When I run a benchmark and want to measure the CPU efficiency of something, I find it’s often a good choice to run a benchmark program, as well as the database, on the same server. This is in order to eliminate network impact and to look at single-thread performance, to eliminate contention.

Usually, this approach gives rather stable results; for example, benchmarking MySQL with Sysbench OLTP Read-Only workload I get a variance of less than one percent between 1-minute runs.

In this case, though, I was seeing some 20 percent difference between the runs, which looked pretty random and would not go away even with longer 10-minute runs.

The benchmark I did was benchmarking MySQL through ProxySQL (all running on the same machine):

Sysbench -> ProxySQL -> MySQL 

As I thought more about possible reasons, I thought CPU scheduling might be a problem. As requests pass …

When connecting to a local MySQL instance, you have two commonly used methods: use TCP/IP protocol to connect to local address –  “localhost” or 127.0.0.1  – or use Unix Domain Socket.

If you have a choice (if your application supports both methods), use Unix Domain Socket as this is both more secure and more efficient.

How much more efficient, though?  I have not looked at this topic in years, so let’s see how a modern MySQL version does on relatively modern hardware and modern Linux.

Benchmarking  TCP/IP Connection vs Unix Domain Socket for MySQL

I’m testing Percona Server for MySQL 8.0.19 running on Ubuntu 18.04 on a Dual Socket 28 Core/56 Threads Server.  (Though I have validated results on 4 …

In this post, I want to evaluate Group Replication Scaling capabilities in cases when we increase the number of nodes and increase user connections. While this setup is identical to that in my post “Evaluating Group Replication Scaling Capabilities in MySQL”,  in this case, I will use an I/O bound workload.

For this test, I will deploy multi-node bare metal servers, where each node and client are dedicated to an individual server and connected between themselves by a 10Gb network.

Also, I will use 3-nodes and 5-nodes Group Replication setup. In both cases, the load is directed only to ONE node, but I expect with five nodes there is some additional overhead from replication.

Hardware specifications:

System | Supermicro; SYS-F619P2-RTN; v0123456789 (Other)
Service Tag | …

In this blog, I want to evaluate Group Replication Scaling capabilities to handle several writers, that is, when the read-write connection is established to multiple nodes, and in this case, two nodes. This setup is identical to my previous post, Evaluating Group Replication Scaling Capabilities in MySQL.

For this test, I deploy multi-node bare metal servers, where each node and client are dedicated to an individual server and connected between themselves by a 10Gb network.

I use the 3-nodes Group Replication setup.

Hardware specifications:

System | Supermicro; SYS-F619P2-RTN; v0123456789 (Other)
Service Tag | S292592X0110239C
   Platform | Linux
    Release | Ubuntu 18.04.4 LTS (bionic)
     Kernel | 5.3.0-42-generic
Architecture | CPU = 64-bit, OS = 64-bit
  Threading | NPTL 2.27 …

In this blog, I want to evaluate Group Replication Scaling capabilities in cases when we increase the number of nodes and increase user connections.

For testing, I will deploy multi-node bare metal servers, where each node and client are dedicated to an individual server and connected between themselves by a 10Gb network.

Also, I will use 3-nodes and 5-nodes Group Replication setup.

Hardware specifications:

System | Supermicro; SYS-F619P2-RTN; v0123456789 (Other)
Service Tag | S292592X0110239C
   Platform | Linux
    Release | Ubuntu 18.04.4 LTS (bionic)
     Kernel | 5.3.0-42-generic
Architecture | CPU = 64-bit, OS = 64-bit
  Threading | NPTL 2.27
    SELinux | No SELinux detected
Virtualized | No virtualization detected
# Processor ##################################################
 Processors | physical = 2, cores = 40, virtual = 80, hyperthreading = yes
     Models | 80xIntel(R) Xeon(R) Gold 6230 CPU @ …

More and more people are nowadays thinking of cloud migration. The question of “Which cloud provider performs better for my MySQL workload?” is really common but cannot always be easily answered. However, there are ways to come up with an answer. This question also applies when thinking of moving to any provider, not necessarily a cloud one or DBaaS.

The Problem

The most reliable conclusion can be found if you have a testing environment that is fully identical and can produce the same amount of traffic compared to your production version. In this case, the comparison should be straightforward as what you have to do is point your testing environment against the under-evaluation (cloud) providers, and evaluate the differences in performance. But this is not always easy, as many organizations do not have such environments or it’s not always possible to replay all traffic.

The Idea

In this …

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 …

Making backups over the network can be done in two ways: either save on disk and transfer or just transfer without saving. Both ways have their strong and weak points. The second way, particularly, is highly dependent on the upload speed, which would either reduce or increase the backup time. Other factors that influence it are chunk size and the number of upload threads.

Percona XtraBackup 2.4.14 has gained S3 streaming, which is the capability to upload backups directly to s3-compatible storage without saving locally first. This feature was developed because we wanted to improve the upload speeds of backups in Percona Operator for XtraDB Cluster.

There are many implementations of S3 Compatible Storage: …

Recently, at Percona Live Europe 2019, Dimitri Kravchuk from Oracle mentioned that he observed some unclear drop in performance for MySQL on an ext4 filesystem with the latest Linux kernels. I decided to check this case out on my side and found out that indeed, starting from linux kernel 4.9, there are some cases with notable (up to 2x) performance drops for ext4 filesystem in direct i/o mode.

So what’s wrong with ext4? It started in 2016 from the patch that was pushed to kernel 4.9: “ext4: Allow parallel DIO reads”. The purpose of that patch was to help to improve read scalability in direct i/o mode. However, along with improvements in pure read workloads, it also introduced regression in intense mixed random read/write scenarios. And it’s quite weird, but this issue had not been …

AWS is the #1 cloud provider for open-source database hosting, and the go-to cloud for MySQL deployments. As organizations continue to migrate to the cloud, it’s important to get in front of performance issues, such as high latency, low throughput, and replication lag with higher distances between your users and cloud infrastructure. While many AWS users default to their managed database solution, Amazon RDS, there are alternatives available that can improve your MySQL performance on AWS through advanced customization options and unlimited EC2 instance type support. ScaleGrid offers a compelling alternative to hosting MySQL on AWS that offers better performance, more control, and no cloud vendor lock-in and the same price as Amazon RDS. In this post, we compare the performance of MySQL Amazon RDS …