Indexes are frequent friends for SELECT queries; however, if we want to use indexes, we must ensure that our database instances use them first. Indexes that are not utilized by our database instances are useless – they waste space and make INSERT, UPDATE, and DELETE queries slower.
If you have found yourself in the MySQL world (at least for a little while), chances are that you have probably heard about the usage of EXPLAIN. In the MySQL world, this keyword is everywhere.
Some MySQL engineers might call them the cornerstone of improving performance in MySQL: indexes are data structures that are frequently used to quickly find rows matching a given query.
During my work tenure, I got a chance to work with MySQL databases with as many as 700 million rows. This post will emphasize how to index and optimize your MySQL queries for indexes.
By
Akhil Mathew
As developers, we are aware of various best practices for SQL query optimization. While a few of these are specific to individual relational database management systems (RDBMSs), most can apply to virtually any database.
A PostgreSQL database health dashboard allows us to monitor the database in real-time and helps us to more fully understand some features which are unique to PostgreSQL.
Sometimes MySQL (or MariaDB) can be great for big data too! In this article, we will explore the things you should consider when answering the question: “Is MySQL a good option for my big data project?”
The first thing to look at to start optimizing a query is the Query Planner. In this post we will explain how a query gets executed and how to understand the EXPLAIN command.
Learn how deadlocks may be impacting your database performance, and how you can resolve them
