[My apologies if this is a re-post. I wasn't subscribed the first time I
sent this message, and I'm hoping this second message will get through.]
I’ve been using Spark 1.3.0 and MLlib for some machine learning tasks. In a
fit of blind optimism, I decided to try running MLlib’s Principal Components
Analayis (PCA) on a dataset with approximately 10,000 columns and 200,000
rows.
The Spark job has been running for about 5 hours on a small cluster, and it
has been stuck on a particular job ("treeAggregate at RowMatrix.scala:119")
for most of that time. The treeAggregate job is now on "retry 5", and after
each failure it seems that the next retry uses a smaller number of tasks.
(Initially, there were around 80 tasks; later it was down to 50, then 42;
now it’s down to 16.) The web UI shows the following error under "failed
stages": "org.apache.spark.shuffle.MetadataFetchFailedException: Missing an
output location for shuffle 1".
This raises a few questions:
1. What does "missing an output location for shuffle 1" mean? I’m guessing
this cryptic error message is indicative of some more fundamental problem
(out of memory? out of disk space?), but I’m not sure how to diagnose it.
2. Why do subsequent retries use fewer and fewer tasks? Does this mean that
the algorithm is actually making progress? Or is the scheduler just
performing some kind of repartitioning and starting over from scratch?
(Also, If the algorithm is in fact making progress, should I expect it to
finish eventually? Or do repeated failures generally indicate that the
cluster is too small to perform the given task?)
3. Is it reasonable to expect that I could get PCA to run on this dataset
using the same cluster simply by changing some configuration parameters? Or
is a larger cluster with significantly more resources per node the only way
around this problem?
4. In general, are there any tips for diagnosing performance issues like the
one above? I've spent some time trying to get a few different algorithms to
scale to larger and larger datasets, and whenever I run into a failure, I'd
like to be able to identify the bottleneck that is preventing further
scaling. Any general advice for doing that kind of detective work would be
much appreciated.
Thanks,
~ Andrew
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