scala - Join time series Cassandra tables in Spark -

i have 2 tables (agg_count_1 , agg_count_2) in cassandra both same schema:

create table agg_count_1 (   pk_1 text,   pk_2 text,   pk_3 text,   window_start timestamp,   count counter,   primary key (( pk_1, pk_2, pk_3 ), window_start) ) clustering order ( window_start desc ) 

window_start timestamp rounded nearest 15 minutes means value same in both tables rows time windows may missing.

i efficiently (inner) join these 2 tables on primary key third table same schema , store value of agg_count_1.counter counter_1 , agg_count_2.counter counter_2 columns:

create table agg_joined (   pk_1 text,   pk_2 text,   pk_3 text,   window_start timestamp,   int counter_1,   int counter_2,   primary key (( pk_1, pk_2, pk_3 ), window_start) ) clustering order ( window_start desc ) 

this can done in many ways using combination of scala, spark , spark-cassandra connector features. what recommended way?

i appreciate hear solutions avoid. joins in general expensive expect kind of "zipping" of time series should efficient if (actually me) don't wrong.

based on spark-cassandra documentation using joinwithcassandratable sounds suboptimal because executes single query every partition:

joinwithcassandratable utilizes java drive execute single query every partition required source rdd no un-needed data requested or serialized.