When you’ve forced an execution plan in SQL Server’s query store and later want to allow the optimizer to choose plans freely again, you’ll need to unforce the plan. You might want to do this because you’ve fixed the underlying issue or the forced plan is no longer optimal. Whatever the reason, the solution is straightforward and easy.
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Generating Staggered Dates with SQL Server Window Functions
SQL Server’s window functions open up some creative possibilities when you need to work with dates. One interesting pattern involves pairing DATEADD() with ROW_NUMBER() to automatically generate sequential dates based on your query results.
This technique gives you a flexible way to calculate dates dynamically without hardcoding values or maintaining separate date tables. This can be useful for doing things like building a scheduling system, creating test data with realistic timestamps, or just spacing events across a timeline.
How to Clear Query Store Data in SQL Server
SQL Server’s Query Store accumulates query execution history, plans, and runtime statistics over time. Eventually you may need to remove this data to free up space, start fresh after troubleshooting, or clear out information that’s no longer relevant. Fortunately, you can clear Query Store data without disabling the feature entirely.
How to Force a Query Execution Plan in SQL Server
When the optimizer consistently chooses a poor execution plan for a query, you can force SQL Server to use a specific better-performing plan from Query Store. This can provide immediate relief while you investigate and fix the root cause of the poor plan choice.
How to Disable Query Store in SQL Server
Query Store captures query execution history, plans, and runtime statistics over time. If you need to stop this data collection (whether to reduce overhead, troubleshoot issues, or prepare for maintenance) you can disable Query Store on a per-database basis.
Building a Survey Results Dashboard in SQL
Survey data can be messy. You’ve got responses scattered across dozens or hundreds of rows, multiple choice answers, rating scales, and so on. And the challenge of turning all that raw data into something stakeholders can actually understand. A well-designed survey dashboard can transform those individual responses into a grid that shows patterns instantly. For example, which questions are getting strong agreement, where opinions diverge, and what trends emerge across different respondent groups.
Calculating Dynamic Date Offsets with Expressions in SQL Server’s DATEADD() Function
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.
How to Check Query Wait Statistics in SQL Server’s Query Store (T-SQL)
SQL Server’s Query Store can capture wait statistics that show what queries are waiting on during execution. This includes things like locks, I/O, CPU, memory, and other resources. This can help you understand not just that a query is slow, but why it’s slow. Wait statistics are available in SQL Server 2017 and later.
Using Window Functions with DATEDIFF() to Calculate Moving Averages of Durations in SQL Server
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.
How to Identify Regressed Queries in SQL Server (T-SQL)
Query regression occurs when a query that previously performed well suddenly becomes slower, often due to a plan change. SQL Server’s Query Store makes it easy to identify these regressions by comparing recent performance against historical baselines.