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 <[email protected]> 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 <[email protected]>
>
>> 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 <[email protected]> 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 <[email protected]>
>>>
>>>> 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 <[email protected]> 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 <[email protected]>
>>>>>
>>>>>> 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 <
>>>>>> [email protected]> 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
>
