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 ?
org.apache.commons.math3.util.MathArrays.distance(MathArrays.java:205)
org.apache.commons.math3.ml.distance.EuclideanDistance.compute(EuclideanDistance.java:34)
org.alitouka.spark.dbscan.spatial.DistanceCalculation$class.calculateDistance(DistanceCalculation.scala:15)
org.alitouka.spark.dbscan.exploratoryAnalysis.DistanceToNearestNeighborDriver$.calculateDistance(DistanceToNearestNeighborDriver.scala:16)
On Thu, Jan 21, 2016 at 5:13 PM, Sanders, Isaac B
<sande...@rose-hulman.edu> wrote:
Hadoop is: HDP 2.3.2.0-2950
Here is a gist (pastebin) of my versions en masse and a stacktrace:
https://gist.github.com/isaacsanders/2e59131758469097651b
Thanks
On Jan 21, 2016, at 7:44 PM, Ted Yu <yuzhih...@gmail.com> wrote:
Looks like you were running on YARN.
What hadoop version are you using ?
Can you capture a few stack traces of the AppMaster during the delay and
pastebin them ?
Thanks
On Thu, Jan 21, 2016 at 8:08 AM, Sanders, Isaac B
<sande...@rose-hulman.edu> wrote:
The Spark Version is 1.4.1
The logs are full of standard fair, nothing like an exception or even
interesting [INFO] lines.
Here is the script I am using:
https://gist.github.com/isaacsanders/660f480810fbc07d4df2
Thanks
Isaac
On Jan 21, 2016, at 11:03 AM, Ted Yu <yuzhih...@gmail.com> wrote:
Can you provide a bit more information ?
command line for submitting Spark job
version of Spark
anything interesting from driver / executor logs ?
Thanks
On Thu, Jan 21, 2016 at 7:35 AM, Sanders, Isaac B
<sande...@rose-hulman.edu> wrote:
Hey all,
I am a CS student in the United States working on my senior thesis.
My thesis uses Spark, and I am encountering some trouble.
I am using
https://github.com/alitouka/spark_dbscan, and to determine parameters, I am
using the utility class they supply,
org.alitouka.spark.dbscan.exploratoryAnalysis.DistanceToNearestNeighborDriver.
I am on a 10 node cluster with one machine with 8 cores and 32G of memory and
nine machines with 6 cores and 16G of memory.
I have 442M of data, which seems like it would be a joke, but the job stalls at
the last stage.
It was stuck in Scheduler Delay for 10 hours overnight, and I have tried a
number of things for the last couple days, but nothing seems to be helping.
I have tried:
- Increasing heap sizes and numbers of cores
- More/less executors with different amounts of resources.
- Kyro Serialization
- FAIR Scheduling
It doesn’t seem like it should require this much. Any ideas?
- Isaac
