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scala - What is RDD in spark - Stack Overflow An RDD is, essentially, the Spark representation of a set of data, spread across multiple machines, with APIs to let you act on it An RDD could come from any datasource, e g text files, a database via JDBC, etc The formal definition is: RDDs are fault-tolerant, parallel data structures that let users explicitly persist intermediate results in memory, control their partitioning to optimize
Difference between DataFrame, Dataset, and RDD in Spark I'm just wondering what is the difference between an RDD and DataFrame (Spark 2 0 0 DataFrame is a mere type alias for Dataset[Row]) in Apache Spark? Can you convert one to the other?
Whats the difference between RDD and Dataframe in Spark? RDD stands for Resilient Distributed Datasets It is Read-only partition collection of records RDD is the fundamental data structure of Spark It allows a programmer to perform in-memory computations In Dataframe, data organized into named columns For example a table in a relational database It is an immutable distributed collection of data
scala - How to print the contents of RDD? - Stack Overflow } Example usage: val rdd = sc parallelize(List(1,2,3,4)) map(_*2) p(rdd) 1 rdd print 2 Output: 2 6 4 8 Important This only makes sense if you are working in local mode and with a small amount of data set Otherwise, you either will not be able to see the results on the client or run out of memory because of the big dataset result
Difference and use-cases of RDD and Pair RDD - Stack Overflow I am new to spark and trying to understand the difference between normal RDD and a pair RDD What are the use-cases where a pair RDD is used as opposed to a normal RDD? If possible, I want to under
Spark: produce RDD[(X, X)] of all possible combinations from RDD[X] Cartesian product and combinations are two different things, the cartesian product will create an RDD of size rdd size() ^ 2 and combinations will create an RDD of size rdd size() choose 2 val rdd = sc parallelize(1 to 5) val combinations = rdd cartesian(rdd) filter{ case (a,b) => a < b }` combinations collect() Note this will only work if an ordering is defined on the elements of the list