reduceByKey算子，聚合

列表中存放二元元组，元组中第一个为key，此算子按key聚合，传入计算逻辑

from pyspark import SparkConf, SparkContext
import os

os.environ["PYSPARK_PYTHON"] = "D:/dev/python/python3.10.4/python.exe"
conf = SparkConf().setMaster("local[*]").setAppName("test_spark")
sc = SparkContext(conf=conf)

准备RDD

rdd = sc.parallelize([('男', 9), ('女', 9), ('男', 7), ('女', 6)])

reduceByKey()

rdd_2 = rdd.reduceByKey(lambda a, b: a + b)

使用collect方法收集结果

print(rdd_2.collect())

关闭pyspark

sc.stop()

案例1，单词计数

数据准备

from pyspark import SparkConf, SparkContext
import os

os.environ["PYSPARK_PYTHON"] = "D:\\dev\\python\\python3.10.4\\python.exe"
conf = SparkConf().setMaster("local[*]").setAppName("test_spark")
sc = SparkContext(conf=conf)

使用textFile()读取文件，使用flatMap()算子，按空格分割单词

rdd = sc.textFile("D:/abc.txt")
rdd_2 = rdd.flatMap(lambda x: x.split(" "))
print(rdd_2.collect())

使用map算子将元素改造为（word，1）形式，便于使用reduceByKey;无法使用for循环

计数

rdd_4 = rdd_3.reduceByKey(lambda a, b: a + b)
print(rdd_4.collect())

# 关闭
sc.stop()

filter方法，过滤

from pyspark import SparkConf, SparkContext
import os

os.environ["PYSPARK_PYTHON"] = "D:/dev/python/python3.10.4/python.exe"
conf = SparkConf().setMaster("local[*]").setAppName("test_spark")
sc = SparkContext(conf=conf)

准备RDD对象

rdd = sc.parallelize([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])

filter()算子过滤，传入逻辑表达式，True留下

rdd_2 = rdd.filter(lambda x: x % 2 == 0)
print(rdd.collect())
print(rdd_2.collect())

sc.stop()

distinct算子，去重,无需传参

**from pyspark import SparkConf, SparkContext
import os

os.environ["PYSPARK_PYTHON"] = "D:/dev/python/python3.10.4/python.exe"
conf = SparkConf().setMaster("local[*]").setAppName("test_pyspark")
sc = SparkContext(conf=conf)**

准备RDD对象

rdd = sc.parallelize([1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 34, 4, 4, 5, 5])

distinct算子去重

print(rdd.distinct().collect())

sc.stop()

sortBy算子,排序,按谁排序就返回谁

from pyspark import SparkConf, SparkContext
import os

os.environ["PYSPARK_PYTHON"] = "D:/dev/python/python3.10.4/python.exe"
conf = SparkConf().setMaster("local[*]").setAppName("test_spark")
sc = SparkContext(conf=conf)

准备RDD对象

rdd = sc.parallelize([('Java', 5), ('python', 7), ('c', 5), ('人生苦短，我用', 2)])

sortBy算子，ascending为True升序，False降序

rdd_2 = rdd.sortBy(lambda x: x[1], ascending=False, numPartitions=1)
print(rdd_2.collect())

sc.stop()