Ok, so now we’re got our InnoDB Cluster a-clustering, MySQL Router a-routing, now we need some disaster to be a-disaster-recovering…

A foreword first.

If you’re looking to use Enterprise Backup to recover a single node and restore that node back into an existing InnoDB Cluster, LeFred takes you through that one nicely here.

Preparing for backup

On our single primary server, the one that allows write, which was ic2/10.0.0.12 in my case:

mysql -uroot -poracle << EOF 
SET sql_log_bin = OFF; 
 create user 'backup'@'%' identified by 'oracle';
 grant all on *.* to 'backup'@'%';
SET sql_log_bin = ON; 
EOF

Let’s create something to backup (if you haven’t already done so of course):

mysqlsh --uri …

Want to setup InnoDB Cluster and be prepared for a Disaster Recovery scenario? Get ready:

Here’s a way to set up InnoDB Cluster using the 3 environments, on Oracle Linux 7.2, 5.7.19 MySQL Commercial Server, MySQL Shell 8.0.3 DMR, MySQL Router. As this is the first blog post for a complete disaster recovery scenario of InnoDB Cluster, we’ll also be installing MySQL Enterprise Backup.

If you’re new to InnoDB Cluster then I’d highly recommend looking at the following to understand how it works and what Group Replication, Shell & Router are.:

• https://dev.mysql.com/doc/refman/5.7/en/mysql-innodb-cluster-production-deployment.html
• …

As a DBA, your top priority is to keep your databases and dependent applications running smoothly at all times. To this end, your best weapon is judicious monitoring of key performance metrics. In a perfect world, you’d want to be up-to-date regarding every aspect of your database’s activity – i.e. how many events occurred, how big they were, precisely when they happened and how long they took.

There certainly is no shortage of tools that can monitor resource consumption, provide instantaneous status snapshots, and generate wait analysis and graphs. The challenge is that some metrics can be expensive to measure, and, perhaps even more importantly, they can require a lot of work to analyze.

The purpose of Part-2 of the blog series is to narrow down the field to those performance metrics that provide the most value for the effort as well as present some tangible ways to capture and study them. It is by tracking the most useful …

In this blog post, I’ll walk through some of the improvements to the Percona Monitoring and Management (PMM) MySQL dashboard in release 1.4.0.

As the part of Percona Monitoring and Management development, we’re constantly looking for better ways to visualize information and help you to spot and resolve problems faster. We’ve made some updates to the MySQL dashboard in the 1.4.0 release. You can see those improvements in action in our Percona Monitoring and Management Demo Site: check out the MySQL Overview and …

This post is following the story of MySQL 8.0 Performance & Scalability started with article about 2.1M QPS obtained on Read-Only workloads. The current story will cover now our progress in Read-Write workloads..
Historically our Read-Only scalability was a big pain, as Read-Only (RO) workloads were often slower than Read-Write (sounds very odd: "add Writes to your Reads to go faster", but this was our reality ;-)) -- and things were largely improved here since MySQL 5.7 where we broke 1M QPS barrier and reached 1.6M QPS for the first time. However, improving Writes or mixed Read+Writes (RW) workloads is a much more complex story..
What are the main scalability show-stoppers …

I ended one of my last posts - Fun with InnoDB Persistent Statistics - with a cryptic sentence: there is more to say about this but I will stop here for now.  What I did not share at the time is the existence of a crashing bug somehow related to what I found.  But let's start with some context.

In Bug#86926, I found a way to put more than 64 characters in the field table_name of the

In this blog post, I’ll provide some guidance on how to choose the MySQL innodb_log_file_size.

Like many database management systems, MySQL uses logs to achieve data durability (when using the default InnoDB storage engine). This ensures that when a transaction is committed, data is not lost in the event of crash or power loss.

MySQL’s InnoDB storage engine uses a fixed size (circular) Redo log space. The size is controlled by innodb_log_file_size and innodb_log_files_in_group (default 2). You multiply those values and get the Redo log space that available to use. While technically it shouldn’t matter whether you change either the innodb_log_file_size or innodb_log_files_in_group variable to control the Redo space size, most people just work with the innodb_log_file_size and leave innodb_log_files_in_group alone.

Configuring …

In this blog post, I’ll discuss some of the MySQL and MariaDB default configuration differences, focusing on MySQL 5.7 and MariaDB 10.2. Percona Server for MySQL uses the same defaults as MySQL, so I will not list them separately.

MariaDB Server is a general purpose open source database, created by the founders of MySQL. MariaDB Server (referred to as MariaDB for brevity) has similar roots as Percona Server for MySQL, but is quickly diverging from MySQL compatibility and growing on its own. MariaDB has become the default installation for several operating systems (such as Red Hat Enterprise Linux/CentOS/Fedora). Changes in the default variables can make a large difference in the out-of-box …

The first release candidate of MySQL 8.0 is here, and I'm happy to share few performance stories about. This article will be about the "most simple" one -- our in-memory Read-Only performance ;-))
However, the used test workload was here for double reasons :

• 1) validate MySQL 8.0 performance
• 2) fully evaluate the "New" Sysbench developed by Alex (https://github.com/akopytov/sysbench)

Going ahead to the second point, the main worry about New Sysbench was about its LUA overhead (the previous version 0.5 was running slower than the old one 0.4 due LUA) -- a long story short, I can confirm now that the New Sysbench is running as fast as the oldest "most lightweight" Sysbench binary I have in use ! so, KUDOS Alex !!! ;-)) …

In my previous blog post, I talked about new general tablespaces in MySQL 8.0. Recently MySQL 8.0.3-rc was released, which includes a new data dictionary. My goal is to create one million tables in MySQL and test the performance.

Background questions

Q: Why million tables in MySQL? Is it even realistic? How does this happen?

Usually, millions of tables in MySQL is a result of “a schema per customer” Software as a Service (SaaS) approach. For the purposes of customer data isolation (security) and logical data partitioning (performance), each “customer” has a dedicated schema. You can think of a WordPress hosting service (or any CMS based hosting) where each …