In a recent podcast on Mastering EOL Migrations: Lessons learned from MySQL 5.7 to 8.0 I discuss with my colleague Adam North not only the technical issues that become a major migration but also key business and management requirements with having a well-articulated strategy that covers:

• Planning
• Testing
• Be Prepared
• Proactiveness

Having a plan is key to any significant task including data migrations. You should heed the warnings and the deprecations and consider all potential downstream product impacts such as connector upgrades. The plan includes a timeline but also needs to define all the stakeholders both technical and business, the definition of a successful migration, and most importantly the decision tree for a non-successful migration that would …

As a data architect I always ensure that for any database schema change there a fully recoverable execution path.
I have generally advised to create a patch/revert process for every change.  For example, if a change adds a new column or index to a table, a revert script would remove the respective column or index.
The goal is to always have a defensive position for any changes. The concept is that simple, it is not complex.

In its simplest form I use the following directory and file structure.

/schema
    schema.sql
    /patch
        YYYYMMDDXX.sql     where XX,ZZ are sequential 2 digit numbers, e.g. 01,02
        YYYYMMDDZZ.sql
   /revert
       YYYYMMDDXX.sql   This is the same file name in the revert sub-directory.
       YYYYMMDDZZ.sql

At any commit or tag in configuration management it is possible to create a current copy of the schema, i.e. use schema.sql.
It is also possible to take the first …

Securing your data is only as good as your weakest link. A clear-text password in a file or history file, shared privileges between test and production or open sudo access when you can connect as an unprivileged user all are security flaws. This talk discusses how to navigate the poor defaults MySQL has in place, how to strengthen processes and how to audit your environment. It also covers the complexity of deploying changes in an always available production environment.

Presented at the Data.Ops Conference in Barcelona, Spain.
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It can be hard for software engineers to understand the following principle, however it is very important for improving performance and obtaining immediate scalability options. The principle is “Do Less Work”. That is, run less SQL statements.

Just one method to achieving the execution of less SQL statements is to eliminate Row At a Time (RAT) processing. In simple terms, do not perform identical repeating SQL statements in a loop. Relational algebra, and the Structure Query Language (SQL) specification is specifically designed to work with sets of data, or as I describe, Chunk At a Time (CAT) processing.

Customer Example

Your online social media website lets you send messages to multiple friends at one time. You enter the message, select the friends you want to receive the message and click send. While the user waits a moment and gets a success message, behind the scenes the application runs the …

What if the data you retrieved from the database did not match the data the application claimed to had successfully stored? How comfortable would your organization feel about your skills and the products that are being used to store important information if data integrity was not guaranteed?

MySQL employs a terrible default technique known as silent truncation where the product determines that it knows about your data better than you. Never has the saying “do not assume” because it makes an “ass” out of “u” and “me” been more applicable.

Customer Example

A HTML form for new customers provide input fields for the customer first and last name. Good design was considered with the HTML form client validation to ensure that each field could not exceed 20 characters in length. However, the database design is different, where the first name is only defined as 10 characters. In most cases this is sufficient, …