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.
How can I convert an RDD (org.apache.spark.rdd.RDD[org.apache.spark.sql.Row]) to a Dataframe org.…
scala apache-spark apache-spark-sql rddAccording to Learning Spark Keep in mind that repartitioning your data is a fairly expensive operation. Spark also has an …
apache-spark distributed-computing rddI have a text file on HDFS and I want to convert it to a Data Frame in Spark. I …
scala apache-spark dataframe apache-spark-sql rddI'm just wondering what is the difference between an RDD and DataFrame (Spark 2.0.0 DataFrame is a mere type alias for …
dataframe apache-spark apache-spark-sql rdd apache-spark-datasetWhat's the difference between an RDD's map and mapPartitions method? And does flatMap behave like map or like mapPartitions? Thanks. (…
performance scala apache-spark rddIn Spark version 1.2.0 one could use subtract with 2 SchemRDDs to end up with only the different content from the first …
apache-spark dataframe rddHow to give more column conditions when joining two dataframes. For example I want to run the following : val Lead_…
apache-spark apache-spark-sql rddIn terms of RDD persistence, what are the differences between cache() and persist() in spark ?
apache-spark distributed-computing rddTrying to read a file located in S3 using spark-shell: scala> val myRdd = sc.textFile("s3n://myBucket/myFile1.…
java scala apache-spark rdd hortonworks-data-platformI am writing a Spark application and want to combine a set of Key-Value pairs (K, V1), (K, V2), ..., (K, …
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