[GitHub] zeppelin issue #1970: ZEPPELIN-2045. Pass interpreter properties with "spark...

2017-06-04 Thread orenpai
Github user orenpai commented on the issue:

https://github.com/apache/zeppelin/pull/1970
  
@zjffdu @Leemoonsoo I believe this is a mistake, and poses a real problem 
for me and other Zeppelin users.
While it's true that the properties file is being skimmed to remove any 
non- spark.* properties, SparkConf.set allows it directly via code:

https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/SparkConf.scala#L84
There are many libraries like es-hadoop for ElasticSearch integration that 
depend on the SparkConf having non spark.* properties, such as "es.nodes".
Since Zeppelin does not allow a user to recreate the sc, this important 
mechanism of directly adding non spark properties via code is being blocked. I 
can't find any workaround either.


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[GitHub] zeppelin issue #1970: ZEPPELIN-2045. Pass interpreter properties with "spark...

2017-06-05 Thread orenpai
Github user orenpai commented on the issue:

https://github.com/apache/zeppelin/pull/1970
  
As far as I can tell from the code, yes - it does some verification of this 
value on the driver, and then passes it after processing to the executors.
@costin might be able to provide more details.


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[GitHub] zeppelin issue #1970: ZEPPELIN-2045. Pass interpreter properties with "spark...

2017-06-06 Thread orenpai
Github user orenpai commented on the issue:

https://github.com/apache/zeppelin/pull/1970
  
Posted 
https://discuss.elastic.co/t/using-es-hadoop-with-zeppelin-and-the-use-of-es-nodes/88392


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[GitHub] zeppelin issue #1970: ZEPPELIN-2045. Pass interpreter properties with "spark...

2017-06-06 Thread orenpai
Github user orenpai commented on the issue:

https://github.com/apache/zeppelin/pull/1970
  
To summarize, es-hadoop is aware that from command line, non spark.* 
parameters get ignored so they created a workaround to allow setting the 
parameters through spark.es.* parameters. 
Either way, as I suspected, the parameters themselves are serialized 
separately to the executors and not through the spark configuration.
I must say that I still think that blocking any non-spark configuration 
settings is overprotective relative to what you can perform in code, but I am 
no longer blocked by this issue.


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