GitHub user saurfang opened a pull request: https://github.com/apache/spark/pull/8726
[SPARK-10543] [CORE] Peak Execution Memory Quantile should be Per-task Basis Read `PEAK_EXECUTION_MEMORY` using `update` to get per task partial value instead of cumulative value. I tested with this workload: ```scala val size = 1000 val repetitions = 10 val data = sc.parallelize(1 to size, 5).map(x => (util.Random.nextInt(size / repetitions),util.Random.nextDouble)).toDF("key", "value") val res = data.toDF.groupBy("key").agg(sum("value")).count ``` Before: ![image](https://cloud.githubusercontent.com/assets/4317392/9828197/07dd6874-58b8-11e5-9bd9-6ba927c38b26.png) After: ![image](https://cloud.githubusercontent.com/assets/4317392/9828151/a5ddff30-58b7-11e5-8d31-eda5dc4eae79.png) Tasks view: ![image](https://cloud.githubusercontent.com/assets/4317392/9828199/17dc2b84-58b8-11e5-92a8-be89ce4d29d1.png) cc @andrewor14 I appreciate if you can give feedback on this since I think you introduced display of this metric. You can merge this pull request into a Git repository by running: $ git pull https://github.com/saurfang/spark stagepage Alternatively you can review and apply these changes as the patch at: https://github.com/apache/spark/pull/8726.patch To close this pull request, make a commit to your master/trunk branch with (at least) the following in the commit message: This closes #8726 ---- commit 3adbc33fce5752c0d80581fc5981490a141d222c Author: Forest Fang <forest.f...@outlook.com> Date: 2015-09-10T21:49:38Z use partial update value instead of cumulative value ---- --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org