How to refer broadcast variable in dataframes

Question

I use spark1.6. I tried to broadcast a RDD and am not sure how to access the broadcasted variable in the data frames?

I have two dataframes employee & department.

Employee Dataframe

-------------------
Emp Id | Emp Name | Emp_Age
------------------
1 | john | 25

2 | David | 35

Department Dataframe

--------------------
Dept Id | Dept Name | Emp Id
-----------------------------
1 | Admin | 1

2 | HR | 2

import scala.collection.Map

val df_emp = hiveContext.sql("select * from emp")

val df_dept = hiveContext.sql("select * from dept")

val rdd = df_emp.rdd.map(row => (row.getInt(0),row.getString(1)))

val lkp = rdd.collectAsMap()

val bc = sc.broadcast(lkp)

print(bc.value.get(1).get)

--Below statement doesn't work

val combinedDF = df_dept.withColumn("emp_name",bc.value.get($"emp_id").get)

1. How do I refer the broadcast variable in the above combinedDF statement?
2. How to handle if the lkp doesn't return any value?
3. Is there a way to return multiple records from the lkp (lets assume if there are 2 records for emp_id=1 in the look up, I would like to get both records)
4. How to return more than one value from broadcast...(emp_name & emp_age)

Answer 1

How do I refer the broadcast variable in the above combinedDF statement?

Use udf. If emp_id is Int

val f = udf((emp_id: Int) =>  bc.value.get(emp_id))

df_dept.withColumn("emp_name", f($"emp_id"))

How to handle if the lkp doesn't return any value?

Don't use get as shown above

Is there a way to return multiple records from the lkp

Use groupByKey:

val lkp = rdd.groupByKey.collectAsMap()

and explode:

df_dept.withColumn("emp_name", f($"emp_id")).withColumn("emp_name", explode($"emp_name"))

or just skip all the steps and broadcast:

import org.apache.spark.sql.functions._

df_emp.join(broadcast(df_dep), Seq("Emp Id"), "left")