Hi Daniel, I am glad you already ran the numbers on this change ☺ (for anyone reading, they can be found on slide 19 in http://www.slideshare.net/SparkSummit/interactive-graph-analytics-daniel-darabos ). I haven’t done any formal benchmarking, but the speedup in our jobs is highly noticeable.
I agree it can be done without modifying Spark (we also have our own implementation in my codebase), but it seems a pity that anyone using the RDD API won’t get the benefits having a sorted RDD (which happens quite often since the shuffle phase can sort!). Ximo. De: Daniel Darabos [mailto:daniel.dara...@lynxanalytics.com] Enviado el: lunes, 21 de marzo de 2016 16:20 Para: Ted Yu <yuzhih...@gmail.com> CC: JOAQUIN GUANTER GONZALBEZ <joaquin.guantergonzal...@telefonica.com>; dev@spark.apache.org Asunto: Re: Performance improvements for sorted RDDs There is related discussion in https://issues.apache.org/jira/browse/SPARK-8836. It's not too hard to implement this without modifying Spark and we measured ~10x improvement over plain RDD joins. I haven't benchmarked against DataFrames -- maybe they also realize this performance advantage. On Mon, Mar 21, 2016 at 11:41 AM, Ted Yu <yuzhih...@gmail.com<mailto:yuzhih...@gmail.com>> wrote: Do you have performance numbers to backup this proposal for cogroup operation ? Thanks On Mon, Mar 21, 2016 at 1:06 AM, JOAQUIN GUANTER GONZALBEZ <joaquin.guantergonzal...@telefonica.com<mailto:joaquin.guantergonzal...@telefonica.com>> wrote: Hello devs, I have found myself in a situation where Spark is doing sub-optimal computations for my RDDs, and I was wondering whether a patch to enable improved performance for this scenario would be a welcome addition to Spark or not. The scenario happens when trying to cogroup two RDDs that are sorted by key and share the same partitioner. CoGroupedRDD will correctly detect that the RDDs have the same partitioner and will therefore create narrow cogroup split dependencies, as opposed to shuffle dependencies. This is great because it prevents any shuffling from happening. However, the cogroup is unable to detect that the RDDs are sorted in the same way, and will still insert all elements of the RDD in a map in order to join the elements with the same key. When both RDDs are sorted using the same order, the cogroup can just join by doing a single pass over the data (since the data is ordered by key, you can just keep iterating until you find a different key). This would greatly reduce the memory requirements for these kind of operations. Adding this to spark would require adding an “ordering” member to RDD of type Option[Ordering], similarly to how the “partitioner” field works. That way, the sorting operations could populate this field and the operations that could benefit from this knowledge (cogroup, join, groupbykey, etc.) could read it to change their behavior accordingly. Do you think this would be a good addition to Spark? 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