I’m working with a payments database and need to calculate the sum of transactions for each day. My table uses columns named ‘pmttotamt’ and ‘date’, but my query unexpectedly returns 518 rows instead of the 31 expected for January.
Below is an alternative approach I attempted:
SELECT CONVERT(VARCHAR(10), payment_date, 23) AS DayLabel,
SUM(transaction_value) AS TotalDaily
FROM PaymentRecords
WHERE YEAR(payment_date) = 2025
AND MONTH(payment_date) = 1
GROUP BY CONVERT(VARCHAR(10), payment_date, 23);
hey, try cast(payment_date as date) to group by day. sometimes convert creates slight differences if time data sticks around. might be worth a look, imho.
It appears that discrepancies in handling the time component in your payment_date column could be creating unintended groupings. In my experience, ensuring that the time values are fully excluded from the grouping is critical. I once encountered a similar issue where a mixed data type caused aggregation functions to split values that should have been combined. I resolved this by explicitly converting the payment_date column to a date type using TRUNC or a similar function, depending on the SQL flavor. This approach helps in maintaining consistency across records and ensures that the filtering criteria accurately returns the expected number of groups.
hey, i wonder if payment_date is storing times too? maybe casting it directly to date might help, dunno. what your database specifics are make a difference?
hey, im not sure but maybe tiny timezone glitches could mess things up? i wonder if using floor or a similar trick in your db might help. what database version are you on? could be interesting to explore if its treating dates strictly or not…