rdd

如下 package com.my.scala import org.apache.spark.SparkConf import org.apache.spark.streaming.{Durations, StreamingContext} /** * * 使用开窗函数实现spark streaming，版本统计一段时间内前三的热搜词汇 * * 测试结果：测试成功 * 步骤： 先开启hadoop集群，start-all.sh * 再在h15上启动端口：nc -lk 8888 * 再输入数据：如---》"ds sdf sdfa wfasd sdf",一定要以空格分开 * 启动本程序 * 查看控制台是否正常 * */ object WindowBasedTopWord { def main(args: Array[String]) { val conf = new SparkConf().setAppName("WindowBasedTopWord").setMaster("local[2]") val ssc = new StreamingContext(conf,Durations.seconds(5)) //这里的5秒是指切分RDD的间隔 ssc.checkpoint("hdfs://h15:8020/wordcount_checkpoint") /

Question How to load a file from the local file system to Spark using sc.textFile? Do I need to change any -env variables? Also when I tried the same on my windows where Hadoop is not installed I got the same error. Code > val inputFile = sc.textFile("file///C:/Users/swaapnika/Desktop/to do list") /17 22:28:18 INFO MemoryStore: ensureFreeSpace(63280) called with curMem=0, maxMem=278019440 /17 22:28:18 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 61.8 KB, free 265.1 MB) /17 22:28:18 INFO MemoryStore: ensureFreeSpace(19750) called with curMem=63280, maxMem

TL;DR - I have what looks like a DStream of Strings in a PySpark application. I want to send it as a DStream[String] to a Scala library. Strings are not converted by Py4j, though. I'm working on a PySpark application that pulls data from Kafka using Spark Streaming. My messages are strings and I would like to call a method in Scala code, passing it a DStream[String] instance. However, I'm unable to receive proper JVM strings in the Scala code. It looks to me like the Python strings are not converted into Java strings but, instead, are serialized. My question would be: how to get Java strings

Now I am learning how to use spark.I have a piece of code which can invert a matrix and it works when the order of the matrix is small like 100.But when the order of the matrix is big like 2000 I have an exception like this: 15/05/10 20:31:00 ERROR DiskBlockObjectWriter: Uncaught exception while reverting partial writes to file /tmp/spark-local-20150510200122-effa/28/temp_shuffle_6ba230c3-afed-489b-87aa-91c046cadb22 java.io.IOException: No space left on device In my program I have lots of lines like this: val result1=matrix.map(...).reduce(...) val result2=result1.map(...).reduce(...) val

I have a small Scala program that runs fine on a single-node. However, I am scaling it out so it runs on multiple nodes. This is my first such attempt. I am just trying to understand how the RDDs work in Spark so this question is based around theory and may not be 100% correct. Let's say I create an RDD: val rdd = sc.textFile(file) Now once I've done that, does that mean that the file at file is now partitioned across the nodes (assuming all nodes have access to the file path)? Secondly, I want to count the number of objects in the RDD (simple enough), however, I need to use that number in a

Apache Spark: comparison of map vs flatMap vs mapPartitions vs mapPartitionsWithIndex Suggestions are welcome to improve our knowledge. map(func) What does it do? Pass each element of the RDD through the supplied function; i.e. func flatMap(func) “Similar to map, but each input item can be mapped to 0 or more output items (so func should return a Seq rather than a single item).” Compare flatMap to map in the following mapPartitions(func) Consider mapPartitions a tool for performance optimization. It won’t do much for you when running examples on your local machine compared to running across a