pretty simple, a similar construct to tables projected as DF val c = HiveContext.table("channels").select("CHANNEL_ID","CHANNEL_DESC") val t = HiveContext.table("times").select("TIME_ID","CALENDAR_MONTH_DESC") val rs = s.join(t,"time_id").join(c,"channel_id")
HTH Dr Mich Talebzadeh LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* http://talebzadehmich.wordpress.com On 17 May 2016 at 21:52, Bijay Kumar Pathak <bkpat...@mtu.edu> wrote: > Hi, > > Try this one: > > > df_join = df1.*join*(df2, 'Id', "fullouter") > > Thanks, > Bijay > > > On Tue, May 17, 2016 at 9:39 AM, ram kumar <ramkumarro...@gmail.com> > wrote: > >> Hi, >> >> I tried to join two dataframe >> >> df_join = df1.*join*(df2, ((df1("Id") === df2("Id")), "fullouter") >> >> df_join.registerTempTable("join_test") >> >> >> When querying "Id" from "join_test" >> >> 0: jdbc:hive2://> *select Id from join_test;* >> *Error*: org.apache.spark.sql.AnalysisException: Reference 'Id' is >> *ambiguous*, could be: Id#128, Id#155.; line 1 pos 7 (state=,code=0) >> 0: jdbc:hive2://> >> >> Is there a way to merge the value of df1("Id") and df2("Id") into one "Id" >> >> Thanks >> > >