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From Exie <tfind...@prodevelop.com.au>
Subject Spark 1.4.0: Parquet partitions / folder hierarchy changed from 1.3.1
Date Wed, 01 Jul 2015 01:33:16 GMT
So I was delighted with Spark 1.3.1 using Parquet 1.6.0 which would
"partition" data into folders. So I set up some parquet data paritioned by
date. This enabled is to reference a single day/month/year minimizing how
much data was scanned. 

eg: 
val myDataFrame =
hiveContext.read.parquet("s3n://myBucket/myPath/2014/07/01") 
or 
val myDataFrame = hiveContext.read.parquet("s3n://myBucket/myPath/2014/07") 

However since upgrading to Spark 1.4.0 it doesnt seem to be working the same
way. 
The first line works, in the "01" folder is all the normal files: 
2015-06-02 20:01         0   s3://myBucket/myPath/2014/07/01/_SUCCESS 
2015-06-02 20:01      2066  
s3://myBucket/myPath/2014/07/01/_common_metadata 
2015-06-02 20:01   1077190   s3://myBucket/myPath/2014/07/01/_metadata 
2015-06-02 19:57    119933  
s3://myBucket/myPath/2014/07/01/part-r-00001.parquet 
2015-06-02 19:57     48478  
s3://myBucket/myPath/2014/07/01/part-r-00002.parquet 
2015-06-02 19:57    576878  
s3://myBucket/myPath/2014/07/01/part-r-00003.parquet 

... but if I now use the second line above, to read in all days, it comes
back empty. 

Is there an option I can set somewhere to fix this ?



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