That was a) fuzzy b) insufficient – one can certainly use forach (only) on DStream RDDs – it works as empirical observation
As another empirical observation: For each partition results in having one instance of the lambda/closure per partition when e.g. publishing to output systems like message brokers, databases and file systems - that increases the level of parallelism of your output processing As an architect I deal with gazillions of products and don’t have time to read the source code of all of them to make up for documentation deficiencies. On the other hand I believe you have been involved in writing some of the code so be a good boy and either answer this question properly or enhance the product documentation of that area of the system From: Sean Owen [mailto:so...@cloudera.com] Sent: Wednesday, July 8, 2015 2:52 PM To: dgoldenberg; user@spark.apache.org Subject: Re: foreachRDD vs. forearchPartition ? These are quite different operations. One operates on RDDs in DStream and one operates on partitions of an RDD. They are not alternatives. On Wed, Jul 8, 2015, 2:43 PM dgoldenberg <dgoldenberg...@gmail.com> wrote: Is there a set of best practices for when to use foreachPartition vs. foreachRDD? Is it generally true that using foreachPartition avoids some of the over-network data shuffling overhead? When would I definitely want to use one method vs. the other? Thanks. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/foreachRDD-vs-forearchPartition-tp23714.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
