For your first question, the partitioning strategy can be tuned by applying different partitioner. You can use existing ones such as HashPartitioner or write your own.See this link( http://ampcamp.berkeley.edu/wp-content/uploads/2012/06/matei-zaharia-amp-camp-2012-advanced-spark.pdf) for some instructions.
On Fri, Jul 11, 2014 at 1:35 PM, Gonzalo Zarza <[email protected]> wrote: > Hi all, > > We've been evaluating Spark for a long-term project. Although we've been > reading several topics in forum, any hints on the following topics we'll be > extremely welcomed: > > 1. Which are the data partition strategies available in Spark? How > configurable are these strategies? > > 2. How would be the best way to use Spark if queries can touch only 3-5 > entries/records? Which strategy is the best if they want to perform a full > scan of the entries? > > 3. Is Spark capable of interacting with RDBMS? > > Thanks a lot! > > Best regards, > > -- > *Gonzalo Zarza* | PhD in High-Performance Computing | Big-Data Specialist > | > *GLOBANT* | AR: +54 11 4109 1700 ext. 15494 | US: +1 877 215 5230 ext. > 15494 | [image: Facebook] <https://www.facebook.com/Globant> [image: > Twitter] <http://www.twitter.com/globant> [image: Youtube] > <http://www.youtube.com/Globant> [image: Linkedin] > <http://www.linkedin.com/company/globant> [image: Pinterest] > <http://pinterest.com/globant/> [image: Globant] <http://www.globant.com/> >
