MySQL management, tutorials, scripts, coding, programming and tips for database administrators
To err is human, or so the saying goes. Unfortunately, in the world of IT, innocuous mistakes made early on can lead to really daunting problems down the road. In today’s article, we’ll go over a few of the most common DBA mistakes to avoid. Some of these relate specifically to MySQL, while others may be applicable to any RDBMS.
A common business requirement is to fetch the top N rows of each group, for example, largest cities for each country. Unfortunately many solutions utilize non-MySQL vendor-specific functions. Rob Gravelle covers a couple of approaches that will work with MySQL.
It’s been said that one of the drawbacks to normalization to the third form (3NF) is more cumbersome data extraction due to the greater number of tables. These require careful linking via JOIN clauses. Improper table joining can easily result in erroneous results or even in the dreaded Cartesian Product. In today’s article, we’ll explore how table joins are achieved in MySQL.
While there is no silver bullet to keeping your MySQL database running briskly, there are a few performance tweaks that you can do to keep things chugging along when the data starts to pile up. In today’s article, we’ll take a look at a few of these.
If you’ve ever tried to calculate how much disk space your MySQL data entities – databases, tables, and columns – are taking up, you may have been left scratching your head for an answer. You’ll be happy to know that it can be done. Rob Gravelle shows you a few sure fire ways to get at the information you’re looking for.
As in all aspects of database development and management, specialized software has emerged to make the process of proper database design much easier to accomplish. In today’s tutorial, Rob Gravelle shows you how to design, implement, maintain, and synchronize MySQL database schemas using the Navicat Data Modeler.
While MySQL does feature Regular Expression matching via REGEXP operator it does not offer an equivalent to Oracle’s REGEXP_REPLACE function, which is all the more mysterious when you consider that Oracle owns MySQL! Join Rob Gravelle as he tries to account for this incongruence between the two DBMSes and searches for user-defined replacements.
When you think of Cloud services for database administration, Database as a Service (DBaaS) is what tends to comes to mind, but one of the ways that the Cloud has had an enormous impact on our work productivity is in the area of collaboration.
The MySQL 5.7.7 JSON Lab release introduced a native JSON datatype, which opened the door for handling JSON data in ways that were previously impossible. Several new functions assist in the selecting of data from a JSON document. We covered a few of them; in this article Rob Gravelle shows you some additional functions for searching on JSON values in MySQL 7.
Both management and various departments rely on data reports to help guide their business decisions. Rob Gravelle presents ten open source and commercial report generation tools for MySQL and other relational databases.
In this follow-up to Beyond the LIKE Operator: Advanced Pattern Matching with MySQL, Rob Gravelle shows how to use the REGEXP operator to match alphanumeric characters, explores the role of meta-characters, and compares standard regular expression syntax to that of MySQL.