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From Josh Wills <>
Subject Re: Crunch Spark pipeline seems to stall?
Date Wed, 22 Jul 2015 18:21:01 GMT
Hey Everett,

I would ideally swing by to see the pipeline in person, but I'm traveling
all over the place the next couple of weeks. Most common cause of job
stalls in Spark in my experience has been during very large shuffles where
not enough tasks are allocated to do the work the job requires. This series
of blog posts is worth a read:

To get more tasks going on a Crunch-on-Spark pipeline, I think your best
options are to:

1) Disable combine file by setting crunch.disable.combine.file to true in
the conf,
2) Increase the parallelism of your jobs, either by manually increasing the
number of "reducers" in a GBK or lowering crunch.bytes.per.reduce.task so
that the automatic partitioning will be higher.


On Wed, Jul 22, 2015 at 10:42 AM, Everett Anderson <> wrote:

> Hi,
> I have a fairly complex Crunch pipeline with many joins and multiple
> inputs and outputs. I've been using the MRPipeline on AWS with EMR/Hadoop
> successfully, but was curious to try out the SparkPipeline.
> I'm using Crunch 0.12.0 and tried Spark 1.4.0 with 25 core instances.
> Spark seemed to successfully run one small part of the pipeline, but then
> stalled, showing that all submitted jobs had succeeded, but that only 16
> jobs had been submitted. It never terminated, but all the workers seemed
> idle.
> Has anyone seen something like that before? Is there a configuration
> parameter that controls how many jobs Crunch will submit to Spark?
> Thanks!
> - Everett
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Director of Data Science
Cloudera <>
Twitter: @josh_wills <>

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