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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?
- 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:
- scala - How to print the contents of RDD? - Stack Overflow
But I think I know where this confusion comes from: the original question asked how to print an RDD to the Spark console (= shell) so I assumed he would run a local job, in which case foreach works fine
- (Why) do we need to call cache or persist on a RDD
193 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?
- Removing duplicates from rows based on specific columns in an RDD Spark . . .
Removing duplicates from rows based on specific columns in an RDD Spark DataFrame Asked 10 years, 7 months ago Modified 2 years, 1 month ago Viewed 252k times
- Difference between Spark RDDs and HDFS data blocks
Is there any relation to HDFS' data blocks? In general not They address different issues RDDs are about distributing computation and handling computation failures HDFS is about distributing storage and handling storage failures Distribution is common denominator, but that is it, and failure handling strategy are obviously different (DAG re-computation and replication respectively) Spark
- 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
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