Resilient Distributed Datasets (RDDs) are a distributed memory abstraction that allows programmers to perform in-memory computations on large clusters while retaining the fault tolerance of data flow models like MapReduce.
I've come across the glom() method on RDD. As per the documentation Return an RDD created by coalescing all elements …
apache-spark rddSo assume ive got an rdd with 3000 rows. The 2000 first rows are of class 1 and the 1000 last rows are of …
apache-spark rddCan any one please correct my understanding on persisting by Spark. If we have performed a cache() on an RDD …
apache-spark apache-spark-sql rddI have get an error when using mllib RandomForest to train data. As my dataset is huge and the default …
scala apache-spark rddLets start with a simple function which always returns a random integer: import numpy as np def f(x): return …
python random apache-spark pyspark rddI have have the following parser to parse arithmetic expressions containing Float and RDD : import scalaz._ import Scalaz._ def term2: …
scala parsing rdd type-mismatch scalaz7The Apache Spark pyspark.RDD API docs mention that groupByKey() is inefficient. Instead, it is recommend to use reduceByKey(), aggregateByKey(), …
apache-spark rdd pysparkI would like to dynamically generate a dataframe containing a header record for a report, so creating a dataframe from …
apache-spark dataframe spark-dataframe rdd spark-csvI need to split an RDD into 2 parts: 1 part which satisfies a condition; another part which does not. I can …
apache-spark rddWe all know Spark does the computation in memory. I am just curious on followings. If I create 10 RDD in …
hadoop apache-spark pyspark hdfs rdd