PS. In my reduceByKey operation I have two mutable object. What I do is
merge mutable2 into mutable1 and return mutable1. I read that it works for
aggregateByKey so thought it will work for reduceByKey as well. I might be
wrong here. Can someone verify if this will work or be un predictable?
On Wed, Jun 22, 2016 at 11:52 AM, Nirav Patel <npa...@xactlycorp.com> wrote:
> Hi,
>
> I do not see any indication of errors or executor getting killed in spark
> UI - jobs, stages, event timelines. No task failures. I also don't see any
> errors in executor logs.
>
> Thanks
>
> On Wed, Jun 22, 2016 at 2:32 AM, Ted Yu <yuzhih...@gmail.com> wrote:
>
>> For the run which returned incorrect result, did you observe any error
>> (on workers) ?
>>
>> Cheers
>>
>> On Tue, Jun 21, 2016 at 10:42 PM, Nirav Patel <npa...@xactlycorp.com>
>> wrote:
>>
>>> I have an RDD[String, MyObj] which is a result of Join + Map operation.
>>> It has no partitioner info. I run reduceByKey without passing any
>>> Partitioner or partition counts. I observed that output aggregation result
>>> for given key is incorrect sometime. like 1 out of 5 times. It looks like
>>> reduce operation is joining values from two different keys. There is no
>>> configuration change between multiple runs. I am scratching my head over
>>> this. I verified results by printing out RDD before and after reduce
>>> operation; collecting subset at driver.
>>>
>>> Besides shuffle and storage memory fraction I use following options:
>>>
>>> sparkConf.set("spark.driver.userClassPathFirst","true")
>>> sparkConf.set("spark.unsafe.offHeap","true")
>>> sparkConf.set("spark.reducer.maxSizeInFlight","128m")
>>> sparkConf.set("spark.serializer",
>>> "org.apache.spark.serializer.KryoSerializer")
>>>
>>>
>>>
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>>
>>
>>
>
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