
        group_by_key_rdd.mapValues(len).collect()
    ))

    reduce_by_key_rdd = word_rdd.reduceByKey(add)
    print("reduceByKey:{}".format(reduce_by_key_rdd.collect()))

    print("sortByKey:{}".format(reduce_by_key_rdd.map(
        lambda x: (x[1], x[0])
    ).sortByKey().map(lambda x: (x[0], x[1])).collect()))

func_by_key()
sc.stop()

"""

result：

groupByKey:[('fly', [1, 1, 1, 1]), ('world', [1, 1]), ('hello', [1, 1, 1, 1, 1, 1])]
groupByKey mapValues(len):[('fly', 4), ('world', 2), ('hello', 6)]
reduceByKey:[('fly', 4), ('world', 2), ('hello', 6)]
sortByKey:[(2, 'world'), (4, 'fly'), (6, 'hello')]

"""

从结果可以看出，groupByKey对分组后的每个key的value做mapValues(len)后的结果与reduceByKey的结果一致，即：如果分组后要对每一个key所对应的值进行操作则应直接用reduceByKey；sortByKey是按key排序，如果要对value排序，可以交换key与value的位置，再排序。