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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 . . .
In Apache Spark, what are the differences between those API? Why and when should we choose one over the others?
- 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
- 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
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