PySpark环境配置 windowns使用PySpark环境配置和基本操作
Nick_Spider 人气:0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
HADOOP_HOME => /path/hadoop SPARK_HOME => /path/spark
安装pyspark。
pip install pyspark
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。
>>> from pyspark import SparkContext >>> sc = SparkContext("local", "First App")
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。
>>> sc.stop()
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。
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))
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:
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)
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。
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)
返回结果:
Number of elements in RDD -> 8
Collect
collect返回RDD中的所有元素。
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)
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。
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)
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。
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']
也可以改写成这样:
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))
map
将函数应用于RDD中的每个元素并返回新的RDD。
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))
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。
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))
这里的add是python内置的函数,可以使用ide查看:
def add(a, b): "Same as a + b." return a + b
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。
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))
返回结果:
x => [('spark', 1), ('hadoop', 4), ('python', 4)]
y => [('spark', 2), ('hadoop', 5)]
Join RDD -> [('hadoop', (4, 5)), ('spark', (1, 2))]
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