Alias Approach using scala (this is example given for older version of spark for spark 2.x see my other answer) :

You can use case class to prepare sample dataset …
which is optional for ex: you can get DataFrame from hiveContext.sql as well..

import org.apache.spark.sql.functions.col

case class Person(name: String, age: Int, personid : Int)

case class Profile(name: String, personid  : Int , profileDescription: String)

    val df1 = sqlContext.createDataFrame(
   Person("Bindu",20,  2) 
:: Person("Raphel",25, 5) 
:: Person("Ram",40, 9):: Nil)


val df2 = sqlContext.createDataFrame(
Profile("Spark",2,  "SparkSQLMaster") 
:: Profile("Spark",5, "SparkGuru") 
:: Profile("Spark",9, "DevHunter"):: Nil
)

// you can do alias to refer column name with aliases to  increase readablity

val df_asPerson = df1.as("dfperson")
val df_asProfile = df2.as("dfprofile")


val joined_df = df_asPerson.join(
    df_asProfile
, col("dfperson.personid") === col("dfprofile.personid")
, "inner")


joined_df.select(
  col("dfperson.name")
, col("dfperson.age")
, col("dfprofile.name")
, col("dfprofile.profileDescription"))
.show

sample Temp table approach which I don’t like personally…

The reason to use the registerTempTable( tableName ) method for a DataFrame, is so that in addition to being able to use the Spark-provided methods of a DataFrame, you can also issue SQL queries via the sqlContext.sql( sqlQuery ) method, that use that DataFrame as an SQL table. The tableName parameter specifies the table name to use for that DataFrame in the SQL queries.

df_asPerson.registerTempTable("dfperson");
df_asProfile.registerTempTable("dfprofile")

sqlContext.sql("""SELECT dfperson.name, dfperson.age, dfprofile.profileDescription
                  FROM  dfperson JOIN  dfprofile
                  ON dfperson.personid == dfprofile.personid""")

If you want to know more about joins pls see this nice post : beyond-traditional-join-with-apache-spark

Note :
1) As mentioned by @RaphaelRoth ,

val resultDf = PersonDf.join(ProfileDf,Seq("personId")) is good
approach since it doesnt have duplicate columns from both sides if you are using inner join with same table.
2) Spark 2.x example updated in another answer with full set of join
operations supported by spark 2.x with examples + result

TIP :

Also, important thing in joins : broadcast function can help to give hint please see my answer