rdd | 易学教程

Spark rdd write in global list

阅读更多关于 Spark rdd write in global list

问题 How to write in global list with rdd? Li = [] Fn(list): If list.value == 4: Li.append(1) rdd.mapValues(lambda x:fn(x)) When I try to print Li the result is: [] What I'm trying to do is to transform another global liste Li1 while transforming the rdd object. However, when I do this I have always an empty list in the end. Li1 is never transformed. 回答1: The reason why you get Li value set to [] after executing mapValue s - is because Spark serializes Fn function (and all global variables that it

Comparing two RDDs

阅读更多关于 Comparing two RDDs

问题 I have two RDD[Array[String]], let's call them rdd1 and rdd2. I would create a new RDD containing just the entries of rdd2 not in rdd1 (based on a key). I use Spark on Scala via Intellij. I grouped rdd1 and rdd2 by a key (I will compare just the keys of the two rdds): val rdd1Grouped = rdd1.groupBy(line => line(0)) val rdd2Grouped = rdd2.groupBy(line => line(0)) Then, I used a leftOuterJoin : val output = rdd1Grouped.leftOuterJoin(rdd2Grouped).collect { case (k, (v, None)) => (k, v) } but

How to parse a csv string into a Spark dataframe using scala?

阅读更多关于 How to parse a csv string into a Spark dataframe using scala?

问题 I would like to convert a RDD containing records of strings, like below, to a Spark dataframe. "Mike,2222-003330,NY,34" "Kate,3333-544444,LA,32" "Abby,4444-234324,MA,56" .... The schema line is not inside the same RDD , but in a another variable: val header = "name,account,state,age" So now my question is, how do I use the above two, to create a dataframe in Spark? I am using Spark version 2.2. I did search and saw a post: Can I read a CSV represented as a string into Apache Spark using spark

pyspark.sql DataFrame创建及常用操作

阅读更多关于 pyspark.sql DataFrame创建及常用操作

Spark SQL 简介及参考链接 Spark 是一个基于内存的用于处理大数据的集群计算框架。它提供了一套简单的编程接口，从而使得应用程序开发者方便使用集群节点的CPU，内存，存储资源来处理大数据。 Spark API提供了Scala, Java, Python和R的编程接口，可以使用这些语言来开发Spark应用。为了用Spark支持Python，Apache Spark社区发布了一个工具PySpark。使用PySpark，您也可以使用Python编程语言处理RDD。 Spark SQL将 SQL和HiveSQL的简单与强大融合到一起。 Spark SQL是一个运行在Spark上的Spark库。它提供了比Spark Core更为高层的用于处理结构化数据的抽象. Spark DataFrame 派生于RDD类，分布式但是提供了非常强大的数据操作功能。本文主要梳理Spark DataFrame的常用方法，之后写一下与DataFrame操作密切配合的Spark SQL内置函数和用户UDF (用户定义函数) 和 UDAF (用户定义聚合函数) pyspark.sql 核心类 pyspark.SparkContext: Spark 库的主要入口点，它表示与Spark集群的一个连接，其他重要的对象都要依赖它.SparkContext存在于Driver中，是Spark功能的主要入口

Reuse a cached Spark RDD

阅读更多关于 Reuse a cached Spark RDD

问题 Is there a possibility in Spark to re-use a cached RDD in another application (or in another run of the same application)? JavaRDD<ExampleClass> toCache = ... // transformations on the RDD toCache.cache(); // can this be reused somehow in another application or further runs? 回答1: No, Spark RDD cannot be used in other application or in another run. You can connect Spark with for example Hazelcast or Apache Ignite to save RDDs in memory. Other application will have possibility to read data

RDD DataSet DataFrame的定义和区别

阅读更多关于 RDD DataSet DataFrame的定义和区别

RDD、DataFrame和DataSet的定义在开始Spark RDD与DataFrame与Dataset之间的比较之前，先让我们看一下Spark中的RDD，DataFrame和Datasets的定义： Spark RDD RDD代表弹性分布式数据集。它是记录的只读分区集合。 RDD是Spark的基本数据结构。它允许程序员以容错方式在大型集群上执行内存计算。 Spark Dataframe 与RDD不同，数据组以列的形式组织起来，类似于关系数据库中的表。它是一个不可变的分布式数据集合。 Spark中的DataFrame允许开发人员将数据结构(类型)加到分布式数据集合上，从而实现更高级别的抽象。 Spark Dataset Apache Spark中的Dataset是DataFrame API的扩展，它提供了类型安全(type-safe)，面向对象(object-oriented)的编程接口。 Dataset利用Catalyst optimizer可以让用户通过类似于sql的表达式对数据进行查询。 RDD、DataFrame和DataSet的比较 Spark版本 RDD – 自Spark 1.0起 DataFrames – 自Spark 1.3起 DataSet – 自Spark 1.6起数据表示形式 RDD RDD是分布在集群中许多机器上的数据元素的分布式集合。

Scan a Hadoop Database table in Spark using indices from an RDD

阅读更多关于 Scan a Hadoop Database table in Spark using indices from an RDD

问题 So if there is a table in the database shown as below: Key2 DateTimeAge AAA1 XXX XXX XXX AAA2 XXX XXX XXX AAA3 XXX XXX XXX AAA4 XXX XXX XXX AAA5 XXX XXX XXX AAA6 XXX XXX XXX AAA7 XXX XXX XXX AAA8 XXX XXX XXX BBB1 XXX XXX XXX BBB2 XXX XXX XXX BBB3 XXX XXX XXX BBB4 XXX XXX XXX BBB5 XXX XXX XXX CCC1 XXX XXX XXX CCC2 XXX XXX XXX CCC3 XXX XXX XXX CCC4 XXX XXX XXX CCC5 XXX XXX XXX CCC6 XXX XXX XXX CCC7 XXX XXX XXX DDD1 XXX XXX XXX DDD2 XXX XXX XXX DDD3 XXX XXX XXX DDD4 XXX XXX XXX DDD5 XXX XXX XXX

How to classify images using Spark and Caffe

阅读更多关于 How to classify images using Spark and Caffe

问题 I am using Caffe to do image classification, can I am using MAC OS X, Pyhton. Right now I know how to classify a list of images using Caffe with Spark python, but if I want to make it faster, I want to use Spark. Therefore, I tried to apply the image classification on each element of an RDD, the RDD created from a list of image_path. However, Spark does not allow me to do so. Here is my code: This is the code for image classification: # display image name, class number, predicted label def

Converting RDD[org.apache.spark.sql.Row] to RDD[org.apache.spark.mllib.linalg.Vector]

阅读更多关于 Converting RDD[org.apache.spark.sql.Row] to RDD[org.apache.spark.mllib.linalg.Vector]

问题 I am relatively new to Spark and Scala. I am starting with the following dataframe (single column made out of a dense Vector of Doubles): scala> val scaledDataOnly_pruned = scaledDataOnly.select("features") scaledDataOnly_pruned: org.apache.spark.sql.DataFrame = [features: vector] scala> scaledDataOnly_pruned.show(5) +--------------------+ | features| +--------------------+ |[-0.0948337274182...| |[-0.0948337274182...| |[-0.0948337274182...| |[-0.0948337274182...| |[-0.0948337274182...| +----

Spark filtering with regex

阅读更多关于 Spark filtering with regex

问题 I am trying to filter file data into good and bad data per the date, hence will get 2 result files. From test file, first 4 lines need to go in good data and last 2 lines in bad data. I am having 2 issues I am not getting any good data, result file is empty and bad data result looks like following - picking up the name characters only (,C,h) (,J,u) (,T,h) (,J,o) (,N,e) (,B,i) Test file Christopher|Jan 11, 2017|5 Justin|11 Jan, 2017|5 Thomas|6/17/2017|5 John|11-08-2017|5 Neli|2016|5 Bilu||5