SPARK SQL Equivalent of Qualify + Row_number statements

Question

Does anyone know the best way for Apache Spark SQL to achieve the same results as the standard SQL qualify() + rnk or row_number statements?

For example:

• I have a Spark Dataframe called statement_data with 12 monthly records each for 100 unique account_numbers, therefore 1200 records in total
• Each monthly record has a field called "statement_date" that can be used for determining the most recent record

I want my final result to be a new Spark Dataframe with the 3 most recent records (as determined by statement_date descending) for each of the 100 unique account_numbers, therefore 300 final records in total.

In standard Teradata SQL, I can do the following:

select * from statement_data
qualify row_number ()
over(partition by acct_id order by statement_date desc) <= 3

Apache Spark SQL does not have a standalone qualify function that I'm aware of, maybe I'm screwing up the syntax or can't find documentation that qualify exists.

It is fine if I need to do this in two steps as long as those two steps are:

• A select query or alternative method to assign rank/row numbering for each account_number's records
• A select query where I'm selecting all records with rank <= 3 (i.e. choose 1st, 2nd, and 3rd most recent records).

EDIT 1 - 7/23 2:09pm: The initial solution provided by zero323 was not working for me in Spark 1.4.1 with Spark SQL 1.4.1 dependency installed.

EDIT 2 - 7/23 3:24pm: It turns out the error was related to using SQL Context objects for my query instead of Hive Context. I am now able to run the below solution correctly after adding the following code to create and use a Hive Context:

final JavaSparkContext sc2;
final HiveContext hc2;
DataFrame df;
hc2 = TestHive$.MODULE$;
sc2 = new JavaSparkContext(hc2.sparkContext()); 
....
// Initial Spark/SQL contexts to set up Dataframes  
SparkConf conf = new SparkConf().setAppName("Statement Test");
...
DataFrame stmtSummary = 
    hc2.sql("SELECT * FROM (SELECT acct_id, stmt_end_dt, stmt_curr_bal, row_number() over (partition by acct_id order by stmt_curr_bal DESC) rank_num FROM stmt_data) tmp WHERE rank_num <= 3");

Answer 1

There is currently no QUALIFY in Spark SQL, but we really need it. As mentioned in a comment above, there is a JIRA for it, but it does not seem to be getting any traction of late: https://issues.apache.org/jira/browse/SPARK-31561

Answer 2

There is Qualify Statement.

SELECT * 
FROM df
QUALIFY row_number() OVER (PARTITION BY acct_id ORDER BY statement_date DESC) = 1 

Answer 3

There is no qualify (it is usually useful to check parser source) but you can use subquery like this:

SELECT * FROM (
    SELECT *, row_number() OVER (
        PARTITION BY acct_id ORDER BY statement_date DESC
    ) rank FROM df
 ) tmp WHERE rank <= 3

See also SPARK : failure: ``union'' expected but `(' found