pythonspark实例_spark+python快速入门实战小例子(PySpark)

1、集群测试实例

代码如下：

from pyspark.sql import SparkSession

if __name__ == "__main__":

spark = SparkSession\

.builder\

.appName("PythonWordCount")\

.master("spark://mini1:7077") \

.getOrCreate()

spark.conf.set("spark.executor.memory", "500M")

sc = spark.sparkContext

a = sc.parallelize([1, 2, 3])

b = a.flatMap(lambda x: (x,x ** 2))

print(a.collect())

print(b.collect())1

运行结果：

2、从文件中读取

为了方便调试，这里采用本地模式进行测试

from py4j.compat import long

from pyspark.sql import SparkSession

def formatData(arr):

# arr = arr.split(",")

mb = (arr[0], arr[2])

flag = arr[3]

time = long(arr[1])

# time = arr[1]

if flag == "1":

time = -time

return (mb,time)

if name == “main”:

spark = SparkSession

.builder

.appName(“PythonWordCount”)

.master(“local”)

.getOrCreate()

sc = spark.sparkContext

# sc = spark.sparkContext

line = sc.textFile("D:\\code\\hadoop\\data\\spark\\day1\\bs_log").map(lambda x: x.split(','))

count = line.map(lambda x: formatData(x))

rdd0 = count.reduceByKey(lambda agg, obj: agg + obj)

# print(count.collect())

line2 = sc.textFile("D:\\code\\hadoop\\data\\spark\\day1\\lac_info.txt").map(lambda x: x.split(','))

rdd = count.map(lambda arr: (arr[0][1], (arr[0][0], arr[1])))

rdd1 = line2.map(lambda arr: (arr[0], (arr[1], arr[2])))

rdd3 = rdd.join(rdd1)

rdd4 =rdd0.map(lambda arr: (arr[0][0], arr[0][1], arr[1]))

# .map(lambda arr: list(arr).sortBy(lambda arr1: arr1[2]).reverse)

rdd5 = rdd4.groupBy(lambda arr: arr[0]).values().map(lambda das: sorted(list(das), key=lambda x: x[2], reverse=True))

print(rdd5.collect())

原文件数据：

结果如下：

[[('18688888888', '16030401EAFB68F1E3CDF819735E1C66', 87600), ('18688888888', '9F36407EAD0629FC166F14DDE7970F68', 51200), ('18688888888', 'CC0710CC94ECC657A8561DE549D940E0', 1300)], [('18611132889', '16030401EAFB68F1E3CDF819735E1C66', 97500), ('18611132889', '9F36407EAD0629FC166F14DDE7970F68', 54000), ('18611132889', 'CC0710CC94ECC657A8561DE549D940E0', 1900)]]1

3、读取文件并将结果保存至文件

from pyspark.sql import SparkSession

from py4j.compat import long

def formatData(arr):

# arr = arr.split(",")

mb = (arr[0], arr[2])

flag = arr[3]

time = long(arr[1])

# time = arr[1]

if flag == “1”:

time = -time

return (mb,time)

if name == “main”:

spark = SparkSession

.builder

.appName(“PythonWordCount”)

.master(“local”)

.getOrCreate()

sc = spark.sparkContext

line = sc.textFile(“D:\code\hadoop\data\spark\day1\bs_log”).map(lambda x: x.split(’,’))

rdd0 = line.map(lambda x: formatData(x))

rdd1 = rdd0.reduceByKey(lambda agg, obj: agg + obj).map(lambda t: (t[0][1], (t[0][0], t[1])))

line2 = sc.textFile(“D:\code\hadoop\data\spark\day1\lac_info.txt”).map(lambda x: x.split(’,’))

rdd2 = line2.map(lambda x: (x[0], (x[1], x[2])))

rdd3 = rdd1.join(rdd2).map(lambda x: (x[1][0][0], x[0], x[1][0][1], x[1][1][0], x[1][1][1]))

rdd4 = rdd3.groupBy(lambda x: x[0])

rdd5 = rdd4.mapValues(lambda das: sorted(list(das), key=lambda x: x[2], reverse=True)[:2])

print(rdd1.join(rdd2).collect())

print(rdd5.collect())

rdd5.saveAsTextFile("D:\\code\\hadoop\\data\\spark\\day02\\out1")

sc.stop()

结果如下：

4、根据自定义规则匹配

import urllib

from pyspark.sql import SparkSession

def getUrls(urls):

url = urls[0]

parsed = urllib.parse.urlparse(url)

return (parsed.netloc, url, urls[1])

if name == “main”:

spark = SparkSession

.builder

.appName(“PythonWordCount”)

.master(“local”)

.getOrCreate()

sc = spark.sparkContext

line = sc.textFile(“D:\code\hadoop\data\spark\day02\itcast.log”).map(lambda x: x.split(’\t’))

//从数据库中加载规则

arr = [“java.itcast.cn”, “php.itcast.cn”, “net.itcast.cn”]

rdd1 = line.map(lambda x: (x[1], 1))

rdd2 = rdd1.reduceByKey(lambda agg, obj: agg + obj)

rdd3 = rdd2.map(lambda x: getUrls(x))

for ins in arr:

rdd = rdd3.filter(lambda x:x[0] == ins)

result = rdd.sortBy(lambda x: x[2], ascending = False).take(2)

print(result)

spark.stop()

结果如下：

5、自定义类排序

from operator import gt

from pyspark.sql import SparkSession

class Girl:

def init(self, faceValue, age):

self.faceValue = faceValue

self.age = age

def __gt__(self, other):

if other.faceValue == self.faceValue:

return gt(self.age, other.age)

else:

return gt(self.faceValue, other.faceValue)

if name == “main”:

spark = SparkSession

.builder

.appName(“PythonWordCount”)

.master(“local”)

.getOrCreate()

sc = spark.sparkContext

rdd1 = sc.parallelize([(“yuihatano”, 90, 28, 1), (“angelababy”, 90, 27, 2), (“JuJingYi”, 95, 22, 3)])

rdd2 = rdd1.sortBy(lambda das: Girl(das[1], das[2]),False)

print(rdd2.collect())

sc.stop()

结果如下：

6、JDBC

from pyspark import SQLContext

from pyspark.sql import SparkSession

if name == “main”:

spark = SparkSession

.builder

.appName(“PythonWordCount”)

.master(“local”)

.getOrCreate()

sc = spark.sparkContext

sqlContext = SQLContext(sc)

df = sqlContext.read.format(“jdbc”).options(url=“jdbc:mysql://localhost:3306/hellospark”,driver=“com.mysql.jdbc.Driver”,dbtable="(select * from actor) tmp",user=“root”,password=“123456”).load()

print(df.select(‘description’,‘age’).show(2))

# print(df.printSchema)

sc.stop()