Understanding the Problem and Solution

In this article, we will delve into the world of window functions in SQL, specifically focusing on how to ensure that every date and deployment is present in a table and how to modify window functions to skip days if data is not present.

The problem presented in the question revolves around creating a table with several measures for each iteration of date and deployment using window functions. The query provided generates a table with several measures, including count(“macaddress”), but it does not account for missing dates or deployments. This results in a table where some days have no data.

Introduction to Window Functions

Window functions are used to calculate values over rows that are related to the current row. In this case, we want to calculate the sum of “count”(“macaddress”) over a specific number of preceding rows (or days) for each deployment and date.

The window function in question is SUM() with an overlapping clause (ROWS BETWEEN 6 PRECEDING AND CURRENT ROW). This means that it sums up the values from 6 rows before the current row to the current row. However, since we are dealing with dates and deployments, this approach does not account for missing days or deployments.

The Solution

The solution provided by the answerer is to create a deploymentcalendar CTE (Common Table Expression) that cross joins the deployments with the calendar table. This allows us to assign each deployment a specific date from the calendar table.

Here’s an example of how this works:

WITH 
  core AS (
   SELECT /* create dataset for window functions to work on */
     "deployment"
   , "macaddress"
   , "date"
   FROM 
     (
      SELECT /* de-dupe the base dataset based on a macaddress being valid once per day per deployment */
        "_deployment" "deployment"
      , "macaddress"
      , "date"("timestamp") "date"
      , "row_number"() OVER (PARTITION BY "_deployment", "macaddress" ORDER BY "date"("timestamp") ASC) "rn"
      FROM
        registration
     ) 
   WHERE (CAST("rn" AS varchar(2)) = '1')
  ),
  deploymentcalendar as (
    select t.deployment, ct.date 
    from (select distinct deploymet from core) t
    cross join calendar_table ct
  )
SELECT /* window functions to aggregate */
  "count"("macaddress") "registrations"
, "deployment"
, "date"
, "lag"("count"("macaddress"), 1) OVER (PARTITION BY dc."deployment" ORDER BY "date" ASC) "yesterdayCount"
, "sum"("count"("macaddress")) OVER (PARTITION BY dc."deployment" ORDER BY dc."date" ASC ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) "Past7Days"
, "sum"("count"("macaddress")) OVER (PARTITION BY dc."deployment" ORDER BY dc."date" ASC ROWS BETWEEN 13 PRECEDING AND 7 PRECEDING) "7daysTarget"
, "sum"("count"("macaddress")) OVER (PARTITION BY dc."deployment" ORDER BY dc."date" ASC ROWS BETWEEN 29 PRECEDING AND CURRENT ROW) "Past30Days"
, "sum"("count"("macaddress")) OVER (PARTITION BY dc."deployment" ORDER BY dc."date" ASC ROWS BETWEEN 59 PRECEDING AND 30 PRECEDING) "30daysTarget"
, "avg"("count"("macaddress")) OVER (PARTITION BY dc."deployment" ORDER BY dc."date" ASC ROWS BETWEEN 9 PRECEDING AND CURRENT ROW) "10DayTrend"
FROM
  core
RIGHT JOIN deploymentcalendar dc on dc.deployment=core.deployment and dc.date=core.date
GROUP BY core."deployment", core."date"

Understanding the Solution

The deploymentcalendar CTE assigns each deployment a specific date from the calendar table using a cross join. This allows us to partition the data by both deployment and date, rather than just deployment.

The main difference between this solution and the original query is that it uses a RIGHT JOIN instead of a regular JOIN. This ensures that all rows in the core CTE are included in the final result, even if there is no matching row in the deploymentcalendar CTE.

Conclusion

In conclusion, we have discussed how to ensure that every date and deployment is present in a table using window functions. We also explored alternative solutions to account for missing days or deployments.

The solution provided by the answerer uses a deploymentcalendar CTE to cross join the deployments with the calendar table, which allows us to assign each deployment a specific date from the calendar table.

By understanding how window functions work and how to use them effectively, we can create more efficient and accurate queries for our SQL needs.

Last modified on 2023-11-13