I am not sure. But in their RDD paper they have mentioned the usage of broadcast variable. Sometimes you may need local variable in many map-reduce jobs and you do not want to copy them to all worker nodes multiple times. Then the broadcast variable is a good choice


2013/11/7 Walrus theCat <walrusthecat@gmail.com>
Shangyu,

Thanks for the tip re: the flag!  Maybe the broadcast variable is only for "complex" data structures?


On Sun, Nov 3, 2013 at 7:58 PM, Shangyu Luo <lsyurd@gmail.com> wrote:
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




--
--

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