How can I group by datetime column without considering time

Diving deep: Grouping operations in detail

Mastering date conversion: CAST vs CONVERT

Time for SQL gymnastics! When dealing with datetime columns and you wish to perform some day-by-day data aggregation, the best tack to take involves converting the datetime to date. The CAST and CONVERT functions are your go-to tools in this process. While CAST is more universally accepted across SQL dialects, CONVERT flaunts stronger formatting capabilities in SQL Server.

For the love of months and years

Not just a daily fan? You can swing to a different rhythm and group by month or year. The MONTH and YEAR functions can hook you up, or you can get granular with the DATEADD and DATEDIFF functions to come up with custom groupings:

-- Because some people like to see months in action
SELECT MONTH(dateTimeColumn) AS Month, COUNT(*)
FROM tableName
GROUP BY MONTH(dateTimeColumn);

-- Sometimes, it's all in the year!
SELECT YEAR(dateTimeColumn) AS Year, COUNT(*)
FROM tableName
GROUP BY YEAR(dateTimeColumn);
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Keeping things 'real' old with legacy SQL versions

Let's time travel! Sadly, SQL Server pre-2008 lacks the date datatype. In such scenarios, we turn into string maestros: we convert the datetime into a string and proceed to group by said string:

-- When push comes to shove, we go old school
SELECT CONVERT(CHAR(8), dateTimeColumn, 112) AS DateOnly, COUNT(*)
FROM tableName
GROUP BY CONVERT(CHAR(8), dateTimeColumn, 112);
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Just a heads-up: use the perfect CONVERT style (112 for the yyyymmdd format). Do this religiously, and it's consistent results all the way!

Pro Tips & Traps: Performant Grouping & Potential Pitfalls

Big data demands big thinking! When handling large datasets or dealing with a flood of order groupings, performance can take a dip. Clever indexing and query optimization strategies are a must-have in your SQL toolbox. While an index on the datetime column appears tempting, the use of conversion functions could render it useless. A smart workaround? Materialize the conversion in a persisted computed column. If speed is non-negotiable, consider bullet-trains like temporary tables or table variables — they will serve you well.