Hi, Somewhat similar to Daniel Mescheder's mail yesterday on SparkSql, I have a data point regarding the performance of Group By, indicating there's excessive GC and it's impacting the throughput. I want to know if the new memory manager for aggregations (https://github.com/apache/spark/pull/5725/) is going to address this kind of issue.
I only have a small amount of data on each node (~360MB) with a large heap size (18 Gig). I still see 2-3 minor collections happening whenever I do a Select Sum() with a group by(). I have tried with different sizes for Young Generation without much effect, though not with different GC algorithms (Hm..I ought to try reducing the rdd storage fraction perhaps). I have made a chart of my results [1] by adding timing code to Aggregates.scala. The query is actually Query 2 from Berkeley's AmpLab benchmark, running over 10 million records. The chart is from one of the 4 worker nodes in the cluster. I am trying to square this with a claim on the Project Tungsten blog post [2]: "When profiling Spark user applications, we’ve found that a large fraction of the CPU time is spent waiting for data to be fetched from main memory. " Am I correct in assuming that SparkSql is yet to reach that level of efficiency, at least in aggregation operations? Thanks. [1] - https://docs.google.com/spreadsheets/d/1HSqYfic3n5s9i4Wsi1Qg0FKN_AWz2vV7_6RRMrtzplQ/edit#gid=481134174 [2] https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html Pramod
