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.
In Pyspark, I can create a RDD from a list and decide how many partitions to have: sc = SparkContext() sc.…
performance apache-spark pyspark rddI'm just wondering what is the difference between an RDD and DataFrame (Spark 2.0.0 DataFrame is a mere type alias for …
apache-spark apache-spark-sql rdd apache-spark-datasetI want to share this particular Apache Spark with Python solution because documentation for it is quite poor. I wanted …
python apache-spark aggregate average rddI have a very big pyspark.sql.dataframe.DataFrame named df. I need some way of enumerating records- thus, being …
python apache-spark bigdata pyspark rddWhy does the rdd.sample() function on Spark RDD return a different number of elements even though the fraction parameter …
apache-spark sample rddI have the following spark job, trying to keep everything in memory: val myOutRDD = myInRDD.flatMap { fp => val tuple2…
apache-spark shuffle rdd persistI have an RDD called JavaPairRDD<String, List<String>> existingRDD; Now I need to initialize this …
java apache-spark rddI used to think that rdd.take(1) and rdd.first() are exactly the same. However I began to wonder if …
apache-spark pyspark rddI've been playing around with converting RDDs to DataFrames and back again. First, I had an RDD of type (Int, …
scala apache-spark dataframe rddLet us say I have the following two RDDs, with the following key-pair values. rdd1 = [ (key1, [value1, value2]), (key2, [value3, …
python scala apache-spark rdd