UNION ALL is SQL’s way of combining result sets from multiple queries without worrying about duplicates. If you’ve used UNION before, UNION ALL is its faster, less fussy sibling that keeps every single row from all your queries, even if some rows are identical.
So UNION removes duplicate rows automatically, while UNION ALL keeps everything.
If you ever find yourself needing to combine the results from multiple SELECT statements into a single result set, UNION is probably going to be the tool for the job. By “UNION“, I mean the UNION operator.
The UNION operator takes the output from two SELECT queries and stacks them on top of each other. It basically merges two lists into one, removing any duplicates along the way.
Cross joins are one of the more straightforward join types in SQL, but they’re also one of the most misunderstood and potentially dangerous if used carelessly. Understanding when and how to use them properly can help you solve certain data problems efficiently while avoiding performance disasters.
A cross join returns the Cartesian product of two tables. This means that it combines every row from the first table with every row from the second table. If you have 10 rows in one table and 5 rows in another, you might immediately assume that it will return 15 rows. But you’d be wrong. A cross join will return 50 rows. No join condition in the ON clause. Just every possible combination of rows.
When working with SQL databases, you’ll sometimes encounter scenarios where the data you need to compare or relate exists in the same table. Typical examples of this include employees who manage other employees, tasks that depend on other tasks, or categories nested within categories. These situations call for a specific querying approach called a self join.
A self join is a technique that lets you compare and relate rows within a single table. This makes them perfect for working with hierarchical data, finding relationships between records, and solving a wide range of queries that would otherwise be difficult or impossible with standard joins alone.
A subquery is a query nested inside another SQL statement. It’s basically a query within a query. You’re using the results of one SELECT statement to help another SQL statement do its job.
Subqueries let you break down complex problems into smaller, more manageable pieces, making your SQL more readable and often more powerful. The outer query relies on the inner query (the subquery) to provide data, filter results, or perform calculations. Once the subquery executes and returns its results, the outer query uses that information to complete its task.
A materialized view is a database object that stores the results of a query physically on disk, rather than computing them on the fly every time you need them. It’s basically a snapshot of your query results that you can refresh periodically. Unlike regular views (which are just saved queries that execute each time you use them), materialized views pre-compute and cache the data, making subsequent reads much faster.
If you’ve been working with SQL for a while, you’ve probably heard someone mention cursors, usually followed by a warning to avoid them. Maybe you’ve used them yourself. But what exactly are cursors, and why do they get such a bad rap? Let’s take a look at what cursors are, how they work, and when (if ever) you should actually use them.
If you’ve been writing SQL for a while, you’ve probably run into situations where you need to query data that’s already been aggregated, filtered, or transformed in some way. Maybe you need to calculate an average first, then find all the rows above that average. Or perhaps you need to group data by category, then filter those grouped results.
This is where derived tables can come in handy. Derived tables let you build queries in layers, where one query’s output becomes another query’s input, all within a single SQL statement.
Need a high-performance, disposable workspace for your SQL queries? Meet temporary tables, also known simply as temp tables. As their name implies, these tables offer a short-lived storage solution, ideal for holding intermediate data or simplifying complex multi-step processing. They exist just long enough to get the job done, providing a handy scratch pad that vanishes automatically to keep your database clean.
If you’ve ever wondered how databases manage to recover your data after a crash without losing everything you just did, checkpoints are a big part of that. They’re one of those behind-the-scenes mechanisms that keep databases running smoothly, even when things go wrong.