rdd

How to give more column conditions when joining two dataframes. For example I want to run the following : val Lead_all = Leads.join(Utm_Master, Leaddetails.columns("LeadSource","Utm_Source","Utm_Medium","Utm_Campaign") == Utm_Master.columns("LeadSource","Utm_Source","Utm_Medium","Utm_Campaign"), "left") I want to join only when these columns match. But above syntax is not valid as cols only takes one string. So how do I get what I want. rchukh There is a Spark column/expression API join for such case: Leaddetails.join( Utm_Master, Leaddetails("LeadSource") <=> Utm_Master("LeadSource") &&

I am relatively new to Apache Spark and Python and was wondering if something like what I am going to describe was doable? I have a RDD of the form [m 1 , m 2 , m 3 , m 4 , m 5 , m 6 .......m n ] (you get this when you run rdd.collect()). I was wondering if it was possible to transform this RDD into another RDD of the form [(m 1 , m 2 , m 3 ), (m 4 , m 5 , m 6 ).....(m n-2 , m n-1 , m n )]. The inner tuples should be of size k. If n is not divisible by k, then one of the tuples should have less than k elements. I tried using the map function but was not able to get the desired output. It seems

I have the following Python test code (the arguments to ALS.train are defined elsewhere): r1 = (2, 1) r2 = (3, 1) test = sc.parallelize([r1, r2]) model = ALS.train(ratings, rank, numIter, lmbda) predictions = model.predictAll(test) print test.take(1) print predictions.count() print predictions Which works, because it has a count of 1 against the predictions variable and outputs: [(2, 1)] 1 ParallelCollectionRDD[2691] at parallelize at PythonRDD.scala:423 However, when I try and use an RDD I created myself using the following code, it doesn't appear to work anymore: model = ALS.train(ratings,

I'm still struggling to understand the full power of the recently introduced Spark Datasets. Are there best practices of when to use RDDs and when to use Datasets? In their announcement Databricks explains that by using Datasets staggering reductions in both runtime and memory can be achieved. Still it is claimed that Datasets are designed ''to work alongside the existing RDD API''. Is this just a reference to downward compatibility or are there scenarios where one would prefer to use RDDs over Datasets? zero323 At this moment (Spark 1.6.0) DataSet API is just a preview and only a small subset

RDD 介绍 RDD，全称Resilient Distributed Datasets（弹性分布式数据集），是Spark最为核心的概念，是Spark对数据的抽象。 RDD是分布式的元素集合，每个RDD只支持读操作，且每个RDD都被分为多个分区存储到集群的不同节点上。除此之外，RDD还允许用户显示的指定数据存储到内存和磁盘中，掌握了RDD编程是SPARK开发的第一步。 1:创建操作（creation operation）：RDD的创建由SparkContext来负责。 2:转换操作（transformation operation）：将一个RDD通过一定操作转换为另一个RDD。 3:行动操作（action operation）：Spark为惰性计算，对RDD的行动操作都会触发Spark作业的运行 4:控制操作（control operation）：对RDD进行持久化等。 DEMO代码地址： https://github.com/zhp8341/sparkdemo/blob/master/src/main/java/com/demo/spark/rdddemo/OneRDD.java 一：创建操作 创建RDD有两种方式： 1 读取一个数据集(SparkContext.textFile()) : JavaDStreamlines=jssc.textFileStream("/Users

大数据追求相关性，不追求因果性。 一、Spark目的 map reuce:先写入磁盘，再从网络读取磁盘数据，mapreduce只适合处理对速度不敏感的离线批量处理。 spark：在一个物理节点，用内存完成各种各样的计算。（有时也用磁盘） storm：流式，纯粹实时计算框架，吞吐量不高，每条数据过来直接处理这样每次传输校验通信。 sparkstreaming ：分布式，准实时框架，把例如秒里的数据都成绩，然后一次性作为batch计算，吞吐量远高于storm。 二、sprk基本工作原理 从hadoop里的hive等读取数据------分布式的放到多个节点上（内存上）----处理好的数据可能移动到其他节点的内存-----迭代式计算（计算操作多个节点并行计算操作）----放入hive，mysql。 PS：mapreduce于spark最大不同在于迭代式计算，map+Reduce就结束了，sprak可以分为n个阶段，因为他是迭代式的。 三、RDD：核心抽象 一个RDD在抽象上代表一个hdfs文件， 分布式数据集：元素集合包含数据，实际上是被分区的，分为多个分区散落在spark集群中的不同节点（一批节点上的一批数据就是RDD）。 最重要特性：提供了容错性，节点失败中自动恢复。默认放在内存，内存不够，被写入磁盘。 四、架构 Spark核心编程：1）定义初始RDD，从哪里读取数据2