[ 
https://issues.apache.org/jira/browse/SPARK-31437?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17084552#comment-17084552
 ] 

Hongze Zhang edited comment on SPARK-31437 at 5/20/20, 8:43 AM:
----------------------------------------------------------------

Thanks [~tgraves]. I got your point of making them tied. 

Actually I was thinking of something like this:

1. to break ResourceProfile up to ExecutorResourceProfile and ResourceProfile;
2. ResourceProfile still contains both resource requirement of executor and 
task;
3. ExecutorResourceProfile only includes executor's resource req;
4. ExecutorResourceProfile is required to allocate new executor instances from 
scheduler backend; 
5. Similar to current solution, user specifies ResourceProfile for RDD, then 
tasks are scheduled onto executors that are allocated using 
ExecutorResourceProfile;
6. Each time ResourceProfile comes, ExecutorResourceProfile is created/selected 
within one of several strategies;

Strategies types:

s1. Always creates new ExecutorSpec;
s2. If executor resource requirement in ResourceProfile meets existing 
ExecutorSpec, use the existing one;
s3. ...

bq. My etl tasks uses 8 cores, my ml tasks use 8 cores and 4 cpus.  How do I 
keep my etl tasks from running on the ML executors without wasting resources?

By just using strategy s1, everything should work as current implementation.




was (Author: zhztheplayer):
Thanks [~tgraves]. I got your point of making them tied. 

Actually I was thinking of something like this:

1. to break ResourceProfile up to ExecutorSpec and ResourceProfile;
2. ResourceProfile's structure remains as is;
3. ExecutorSpec only includes resource requirements for executor, task resource 
requirement is removed;
4. ExecutorSpec is required to allocate new executor instances from scheduler 
backend; 
5. Similar to current solution, user specifies ResourceProfile for RDD, then 
tasks are scheduled onto executors that are allocated using ExecutorResource;
6. Each time ResourceProfile comes, ExecutorSpec is created/selected within one 
of several strategies;

Strategies types:

s1. Always creates new ExecutorSpec;
s2. If executor resource requirement in ResourceProfile meets existing 
ExecutorSpec, use the existing one;
s3. ...

bq. My etl tasks uses 8 cores, my ml tasks use 8 cores and 4 cpus.  How do I 
keep my etl tasks from running on the ML executors without wasting resources?

By just using strategy s1, everything should work as current implementation.



> Try assigning tasks to existing executors by which required resources in 
> ResourceProfile are satisfied
> ------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-31437
>                 URL: https://issues.apache.org/jira/browse/SPARK-31437
>             Project: Spark
>          Issue Type: Improvement
>          Components: Scheduler, Spark Core
>    Affects Versions: 3.1.0
>            Reporter: Hongze Zhang
>            Priority: Major
>
> By the change in [PR|https://github.com/apache/spark/pull/27773] of 
> SPARK-29154, submitted tasks are scheduled onto executors only if resource 
> profile IDs strictly match. As a result Spark always starts new executors for 
> customized ResourceProfiles.
> This limitation makes working with process-local jobs unfriendly. E.g. Task 
> cores has been increased from 1 to 4 in a new stage, and executor has 8 
> slots, it is expected that 2 new tasks can be run on the existing executor 
> but Spark starts new executors for new ResourceProfile. The behavior is 
> unnecessary.



--
This message was sent by Atlassian Jira
(v8.3.4#803005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org

Reply via email to