Probably we should open a ticket for this.There's definitely a deadlock
situation occurring in spark under certain conditions.
The only clue I have is it always happens on the last stage. And it does seem
sensitive to scale. If my job has 300mb of data I'll see the deadlock. But if I
only run 10mb of it it will succeed. This suggest a serious fundamental scaling
problem.
Workers have plenty of resources.
Sent from my Verizon Wireless 4G LTE smartphone
-------- Original message --------
From: "Sanders, Isaac B" <sande...@rose-hulman.edu>
Date: 01/24/2016 2:54 PM (GMT-05:00)
To: Renu Yadav <yren...@gmail.com>
Cc: Darren Govoni <dar...@ontrenet.com>, Muthu Jayakumar <bablo...@gmail.com>,
Ted Yu <yuzhih...@gmail.com>, user@spark.apache.org
Subject: Re: 10hrs of Scheduler Delay
I am not getting anywhere with any of the suggestions so far. :(
Trying some more outlets, I will share any solution I find.
- Isaac
On Jan 23, 2016, at 1:48 AM, Renu Yadav <yren...@gmail.com> wrote:
If you turn on spark.speculation on then that might help. it worked for me
On Sat, Jan 23, 2016 at 3:21 AM, Darren Govoni
<dar...@ontrenet.com> wrote:
Thanks for the tip. I will try it. But this is the kind of thing spark is
supposed to figure out and handle. Or at least not get stuck forever.
Sent from my Verizon Wireless 4G LTE smartphone
-------- Original message --------
From: Muthu Jayakumar <bablo...@gmail.com>
Date: 01/22/2016 3:50 PM (GMT-05:00)
To: Darren Govoni <dar...@ontrenet.com>, "Sanders, Isaac B"
<sande...@rose-hulman.edu>, Ted Yu <yuzhih...@gmail.com>
Cc: user@spark.apache.org
Subject: Re: 10hrs of Scheduler Delay
Does increasing the number of partition helps? You could try out something 3
times what you currently have.
Another trick i used was to partition the problem into multiple dataframes and
run them sequentially and persistent the result and then run a union on the
results.
Hope this helps.
On Fri, Jan 22, 2016, 3:48 AM Darren Govoni <dar...@ontrenet.com> wrote:
Me too. I had to shrink my dataset to get it to work. For us at least Spark
seems to have scaling issues.
Sent from my Verizon Wireless 4G LTE smartphone
-------- Original message --------
From: "Sanders, Isaac B" <sande...@rose-hulman.edu>
Date: 01/21/2016 11:18 PM (GMT-05:00)
To: Ted Yu <yuzhih...@gmail.com>
Cc: user@spark.apache.org
Subject: Re: 10hrs of Scheduler Delay
I have run the driver on a smaller dataset (k=2, n=5000) and it worked quickly
and didn’t hang like this. This dataset is closer to k=10, n=4.4m, but I am
using more resources on this one.
- Isaac
On Jan 21, 2016, at 11:06 PM, Ted Yu <yuzhih...@gmail.com> wrote:
You may have seen the following on github page:
Latest commit 50fdf0e on Feb 22, 2015
That was 11 months ago.
Can you search for similar algorithm which runs on Spark and is newer ?
If nothing found, consider running the tests coming from the project to
determine whether the delay is intrinsic.
Cheers
On Thu, Jan 21, 2016 at 7:46 PM, Sanders, Isaac B
<sande...@rose-hulman.edu> wrote:
That thread seems to be moving, it oscillates between a few different traces…
Maybe it is working. It seems odd that it would take that long.
This is 3rd party code, and after looking at some of it, I think it might not
be as Spark-y as it could be.
I linked it below. I don’t know a lot about spark, so it might be fine, but I
have my suspicions.
https://github.com/alitouka/spark_dbscan/blob/master/src/src/main/scala/org/alitouka/spark/dbscan/exploratoryAnalysis/DistanceToNearestNeighborDriver.scala
- Isaac
On Jan 21, 2016, at 10:08 PM, Ted Yu <yuzhih...@gmail.com> wrote:
You may have noticed the following - did this indicate prolonged computation in
your code ?
