"What happens when you run a 10 TB MySQL database on Kubernetes?" 

That's the question many of our customers and users asked and honestly, we were extremely curious ourselves.

So, we ditched the weekend plans, rolled up our sleeves, and jumped down the rabbit hole. What we found was far more challenging (and perhaps a bit more "psychedelic") than expected. We spent days rigorously testing the Percona Operator for PXC at massive scale. 

This blog post distills all our findings into the most critical, actionable advice you need to ensure your high-scale environment not only survives but operates reliably.

If you are lazy and prefer to watch a video, here is the presentation I did at the MySQL Belgian days in January 2026.

First of all, what environment did we use? 

Well forget about 4Gb ram or using the millesimal set on CPUs. You need heavy stuff, you need power, you need the mega-super-duper tank not the small bike, well if you can pay for it of course. Anyhow we have tested on something that is not small and not too huge as well, all in AWS/EKS.

The AWS instance type chosen for the testing environment was m7i.16xlarge, combined with an io2 storage class provisioned at 80,000 IOPS.

This configuration was selected because:

• The m7i.16xlarge is a general-purpose instance with custom 4th Generation Intel Xeon Scalable processors, offering 64 vCPUs and 256 GiB of high-bandwidth DDR5 memory. This robust computational and memory capacity ensures that CPU or memory are unlikely to be limiting factors during initial testing of a large-scale, high-performance MySQL cluster on EKS.
• Its 20 Gbps dedicated EBS bandwidth and up to 80,000 maximum IOPS to EBS perfectly complement the high-performance io2 Block Express storage volume.
• By provisioning the storage to the maximum rated IOPS of the underlying instance type, the aim was to create a stress-testing environment that isolates potential performance constraints in the MySQL operator, Percona architecture, Kubernetes storage mechanisms (CSI/EBS), or database configuration, rather than hitting a hardware-imposed ceiling. This allows for a precise analysis of operational overheads and bottlenecks when the I/O subsystem is not the weakest link, which is critical when scaling a database to 10 TB of data.

Did it go as we would like to? Well long story short, not in full but I was expecting worse … into the rabbit hole we go.

We fully validated the cluster's ability to scale, but here's what surprised (and alarmed) us the most:

• The 16-Hour Naptime MTTR (Mean Time To Recover): Full cluster recovery time for the 10 TB dataset clocked in at an eye-watering 960 minutes (16 hours)! If your business requires a guaranteed 1-hour Recovery Time Objective (RTO), you're going to have a very awkward conversation with your boss.

The rebuild operation from cluster crash was executed recovering the first node from snapshot then, letting the cluster recover using internal recovery mechanism.

• The Unruly Cloud Storage: We provisioned high-performance AWS EBS volumes (80,000 IOPS), yet we hit a wall due to Galera Flow Control. The root cause? One node's volume had a 21% performance degradation compared to the others. The entire synchronous cluster was choked by the single slowest volume.

• The Starved Recovery Container: We discovered the restore process's most time-consuming phase the single-threaded redolog apply was bottlenecked because the default recovery container only gets a laughably default memory allocation (around 100 MB). This process consumed 92% of the total restore time.

The Safe Operation Playbook: Critical Fixes

You can tame the data monster, but you have to stop using the default settings. Your focus must shift to I/O consistency, recovery optimization, and mandatory zero-downtime practices.

1. Stop Starving Your Recovery Container

The single most effective action to improve your RTO is giving the restore container more memory to efficiently process the redolog.

• The Fix: You must override the inadequate defaults in your recovery.yaml specification.
• Recommendation: Set memory: 4Gi and cpu: 2000m (2 full cores). This directly reduces the time spent on the single-threaded redolog apply phase, chipping away at that painful 16-hour recovery window. This will alleviate and not resolve the situation though. 
• Redefine Recovery SLO: Since the Operator waits for full cluster recovery before HAProxy serves traffic, you must redefine your internal service goal to Minimal High Availability (HA) (two out of three nodes synced), you can achieve it increasing the size of the cluster by steps, so first one node then add the second and so on. This allows you to claim service is resumed in hours (or less, if optimized) rather than 16 hours.

2. The Cloud Storage Problem: EBS is Not Trustable

For critical, high-scale PXC clusters, the testing proved that high-cost abstracted cloud volumes (EBS) are too unpredictable due to the risk of I/O variability.

• The Problem: I/O variance is a single point of failure that triggers Galera Flow Control. Because PXC is synchronous, if one node is 21% slower, the entire cluster is 21% slower.
• The Long-Term Solution: For mission-critical 10 TB systems, mandate a review for dedicated storage solutions. Look for cloud alternatives that offer strict I/O guarantees, such as dedicated NVMe instances, to bypass the performance abstraction layer and ensure every node performs equally. Or opt for direct attach (fiberchannel) solutions with a SAN tune to prevent volume overlaps. 

3.  Zero-Downtime is Mandatory (Say No to Direct SQL DDL)

On a Percona XtraDB Cluster, DDL is replicated using Total Order Isolation (TOI). This means that direct ALTER TABLE operations cause a full, unacceptable table lock, resulting in downtime.

• The Rule: Use PTOSC (Percona Online Schema Change) for all DDL, including simple index operations. It takes significantly longer, but it is the only way to avoid service interruption.
• The Capacity Warning: PTOSC temporarily duplicates the entire target table. Ensure you have meticulous capacity planning for temporary disk space to prevent a cluster-wide storage saturation crash!
• Large DML: Break all large UPDATE and DELETE operations into small, iterative batches (chunks) to minimize the scope and duration of transactional locks.

Hot Data Subset vs. The Whole Barn

The most fascinating performance finding was how much the application's access pattern affects performance at scale. This dictates how well your InnoDB Buffer Pool can save you from a slow disk.

Remember that the tests used a 50/50 read/write workload. Even when operating on a "hot" subset in the buffer pool, the persistent write and purge operations still required disk I/O, which likely reduced the visible performance difference (delta) between the hot subset and uniform access patterns. This delta would be larger with a higher percentage of read operations. 

The Bottom Line is that your 10 TB cluster is faster if your application keeps queries focused on the "hot" data subset. Uniform access patterns force the system to pay the full 10 TB I/O cost, every time. 

Actionable Summary for the Team

1. Recovery Override: Set memory: 4Gi in the recovery spec to reduce the negative impact of the 92% redolog apply bottleneck.
2. DDL Enforcement: PTOSC is mandatory for all schema changes. Batch large DML.
3. Storage Fix: Do not trust EBS/abstracted storage for 10 TB. Plan an architectural shift toward guaranteed I/O (e.g., dedicated NVMe instances).
4. Data Mobility: Standardize on MyDumper/Loader for all bulk operations.
5. Data distribution/access: If You Have a Hot Data Subset, Offload the Rest. The better performance seen with the "hot" data subset (Pareto) is your green light!
   Any data your application accesses uniformly (or rarely) is only increasing your recovery time and operational cost.
   You should treat the remaining data as a candidate for immediate migration to an OLAP container or data lake, ensuring your PXC cluster remains lean and fast.

Conclusions

Ultimately, our deep dive proved that running Percona XtraDB Cluster on Kubernetes with a 10 TB dataset is less about raw scalability and more about operational rigor and resource tuning. 

The cluster is fundamentally resilient, but its success hinges on bypassing the hidden pitfalls like:

• the unpredictable nature of abstract cloud I/O
• the crippling cost of default recovery settings
• the unacceptable downtime from standard DDL practices.

However we also identify some areas for improvements like in the case of full cluster recovery, we will work on that. 

The key takeaway is simple: default configurations are for small data, large datasets require deliberate engineering.

Notes:

*  In this context means: Mean Time To Recover / Restore (The "User" Metric). Which is the average time from the start of the outage until the system is fully back online and usable for the customer. Includes: Detection time + Response time + Repair time + Testing time.

This article is focused on describing the latest performance benchmarking executed on the latest releases of Community MySQL, Percona Server for MySQL and MariaDB. 

In this set of tests I have used the machine described here. 

Assumptions

There are many ways to run tests, and we know that results may vary depending on how you play with many factors, like the environment or the MySQL server settings. However, if we compare several versions of the same product on the same platform, it is logical to assume that all the versions will have the same “chance” to behave well or badly unless we change the MySQL server settings. 

Because of this, I ran the tests changing only things in a consistent way, with the intent to give the same opportunity to each solution. There is a clear assumption that if you release your product based on the defaults, that implies you have tested with them and consider them the safest for generic use. 

I also applied some modifications and ran the tests again to see how optimization would impact performance. 

What tests do we run?

High level, I run one set of test:

• TPC-C (https://www.tpc.org/tpcc/) like 

The full methodology and test details can be found here, while actual commands are available:

• Sysbench
• TPC-C

Why do I (normally) only publish TPC-C tests?  

Well, I am, normally, more interested in testing scenarios that are closer to reality than a single function test as we normally do with sysbench. 

This is it, while it is not possible to get the perfect benchmark test fitting all real usage, we need to keep in mind the rule of 80%. 

If you use MySQL/InnoDB I expect that you will have an OLTP kind of traffic, more than Key/Value or else. 

Given that while testing the single function, as we do with sysbench, it may be useful to identify regression points or so. To get the wide scenario, TPC-C is a better way to go, given it implies not only a more intense write load, TPC-C test is 50/50 r/w, but also a schema structure with relations, foreign keys and constraints. In short, it is closer to the common use of a relational database management system.

However, this time things are a bit different and to be fair I must dig a bit also in the Sysbench single tests. This is making this blog a bit longer than the previous ones, but I think it is worth it and you will see why. 

Results

The tests done have two different kinds of isolation levels. Repeatable Read and Read Committed. The first is the default in MySQL/InnoDB, while the second is the default in many other very well-known RDBMS. 

First let us review how Community MySQL and Percona Server MySQL are doing in comparison with the previous versions, especially after the debacle we had some time ago (see https://www.tusacentral.net/joomla/index.php/mysql-blogs/256-sakila-where-are-you-going) 

This time I have finally dropped 5.7, given it's EOL from some time. (Folks, you must move out of it, if you don’t know how to do it, call us for help.) 

As usual an image is more descriptive than many words:

You can also compare these trends (not the values) with the previous results published here.

Let us comment a bit on these images. 

The first comment we should make is that nowadays our systems must be ready to scale; period, no discussion. Also, doing benchmarks up to 1024 threads is not enough. In the real world we have 4000 or more connections. Given that, doing benchmarking exercises and stopping the load at 128 threads or lower, makes no sense. 

This is going to be very clear in the next section. For now, what we can notice is that Percona Server and MySQL community are very close in terms of performance. The minor difference is inside the %variance so it is not relevant. This is expected given we share a large part of the code. What is relevant here is that Percona Server comes with similar/same performance and is a more feature-rich version, given that Percona Server MySQL has features that exist only in MySQL Enterprise.  

The second comment is that in previous tests, MySQL had not only stabilized the server behavior, but it has also done significant fixes to prevent the performance loss while scaling.

Now what we can see is that the newer versions are able to keep on with the traffic and are able to scale a bit more. So, in short, everything seems going good, or at least much better than before. 

Let us now add MariaDB 11.8.5 and stay focused on MySQL/Percona 8.4 and 9.5

This doesn’t look very good, right? What it seems is that MariaDB is doing great up to 32 threads, then just stops and cannot serve traffic. 

Restricting the concurrency to a maximum of 32 threads would have prevented the observation of MariaDB's saturation point, thereby yielding inaccurate test results and erroneous conclusions. This is why I am always first trying to identify the saturation point, then scale the tests.   

Why did we have that? We need to keep in mind that in TPC-C like tests execute a lot of reads and writes 50/50 inside the same transaction and as concurrent operations, better reflecting the reality of what happens on a busy system. 

In any case, I was really puzzled by this MariaDB behavior, which seems very weird to me. So I decided to also run all the sysbench tests I have and see what happens (to see the full results, see this.) 

What comes up from this exercise is a different scenario. Executing isolated tests, MariaDB is doing great and often surpassing MySQL or Percona server in reads.
Let us see some examples like the range tests all, where I execute all the range tests:

As we can see MariaDB is performing great and in the case of unordered pages is less affected than MySQL/Percona. 

And this is the trend for all reads. What about the writes then?

Well in some cases like when we have in list update with hotspot MariaDB it is still doing good:

But if we do not have hotspots, when the writes are distributed across the whole dataset:

This is even clearer when using insert/delete:

Finally, if we collect the tests that execute read/write operations:

We can see we have a very similar behavior as we had with TPC-C tests. 

I did not investigate in detail what is causing MariaDB to fail so much in write operations, but my gut tells me to point toward the InnoDB Buffer pool restructuring they had done, which is probably also the reason why MariaDB is doing better in reads. I am obviously open to collaborate with any MariaDB folk that would like to tune and test again, just ping me.

If you want to browse all tests go: here for tpcc

Conclusions

The tests performed using TPC-C like tests confirm that the MySQL debacle should be over. At the same time, they indicate that the race to get better performance is open again, and I am intrigued to see what will come next, hoping we will see Percona sprint again and add a significant performance gain, not only enterprise features.

For now, MySQL/Percona 9.5 confirms to be the better performing MySQL version currently available, for its stability and scalability. 

I also think we should investigate a bit more on MariaDB implementation and identify what is making that solution operating much better in reads and acting so poorly for writes. For sure, we are not in the LAMP era anymore, when the majority of operations were read. Nowadays, the standard load and utilization on a DB sees read/write operations executed with a high level of threads, so a behavior like the one MariaDB has is not acceptable. But I am sure that if they investigate and focus on that, they will be able to identify the bottleneck and fix it.

Finally, I can't wait to see this product become even better. I truly hope that our push for the community to step up and take charge yields the results we're aiming for.

In the meanwhile, have a great MySQL (and MariaDB)!

MySQL Shell is a powerful tool for managing MySQL databases, and one of its most exciting features is the ability to extend its functionality using plugins. If you're a Python developer, you can create custom plugins to automate tasks, add new commands, or
integrate MySQL Shell with other tools. However debugging the python code could be cumbersome for mysql-shell given the python code requires some specific objects available only when running inside the shell. 

In this blog post, we'll walk you through the steps to set up your development environment for creating MySQL Shell plugins in Python while able to modify and debug the mysql-shell itself.

Prerequisites

Before we dive into the setup, ensure you have the following installed on your system:

• MySQL Shell: Download and install MySQL Shell from the official MySQL website.
• Python 3.8 or later: MySQL Shell plugins require Python 3.8+. You can download Python from python.org.
• A code editor: Use your favorite code editor, such as VS Code
• Git: Optional, but useful for version control.

A directory structure as follow:

mysql_source/
├── boost
├── mysql-8.4.4  <-- directory where we compile the mysql server 
├── mysql-server <-- directory with the source code (from git or package)
├── mysql-shell  <-- directory with the mysql-shell code from git
├── mysql-shell-build-vs <-- directory where we build using Visual Studio
└── mysql-shell-plugins  <-- directory with the example plugin 

Step 1: Collect and compile MySQL server code

If you want to clone the MySQL repository:

git clone https://github.com/mysql/mysql-server.git

git fetch

git checkout tags/mysql-8.4.4 -b mysql-8.4.4 

Or just download the source code from the website for that specific release:

curl https://dev.mysql.com/get/Downloads/MySQL-8.4/mysql-8.4.4.tar.gz - o mysql-8.4.4.tar.gz

In both cases once you have the code locally we need to configure and build the libraries.

For more information about how to compile and dependencies check here

Then we go inside the compile directory (mysql-8.4.4) and configure it:

cmake /<path>/mysql_source/mysql-server -DCMAKE_INSTALL_PREFIX=/<path>/mysql_templates/mysql-8.4.4 -DWITH_DEBUG=1 -Dprotobuf_BUILD_SHARED_LIBS=ON

And we compile the needed libraries:

  cmake --build . --target mysqlclient

  cmake --build . --target mysqlxclient

There is no need to compile the whole server, unless you really want to. 

Step 2: Collect and compile MySQL Shell code

Once the server libs are prepared then go for mysql-shell.

Get the code:

git clone https://github.com/mysql/mysql-server.git

git fetch

git checkout tags/8.4.4 -b 8.4.4

Now my advice is to always do a compilation outside the code editor to be sure we have all the needed components, for more info check here

To configure:

cmake /<path>/mysql_source/mysql-shell -DMYSQL_SOURCE_DIR=/<path>/mysql_source/mysql-server/ -DMYSQL_BUILD_DIR=/<path>/mysql_source/mysql-8.4.4 -DHAVE_PYTHON=1 -DHAVE_V8=0 -DWITH_TESTS=1 -DCMAKE_BUILD_TYPE=Debug -DCMAKE_INSTALL_PREFIX=/<path>/mysql_source/mysql_shell_bin 

If all went well you can check if the mysql-shell works properly running it from the destination path (in the example) /opt/mysql_source/mysql_shell_bin/bin/mysqlsh 

Step 3: Set Up a Python Virtual Environment

To avoid conflicts with other Python projects, it's a good idea to create a virtual environment for your MySQL Shell plugin development.

Open your terminal and navigate to your project directory.

Create a virtual environment:

python -m venv mysqlsh-plugin-env

Activate the virtual environment:

On macOS/Linux:

source mysqlsh-plugin-env/bin/activate

Verify that the virtual environment is active. Your terminal prompt should now show the name of the virtual environment.

Step 4: For lazy people (as myself) download and use existing plugin demos.

Our dear friend Lefred has built a very nice suite that I normally use as a starting point.

Get the code, go to your mysql_source directory and:

git clone https://github.com/lefred/mysqlshell-plugins.git 

In mysql-shell at the moment you can load the plugin at startup only if they are located in the sub-directory ~/.mysqlsh/plugins or where the lib/mysqlsh/plugins is located. See FR here for a more convenient way to dynamically pass the path to browse. 

Anyhow for now and the purpose of this blog let us use a a trick and create a symlink:

mkdir ~/.mysqlsh/plugins

ln -s <path>/mysql_source/mysql-shell-plugins/demo ~/.mysqlshell/plugins/demo

Ok now our code side is set. 

Download and configure VisualStudio.

Add few extension, mine are the following:

Then open the directory where you have clone the mysql-shell code inside VS and save it as workspace:

Then click again on the File and add Folder to workspace and add mysql-shell-plugin/demo directory. 

Now we need to configure the cmake side of the project:

Click on the icon on the left first then on the settings wheel to open the specific menu. 

I suggest you to change things at workspace level choosing the workspace tab:

Look for the Cmake: Build Directory option and set it to /<add full path>/mysql_source/mysql-shell-build-vs as outlined before. 

Then look for the Cmake: Configure Settings option and click on edit settings.json and set as follow:
"cmake.configureSettings": {

"MYSQL_SOURCE_DIR":"/<add full path>/mysql_source/mysql-server/",

"MYSQL_BUILD_DIR":"/<add full path>/mysql_source/mysql-8.4.4",

"HAVE_PYTHON":true,

"BUNDLED_POLYGLOT_DIR":0,

"WITH_TESTS":true,

"CMAKE_BUILD_TYPE":"Debug",

},

The above will create the Cmake configuration and structure in /<add full path>/mysql_source/mysql-shell-build-vs. 

Once this is done compile the project clicking on the small icon close to build

This will build the whole project, sit down and relax while compilation will complete. 

Once this is complete click on the run-and-debug  icon:

Then click on create a launch.json

Choose mysql-shell-vs:

Then pick C/C++ gdb Attach.

This will open a json file, we are interested in the values inside the “configurations”, it should be something like this:
      {

            "name": "(gdb) Launch",

            "type": "cppdbg",

            "request": "launch",

            "program": "/<add full path>/mysql_source/mysql-shell-build-vs/bin/mysqlsh",

            "args": [

                "--verbose=1"

            ],

            "stopAtEntry": false,

            "cwd": "/<add full path>/mysql_source/mysql-shell-build-vs/",

            "environment": [{"name": "PYTHONPATH", "value": "PATH to your python directory"}],

            "externalConsole": false,

            "MIMode": "gdb",

            "setupCommands": [

                {

                    "description": "Enable pretty-printing for gdb",

                    "text": "-enable-pretty-printing",

                    "ignoreFailures": true

                },

                {

                    "description": "Set Disassembly Flavor to Intel",

                    "text": "-gdb-set disassembly-flavor intel",

                    "ignoreFailures": true

                }

            ]

        }

After that you need to add the Python debugger information:

      {

            "name": "plugin-p",

            "type": "debugpy",

            "request": "attach",

            "connect": {

                "host": "localhost",

                "port": 5678

            },

            "pathMappings": [

                {

                    "localRoot": "${workspaceFolder}",

                    "remoteRoot": "."

                }

            ],

            "justMyCode": false

        },

As you can see what we will do is to attach the debugger to a process that will spin up inside the code.

Save the file, and in the left panel you will now have the option to run and debug using both debuggers. 

Before running the debug let us do a couple of more things.

Click on the project explorer and open the init.py file in the demo directory 

Before the instruction: import mysqlsh, add this text:

import debugpy

debugpy.listen(("localhost", 5678))

print("Waiting for debugger attach...")

debugpy.wait_for_client()

print("Debugger attached.")

If the module debugpy is not installed do it with:

pip install debugpy

Then before the last line add:

import mysqlsh

shell = mysqlsh.globals.shell

This to create the shell object that is available only internally to the mysql-shell.

Now let us add a couple of breakpoints one in the mysql-shell code and another inside the python code.

In mysql_shell.cc look for:

  for (const auto &dir : plugin_directories) {

    get_plugins(file_list, dir, true);

  }

In mysql-shell 8.4.4 is at line 964.

Put a breakpoint on get_plugins

Then open oracle8ball.py and put a breakpoint on the last line:

 print(answers[index]) 

Run the C/C++ debugger… 

The execution will stop and in the debug left panel you will see the variable informations plus the debug toolbar:

This is standard debug for C/C++ projects … Let us click to proceed.

Execution will continue until loading the plugins then will stop. It will hang because the line we have inserted:

debugpy.wait_for_client()

Now run the Python debugger it will connect to the dbugger we defined in the code allowing the process to continue.

In the terminal window the mysql-shell prompt will appear but not only:

We can see that the python debugger is now also attached. 

Let us move to python and execute the demo.oracle8ball()

Execution is suspended and our debugger stop at our breakpoint:

Please note two things, the first is that also in this case we can see the whole set of information related to local and global variables including the shell object and the other is that the debugger menu shows that now the navigation button refers to the python debugger. 

This level of integration will give anyone a more friendly environment where to develop your mysql-shell extensions. This is particularly important for the ones who wish to standardize and simplify the DBA operation or to give developers easy access to independently and safely perform actions on the databases. 

Tips for Developing MySQL Shell Plugins

Use the MySQL Shell API: MySQL Shell provides a rich API for interacting with MySQL databases. Refer to the official documentation for details.

• Test Incrementally: Test your plugin functions frequently to catch errors early.
• Leverage Python Libraries: You can use any Python library in your plugin, so take advantage of the vast Python ecosystem.

Conclusion

Setting up a development environment for MySQL Shell plugins in Python is straightforward. With the right tools and a bit of Python knowledge, you can create powerful extensions to enhance MySQL Shell's functionality. Whether you're automating database tasks or integrating with other systems, MySQL Shell plugins offer endless possibilities.

Happy coding!

This article is focused on describing the latest performance benchmarking executed on the latest release of MySQL and Percona. 

In this set of tests I have used the machine described here.  

Assumptions

There are many ways to run tests, and we know that results may vary depending on how you play with many factors, like the environment or the MySQL server settings. However, if we compare several versions of the same product on the same platform, it is logical to assume that all the versions will have the same “chance” to behave well or badly unless we change the MySQL server settings. 

Because of this, I ran the tests changing only things in a consistent way, with the intent to give the same opportunity to each solution., with the clear assumption that if you release your product based on the defaults, that implies you had tested with them and consider them the safest for generic use. 

I also applied some modifications and ran the tests again to see how optimization would impact performance. 

What tests do we run?

High level, I run one set of test:

• TPC-C (https://www.tpc.org/tpcc/) like 

The full methodology and test details can be found here, while actual commands are available:

• Sysbench
• TPC-C 

Why do I only run TPC-C tests?  

Well I am, normally, more interested in testing scenarios that are closer to reality than a single function test as we normally do with sysbench. 

This is it, while it is not possible to get the perfect benchmark test fitting all real usage, we need to keep in mind the rule of 80%. 

If you use MYSQL/InnoDB I expect that you have an OLTP kind of traffic, more than Key/Value or else. Given that while testing the single function, as we do with sysbench, may be useful to identify regression points or so. To get the wide scenario, TPC-C is a better way to go, given it implies not only a more intense write load, TPC-C test is 50/50 r/w, but also a schema structure with relations, foreign keys and constraints. In short, it is closer to the common use of a relational database management system. 

Results

The tests done have two different kinds of isolation levels. Repeatable Read and Read Committed. The first is the default in MySQL/InnoDB, while the second is the default in many other very well known RDBMS. 

As usual an image is more descriptive than many words:

You can also compare these trends (not the values) with the previous results published here.

Let us comment a bit on these images. 

The first comment we should make is that nowadays our systems must be ready to scale. Period, no discussion, also doing benchmarks up to 1024 threads is not enough. In reality we have 4000 or even more connections, given that doing benchmarking exercises and stopping the load at 128 threads or lower, makes no sense. Here it is quite clear that doing something like that could be very misleading. 

Old MySQL versions are still faster than newer with low level of concurrency, but they do not scale. So if I stop my tests at 64 or 128 threads I will conclude that MySQL 5.7 is always better than newer versions, while if I go on loading, I can see that old version performance drop after 64 concurrent threads. 

The second comment is that while in previous tests (see mentioned article) we saw that newer versions were not performing better or even consistently. With the latest releases MySQL had not only stabilized the server behaviour, but done significant fixes to the performance issues it had. 

If we remove the 5.7 version from the graphs we can see clearer what is going on:

If you notice the servers lines after 32 threads and especially after 64 threads diverge and we have two groups one see Percona and MySQL 8.0.40 in the lower set while Percona and MySQL 8.4 and 9.x are in the upper group. 

This is quite great news and very nice to see, because if I have to read it in a way I see a positive sign indicating how Oracle/MySQL is progressively resolving some performance issues and gaining ground again. 

Conclusions

The tests performed using TPC-C like tests confirms the initial finding of my colleague here, and give us a picture that is more positive of what we had. At the same time they indicate that the race to get better performance is open again, and I am intrigued to see what will come next.

For now we can say that MySQL 9.2 is the better performing MySQL version currently available, for its stability and scalability. 

Great job!