Common Table Expressions (CTEs) in Databricks and Spark

Question

I have a spark dataframe in Databricks. I am trying to run some sql query with Common Table Expressions (CTEs). Here is a first 10 rows of data

+----------+----------+------+---+---+---------+-----------------+
| data_date|   user_id|region|sex|age|age_group|sum(duration_min)|
+----------+----------+------+---+---+---------+-----------------+
|2020-01-01|22600560aa|     1|  1| 28|        2|              0.0|
|2020-01-01|17148900ab|     6|  2| 60|        5|           1138.0|
|2020-01-01|21900230aa|     5|  1| 43|        4|              0.0|
|2020-01-01|35900050ac|     8|  1| 16|        1|            224.0|
|2020-01-01|22300280ad|     6|  2| 44|        4|              8.0|
|2020-01-02|19702160ac|     2|  2| 55|        5|              0.0|
|2020-02-02|17900020aa|     5|  2| 64|        5|            264.0|
|2020-02-02|16900120aa|     3|  1| 69|        6|              0.0|
|2020-02-02|11160900aa|     6|  2| 52|        5|              0.0|
|2020-03-02|16900290aa|     5|  1| 37|        3|              0.0|
+----------+----------+------+---+---+---------+-----------------+

Here I stored each user registration date in the regs CTE and then calculate the number of registrations per month. This chunk with CTE is working without any issue in Databricks

%sql


    WITH regs AS (
      SELECT
        user_id,
        MIN(data_date) AS reg_date
      FROM df2
      GROUP BY user_id)
    
    SELECT
      month(reg_date)  AS reg_month,
      COUNT(DISTINCT user_id) AS users
    FROM regs
    GROUP BY reg_month
    ORDER BY reg_month ASC;

However when I add another CTE into my previous sql query, it returns an error ( I test this chunk in sql server and it was working fine). I couldn't figure it out why is not working in spark databricks.

%sql

WITH regs AS (
  SELECT
    user_id,
    MIN(data_date) AS reg_date
  FROM df2
  GROUP BY user_id
  ),

  regs_per_month AS (
    SELECT
      month(reg_date) AS reg_month,
      COUNT(DISTINCT user_id) AS users
    FROM regs
    GROUP BY reg_month
  )

SELECT
  reg_month,
  users,
  LAG(users, 1) OVER (ORDER BY regs_per_month ASC) AS previous_users
FROM regs_per_month
ORDER BY reg_month ASC;

Here is the error message

Error in SQL statement: AnalysisException: cannot resolve '`regs_per_month`' given input columns: [regs_per_month.reg_month, regs_per_month.users]; line 20 pos 31;
'Sort ['reg_month ASC NULLS FIRST], true

user_1527504 · Answer

You can nest common table expressions (CTEs) in Spark SQL simply using commas, eg

%sql
;WITH regs AS (
SELECT
  user_id,
  MIN(data_date) AS reg_date
FROM df2
GROUP BY user_id
),
regs_per_month AS (
SELECT
  month(reg_date) AS reg_month,
  COUNT(DISTINCT user_id) AS users
FROM regs
GROUP BY reg_month
)
SELECT
  reg_month,
  users,
  LAG(users, 1) OVER (ORDER BY reg_month ASC) AS previous_users
FROM regs_per_month
ORDER BY reg_month ASC;

My results:

As already mentioned your LAG statement should reference the reg_month column and not the regs_per_month CTE.

As an alternative way of nesting CTEs you can use multiple, WITH statements, eg

%sql
;WITH regs_per_month AS ( 
  WITH regs AS ( 
  SELECT
    user_id,
    MIN(data_date) AS reg_date
  FROM df2
  GROUP BY user_id
  )
  SELECT 
    month(reg_date) AS reg_month,
    COUNT(DISTINCT user_id) AS users
  FROM regs
  GROUP BY reg_month
)
SELECT 
  reg_month, 
  users,
  LAG( users, 1 ) OVER ( ORDER BY reg_month ASC ) AS previous_users
FROM regs_per_month
ORDER BY reg_month ASC;

Common Table Expressions (CTEs) in Databricks and Spark

1 Answers