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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?
java - What are the differences between Dataframe, Dataset, and RDD in . . . The APIs RDD It's the first API provided by spark To put is simply it is a not-ordered sequence of scala java objects distributed over a cluster All operations executed on it are jvm methods (passed to map, flatmap, groupBy, ) that need to be serialized, send to all workers, and be applied to the jvm objects there
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
Spark: Best practice for retrieving big data from RDD to local machine Update: RDD toLocalIterator method that appeared after the original answer has been written is a more efficient way to do the job It uses runJob to evaluate only a single partition on each step TL;DR And the original answer might give a rough idea how it works: First of all, get the array of partition indexes: val parts = rdd partitions Then create smaller rdds filtering out everything but a
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
apache spark - What is differences between RDD and a traditional . . . RDD (Resilient Distributed Dataset) is a in memory data structure used by Spark It is immutable data structure Think of it as , spark has loaded data in memory in a specific structure and that structure is called RDD Once your spark job stops, there is no RDD existence
(Why) do we need to call cache or persist on a RDD When a resilient distributed dataset (RDD) is created from a text file or collection (or from another RDD), do we need to call "cache" or "persist" explicitly to store the RDD data into memory? Or is the RDD data stored in a distributed way in the memory by default?