[Spark][Python][DataFrame][RDD]DataFrame中抽取RDD例子 sqlContext = HiveContext(sc) peopleDF = sqlContext.read.json("people.json") peopleRDD = peopleDF.map(lambda row: (row.pcode,row.name)) peopleRDD.take(5) Out[5]: [(u'94304', u'Alice'),(u'94304', u'…

[Spark][Python]获得 key,value形式的 RDD [training@localhost ~]$ cat users.txtuser001 Fred Flintstoneuser090 Bugs Bunnyuser111 Harry Potter[training@localhost ~]$ hdfs dfs -put users.txt[training@localhost ~]$ [training@localhost ~]$ [training@localhost ~]…

针对RDD, 使用 keyBy 来构筑 key-line 对: [training@localhost ~]$ cat webs.log 56.31.230.188 - 90700 "GET/KDDOC-00101.html HTTP/1.0"56.32.230.186 - 90700 "GET/contents.css HTTP/1.0"202.156.27.99 - 25223 "GET /KDDOC-00220.html HTTP/1.0"…

一.前言 初学python,看<python基础教程>,第20章实现了将文本转化成html的功能.由于本人之前有DIY一个markdown转html的算法,所以对这个例子有兴趣.可仔细一看,发现很难看懂,一个功能分散在几个文件中,各个类的耦合非常紧.虽然自己有几年的c++开发经验,但初看这个python代码也觉得头晕. 二.原版 以下是其源码 from __future__ import generators def lines(file): for line in file: yield l…

[Spark][Python][RDD][DataFrame]从 RDD 构造 DataFrame 例子 from pyspark.sql.types import * schema = StructType( [ StructField("age",IntegerType(),True), StructField("name",StringType(),True), StructField("pcode",StringType(),True)…

[Spark][Python][DataFrame][RDD]从DataFrame得到RDD的例子 $ hdfs dfs -cat people.json {"name":"Alice","pcode":"94304"}{"name":"Brayden","age":30,"pcode":"94304"}{"name&…

什么是DataFrame 在Spark中,DataFrame是一种以RDD为基础的分布式数据集,类似于传统数据库中的二维表格. RDD和DataFrame的区别 DataFrame与RDD的主要区别在于,DataFrame带有schema元信息,即DataFrame所表示的二维表数据集的每一列都带有名称和类型.使得Spark SQL得以洞察更多的结构信息,从而对藏于DataFrame背后的数据源以及作用于DataFrame之上的变换进行了针对性的优化,最终达到大幅提升运行时效率的目标. RDD,…

[Spark][Python]DataFrame中取出有限个记录的例子: sqlContext = HiveContext(sc) peopleDF = sqlContext.read.json("people.json") peopleDF.limit(3).show() === [training@localhost ~]$ hdfs dfs -cat people.json{"name":"Alice","pcode":…

转载自:http://blog.csdn.net/wo334499/article/details/51689549 RDD 优点: 编译时类型安全 编译时就能检查出类型错误 面向对象的编程风格 直接通过类名点的方式来操作数据 缺点: 序列化和反序列化的性能开销 无论是集群间的通信, 还是IO操作都需要对对象的结构和数据进行序列化和反序列化. GC的性能开销 频繁的创建和销毁对象, 势必会增加GC   import org.apache.spark.sql.SQLContext import o…

What’s New, What’s Changed and How to get Started. Are you ready for Apache Spark 2.0? If you are just getting started with Apache Spark, the 2.0 release is the one to start with as the APIs have just gone through a major overhaul to improve ease-of-…