In MySQL 8.0 we introduced a totally new design for InnoDB REDO Log management. The main difference was about implementing a lock-free solution for user threads, and use dedicated REDO threads for all background IO write work.

for more details, see an excellent and very detailed article by Pawel : https://dev.mysql.com/blog-archive/mysql-8-0-new-lock-free-scalable-wal-design/

However, over a time we also added an option to let users to switch REDO threads=OFF to enforce REDO log processing efficiency in some particular cases. Unfortunately this feature created a lot of confusions for MySQL users, and many ones interpreted this in different ways, providing different and sometimes opposite advices, etc..

My main advice will be always : test each feature yourself and within your …

The following is a short HOWTO about deployment and use of Benchmark-kit (BMK-kit). The main idea of this kit is to simplify your life in running various MySQL benchmark workloads with less blood and minimal potential errors.

Generally as simple as the following :

$ bash /BMK/sb_exec/sb11-Prepare_50M_8tab-InnoDB.sh 32   # prepare data

$ for users in 1 2 4 8 16 32 64 128 256 512 1024 2048
do   
  # run OLTP_RW for 5min each load level..
  bash /BMK/sb_exec/sb11-OLTP_RW_50M_8tab-uniform-ps-trx.sh $users 300
  sleep 15
done

the latest public online version of the following HOWTO is always available from here : http://dimitrik.free.fr/blog/posts/mysql-perf-bmk-kit.html

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As some of you likely know, I have a favorable view of ZFS and especially of MySQL on ZFS. As I published a few years ago, the argument for ZFS was less about performance than its useful features like data compression and snapshots. At the time, ZFS was significantly slower than xfs and ext4 except when the L2ARC was used.

Since then, however, ZFS on Linux has progressed a lot and I also learned how to better tune it. Also, I found out the sysbench benchmark I used at the time was not a fair choice since the dataset it generates compresses much less than a realistic one. For all these reasons, I believe that it is time to revisit the performance aspect of MySQL on ZFS.

ZFS Evolution

In 2018, I reported ZFS performance results based on version 0.6.5.6, the default version available in Ubuntu Xenial. The present post is using …

Few weeks ago with a big curiosity I was reading several articles published by Percona about TPCC Benchmark results and MySQL 8.0 "checkpointing" issues..

Unfortunately, in these articles there was no any explanation nor any tentative to understand what is going on, an probably at least try and validate some "first coming in mind" tuning / troubleshooting options.. (And even no any try to show in action so often advertised PMM, and see on what it'll point ?)..

All in all, in the following article I'll try to feel up the "white holes" left in this TPCC testing..

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The TPCC workload "mystery" exposed in the following post was already clarified the last year, and I've presented explanations about the observed problem during PerconaLIVE-2019. But slides are slides, while article is article ;-)) So, I decided to take a time to write a few lines more about, to keep this post as a reference for further TPCC investigations..

The "mystery" is related to observed scalability issues on MySQL 8.0 under the given TPCC workload -- just that on the old aged DBT-2 workload (TPCC variation) I was getting much higher TPS when running on 2 CPU Sockets, comparing to1 CPU Socket, which is was not at all the case for Sysbench-TPCC.

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This post is mainly inspired by findings from the previous testing of MySQL 8.0 on TPCC workload(s) and observations from IO-bound Sysbench OLTP on Optane -vs- SSD. But also by several "urban myths" I'm often hearing when discussing with users about their IO-bound OLTP performance problems :
Myth #1 : "if I'll double the number of my storage drives -- I'll get x2 times better TPS !"

• this was mostly true during "HDD era", and again..
• (ex.: a single thread app doing single random IO reads from a single HDD will not go faster by doing the same from 2x HDD -- similar like single thread workload will not run faster on 8CPU cores -vs- 2CPU cores, etc.)
• all depends …

Generally TPC-C benchmark workload is considered as one of the #1 references for Database OLTP Performance. On the same time, for MySQL users it's often not something which is seen as "the most compelling" for performance evaluations.. -- well, when you're still fighting to scale with your own very simple queries, any good result on something more complex may only look as "fake" ;-)) So, since a long time Sysbench workloads remained (and will remain) as the main #1 "entry ticket" for MySQL evaluation -- the most simple to install, to use, and to point on some sensible issues (if any). Specially that since new Sysbench version 1.0 a lot of improvements were made in Sysbench code itself, it really scales now, has the lowest ever overhead, and also allowing you to add your own test scenario via extended LUA scripts (and again, with lowest ever overhead) -- so, anyone can easily add …

Recently Mark Callaghan blogged about using O_DIRECT for the InnoDB transaction log. He noted that there was not a significant performance gain if the number of concurrent connections increases. I've done my share of testing and retesting over the past months to determine how useful this is. Based on a small TPCC workload (100 warehouses, 64 connections, 1 hour test, 5 minute rampup), I've seen huge performance gains by setting ALL_O_DIRECT for the variable innodb_flush_method using Percona XtraDB.

Without using Direct I/O, the benchmark generated a TpmC score of approximately 24,500 (HP DL160 G6, 2 x Xeon E5620 2.40GHz, 16GB mem, 4x300 GB SAS, RAID-10). After setting the variable to ALL_O_DIRECT, TpmC score went up to 48,000. Huge increase. This deserves some more investigation and some more testing. I want to also try this out on some older …

Percona’s TPCC for MySQL toolset allows one to measure the query performance for an OLTP workload on various MySQL storage engines.  The toolset includes a program to load the database tables, and a program to run queries and measure performance.  We have found Percona’s TPCC toolset to be extremely useful for tuning our software.  However, we want to take advantage of TokuDB’s bulk load capability when loading the database.

We created a new tool, a simple variant of the existing code, that generates CSV files for the TPCC database.  These CSV files can be bulk loaded into TokuDB with a “LOAD DATA INFILE” statement. TokuDB’s bulk loader uses a parallel merge sort algorithm that is implemented in CILK, an extension to the C language that …