Both GROUP BY and ORDER BY are clauses (or statements) that serve similar functions; that is to sort query results. However, each of these serve very different purposes; so different in fact, that they can be employed separately or together. And that is where things can get a little dicey if you are unsure of what you're doing. In today's blog, we'll learn what each clause does and how to use them together for the ultimate control over your query output. To do that we'll be using Navicat Premium against the Sakila Sample Database.
In previous blog, we tabulated the average daily counts for a given column in SQL Server using Navicat for SQL Server. In today's follow-up, we're going to raise the difficulty factor slightly by calculating the daily average date/time interval that is based on start and end date columns. For demonstration purposes, I'll be working with MySQL using Navicat Premium.
Normally, querying a normalized database necessitates joining tables together on one or more common fields. Otherwise, you risk generating a cartesian product. That is a result set whose number of rows equals those in the first table multiplied by the number of rows in the second table. So, if the input contains 1000 persons and 1000 phone numbers, the result consists of 1,000,000 pairs! Not good. Having said that, if you wanted to aggregate data from similar tables that are not directly related, you can do that using the UNION operator. In today's blog, we'll learn some of the finer points on using UNION, along with its close cousin, UNION ALL.
I recently wrote a node.js script to iterate over millions of files per day and insert their contents into a MySQL database. Rather than process one record at a time, the script stored file contents in memory and then ran an INSERT statement every 1000 files. To do that, I used the bulk insert form of the INSERT statement. Depending on your particular requirements, you may opt to go with a different solution. In today's blog, we'll go over a few alternatives.
Joins and subqueries are both used to combine data from different tables into a single result set. As such, they share many similarities as well as differences. One key difference is performance. If execution speed is paramount in your business, then you should favor one over the other. Which one? Read on to find out!
- 2024 (1)
- 2023 (1)
- 2022 (1)
- 2021 (1)
- 2020 (1)
- 2019 (1)
- 2018 (1)
- 2017 (1)
