Spark-windows安装

 

 

 

Spark

 

目的:达到能在pycharm中测试

1.安装必要的文件:

JDK

AnaConda

spark

hadoop

jdk测试:java -version

Anaconda测试: 打开Anaconda Prompt输入conda list

spark测试(注意spark的安装路径不能有空格):spark-shell

2.配置环境变量

 

3.打开pycharm测试

import os
from pyspark import SparkConf, SparkContext
os.environ['JAVA_HOME']='G:\Program Files\Java\jdk1.8.0_181'
conf = SparkConf().setMaster('local[*]').setAppName('word_count')
sc = SparkContext(conf=conf)
d = ['a b c d', 'b c d e', 'c d e f']
d_rdd = sc.parallelize(d)
rdd_res = d_rdd.flatMap(lambda x: x.split(' ')).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a+b)
print(rdd_res)
print(rdd_res.collect())

运行结果：

G:\ProgramData\Anaconda3\python.exe "H:/1.study/资料(1)/机器学习2/Maching Learning_2/chapter13/spark_test.py"
19/07/18 17:12:13 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
PythonRDD[5] at RDD at PythonRDD.scala:53
[('a', 1), ('e', 2), ('b', 2), ('c', 3), ('d', 3), ('f', 1)]
​
Process finished with exit code 0

利用spark求圆周率代码

import random
import os
from pyspark import SparkConf, SparkContext
os.environ['JAVA_HOME']='G:\Program Files\Java\jdk1.8.0_181'
conf = SparkConf().setMaster('local[*]').setAppName('word_count')
sc = SparkContext(conf=conf)
NUM_SAMPLES = 100000
​
def inside(p):
    x, y = random.random(), random.random()
    return x*x + y*y < 1
​
count = sc.parallelize(range(0, NUM_SAMPLES)).filter(inside).count()
print("π粗糙的值: %f" % (4.0 * count / NUM_SAMPLES))

得到结果：

[Stage 0:============================================>              (6 + 2) / 8]
 π粗糙的值: 3.129680