This blog post introduces the new MySQL Configurator tool, available starting with MySQL Server 8.1.0.

MySQL HeatWave Lakehouse is a fully managed database service, powered by a highly parallel in-memory query accelerator. It is the only cloud database service that combines transactions, analytics, automatic machine learning and now querying object store into one MySQL database, delivering real-time, and secure analytics without the complexity, latency, and cost of ETL duplication.

This blog post shows how to get started with MySQL HeatWave Lakehouse, create a cluster, run Autopilot to infer your schema and estimate capacity, and finally load and query data.

Making waves as an industry first, MySQL HeatWave Lakehouse enables querying data in object storage at speeds as fast as querying data inside the database while delivering record-setting performance and price-performance on a 500TB workload.  

Thank you for your time and for reading OpenLampTech. Let me know what you want to see more of in the weekly publication. Enjoy issue #87.

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In OpenLampTech issue #87, there are great reads covering:

• MySQL datetimes versus timestamps
…

Well, “easy” if you know just a tiny bit of C++.

MySQL is well known for its ease of use, being easy to install, easy to configure, and easy to maintain. What if there is something more that you’d like MySQL to do? How would you integrate some new fancy processing library into MySQL without having to recreate the complexities in pure SQL?

MySQL Loadable Functions would be the way to go. In this blog post, you’ll learn how to set up a build environment for compiling your own MySQL plugin to be loaded into MySQL as a function. Our function will implement a ULID generator using a C++ library from ChrisBove/ulid.

Creating the build environment

The first step is downloading the …

Introducing MySQL Innovation and Long-Term Support (LTS) versions.

This blog was originally published in January 2022 and was updated in July 2023.

Working with hundreds of different customers, I often face similar problems around running queries. One very common problem when trying to optimize a database environment is index usage. A query that cannot use an index is usually a long-running one, consuming more memory or triggering more disk iops.

A very common case is when a query uses a filter condition against a column that is involved in some kind of functional expression. An index on that column can not be used.

Starting from MySQL 8.0.13, functional indexes are supported. In this article, I will first explain an overview of indexes in MySQL and cover the MySQL CREATE INDEX before diving into showing what functional indexes are and how they work.

Introduction to MySQL Indexes

Working with my students to create an Ubuntu virtual environment for Python development with the MySQL database. After completing the general provisioning covered in this older post, I’d recommend you create a python symbolic link before installing the MySQL-Python driver.

sudo ln -s /usr/bin/python3 /usr/bin/python

You install the Python development driver with the following:

sudo apt-get -y install python3-mysql.connector

Create a python_connect.py file to test your Python deployment’s ability to connect to the MySQL database:

#!/usr/bin/python

# Import the library.
import mysql.connector
from mysql.connector import errorcode
 
try:
  # Open connection.
  cnx = mysql.connector.connect(user='student', password='student',
                                host='localhost', …

MySQL HeatWave on AWS is now available in AWS Europe London and Frankfurt regions