If you use SQL on a regular basis, then you are well aware that Window Functions are powerful. They allow us to simplify queries that would otherwise be quite the mess. We can provide meaningful insight across rows of data without collapsing the results into a single value. I have written numerous blog posts on Window Functions, many here recently. I decided to make this blog post a compilation of all the Window Function posts I have written, providing a one-stop source for any readers interested in learning more about Window Functions…
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Top n Window Function queries in MySQL
Top n Window Function queries over a specific subset of data are common in analysis and reporting requirements. Luckily, in MySQL, there are Window functions we can use for this type of query. To be quite honest, you don’t necessarily need Window Functions. You can retrieve those top 3 (or whatever) types of results with a regular SQL query. But, since we have those powerful Window Functions, why not use them? My thoughts exactly! Besides, no one wants a spaghetti code mess of SQL to try and understand. Not to mention, Window functions are often better optimized for querying larger data sets. Continue reading and see example queries for more understanding…
[Keep reading for more SQL database and PHP/Python-centric content >>>]PHP trim functions: trim, rtrim, and ltrim
String variables are common in any programming language. You are likely going to use or manipulate a string at some point while programming. Sometimes, string variables are not at all perfect. Many of them may even have characters that need to be removed. Targeting specific locations of a string, PHP provides 3 string functions we can use to remove unwanted characters from a string variable: trim(), ltrim() and rtrim(). Continue reading to see examples of each function…
Rolling sum and average – Window Functions MySQL
Rolling sum and average query results are possible by combining the aggregate functions SUM() or AVG() with the OVER() clause, making for powerful analytic queries. I recently learned how to compute a rolling average or sum of values by using the Windowing option of the OVER() clause, applying the concepts to a data set I am familiar with. I wanted to share my learning with any readers who might be interested…
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RANK() and DENSE_RANK() differences
The Window Ranking functions: ROW_NUMBER(), RANK(), and DENSE_RANK() each rank rows with an increasing integer value. I wrote a previous blog post, ROW_NUMBER() Window Function – find duplicate values, where I covered how the ROW_NUMBER() window function can be used to target any duplicate rows, with the use of the PARTITION BY clause in the OVER() clause. In this post, I cover the differences between RANK(), and DENSE_RANK() in handling any ties according to the sorting performed by the ORDER BY clause with regards to the assigned increasing integer. Continue reading and see examples…