I understand that the following are equivalent df.filter('account === "acct1")
sql("select * from tempTableName where account = 'acct1'") But is Spark SQL "smart" to also push filter predicates down for the initial load? e.g. sqlContext.read.jdbc(…).filter('account=== "acct1") Is Spark "smart enough" to this for each partition? ‘select … where account= ‘acc1’ AND (partition where clause here)? Or do I have to put it on each partition where clause otherwise it will load the entire set and only then filter it in memory? ᐧ