[Spark][Python]spark 从 avro 文件获取 Dataframe 的例子 从如下地址获取文件: https://github.com/databricks/spark-avro/raw/master/src/test/resources/episodes.avro 导入到 hdfs 系统: hdfs dfs -put episodes.avro 读入: mydata001=sqlContext.read.format("com.databricks.spark.avro&qu…

[Spark][Python]Spark 访问 mysql , 生成 dataframe 的例子: mydf001=sqlContext.read.format("jdbc").option("url","jdbc:mysql://localhost/loudacre")\ .option("dbtable","accounts").option("user","trainin…

Spark Python 索引页 为了查找方便,建立此页 === RDD 基本操作: [Spark][Python]groupByKey例子…

map 就是对一个RDD的各个元素都施加处理,得到一个新的RDD 的过程 [training@localhost ~]$ cat names.txtYear,First Name,County,Sex,Count2012,DOMINIC,CAYUGA,M,62012,ADDISON,ONONDAGA,F,142012,ADDISON,ONONDAGA,F,142012,JULIA,ONONDAGA,F,15[training@localhost ~]$ hdfs dfs -put names.t…

[training@localhost ~]$ hdfs dfs -cat people.json {"name":"Alice","pcode":"94304"}{"name":"Brayden","age":30,"pcode":"94304"}{"name":"Carla",&quo…

周末的任务是更新Learning Spark系列第三篇,以为自己写不完了,但为了改正拖延症,还是得完成给自己定的任务啊 = =.这三章主要讲Spark的运行过程(本地+集群),性能调优以及Spark SQL相关的知识,如果对Spark不熟的同学可以先看看之前总结的两篇文章: [原]Learning Spark (Python版) 学习笔记(一)----RDD 基本概念与命令 [原]Learning Spark (Python版) 学习笔记(二)----键值对.数据读取与保存.共享特性 #####…

[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]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":…

[Spark][python]以DataFrame方式打开Json文件的例子: [training@localhost ~]$ cat people.json{"name":"Alice","pcode":"94304"}{"name":"Brayden","age":30,"pcode":"94304"}{"name…

[Spark][Python]sortByKey 例子: [training@localhost ~]$ hdfs dfs -cat test02.txt00002 sku01000001 sku93300001 sku02200003 sku88800004 sku41100001 sku91200001 sku331[training@localhost ~]$ mydata001=sc.textFile("test02.txt")mydata002=mydata001.map(l…