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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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What is a MERGE Statement in SQL?

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The MERGE statement is SQL’s convenient tool for synchronizing data between two tables. It lets you perform INSERT, UPDATE, and DELETE operations in a single statement based on whether matching records exist. Instead of writing separate logic to check if a record exists and then deciding what to do with it, MERGE handles all of that in one go.

Most major database systems support MERGE, including SQL Server, Oracle, and DB2. PostgreSQL added native MERGE support in version 15, but if you’re on an older version, you can use INSERT … ON CONFLICT as an alternative. MySQL doesn’t have MERGE but offers INSERT … ON DUPLICATE KEY UPDATE for similar functionality.

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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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What is the Query Store in SQL Server?

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Query Store is SQL Server’s built-in query performance tracking system. It captures a history of queries, their execution plans, and runtime statistics, storing everything in the database itself. It constantly records what’s happening so you can analyze performance issues after the fact.

Query performance issues can be notoriously hard to debug. A query runs fine for weeks, then suddenly it’s slow, and by the time you check, the problem has vanished or the execution plan is no longer in cache. SQL Server 2016 introduced Query Store to address this. Once enabled on a database, it continuously records query execution history, giving you the data you need to investigate performance problems after they happen. It won’t tell you what’s wrong or how to fix it, but at least you’ll have evidence to work with instead of flying blind.

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What is a Query Hint?

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A query hint is a directive you add to your SQL statement that tells the database optimizer how to execute that query. You’re basically overriding the optimizer’s judgment with your own instructions.

Most of the time, your database’s query optimizer does a pretty solid job figuring out the best execution plan. It analyzes statistics, indexes, and table structures to determine the most efficient path. But sometimes you know better (or at least you think you do) and that’s where query hints can be useful.

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What is a Query Execution Plan?

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A query execution plan is a detailed roadmap that shows exactly how a database will execute your SQL query. When you submit a query, the database doesn’t just start grabbing data randomly. Rather, it creates a step-by-step strategy for retrieving and processing your data in the most efficient way possible.

The query execution plan is that strategy made visible.

Basically, the SQL you write tells the database what you want, but the execution plan shows you how it’s actually going to get it. This includes which tables it’ll scan, what indexes it’ll use, how it’ll join tables together, and in what order everything will happen.

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What is an Index Scan?

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An index scan is a method databases use to retrieve data by reading through an index from start to finish. The database reads every entry in the index sequentially, checking each one to see if it matches your query conditions.

This is different from an index seek, where the database jumps directly to specific values in the index. Index scans happen when the database determines it needs to examine a large portion of the index, or when it can’t use the index’s sorted structure to go directly to the data you need.

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What is an Index Seek?

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An index seek is the fastest way a database can use an index to find data. When you perform a seek, the database jumps directly to the exact location in the index where your data lives, grabs what it needs, and moves on. No scanning, no reading through irrelevant entries. Just a precise lookup using the index’s sorted structure.

This is fundamentally different from an index scan, where the database reads through the index sequentially. Seeks are only possible when your query conditions allow the database to pinpoint specific index entries without examining others.

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What is Query Optimization?

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Query optimization is the process of finding the most efficient way to execute a database query.

When you write a SQL query, you’re basically telling the database what data you want, but the database has to figure out how to actually retrieve it. That’s the main job of the query optimizer. The query optimizer is a dedicated component of the database management system (DBMS) that evaluates various possible execution paths and selects the most efficient one.

But there are also things that we can do to help the query optimizer, such as writing efficient SQL, properly indexing tables, maintaining up-to-date statistics, etc.

Understanding how the optimizer works and knowing how to steer it toward better execution plans is what we mean by query optimization.

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