rdd

来源： https://stackoverflow.com/questions/40255149/rdd-to-dataframe-in-pyspark-columns-from-rdds-first-element

来源： https://stackoverflow.com/questions/40255149/rdd-to-dataframe-in-pyspark-columns-from-rdds-first-element

来源： https://stackoverflow.com/questions/40255149/rdd-to-dataframe-in-pyspark-columns-from-rdds-first-element

问题 I want to know how to use ssd for SPARK RDD. Originally, SPARK RDD is using in Memory. But I want to use ssd for RDD. 回答1: Check this link Check for RDD Persistence and select storage level as DISK_ONLY Also recommended to check this 来源： https://stackoverflow.com/questions/29762946/use-ssd-for-spark-rdd

问题 public static void main(String[] args) { SparkSession sessn = SparkSession.builder().appName("RDD2DF").master("local").getOrCreate(); List<Integer> lst = Arrays.asList(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20); Dataset<Integer> DF = sessn.createDataset(lst, Encoders.INT()); System.out.println(DF.javaRDD().getNumPartitions()); JavaRDD<Integer> mappartRdd = DF.repartition(3).javaRDD().mapPartitions(it-> Arrays.asList(JavaConversions.asScalaIterator(it).length()).iterator()); } From

问题 Hardest thing in Spark is Serialization imho. This https://medium.com/onzo-tech/serialization-challenges-with-spark-and-scala-a2287cd51c54 I looked at some time ago and I think I am pretty sure I get it, the Object aspects. I run the code and it is as per the examples. However, I am curious on a few other aspects when testing in a Notebook on a Databricks Community Edition account - not a real cluster BTW. I did check, confirm also on a Spark Standalone cluster via the spark-shell. This does

问题 To reduce shuffling during the joining of two RDDs, I decided to partition them using HashPartitioner first. Here is how I do it. Am I doing it correctly, or is there a better way to do this? val rddA = ... val rddB = ... val numOfPartitions = rddA.getNumPartitions val rddApartitioned = rddA.partitionBy(new HashPartitioner(numOfPartitions)) val rddBpartitioned = rddB.partitionBy(new HashPartitioner(numOfPartitions)) val rddAB = rddApartitioned.join(rddBpartitioned) 回答1: To reduce shuffling

问题 To reduce shuffling during the joining of two RDDs, I decided to partition them using HashPartitioner first. Here is how I do it. Am I doing it correctly, or is there a better way to do this? val rddA = ... val rddB = ... val numOfPartitions = rddA.getNumPartitions val rddApartitioned = rddA.partitionBy(new HashPartitioner(numOfPartitions)) val rddBpartitioned = rddB.partitionBy(new HashPartitioner(numOfPartitions)) val rddAB = rddApartitioned.join(rddBpartitioned) 回答1: To reduce shuffling

问题 I am new for Apache Spark and I have couple of basic questions in spark which I could not understand while reading the spark material. Every materials have their own style of explanation. I am using PySpark Jupyter notebook on Ubuntu to practice. As per my understanding, When I run the below command, the data in the testfile.csv is partitioned and stored in memory of the respective nodes.( actually I know its a lazy evaluation and it will not process until it sees action command ), but still

问题 I have a dataframe with text and category. I want to count the words which are common in these categories. I am using nltk to remove the stop words and tokenize however not able to include the category in the process. Below is my sample code of the problem. from pyspark import SparkConf, SparkContext from pyspark.sql import SparkSession,Row import nltk spark_conf = SparkConf()\ .setAppName("test") sc=SparkContext.getOrCreate(spark_conf) def wordTokenize(x): words = [word for line in x for