The new MySQL-8.0.20 release is coming with re-designed InnoDB Double Write Buffer (DBLWR), and, indeed, it's one huge historical PITA less.. -- why it was so painful and cost us much blood in the past, I could not better explain than already done it in the following article yet from 2018 about MySQL on IO-bound workloads.. The story is not complete, as it's missing the 2019's chapter (will tell it later, np) -- but if you'll (re)read the mentioned above article first, you'll better understand the next ;-))

But at least the current post is only about good news now -- the new DBLWR and how it helps to solve historical MySQL performance problems ! -- and as one picture is better than million words, I'll try to save 3M words here (as there are 3 pictures in this article ;-))

Well, I'll also skip all new design details …

What You May Not Know About Random Number Generation in Sysbench

Sysbench is a well known and largely used tool to perform benchmarking. Originally written by Peter Zaitsev in early 2000, it has become a de facto standard when performing testing and benchmarking. Nowadays it is maintained by Alexey Kopytov and can be found in Github at https://github.com/akopytov/sysbench.

What I have noticed though, is that while widely-used, some aspects of sysbench are not really familiar to many. For instance, the easy way to expand/modify the MySQL tests is using the lua extension, or the embedded way it handles the random number generation. 

Why This Article? 

I wrote this article with the intent to show how easy it can be to customize sysbench to make it what you need. There are many different ways to extend sysbench use, and one of these is …

As our Percona Kubernetes Operator for Percona XtraDB Cluster gains in popularity, I am getting questions about its performance and how to measure it properly. Sysbench is the most popular tool for database performance evaluation, so let’s review how we can use it with Percona XtraDB Cluster Operator.

Operator Setup

I will assume that you have an operator running (if not, this is the topic for a different post). We have the documentation on how to get it going, and we will start a three-node cluster using the following cr.yaml file:

apiVersion: pxc.percona.com/v1-3-0
kind: PerconaXtraDBCluster
metadata:
  name: cluster1
  finalizers:
    - delete-pxc-pods-in-order
spec:
  secretsName: my-cluster-secrets
  sslSecretName: …

By having appropriate indexes on your MySQL tables, you can greatly enhance the performance of SELECT queries. But, did you know that adding indexes to your tables in itself is an expensive operation, and may take a long time to complete depending on the size of your tables? During this time, you are also likely to experience a degraded performance of queries as your system resources are busy in index-creation work as well. In this blog post, we discuss an approach to optimize the MySQL index creation process in such a way that your regular workload is not impacted.

MySQL Rolling Index Creation

We call this approach a ‘Rolling Index Creation’ - if you have a MySQL master-slave replica set, you can create the index one node at a time in a rolling fashion. You should create the index only on the slave nodes so the master’s …

This is a follow-up post in the MySQL Master Replication Crash Safety series.  In the previous posts, we explored the consequences of reducing durability on masters (different data inconsistencies after an OS crash depending on replication type) and the performance boost associated with this configuration (benchmark results done on Google Cloud Platform / GCP).  The consequences are summarised in

This post is a sister post to MySQL Master Replication Crash Safety Part #5: making things faster without reducing durability.  There is no introduction or conclusion to this post, only landing sections: reading this post without its context is not not recommended. You should start with the main post and come back here for more details.

And this Part #5 of the series has many sub-parts.  So far,

This is a follow-up post in the MySQL Master Replication Crash Safety series.  In the three previous posts, we explored the consequence of reducing durability on masters (including setting sync_binlog to a value different from 1).  But so far, I only quickly presented why a DBA would run MySQL with such configuration.  In this post, I present actual benchmark results.  I also present a

This post is a sister post to MySQL Master Replication Crash Safety Part #4: benchmarks of high and low durability.  There are no introduction or conclusion to this post, only landing sections: reading this post without its context is not recommended. You should start with the main post and come back here for more details.

Environment

My benchmark environment is composed of three vms in

I am currently running tests with sysbench and dbdeployer on a Google Cloud Platform Debian 9 instance.  I ran into an interesting limit and lifting it was not straightforward.  I hope that by sharing this, you can avoid losing too much time.

The benchmark I am using is the insert benchmark.  I am able to run it with 2048 threads but I am not able to run it with 4096 threads (and yes, it makes

Sysbench has long been established as the de facto standard when it comes to benchmarking MySQL performance. Percona relies on it daily, and even Oracle uses it when blogging about new features in MySQL 8. Sysbench comes with several pre-defined benchmarking tests. These tests are written in an easy-to-understand scripting language called Lua. Some of these tests are called: oltp_read_write, oltp_point_select, tpcc, oltp_insert. There are over ten such scripts to emulate various behaviors found in standard OLTP applications.

But what if your application does not fit the pattern of traditional OLTP? How can you continue to utilize the power of load-testing, benchmarking, …