下载依赖
首先需要下载hadoop和spark,解压,然后设置环境变量。
hadoop清华源下载
spark清华源下载
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HADOOP_HOME => /path/hadoop
SPARK_HOME => /path/spark
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安装pyspark。
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pip install pyspark
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基本使用
可以在shell终端,输入pyspark,有如下回显:
输入以下指令进行测试,并创建SparkContext,SparkContext是任何spark功能的入口点。
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>>> from pyspark import SparkContext
>>> sc = SparkContext( "local" , "First App" )
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如果以上不会报错,恭喜可以开始使用pyspark编写代码了。
不过,我这里使用IDE来编写代码,首先我们先在终端执行以下代码关闭SparkContext。
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>>> sc.stop()
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下面使用pycharm编写代码,如果修改了环境变量需要先重启pycharm。
在pycharm运行如下程序,程序会起本地模式的spark计算引擎,通过spark统计abc.txt文件中a和b出现行的数量,文件路径需要自己指定。
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from pyspark import SparkContext
sc = SparkContext( "local" , "First App" )
logFile = "abc.txt"
logData = sc.textFile(logFile).cache()
numAs = logData. filter ( lambda s: 'a' in s).count()
numBs = logData. filter ( lambda s: 'b' in s).count()
print ( "Line with a:%i,line with b:%i" % (numAs, numBs))
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运行结果如下:
20/03/11 16:15:57 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).
20/03/11 16:15:58 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
Line with a:3,line with b:1
这里说一下,同样的工作使用python可以做,spark也可以做,使用spark主要是为了高效的进行分布式计算。
戳pyspark教程
戳spark教程
RDD
RDD代表Resilient Distributed Dataset,它们是在多个节点上运行和操作以在集群上进行并行处理的元素,RDD是spark计算的操作对象。
一般,我们先使用数据创建RDD,然后对RDD进行操作。
对RDD操作有两种方法:
Transformation(转换) - 这些操作应用于RDD以创建新的RDD。例如filter,groupBy和map。
Action(操作) - 这些是应用于RDD的操作,它指示Spark执行计算并将结果发送回驱动程序,例如count,collect等。
创建RDD
parallelize是从列表创建RDD,先看一个例子:
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from pyspark import SparkContext
sc = SparkContext( "local" , "count app" )
words = sc.parallelize(
[ "scala" ,
"java" ,
"hadoop" ,
"spark" ,
"akka" ,
"spark vs hadoop" ,
"pyspark" ,
"pyspark and spark"
])
print (words)
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结果中我们得到一个对象,就是我们列表数据的RDD对象,spark之后可以对他进行操作。
ParallelCollectionRDD[0] at parallelize at PythonRDD.scala:195
Count
count方法返回RDD中的元素个数。
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from pyspark import SparkContext
sc = SparkContext( "local" , "count app" )
words = sc.parallelize(
[ "scala" ,
"java" ,
"hadoop" ,
"spark" ,
"akka" ,
"spark vs hadoop" ,
"pyspark" ,
"pyspark and spark"
])
print (words)
counts = words.count()
print ( "Number of elements in RDD -> %i" % counts)
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返回结果:
Number of elements in RDD -> 8
Collect
collect返回RDD中的所有元素。
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from pyspark import SparkContext
sc = SparkContext( "local" , "collect app" )
words = sc.parallelize(
[ "scala" ,
"java" ,
"hadoop" ,
"spark" ,
"akka" ,
"spark vs hadoop" ,
"pyspark" ,
"pyspark and spark"
])
coll = words.collect()
print ( "Elements in RDD -> %s" % coll)
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返回结果:
Elements in RDD -> ['scala', 'java', 'hadoop', 'spark', 'akka', 'spark vs hadoop', 'pyspark', 'pyspark and spark']
foreach
每个元素会使用foreach内的函数进行处理,但是不会返回任何对象。
下面的程序中,我们定义的一个累加器accumulator,用于储存在foreach执行过程中的值。
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from pyspark import SparkContext
sc = SparkContext( "local" , "ForEach app" )
accum = sc.accumulator( 0 )
data = [ 1 , 2 , 3 , 4 , 5 ]
rdd = sc.parallelize(data)
def increment_counter(x):
print (x)
accum.add(x)
return 0
s = rdd.foreach(increment_counter)
print (s) # None
print ( "Counter value: " , accum)
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返回结果:
None
Counter value: 15
filter
返回一个包含元素的新RDD,满足过滤器的条件。
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from pyspark import SparkContext
sc = SparkContext( "local" , "Filter app" )
words = sc.parallelize(
[ "scala" ,
"java" ,
"hadoop" ,
"spark" ,
"akka" ,
"spark vs hadoop" ,
"pyspark" ,
"pyspark and spark" ]
)
words_filter = words. filter ( lambda x: 'spark' in x)
filtered = words_filter.collect()
print ( "Fitered RDD -> %s" % (filtered))
Fitered RDD - > [ 'spark' , 'spark vs hadoop' , 'pyspark' , 'pyspark and spark' ]
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也可以改写成这样:
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from pyspark import SparkContext
sc = SparkContext( "local" , "Filter app" )
words = sc.parallelize(
[ "scala" ,
"java" ,
"hadoop" ,
"spark" ,
"akka" ,
"spark vs hadoop" ,
"pyspark" ,
"pyspark and spark" ]
)
def g(x):
for i in x:
if "spark" in x:
return i
words_filter = words. filter (g)
filtered = words_filter.collect()
print ( "Fitered RDD -> %s" % (filtered))
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map
将函数应用于RDD中的每个元素并返回新的RDD。
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from pyspark import SparkContext
sc = SparkContext( "local" , "Map app" )
words = sc.parallelize(
[ "scala" ,
"java" ,
"hadoop" ,
"spark" ,
"akka" ,
"spark vs hadoop" ,
"pyspark" ,
"pyspark and spark" ]
)
words_map = words. map ( lambda x: (x, 1 , "_{}" . format (x)))
mapping = words_map.collect()
print ( "Key value pair -> %s" % (mapping))
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返回结果:
Key value pair -> [('scala', 1, '_scala'), ('java', 1, '_java'), ('hadoop', 1, '_hadoop'), ('spark', 1, '_spark'), ('akka', 1, '_akka'), ('spark vs hadoop', 1, '_spark vs hadoop'), ('pyspark', 1, '_pyspark'), ('pyspark and spark', 1, '_pyspark and spark')]
Reduce
执行指定的可交换和关联二元操作后,然后返回RDD中的元素。
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from pyspark import SparkContext
from operator import add
sc = SparkContext( "local" , "Reduce app" )
nums = sc.parallelize([ 1 , 2 , 3 , 4 , 5 ])
adding = nums. reduce (add)
print ( "Adding all the elements -> %i" % (adding))
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这里的add是python内置的函数,可以使用ide查看:
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def add(a, b):
"Same as a + b."
return a + b
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reduce会依次对元素相加,相加后的结果加上其他元素,最后返回结果(RDD中的元素)。
Adding all the elements -> 15
Join
返回RDD,包含两者同时匹配的键,键包含对应的所有元素。
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from pyspark import SparkContext
sc = SparkContext( "local" , "Join app" )
x = sc.parallelize([( "spark" , 1 ), ( "hadoop" , 4 ), ( "python" , 4 )])
y = sc.parallelize([( "spark" , 2 ), ( "hadoop" , 5 )])
print ( "x =>" , x.collect())
print ( "y =>" , y.collect())
joined = x.join(y)
final = joined.collect()
print ( "Join RDD -> %s" % (final))
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返回结果:
x => [('spark', 1), ('hadoop', 4), ('python', 4)]
y => [('spark', 2), ('hadoop', 5)]
Join RDD -> [('hadoop', (4, 5)), ('spark', (1, 2))]
到此这篇关于windows使用PySpark环境配置和基本操作的文章就介绍到这了,更多相关PySpark环境配置 内容请搜索服务器之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持服务器之家!
原文链接:https://blog.csdn.net/weixin_39198406/article/details/104798681