Dec 23, 2015 · 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 …

Feb 18, 2020 · 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?

Jan 31, 2018 · 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 …

Mar 11, 2015 · 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 …

Please note that when you run collect (), the RDD - which is a distributed data set is aggregated at the driver node and is essentially converted to a list. So obviously, it won't be a good idea to collect () a …

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.

Aug 18, 2017 · In Spark, Lineage Graph is a dependencies graph in between existing RDD and new RDD. It means that all the dependencies between the RDD will be recorded in a graph, rather than …

Feb 11, 2014 · 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 …

Mar 12, 2014 · Can someone explain to me the difference between map and flatMap and what is a good use case for each? What does "flatten the results" mean? What is it good for?

Feb 1, 2016 · RDD checkpointing is a different concept than a chekpointing in Spark Streaming. The former one is designed to address lineage issue, the latter one is all about streaming reliability and …