Joining 3 SchemaRDDs - apache-spark

Question

I'm doing a three-way join with three SchemaRDDs (each on the order of a million records, stored in Parquet files on HDFS).
The schema is as follows:
table1 has four fields: id, group_id, t2_id and a date
table2 has three fields: id, group_id and t3_id
table3 has three fields: id, group_id and date
I'm trying to figure out the relationships between table1 and table3 within a group.
The SQL Query I'd use would be:
SELECT group_id, t1.id, t3.id
FROM table1, table2, table3
WHERE t1.group_id = t2.group_id and t1.t2_id = t2.id and
and t2.group_id = t3.group_id and t2.t3_id = t3.id and
t3.date < t1.date
However, I'm trying to do it in Spark:
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext._
import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
val tab1 = sqlContext.parquetFile("warehouse/tab1_pq")
val tab2 = sqlContext.parquetFile("warehouse/tab2_pq")
val tab3 = sqlContext.parquetFile("warehouse/tab3_pq")
val relationship = tab1.as('t1).
join(tab2.as('t2), Inner, Some(("t2.group_id".attr === "t1.group_id".attr) && ("t2.id".attr === "t1.t2_id".attr))).
join(tab3.as('t3), Inner, Some(("t3.group_id".attr === "t2.group_id".attr) && ("t3.id".attr === "t2.t3_id".attr))).
where("t3.date".attr <= "t1.date".attr).
select("t1.group_id".attr, "t1.id".attr, "t3.id".attr)
So this seems to work -- however it runs significantly slower than impala on the same (3 unit, EMR) cluster. Is this the right way to do it? Is there a way to make this more performant?
Thanks for any help