Spark SQL: Cache Memory footprint improves with 'order by'

I have two scenarios where I have 23 GB partitioned parquet data and reading few of the columns & caching it upfront to fire a series of subsequent queries later on.

Setup:

• Cluster: 12 Node EMR
• Spark Version: 1.6
• Spark Configurations: Default
• Run Configurations: Same for both cases

Case 1:

val paths = Array("s3://my/parquet/path", ...)
val parqFile = sqlContext.read.parquet(paths:_*)
parqFile.registerTempTable("productViewBase")
val dfMain = sqlContext.sql("select guid,email,eventKey,timestamp,pogId from productViewBase")
dfMain.cache.count

From SparkUI, the input data read is 6.2 GB and the cached object is of 15.1 GB.

Case 1:

val paths = Array("s3://my/parquet/path", ...)
val parqFile = sqlContext.read.parquet(paths:_*)
parqFile.registerTempTable("productViewBase")
val dfMain = sqlContext.sql("select guid,email,eventKey,timestamp,pogId from productViewBase order by pogId")
dfMain.cache.count

From SparkUI, the input data read is 6.2 GB and the cached object is of 5.5 GB.

Any explanation, or code-reference to this behavior?