Are you using the default Java object serialization, or have you tried Kryo
yet? If you haven't tried Kryo please do and let me know how much it
impacts the serialization size. (I know its more efficient, I'm curious to
know how much more efficient, and I'm being lazy - I don't have ~6K 500MB
files on hand.)
You can saveAsObjectFile on maybe a take(1) from an RDD and examine the
serialized output to see if maybe a much larger graph than you expect is
being output.
On Fri Jan 30 2015 at 3:47:31 PM ankits ankitso...@gmail.com wrote:
This is on spark 1.2
I am loading ~6k parquet files, roughly 500 MB each into a schemaRDD, and
calling count() on it.
After loading about 2705 tasks (there is one per file), the job crashes
with
this error:
Total size of serialized results of 2705 tasks (1024.0 MB) is bigger than
spark.driver.maxResultSize (1024.0 MB)
This indicates that the results of each task are about 2705/1024 = 2.6MB
each. Is that normal? I don't know exactly what the result of each task
would be, but 2.6 MB for each seems too high. Can anyone offer an
explanation as to what the normal size should be if this is too high, or
ways to reduce this?
Thanks.
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