Hi Shahid,
I played around with spark driver memory too. In the conf file it was set
to " --driver-memory 20G " first. When i changed the spark driver
maxResultSize from default to 2g ,i changed the driver memory to 30G and
tired too. It gave we same error says "bigger than
spark.driver.maxResultSize (1024.0 MB) " .
One other thing i observed is , in one of the tasks the data its trying to
process is more than 100 MB and that exceutor and task keeps losing
connection and doing retry. I tried increase the Tasks by repartition from
120 to 240 to 480 also. Still i can see in one of my tasks it still is
trying to process more than 100 mb. Other task hardly process 1 mb to 10 mb
, some around 20 mbs, some have 0 mbs .
Any idea how can i try to even the data distribution acrosss multiple node.
On Fri, Oct 30, 2015 at 12:09 AM, shahid ashraf <sha...@trialx.com> wrote:
> Hi
> I guess you need to increase spark driver memory as well. But that should
> be set in conf files
> Let me know if that resolves
> On Oct 30, 2015 7:33 AM, "karthik kadiyam" <karthik.kadiyam...@gmail.com>
> wrote:
>
>> Hi,
>>
>> In spark streaming job i had the following setting
>>
>> this.jsc.getConf().set("spark.driver.maxResultSize", “0”);
>> and i got the error in the job as below
>>
>> User class threw exception: Job aborted due to stage failure: Total size
>> of serialized results of 120 tasks (1082.2 MB) is bigger than
>> spark.driver.maxResultSize (1024.0 MB)
>>
>> Basically i realized that as default value is 1 GB. I changed
>> the configuration as below.
>>
>> this.jsc.getConf().set("spark.driver.maxResultSize", “2g”);
>>
>> and when i ran the job it gave the error
>>
>> User class threw exception: Job aborted due to stage failure: Total size
>> of serialized results of 120 tasks (1082.2 MB) is bigger than
>> spark.driver.maxResultSize (1024.0 MB)
>>
>> So, basically the change i made is not been considered in the job. so my
>> question is
>>
>> - "spark.driver.maxResultSize", “2g” is this the right way to change or
>> any other way to do it.
>> - Is this a bug in spark 1.3 or something or any one had this issue
>> before?
>>
>>
