Github user squito commented on a diff in the pull request:

    https://github.com/apache/spark/pull/7770#discussion_r36087907
  
    --- Diff: core/src/main/scala/org/apache/spark/ui/ToolTips.scala ---
    @@ -62,6 +62,13 @@ private[spark] object ToolTips {
         """Time that the executor spent paused for Java garbage collection 
while the task was
            running."""
     
    +  val PEAK_EXECUTION_MEMORY =
    +    """Execution memory refers to the memory used by internal data 
structures created during
    +       shuffles, aggregations and joins when Tungsten is enabled. The 
value of this accumulator
    +       should be approximately the sum of the peak sizes across all such 
data structures created
    +       in this task. For SQL jobs, this only tracks all unsafe operators, 
broadcast joins, and
    +       external sort."""
    --- End diff --
    
    > I like the idea of something like spark.execution.memoryFraction to 
describe the fraction of memory that is reserved for internal use by Spark's 
own execution operators and not by caching or user-code.
    
    yeah I like that more too ... but especially as you put it that way, I 
wonder should it have "internal" in there somewhere?  I still worry its going 
to sound too much like its the total memory usage for the task (including user 
code), or even the total memory that is used by spark.  
"spark.shuffle.memoryFraction" has been around practically forever, so I doubt 
existing users will see the connection to this metric, but then again I suppose 
we can't be too hamstrung by legacy.
    
    I also wonder if it just be named something far more specific to SQL + 
Tungsten if that is the only case its accurately measured.


---
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

Reply via email to