Not able to COUNT DISTINCT using WINDOW functions (Spark SQL)

Question

Let's say I have a dataset sample (table 1) as shown below -

Here, one customer can use multiple tokens and one token can be used by multiple customers. I am trying to get for each token, customer and creation date of the record, the number of customers used this token before the creation date.

When I am trying to execute this query in Spark SQL, I am getting the following error -

Option 1 (correlated subquery)

SELECT 
t1.token, 
t1.customer_id, 
t1.creation_date,
(SELECT COUNT(DISTINCT t2.customer_id) FROM Table 1  t2
AND t1.token = t2.token 
AND t2.creation_date < t1.creation_date) cust_cnt
FROM Table 1  t1;

Error: Correlated column is not allowed in a non-equality predicate

Option 2 (cross - join)

SELECT 
t1.token, 
t1.customer_id, 
t1.creation_date, 
COUNT(DISTINCT t2.customer_id) AS cust_cnt
FROM Table 1 t1, Table 1 t2
WHERE t1.token = t2.token
AND t2.creation_date < t1.creation_date 
GROUP BY t1.token, t1.customer_id, t1.creation_date;

Problem: Long running query since Table 1 has millions of rows

Is there any workaround (for eg. using window function) to optimize this query in Spark SQL? Note: window functions does not allow distinct count.

Answer 1

Count the first time a customer appears:

SELECT t1.token, t1.customer_id, t1.creation_date,
       SUM(CASE WHEN seqnum = 1 THEN 1 ELSE 0 END) OVER (PARTITION BY token ORDER BY creation_date) as cust_cnt
FROM (SELECT t1.*,
             ROW_NUMBER() OVER (PARTITION BY token, customer_id ORDER BY creation_date) as seqnum
      FROM Table1  t1
     ) t1;

Note: This is also counting the current row. I'm guessing that is acceptable for what you want to do.