Re: bitten by spark.yarn.executor.memoryOverhead
bq. that 0.1 is always enough? The answer is: it depends (on use cases). The value of 0.1 has been validated by several users. I think it is a reasonable default. Cheers On Mon, Mar 2, 2015 at 8:36 AM, Ryan Williams ryan.blake.willi...@gmail.com wrote: For reference, the initial version of #3525 https://github.com/apache/spark/pull/3525 (still open) made this fraction a configurable value, but consensus went against that being desirable so I removed it and marked SPARK-4665 https://issues.apache.org/jira/browse/SPARK-4665 as won't fix. My team wasted a lot of time on this failure mode as well and has settled in to passing --conf spark.yarn.executor.memoryOverhead=1024 to most jobs (that works out to 10-20% of --executor-memory, depending on the job). I agree that learning about this the hard way is a weak part of the Spark-on-YARN onboarding experience. The fact that our instinct here is to increase the 0.07 minimum instead of the alternate 384MB https://github.com/apache/spark/blob/3efd8bb6cf139ce094ff631c7a9c1eb93fdcd566/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala#L93 minimum seems like evidence that the fraction is the thing we should allow people to configure, instead of absolute amount that is currently configurable. Finally, do we feel confident that 0.1 is always enough? On Sat, Feb 28, 2015 at 4:51 PM Corey Nolet cjno...@gmail.com wrote: Thanks for taking this on Ted! On Sat, Feb 28, 2015 at 4:17 PM, Ted Yu yuzhih...@gmail.com wrote: I have created SPARK-6085 with pull request: https://github.com/apache/spark/pull/4836 Cheers On Sat, Feb 28, 2015 at 12:08 PM, Corey Nolet cjno...@gmail.com wrote: +1 to a better default as well. We were working find until we ran against a real dataset which was much larger than the test dataset we were using locally. It took me a couple days and digging through many logs to figure out this value was what was causing the problem. On Sat, Feb 28, 2015 at 11:38 AM, Ted Yu yuzhih...@gmail.com wrote: Having good out-of-box experience is desirable. +1 on increasing the default. On Sat, Feb 28, 2015 at 8:27 AM, Sean Owen so...@cloudera.com wrote: There was a recent discussion about whether to increase or indeed make configurable this kind of default fraction. I believe the suggestion there too was that 9-10% is a safer default. Advanced users can lower the resulting overhead value; it may still have to be increased in some cases, but a fatter default may make this kind of surprise less frequent. I'd support increasing the default; any other thoughts? On Sat, Feb 28, 2015 at 3:34 PM, Koert Kuipers ko...@tresata.com wrote: hey, running my first map-red like (meaning disk-to-disk, avoiding in memory RDDs) computation in spark on yarn i immediately got bitten by a too low spark.yarn.executor.memoryOverhead. however it took me about an hour to find out this was the cause. at first i observed failing shuffles leading to restarting of tasks, then i realized this was because executors could not be reached, then i noticed in containers got shut down and reallocated in resourcemanager logs (no mention of errors, it seemed the containers finished their business and shut down successfully), and finally i found the reason in nodemanager logs. i dont think this is a pleasent first experience. i realize spark.yarn.executor.memoryOverhead needs to be set differently from situation to situation. but shouldnt the default be a somewhat higher value so that these errors are unlikely, and then the experts that are willing to deal with these errors can tune it lower? so why not make the default 10% instead of 7%? that gives something that works in most situations out of the box (at the cost of being a little wasteful). it worked for me. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: bitten by spark.yarn.executor.memoryOverhead
The problem is, you're left with two competing options then. You can go through the process of deprecating the absolute one and removing it eventually. You take away ability to set this value directly though, meaning you'd have to set absolute values by depending on a % of what you set your app memory too. I think there's non-trivial downside that way too. No value can always be right, or else it wouldn't be configurable. I think of this one like any other param that's set in absolute terms, but with an attempt to be smart about the default. On Mon, Mar 2, 2015 at 4:36 PM, Ryan Williams ryan.blake.willi...@gmail.com wrote: For reference, the initial version of #3525 (still open) made this fraction a configurable value, but consensus went against that being desirable so I removed it and marked SPARK-4665 as won't fix. My team wasted a lot of time on this failure mode as well and has settled in to passing --conf spark.yarn.executor.memoryOverhead=1024 to most jobs (that works out to 10-20% of --executor-memory, depending on the job). I agree that learning about this the hard way is a weak part of the Spark-on-YARN onboarding experience. The fact that our instinct here is to increase the 0.07 minimum instead of the alternate 384MB minimum seems like evidence that the fraction is the thing we should allow people to configure, instead of absolute amount that is currently configurable. Finally, do we feel confident that 0.1 is always enough? On Sat, Feb 28, 2015 at 4:51 PM Corey Nolet cjno...@gmail.com wrote: Thanks for taking this on Ted! On Sat, Feb 28, 2015 at 4:17 PM, Ted Yu yuzhih...@gmail.com wrote: I have created SPARK-6085 with pull request: https://github.com/apache/spark/pull/4836 Cheers On Sat, Feb 28, 2015 at 12:08 PM, Corey Nolet cjno...@gmail.com wrote: +1 to a better default as well. We were working find until we ran against a real dataset which was much larger than the test dataset we were using locally. It took me a couple days and digging through many logs to figure out this value was what was causing the problem. On Sat, Feb 28, 2015 at 11:38 AM, Ted Yu yuzhih...@gmail.com wrote: Having good out-of-box experience is desirable. +1 on increasing the default. On Sat, Feb 28, 2015 at 8:27 AM, Sean Owen so...@cloudera.com wrote: There was a recent discussion about whether to increase or indeed make configurable this kind of default fraction. I believe the suggestion there too was that 9-10% is a safer default. Advanced users can lower the resulting overhead value; it may still have to be increased in some cases, but a fatter default may make this kind of surprise less frequent. I'd support increasing the default; any other thoughts? On Sat, Feb 28, 2015 at 3:34 PM, Koert Kuipers ko...@tresata.com wrote: hey, running my first map-red like (meaning disk-to-disk, avoiding in memory RDDs) computation in spark on yarn i immediately got bitten by a too low spark.yarn.executor.memoryOverhead. however it took me about an hour to find out this was the cause. at first i observed failing shuffles leading to restarting of tasks, then i realized this was because executors could not be reached, then i noticed in containers got shut down and reallocated in resourcemanager logs (no mention of errors, it seemed the containers finished their business and shut down successfully), and finally i found the reason in nodemanager logs. i dont think this is a pleasent first experience. i realize spark.yarn.executor.memoryOverhead needs to be set differently from situation to situation. but shouldnt the default be a somewhat higher value so that these errors are unlikely, and then the experts that are willing to deal with these errors can tune it lower? so why not make the default 10% instead of 7%? that gives something that works in most situations out of the box (at the cost of being a little wasteful). it worked for me. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
bitten by spark.yarn.executor.memoryOverhead
hey, running my first map-red like (meaning disk-to-disk, avoiding in memory RDDs) computation in spark on yarn i immediately got bitten by a too low spark.yarn.executor.memoryOverhead. however it took me about an hour to find out this was the cause. at first i observed failing shuffles leading to restarting of tasks, then i realized this was because executors could not be reached, then i noticed in containers got shut down and reallocated in resourcemanager logs (no mention of errors, it seemed the containers finished their business and shut down successfully), and finally i found the reason in nodemanager logs. i dont think this is a pleasent first experience. i realize spark.yarn.executor.memoryOverhead needs to be set differently from situation to situation. but shouldnt the default be a somewhat higher value so that these errors are unlikely, and then the experts that are willing to deal with these errors can tune it lower? so why not make the default 10% instead of 7%? that gives something that works in most situations out of the box (at the cost of being a little wasteful). it worked for me.
Re: bitten by spark.yarn.executor.memoryOverhead
I have created SPARK-6085 with pull request: https://github.com/apache/spark/pull/4836 Cheers On Sat, Feb 28, 2015 at 12:08 PM, Corey Nolet cjno...@gmail.com wrote: +1 to a better default as well. We were working find until we ran against a real dataset which was much larger than the test dataset we were using locally. It took me a couple days and digging through many logs to figure out this value was what was causing the problem. On Sat, Feb 28, 2015 at 11:38 AM, Ted Yu yuzhih...@gmail.com wrote: Having good out-of-box experience is desirable. +1 on increasing the default. On Sat, Feb 28, 2015 at 8:27 AM, Sean Owen so...@cloudera.com wrote: There was a recent discussion about whether to increase or indeed make configurable this kind of default fraction. I believe the suggestion there too was that 9-10% is a safer default. Advanced users can lower the resulting overhead value; it may still have to be increased in some cases, but a fatter default may make this kind of surprise less frequent. I'd support increasing the default; any other thoughts? On Sat, Feb 28, 2015 at 3:34 PM, Koert Kuipers ko...@tresata.com wrote: hey, running my first map-red like (meaning disk-to-disk, avoiding in memory RDDs) computation in spark on yarn i immediately got bitten by a too low spark.yarn.executor.memoryOverhead. however it took me about an hour to find out this was the cause. at first i observed failing shuffles leading to restarting of tasks, then i realized this was because executors could not be reached, then i noticed in containers got shut down and reallocated in resourcemanager logs (no mention of errors, it seemed the containers finished their business and shut down successfully), and finally i found the reason in nodemanager logs. i dont think this is a pleasent first experience. i realize spark.yarn.executor.memoryOverhead needs to be set differently from situation to situation. but shouldnt the default be a somewhat higher value so that these errors are unlikely, and then the experts that are willing to deal with these errors can tune it lower? so why not make the default 10% instead of 7%? that gives something that works in most situations out of the box (at the cost of being a little wasteful). it worked for me. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: bitten by spark.yarn.executor.memoryOverhead
Thanks for taking this on Ted! On Sat, Feb 28, 2015 at 4:17 PM, Ted Yu yuzhih...@gmail.com wrote: I have created SPARK-6085 with pull request: https://github.com/apache/spark/pull/4836 Cheers On Sat, Feb 28, 2015 at 12:08 PM, Corey Nolet cjno...@gmail.com wrote: +1 to a better default as well. We were working find until we ran against a real dataset which was much larger than the test dataset we were using locally. It took me a couple days and digging through many logs to figure out this value was what was causing the problem. On Sat, Feb 28, 2015 at 11:38 AM, Ted Yu yuzhih...@gmail.com wrote: Having good out-of-box experience is desirable. +1 on increasing the default. On Sat, Feb 28, 2015 at 8:27 AM, Sean Owen so...@cloudera.com wrote: There was a recent discussion about whether to increase or indeed make configurable this kind of default fraction. I believe the suggestion there too was that 9-10% is a safer default. Advanced users can lower the resulting overhead value; it may still have to be increased in some cases, but a fatter default may make this kind of surprise less frequent. I'd support increasing the default; any other thoughts? On Sat, Feb 28, 2015 at 3:34 PM, Koert Kuipers ko...@tresata.com wrote: hey, running my first map-red like (meaning disk-to-disk, avoiding in memory RDDs) computation in spark on yarn i immediately got bitten by a too low spark.yarn.executor.memoryOverhead. however it took me about an hour to find out this was the cause. at first i observed failing shuffles leading to restarting of tasks, then i realized this was because executors could not be reached, then i noticed in containers got shut down and reallocated in resourcemanager logs (no mention of errors, it seemed the containers finished their business and shut down successfully), and finally i found the reason in nodemanager logs. i dont think this is a pleasent first experience. i realize spark.yarn.executor.memoryOverhead needs to be set differently from situation to situation. but shouldnt the default be a somewhat higher value so that these errors are unlikely, and then the experts that are willing to deal with these errors can tune it lower? so why not make the default 10% instead of 7%? that gives something that works in most situations out of the box (at the cost of being a little wasteful). it worked for me. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: bitten by spark.yarn.executor.memoryOverhead
There was a recent discussion about whether to increase or indeed make configurable this kind of default fraction. I believe the suggestion there too was that 9-10% is a safer default. Advanced users can lower the resulting overhead value; it may still have to be increased in some cases, but a fatter default may make this kind of surprise less frequent. I'd support increasing the default; any other thoughts? On Sat, Feb 28, 2015 at 3:34 PM, Koert Kuipers ko...@tresata.com wrote: hey, running my first map-red like (meaning disk-to-disk, avoiding in memory RDDs) computation in spark on yarn i immediately got bitten by a too low spark.yarn.executor.memoryOverhead. however it took me about an hour to find out this was the cause. at first i observed failing shuffles leading to restarting of tasks, then i realized this was because executors could not be reached, then i noticed in containers got shut down and reallocated in resourcemanager logs (no mention of errors, it seemed the containers finished their business and shut down successfully), and finally i found the reason in nodemanager logs. i dont think this is a pleasent first experience. i realize spark.yarn.executor.memoryOverhead needs to be set differently from situation to situation. but shouldnt the default be a somewhat higher value so that these errors are unlikely, and then the experts that are willing to deal with these errors can tune it lower? so why not make the default 10% instead of 7%? that gives something that works in most situations out of the box (at the cost of being a little wasteful). it worked for me. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: bitten by spark.yarn.executor.memoryOverhead
Having good out-of-box experience is desirable. +1 on increasing the default. On Sat, Feb 28, 2015 at 8:27 AM, Sean Owen so...@cloudera.com wrote: There was a recent discussion about whether to increase or indeed make configurable this kind of default fraction. I believe the suggestion there too was that 9-10% is a safer default. Advanced users can lower the resulting overhead value; it may still have to be increased in some cases, but a fatter default may make this kind of surprise less frequent. I'd support increasing the default; any other thoughts? On Sat, Feb 28, 2015 at 3:34 PM, Koert Kuipers ko...@tresata.com wrote: hey, running my first map-red like (meaning disk-to-disk, avoiding in memory RDDs) computation in spark on yarn i immediately got bitten by a too low spark.yarn.executor.memoryOverhead. however it took me about an hour to find out this was the cause. at first i observed failing shuffles leading to restarting of tasks, then i realized this was because executors could not be reached, then i noticed in containers got shut down and reallocated in resourcemanager logs (no mention of errors, it seemed the containers finished their business and shut down successfully), and finally i found the reason in nodemanager logs. i dont think this is a pleasent first experience. i realize spark.yarn.executor.memoryOverhead needs to be set differently from situation to situation. but shouldnt the default be a somewhat higher value so that these errors are unlikely, and then the experts that are willing to deal with these errors can tune it lower? so why not make the default 10% instead of 7%? that gives something that works in most situations out of the box (at the cost of being a little wasteful). it worked for me. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: bitten by spark.yarn.executor.memoryOverhead
+1 to a better default as well. We were working find until we ran against a real dataset which was much larger than the test dataset we were using locally. It took me a couple days and digging through many logs to figure out this value was what was causing the problem. On Sat, Feb 28, 2015 at 11:38 AM, Ted Yu yuzhih...@gmail.com wrote: Having good out-of-box experience is desirable. +1 on increasing the default. On Sat, Feb 28, 2015 at 8:27 AM, Sean Owen so...@cloudera.com wrote: There was a recent discussion about whether to increase or indeed make configurable this kind of default fraction. I believe the suggestion there too was that 9-10% is a safer default. Advanced users can lower the resulting overhead value; it may still have to be increased in some cases, but a fatter default may make this kind of surprise less frequent. I'd support increasing the default; any other thoughts? On Sat, Feb 28, 2015 at 3:34 PM, Koert Kuipers ko...@tresata.com wrote: hey, running my first map-red like (meaning disk-to-disk, avoiding in memory RDDs) computation in spark on yarn i immediately got bitten by a too low spark.yarn.executor.memoryOverhead. however it took me about an hour to find out this was the cause. at first i observed failing shuffles leading to restarting of tasks, then i realized this was because executors could not be reached, then i noticed in containers got shut down and reallocated in resourcemanager logs (no mention of errors, it seemed the containers finished their business and shut down successfully), and finally i found the reason in nodemanager logs. i dont think this is a pleasent first experience. i realize spark.yarn.executor.memoryOverhead needs to be set differently from situation to situation. but shouldnt the default be a somewhat higher value so that these errors are unlikely, and then the experts that are willing to deal with these errors can tune it lower? so why not make the default 10% instead of 7%? that gives something that works in most situations out of the box (at the cost of being a little wasteful). it worked for me. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
