I think it is an interesting requirement, but I am not familiar with Spark enough to say it can be done as latest spark version or not.
>From my understanding, you are looking for some API from the spark to read the >source directly into a ShuffledRDD, which indeed needs (K, V and a Partitioner >instance). I don't think Spark provides that directly as now, but in your case, it makes sense to create a JIRA for spark to support in the future. For right now, maybe there are ways to use Spark developerAPI to do what you need, and I will leave that to other Spark expert to confirm. Yong ________________________________ From: sourabh chaki <chaki.sour...@gmail.com> Sent: Friday, March 10, 2017 9:03 AM To: Imran Rashid Cc: Jonathan Coveney; user@spark.apache.org Subject: Re: can spark take advantage of ordered data? My use case is also quite similar. I have 2 feeds. One 3TB and another 100GB. Both the feeds are generated by hadoop reduce operation and partitioned by hadoop hashpartitioner. 3TB feed has 10K partitions whereas 100GB file has 200 partitions. Now when I do a join between these two feeds using spark, spark shuffles both the RDDS and it takes long time to complete. Can we do something so that spark can recognise the existing partitions of 3TB feed and shuffles only 200GB feed? It can be mapside scan for bigger RDD and shuffle read from smaller RDD? I have looked at spark-sorted project, but that project does not utilise the pre-existing partitions in the feed. Any pointer will be helpful. Thanks Sourabh On Thu, Mar 12, 2015 at 6:35 AM, Imran Rashid <iras...@cloudera.com<mailto:iras...@cloudera.com>> wrote: Hi Jonathan, you might be interested in https://issues.apache.org/jira/browse/SPARK-3655 (not yet available) and https://github.com/tresata/spark-sorted (not part of spark, but it is available right now). Hopefully thats what you are looking for. To the best of my knowledge that covers what is available now / what is being worked on. Imran On Wed, Mar 11, 2015 at 4:38 PM, Jonathan Coveney <jcove...@gmail.com<mailto:jcove...@gmail.com>> wrote: Hello all, I am wondering if spark already has support for optimizations on sorted data and/or if such support could be added (I am comfortable dropping to a lower level if necessary to implement this, but I'm not sure if it is possible at all). Context: we have a number of data sets which are essentially already sorted on a key. With our current systems, we can take advantage of this to do a lot of analysis in a very efficient fashion...merges and joins, for example, can be done very efficiently, as can folds on a secondary key and so on. I was wondering if spark would be a fit for implementing these sorts of optimizations? Obviously it is sort of a niche case, but would this be achievable? Any pointers on where I should look?
