Cleanest, most efficient syntax to perform DataFra

In standard SQL, when you join a table to itself, you can create aliases for the tables to keep track of which columns you are referring to:

SELECT a.column_name, b.column_name...
FROM table1 a, table1 b
WHERE a.common_field = b.common_field;

There are two ways I can think of to achieve the same thing using the Spark DataFrame API:

Solution #1: Rename the columns

There are a couple of different methods for this in answer to this question. This one just renames all the columns with a specific suffix:

df.toDF(df.columns.map(_ + "_R"):_*)

For example you can do:

df.join(df.toDF(df.columns.map(_ + "_R"):_*), $"common_field" === $"common_field_R")

Solution #2: Copy the reference to the DataFrame

Another simple solution is to just do this:

val df: DataFrame = ....
val df_right = df

df.join(df_right, df("common_field") === df_right("common_field"))

Both of these solutions work, and I could see each being useful in certain situations. Are there any internal differences between the two I should be aware of?

There are at least two different ways you can approach this either by aliasing:

df.as("df1").join(df.as("df2"), $"df1.foo" === $"df2.foo")

or using name-based equality joins:

// Note that it will result in ambiguous column names
// so using aliases here could be a good idea as well.
// df.as("df1").join(df.as("df2"), Seq("foo"))

df.join(df, Seq("foo"))

In general column renaming, while the ugliest, is the safest practice across all the versions. There have been a few bugs related to column resolution (we found one on SO not so long ago) and some details may differ between parsers (HiveContext / standard SQLContext) if you use raw expressions.

Personally I prefer using aliases because their resemblance to an idiomatic SQL and ability to use outside the scope of a specific DataFrame objects.

Regarding performance unless you're interested in close-to-real-time processing there should be no performance difference whatsoever. All of these should generate the same execution plan.