Access patterns intrigue me because it seems that everyone knows what they are and talks about them, but there’s also very little written about them—in MySQL literature, at least. That’s why I set out to enumerate a list of access patterns (specific to MySQL). Since there’s no apparent standard for access patterns, I cannot say how my list measures up, but after spending most my career with MySQL, I know this: it is necessary to consider these access patterns when evaluating and improving MySQL performance. Simply put: you cannot ignore how the application accesses MySQL.

Is MySQL performance about “more” or “less”? From the title, you can tell that I’m going to argue that it’s about “less”. Here’s the punchline: zero is maximum performance. Let’s see where this philosophical blog post leads us.

Is MySQL performance about “more” or “less”? From the title, you can tell that I’m going to argue that it’s about “less”. Here’s the punchline: zero is maximum performance. Let’s see where this philosophical blog post leads us.

Is MySQL performance about “more” or “less”? From the title, you can tell that I’m going to argue that it’s about “less”. Here’s the punchline: zero is maximum performance. Let’s see where this philosophical blog post leads us.

As of MySQL 8.0.18, EXPLAIN ANALYZE is an indispensable tool for understanding query execution because it breaks down the query execution stage of response time by measuring each step of the query execution plan. The information is illuminating, but the output is not intuitive: it requires practice and some understanding of how MySQL executes queries beyond the table join order shown by traditional EXPLAIN output. This blog post closely examines three different examples of EXPLAIN ANALYZE output.

As of MySQL 8.0.18, EXPLAIN ANALYZE is an indispensable tool for understanding query execution because it breaks down the query execution stage of response time by measuring each step of the query execution plan. The information is illuminating, but the output is not intuitive: it requires practice and some understanding of how MySQL executes queries beyond the table join order shown by traditional EXPLAIN output. This blog post closely examines three different examples of EXPLAIN ANALYZE output.

As of MySQL 8.0.18, EXPLAIN ANALYZE is an indispensable tool for understanding query execution because it breaks down the query execution stage of response time by measuring each step of the query execution plan. The information is illuminating, but the output is not intuitive: it requires practice and some understanding of how MySQL executes queries beyond the table join order shown by traditional EXPLAIN output. This blog post closely examines three different examples of EXPLAIN ANALYZE output.

Editors and technical reviewers suggested that I cover how to configure MySQL query metrics in chapter 1 of Efficient MySQL Performance, but I deferred because it was out of scope for the book, which focuses on engineers using MySQL, not DBAs. As such, there’s only a note in chapter 1 that says: “Ask your DBA or read the MySQL manual.” But I’ll cover the topic here because that’s what this blog post series is for: behind the book.

Editors and technical reviewers suggested that I cover how to configure MySQL query metrics in chapter 1 of Efficient MySQL Performance, but I deferred because it was out of scope for the book, which focuses on engineers using MySQL, not DBAs. As such, there’s only a note in chapter 1 that says: “Ask your DBA or read the MySQL manual.” But I’ll cover the topic here because that’s what this blog post series is for: behind the book.

Editors and technical reviewers suggested that I cover how to configure MySQL query metrics in chapter 1 of Efficient MySQL Performance, but I deferred because it was out of scope for the book, which focuses on engineers using MySQL, not DBAs. As such, there’s only a note in chapter 1 that says: “Ask your DBA or read the MySQL manual.” But I’ll cover the topic here because that’s what this blog post series is for: behind the book.