This post is the third and last of a series of articles dedicated to MySQL Database Service (MDS):

• Using MySQL Database Service in OCI – Part 1: creating a MySQL DB System
• Using MySQL Database Service in OCI – Part 2: importing Data to MySQL DB System
• Using MySQL Database Service in OCI – Part 3: faster with MySQL HeatWave !

The goal of this series if to get familiar with MDS, HeatWave and useful MySQL Shell utilities.

We can now start a query on our MDS instance that has been populated with >200M rows:

 SQL > SELECT year, Reporting_Airline,  
              AVG(ArrDelay) AS avgArrDelay …

This post is the second of a series of articles dedicated to MySQL Database Service (MDS):

• Using MySQL Database Service in OCI – Part 1: creating a MySQL DB System
• Using MySQL Database Service in OCI – Part 2: importing Data to MySQL DB System
• Using MySQL Database Service in OCI – Part 3: faster with MySQL HeatWave !

The goal of this series if to get familiar with MDS, HeatWave and useful MySQL Shell utilities.

Before we start, I would like to highlight that the fastest and recommended way to import data to a MySQL DB System in OCI is to use a parallel dump created using MySQL to Object Storage and load it …

This post is the first of a series of articles dedicated to MySQL Database Service (MDS):

• Using MySQL Database Service in OCI – Part 1: creating a MySQL DB System
• Using MySQL Database Service in OCI – Part 2: importing Data to MySQL DB System
• Using MySQL Database Service in OCI – Part 3: faster with MySQL HeatWave !

The goal of this series if to get familiar with MDS, HeatWave and useful MySQL Shell utilities.

In this first article we will start by creating a MySQL DB System and a Compute instance in OCI. One of the goal is also to see the benefits of HeatWave, so we will start by creating this …

We have a quickstart guide for how to install Percona Distribution for MySQL Operator on minikube. Installing the minimal version works well as it is described in the guide. After that, we will have one HAproxy and one Percona XtraDB Cluster (PXC) node to work with.

Minikube provides Kubernetes locally. One can try using the provided local k8s to try the more advanced scenarios such as the one described here.

Following that guide, everything works well, until we get to the part of deploying a cluster with

deploy/cr.yaml

Even after that, things seemingly work.

$ kubectl get pods
NAME READY STATUS RESTARTS AGE …

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 …

On behalf of the Vitess maintainers, I am pleased to announce the general availability of Vitess 12. Major Themes # In this release, Vitess Maintainers have made significant progress in several areas, including Gen4 planner, VTAdmin, and other improvements. Please take a moment to review the Release Notes. Please read them carefully and report any issues via GitHub. Gen4 Planner # The newest version of the query planner, Gen4, becomes an experimental feature as part of this release.

Recently we published the first part of research comparing Graviton (ARM) with AMD and Intel CPU on AWS. In the first part, we selected general-purpose EC2 instances with the same configurations (amount of vCPU).  The main goal was to see the trend and make a general comparison of CPU types on the AWS platform only for MySQL. We didn’t set the goal to compare the performance of different CPU types. Our expertise is in MySQL performance tuning. We share research “as is” with all scripts, and anyone interested could rerun and reproduce it.
All scripts, raw logs and additional plots are available on GitHub: (2021_10_arm_cpu_comparison_c5,  …

I’m active on multiple platforms (mail, slack, forums, …) and often, when people are looking for help, the first 5 or 10 questions are always the same:

• which version of MySQL are you running ?
• which OS ?
• is it in the cloud ?
• which provider ?
• are you using replication ?
• GTIDs ?
• ….

I’ve added to MySQL Shell Plugin repository and plugin called support which provides an output that user can share when looking for MySQL help.

Usually, I’m focusing only in MySQL 8.0, but this plugin works with older versions too (don’t try MySQL 3.23…. it should be compatible from 5.6).

Let’s see an output if I run it locally:

[fred@fedora ~] $ mysqlsh root@localhost -e "support.fetchInfo()" …

Recently, AWS presented its own CPU on ARM architecture for server solutions.

It was Graviton. As a result, they update some lines of their EC2 instances with new postfix “g” (e.g. m6g.small, r5g.nano, etc.). In their review and presentation, AWS showed impressive results that it is faster in some benchmarks up to 20 percent. On the other hand, some reviewers said that Graviton does not show any significant results and, in some cases, showed fewer performance results than Intel.

We decided to investigate it and do our research regarding Graviton performance, comparing it with other CPUs (Intel and AMD) directly for MySQL.

Disclaimer

1. The test is designed to be CPU bound only, so we will use a read-only test and make sure there is no I/O activity during the test.
2. Tests were run  on m5.* (Intel) , m5a.* (AMD),  m6g.*(Graviton) EC2 instances in the US-EAST-1 region. (List of EC2 see …

There are plenty of ways to run ProxySQL in Kubernetes (K8S). For example, we can deploy sidecar containers on the application pods, or run a dedicated ProxySQL service with its own pods.

We are going to discuss the latter approach, which is more likely to be used when dealing with a large number of application pods. Remember each ProxySQL instance runs a number of checks against the database backends. These checks monitor things like server-status and replication lag. Having too many proxies can cause significant overhead.

Creating a Cluster

For the purpose of this example, I am going to deploy a test cluster in GKE. We need to follow these steps:

1. Create a cluster

gcloud container clusters create ivan-cluster --preemptible --project my-project --zone us-central1-c --machine-type n2-standard-4 --num-nodes=3

2. Configure command-line access

gcloud …