SQL Server

Calculating Dynamic Date Offsets with Expressions in SQL Server’s DATEADD() Function

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One of DATEADD()‘s less obvious features in SQL Server is its ability to accept expressions as the interval parameter rather than just simple numeric values. You can perform calculations, use arithmetic operations, or reference multiple columns right inside the function call. This gives you a more flexible way to calculate dates when the offset itself needs to be computed based on your data.

So, instead of adding or subtracting a fixed number of days, months, or years, you compute that number on the fly using whatever logic makes sense for your situation. Let’s look at an example that demonstrates this concept.

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Using Window Functions with DATEDIFF() to Calculate Moving Averages of Durations in SQL Server

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SQL Server’s window functions allow you to perform calculations across sets of rows that are related to the current row, without collapsing those rows into a single result like traditional GROUP BY aggregates would. When combined with the DATEDIFF() function, they can open up many options for analyzing temporal patterns in your data.

Moving averages smooth out short-term fluctuations to reveal longer-term trends in your data. Unlike a simple overall average that treats all historical data equally, a moving average focuses on a sliding window of recent events. This can be quite relevant when analyzing process durations, response times, or any time-based metric where you want to understand current performance trends without being overly influenced by distant historical data.

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SQL Server UNPIVOT Explained

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Sometimes you need to do the reverse of pivoting – take data that’s spread across multiple columns and convert it back into rows. You might receive data in a wide format from Excel, need to normalize denormalized data for storage, or simply need to reshape data for a different type of analysis. Fortunately, SQL Server has the UNPIVOT operator which is designed for this very scenario.

Whereas PIVOT transforms rows into columns, UNPIVOT transforms column headers back into row values. This creates a narrower, longer dataset from a wide one.

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How to Enable Query Store in SQL Server

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Query Store is SQL Server’s built-in query performance tracking system that captures execution history, plans, and runtime statistics. From SQL Server 2022 it’s enabled by default for all newly created databases. But in earlier versions, it’s disabled by default, which means you’ll need to explicitly enable it on each database where you want to track query performance.

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What is Parameter Sniffing in SQL Server?

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Parameter sniffing is a feature in SQL Server where the query optimizer examines (or “sniffs”) the parameter values the first time a stored procedure or parameterized query executes. It uses these specific values to create an execution plan optimized for those particular parameters. The plan is then cached and reused for subsequent executions, even when different parameter values are passed in.

This behavior can be efficient if the initial parameters are representative of typical queries, but it can also cause performance problems if later calls use very different parameter values that make the cached plan inefficient. 

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How PIVOT Works in SQL Server

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In SQL databases, a pivot operation transforms rows into columns, making it easier to summarize or compare data across categories. It’s commonly used to convert long, vertical datasets into a wider, more readable format. For example, turning a list of monthly sales records into a table where each month becomes its own column.

By applying aggregation functions like SUM(), COUNT(), or AVG() during the pivot, SQL can reorganize and summarize data for reporting or analysis.

In this article, we’ll take a look at SQL Server’s PIVOT operator, which is designed specifically for pivot operations.

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