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Sandy Ryza commented on SPARK-3174: ----------------------------------- bq. for instance, lets say I do some ETL stuff where I want it to do dynamic, but then I need to run an ML algorithm or do some heavy caching where I want to shut it off. IIUC, the proposal only gets rid of executors once they are idle / empty of cached data. If that's the case, do you still see issues with leaving dynamic allocation on during the "ML algorithm phase"? > Provide elastic scaling within a Spark application > -------------------------------------------------- > > Key: SPARK-3174 > URL: https://issues.apache.org/jira/browse/SPARK-3174 > Project: Spark > Issue Type: Improvement > Components: Spark Core, YARN > Affects Versions: 1.0.2 > Reporter: Sandy Ryza > Assignee: Andrew Or > Attachments: SPARK-3174design.pdf, > dynamic-scaling-executors-10-6-14.pdf > > > A common complaint with Spark in a multi-tenant environment is that > applications have a fixed allocation that doesn't grow and shrink with their > resource needs. We're blocked on YARN-1197 for dynamically changing the > resources within executors, but we can still allocate and discard whole > executors. > It would be useful to have some heuristics that > * Request more executors when many pending tasks are building up > * Discard executors when they are idle > See the latest design doc for more information. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org