Hi,

I am sorry for distributing you and thank you for your explication.
However, I find "spark.driver.memory" is used also for standalone.(I set
this in spark/conf/spark-defaults.conf).

Cheers
Gen


Andrew Or-2 wrote
> Hi Maria,
> 
> SPARK_MEM is actually a deprecated because it was too general; the reason
> it worked was because SPARK_MEM applies to everything (drivers, executors,
> masters, workers, history servers...). In favor of more specific configs,
> we broke this down into SPARK_DRIVER_MEMORY and SPARK_EXECUTOR_MEMORY and
> other environment variables and configs. Note that while
> "spark.executor.memory" is an equivalent config, "spark.driver.memory" is
> only used for YARN.
> 
> If you are using Spark 1.0+, the recommended way of specifying driver
> memory is through the "--driver-memory" command line argument of
> spark-submit. The equivalent also holds for executor memory (i.e.
> "--executor-memory").  That way you don't have to wrangle with the
> millions
> of overlapping configs / environment variables for all the deploy modes.
> 
> -Andrew
> 
> 
> 2014-07-23 4:18 GMT-07:00 mrm <

> maria@

> >:
> 
>> Hi,
>>
>> I figured out my problem so I wanted to share my findings. I was
>> basically
>> trying to broadcast an array with 4 million elements, and a size of
>> approximatively 150 MB. Every time I was trying to broadcast, I got an
>> OutOfMemory error. I fixed my problem by increasing the driver memory
>> using:
>> export SPARK_MEM="2g"
>>
>> Using SPARK_DAEMON_MEM or spark.executor.memory did not help in this
>> case!
>> I
>> don't have a good understanding of all these settings and I have the
>> feeling
>> many people are in the same situation.
>>
>>
>>
>> --
>> View this message in context:
>> http://apache-spark-user-list.1001560.n3.nabble.com/driver-memory-tp10486p10489.html
>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>





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