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From Shangyu Luo <lsy...@gmail.com>
Subject Re: cluster hangs for no apparent reason
Date Mon, 04 Nov 2013 03:58:53 GMT
I met the problem of 'Too many open files' before. One solution is adding
'ulimit -n 100000' in the spark-env.sh file.
Basically, I think the local variable may not be a problem as I have
written programs with local variables as parameters for functions and the
programs work.


2013/11/3 Walrus theCat <walrusthecat@gmail.com>

> Hi Shangyu,
>
> I appreciate your ongoing correspondence.  To clarify, my solution didn't
> work, and I didn't expect it to. I was digging through the logs, and I
> found a series of exceptions (in only one of the workers):
>
> 13/11/03 17:51:05 INFO client.DefaultHttpClient: Retrying connect
> 13/11/03 17:51:05 INFO http.AmazonHttpClient: Unable to execute HTTP request: Too many
open files
> java.net.SocketException: Too many open files
> ...
> at com.amazonaws.services.s3.AmazonS3Client.getObject(AmazonS3Client.java:808)
> ...
>
> I don't know why, because I do close those streams, but I'll look into it.
>
> As an aside, I make references to a spark.util.Vector from a parallelized context (in
an RDD.map operation), as per the Logistic Regression example that Spark came with, and it
seems to work out (the following from the examples, you'll see that 'w' is not a broadcast
variable, and 'points' is an RDD):
>
>     var w = Vector(D, _ => 2 * rand.nextDouble - 1)
>     println("Initial w: " + w)
>
>     for (i <- 1 to ITERATIONS) {
>       println("On iteration " + i)
>       val gradient = points.map { p =>
>
>         (1 / (1 + exp(-p.y * (w dot p.x))) - 1) * p.y * p.x
>       }.reduce(_ + _)
>       w -= gradient
>     }
>
>
>
>
> On Sun, Nov 3, 2013 at 10:47 AM, Shangyu Luo <lsyurd@gmail.com> wrote:
>
>> Hi Walrus,
>> Thank you for sharing your solution to your problem. I think I have met
>> the similar problem before (i.e., one machine is working while others are
>> idle.) and I just waits for a long time and the program will continue
>> processing. I am not sure how your program filters an RDD by a locally
>> stored set. If the set is a parameter of a function, I assume it should be
>> copied to all worker nodes. But it is good that you solved your problem
>> with a broadcast variable and the running time seems reasonable!
>>
>>
>> 2013/11/3 Walrus theCat <walrusthecat@gmail.com>
>>
>>> Hi Shangyu,
>>>
>>> Thanks for responding.  This is a refactor of other code that isn't
>>> completely scalable because it pulls stuff to the driver.  This code keeps
>>> everything on the cluster.  I left it running for 7 hours, and the log just
>>> froze.  I checked ganglia, and only one machine's CPU seemed to be doing
>>> anything.  The last output on the log left my code at a spot where it is
>>> filtering an RDD by a locally stored set.  No error was thrown.  I thought
>>> that was OK based on the example code, but just in case, I changed it so
>>> it's a broadcast variable.  The un-refactored code (that pulls all the data
>>> to the driver from time to time) runs in minutes.  I've never had the
>>> problem before of the log just getting non-responsive, and was wondering if
>>> anyone knew of any heuristics I could check.
>>>
>>> Thank you
>>>
>>>
>>> On Sat, Nov 2, 2013 at 2:55 PM, Shangyu Luo <lsyurd@gmail.com> wrote:
>>>
>>>> Yes, I think so. The running time depends on what work your are doing
>>>> and how large it is.
>>>>
>>>>
>>>> 2013/11/1 Walrus theCat <walrusthecat@gmail.com>
>>>>
>>>>> That's what I thought, too.  So is it not "hanging", just
>>>>> recalculating for a very long time?  The log stops updating and it just
>>>>> gives the output I posted.  If there are any suggestions as to parameters
>>>>> to change, or any other data, it would be appreciated.
>>>>>
>>>>> Thank you, Shangyu.
>>>>>
>>>>>
>>>>> On Fri, Nov 1, 2013 at 11:31 AM, Shangyu Luo <lsyurd@gmail.com>
wrote:
>>>>>
>>>>>> I think the missing parent may be not abnormal. From my
>>>>>> understanding, when a Spark task cannot find its parent, it can use
some
>>>>>> meta data to find the result of its parent or recalculate its parent's
>>>>>> value. Imaging in a loop, a Spark task tries to find some value from
the
>>>>>> last iteration's result.
>>>>>>
>>>>>>
>>>>>> 2013/11/1 Walrus theCat <walrusthecat@gmail.com>
>>>>>>
>>>>>>> Are there heuristics to check when the scheduler says it is "missing
>>>>>>> parents" and just hangs?
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Thu, Oct 31, 2013 at 4:56 PM, Walrus theCat <
>>>>>>> walrusthecat@gmail.com> wrote:
>>>>>>>
>>>>>>>> Hi,
>>>>>>>>
>>>>>>>> I'm not sure what's going on here.  My code seems to be working
>>>>>>>> thus far (map at SparkLR:90 completed.)  What can I do to
help the
>>>>>>>> scheduler out here?
>>>>>>>>
>>>>>>>> Thanks
>>>>>>>>
>>>>>>>> 13/10/31 02:10:13 INFO scheduler.DAGScheduler: Completed
>>>>>>>> ShuffleMapTask(10, 211)
>>>>>>>> 13/10/31 02:10:13 INFO scheduler.DAGScheduler: Stage 10 (map
at
>>>>>>>> SparkLR.scala:90) finished in 0.923 s
>>>>>>>> 13/10/31 02:10:13 INFO scheduler.DAGScheduler: looking for
newly
>>>>>>>> runnable stages
>>>>>>>> 13/10/31 02:10:13 INFO scheduler.DAGScheduler: running: Set(Stage
>>>>>>>> 11)
>>>>>>>> 13/10/31 02:10:13 INFO scheduler.DAGScheduler: waiting: Set(Stage
>>>>>>>> 9, Stage 8)
>>>>>>>> 13/10/31 02:10:13 INFO scheduler.DAGScheduler: failed: Set()
>>>>>>>> 13/10/31 02:10:16 INFO scheduler.DAGScheduler: Missing parents
for
>>>>>>>> Stage 9: List(Stage 11)
>>>>>>>> 13/10/31 02:10:16 INFO scheduler.DAGScheduler: Missing parents
for
>>>>>>>> Stage 8: List(Stage 9)
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> --
>>>>>>
>>>>>> Shangyu, Luo
>>>>>> Department of Computer Science
>>>>>> Rice University
>>>>>>
>>>>>> --
>>>>>> Not Just Think About It, But Do It!
>>>>>> --
>>>>>> Success is never final.
>>>>>> --
>>>>>> Losers always whine about their best
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> --
>>>>
>>>> Shangyu, Luo
>>>> Department of Computer Science
>>>> Rice University
>>>>
>>>> --
>>>> Not Just Think About It, But Do It!
>>>> --
>>>> Success is never final.
>>>> --
>>>> Losers always whine about their best
>>>>
>>>
>>>
>>
>>
>> --
>> --
>>
>> Shangyu, Luo
>> Department of Computer Science
>> Rice University
>>
>> --
>> Not Just Think About It, But Do It!
>> --
>> Success is never final.
>> --
>> Losers always whine about their best
>>
>
>


-- 
--

Shangyu, Luo
Department of Computer Science
Rice University

--
Not Just Think About It, But Do It!
--
Success is never final.
--
Losers always whine about their best

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