rdd

I am exploring the spark 2.0 java api and have a doubt regarding collect() & collectAsync() available for javardd. Collect action is basically used to view the content of RDD, basically it is synchronous while collectAsync() is asynchronous meaning it Returns a future for retrieving all elements of this RDD. it allows to run other RDD to run in parallel. for better optimization you can utilize fair scheduler for job scheduling. collect(): It returns an array that contains all of the elements in this RDD. List<Integer> data = Arrays.asList(1, 2, 3, 4, 5); JavaRDD<Integer> rdd = sc.parallelize

How to overwrite RDD output objects any existing path when we are saving time. test1: 975078|56691|2.000|20171001_926_570_1322 975078|42993|1.690|20171001_926_570_1322 975078|46462|2.000|20171001_926_570_1322 975078|87815|1.000|20171001_926_570_1322 rdd=sc.textFile('/home/administrator/work/test1').map( lambda x: x.split("|")[:4]).map( lambda r: Row( user_code = r[0],item_code = r[1],qty = float(r[2]))) rdd.coalesce(1).saveAsPickleFile("/home/administrator/work/foobar_seq1") The first time it is saving properly. now again I removed one line from the input file and saving RDD same location, it

These three Apache Spark Transformations are little confusing. Is there any way I can determine when to use which one and when to avoid one? I think official guide explains it well enough. I will highlight differences (you have RDD of type (K, V) ): if you need to keep the values, then use groupByKey if you no need to keep the values, but you need to get some aggregated info about each group (items of the original RDD, which have the same K ), you have two choices: reduceByKey or aggregateByKey ( reduceByKey is kind of particular aggregateByKey ) 2.1 if you can provide an operation which take

I am trying to write a SparkRDD to HBase table using scala(haven't used before). The entire code is this : import org.apache.hadoop.hbase.client.{HBaseAdmin, Result} import org.apache.hadoop.hbase.{HBaseConfiguration, HTableDescriptor} import org.apache.hadoop.hbase.mapreduce.TableInputFormat import org.apache.hadoop.hbase.io.ImmutableBytesWritable import scala.collection.JavaConverters._ import org.apache.hadoop.hbase.util.Bytes import org.apache.spark._ import org.apache.hadoop.mapred.JobConf import org.apache.spark.rdd.PairRDDFunctions import org.apache.spark.SparkContext._ import org

Spark设计理念与基本架构 https://www.cnblogs.com/swordfall/p/9280006.html 1.基本概念 Spark中的一些概念： RDD(resillient distributed dataset)：弹性分布式数据集。 Partition：数据分区。即一个RDD的数据可以划分为多少个分区。 NarrowDependency：窄依赖，即子RDD依赖于父RDD中固定的Partition。Narrow-Dependency分为OneToOneDependency和RangeDependency两种。 ShuffleDependency：shuffle依赖，也称为宽依赖，即子RDD对父RDD中的所有Partition都有依赖。 Task：是送到某个Executor上的工作单元，即具体执行任务。Task分为ShuffleMapTask和ResultTask两种。ShuffleMapTask和ResultTask分别类似于Hadoop中的Map和Reduce。Task是运行Application的基本单位，多个Task组成一个Stage，而Task的调度和管理等是由TaskScheduler负责的。 Job：用户提交的作业。一个Job包含多个Task组成的并行计算，往往由Spark Action触发。 Stage：每个Job会被拆分成多组Task

1. RDD的使用 1.1 什么是RDD 　　RDD（Resilient Distributed Dataset）是一个抽象数据集，RDD中不保存要计算的数据集，保存的是元数据，即数据的描述信息和运算逻辑，比如数据要从哪里去读取，怎么运算等。RDD可以理解为一个代理，你对RDD进行操作，相当于在Driver端先是记录下计算的描述信息，然后生成Task，将Task调度到Executor端才执行真正的计算逻辑 1.2 RDD的特点 来源： https://www.cnblogs.com/jj1106/p/11965439.html

I have a text file with data that look like this: Type1 1 3 5 9 Type2 4 6 7 8 Type3 3 6 9 10 11 25 I'd like to transform it into an RDD with rows like this: 1 Type1 3 Type1 3 Type3 ...... I started with a case class: MyData[uid : Int, gid : String] New to spark and scala, and I can't seem to find an example that does this. It seems you want something like this? rdd.flatMap(line=>{ val splitLine = line.split(' ').toList splitLine match{ case (gid:String) :: rest => rest.map(x:String =>MyData(x.toInt, gid)) } } 来源： https://stackoverflow.com/questions/31008169/spark-rdd-mapping-one-row-of-data

RDD有两种类型的操作 ，分别是Transformation（返回一个新的RDD）和Action（返回values）。 1.Transformation：根据已有RDD创建新的RDD数据集build （1）map(func)：对调用map的RDD数据集中的每个element都使用func，然后返回一个新的RDD，这个返回的数据集是分布式的数据集。 （2）filter(func) ：对调用filter的RDD数据集中的每个元素都使用func，然后返回一个包含使func为true的元素构成的RDD。 （3）flatMap(func)：和map很像，但是flatMap生成的是多个结果。 （4）mapPartitions(func)：和map很像，但是map是每个element，而mapPartitions是每个partition。 （5）mapPartitionsWithSplit(func)：和mapPartitions很像，但是func作用的是其中一个split上，所以func中应该有index。 （6）sample(withReplacement,faction,seed)：抽样。 （7）union(otherDataset)：返回一个新的dataset，包含源dataset和给定dataset的元素的集合。 （8）distinct([numTasks])