I have a Spark 2.1 job where I maintain multiple Dataset objects/RDD's that represent different queries over our underlying Hive/HDFS datastore. I've noticed that if I simply iterate over the List of Datasets, they execute one at a time. Each individual query operates in parallel, but I feel that we are not maximizing our resources by not running the different datasets in parallel as well.
There doesn't seem to be a lot out there regarding doing this, as most questions appear to be around parallelizing a single RDD or Dataset, not parallelizing multiple within the same job.
Is this inadvisable for some reason? Can I just use a executor service, thread pool, or futures to do this?
Thanks!
- you can find multiple questions and answers in stackoverflow itself for examplestackoverflow.com/questions/31757737/… andstackoverflow.com/questions/30214474/… and there are a lot of materials explaining how to do them in the web as wellAnahcolus– Anahcolus2018-02-17 12:14:26 +00:00CommentedFeb 17, 2018 at 12:14
- yes you can do this, the easiest way is to use scala's parallel collectionRaphael Roth– Raphael Roth2018-02-17 20:39:11 +00:00CommentedFeb 17, 2018 at 20:39
- 1@RameshMaharjan Upon review - yes those questions are relevant, but without understanding that is the question I should be asking, it's hard to find those answers :).Brian– Brian2018-02-18 02:50:12 +00:00CommentedFeb 18, 2018 at 2:50
1 Answer1
Yes you can use multithreading in the driver code, but normally this does not increase performance, unless your queries operate on very skewed data and/or cannot be parallelized well enough to fully utilize the resources.
You can do something like that:
val datasets : Seq[Dataset[_]] = ???datasets .par // transform to parallel Seq .foreach(ds => ds.write.saveAsTable(...)1 Comment
Explore related questions
See similar questions with these tags.
