We know that SQL statement compatibility can change with major database version upgrades and that you should adequately test for them. But what about minor version upgrades?

It is dangerous to assume that your existing SQL statements work with a minor update, especially when using an augmented version of an open-source database such as a cloud provider that may not be as transparent about all changes.

While I have always found reading the release notes an important step in architectural principles over the decades, many organizations skip over this principle and get caught off guard when there are no dedicated DBAs and architects in the engineering workforce.

Real-world examples of minor version upgrade issues

Here are two real-world situations common in the AWS RDS ecosystem using MySQL.

1. You are an organization that uses RDS Aurora MySQL for its production systems, and you upgrade one minor version …

Amazon claims that Aurora has “Up to 5X the throughput of MySQL”. Is it true? It wasn’t easy to find the truth, but I kept digging until I found it. This is a long read; let’s chase the rabbit all the way down the hole.

Amazon claims that Aurora has “Up to 5X the throughput of MySQL”. Is it true? It wasn’t easy to find the truth, but I kept digging until I found it. This is a long read; let’s chase the rabbit all the way down the hole.

Amazon claims that Aurora has “Up to 5X the throughput of MySQL”. Is it true? It wasn’t easy to find the truth, but I kept digging until I found it. This is a long read; let’s chase the rabbit all the way down the hole.

Let’s examine COMMIT latency on Aurora v2 (MySQL 5.7) vs. Aurora v3 (MySQL 8.0) vs. RDS MySQL 8.0 2-AZ vs. RDS MySQL 8.0 3-AZ “cluster”.

Let’s examine COMMIT latency on Aurora v2 (MySQL 5.7) vs. Aurora v3 (MySQL 8.0) vs. RDS MySQL 8.0 2-AZ vs. RDS MySQL 8.0 3-AZ “cluster”.

Let’s examine COMMIT latency on Aurora v2 (MySQL 5.7) vs. Aurora v3 (MySQL 8.0) vs. RDS MySQL 8.0 2-AZ vs. RDS MySQL 8.0 3-AZ “cluster”.

Aurora cluster promises a high availability solution and seamless failover procedure. However, how much is actually the downtime when a failover happens? And how proxySQL can help in minimizing the downtime ? A little sneak peek on the results ProxySQL achieves up to 25x less downtime and the impressive up to ~9800x less errors during unplanned failovers. How proxySQL achieves this: 

1. Less downtime
2. “Queueing” feature when an instance in a hostgroup becomes unavailable.

So what is ProxySQL? ProxySQL is a middle layer between the database and the application. ProxySQL protects databases from high traffic spikes, prevents databases from having high number of connections due to the multiplexing feature and minimizes the impact during planned/unexpected failovers or crashes of DBs. 

This blog will continue with measuring the impact of an unexpected …

With Aurora MySQL 8 now generally available to all, you may want to consider the plan for an upgrade path if you would like to take advantage of the new features for your application, for example, Common Table Expressions (CTE). This new major release has a much improved and streamlined upgrade progress from Aurora MySQL 5.7.

This tutorial will provide all the steps to allow you to try out setting up an Aurora cluster and performing an upgrade without the impact on your existing AWS environment. The two pre-requisites to getting started are:

• An AWS account. The Free 1 year AWS account provides many of the services used in these tutorials at no or little cost.
• The awscli. See …

A recommendation we often give to our customers is along the lines of “archive old data” to reduce your database size. There is a tradeoff between keeping all our data online and archiving part of it to cold storage.

There could also be legal requirements to keep certain data online, or you might want to query old data occasionally without having to go through the hassle of restoring an old backup.

In this post, we will explore a very useful feature of AWS RDS/Aurora that allows us to export data to an S3 bucket and run SQL queries directly against it.

Archiving Data to S3

Let’s start by describing the steps we need to take to put our data into an S3 bucket in the required format, which is called Apache Parquet.

Amazon states the Parquet format is up to 2x faster to export and consumes up to 6x less storage in S3, compared to other text formats.

1. Create a snapshot of the database (or …