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Step 3: Why my sysbench-trained build loses and how to do it right.

Given the topic complexity and the length of this article I have split it in 3 three different blog-post:

1. What is PGO
2. How PGO it works
3. Why my sysbench-trained build loses and how to do it right.

Three compounding reasons:

1. Uncovered code gets pessimized.
   Sysbench-tpcc touches a narrow slice of mysqld.
   Every function with zero counts is treated as cold: GCC optimizes it for size, skips inlining, and shoves it into cold sections.
   But at runtime I still execute plenty of code my training never touched, such as purge, flushing, stats …

Step 2: How it works

Given the topic complexity and the length of this article I have split it in 3 three different blog-post:

1. What is PGO
2. How PGO it works
3. Why my sysbench-trained build loses and how to do it right.

How PGO works: PGO is a two-pass build. First pass compiles with instrumentation (-fprofile-generate): every basic block and branch gets a counter.
You run a training workload, counters are dumped to profraw files.
Second pass recompiles using those counts to drive inlining decisions, branch layout (hot path falls through, cold path jumps away), hot/cold function splitting, code ordering for icache/iTLB locality, …

Given the topic complexity and the length of this article I have split it in 3 three different blog-post:

1. What is PGO
2. How PGO it works
3. Why my sysbench-trained build loses and how to do it right.

Step 1: What is PGO

PGO (Profile-Guided Optimization) is a two-pass compilation technique. 

First, we build the program with instrumentation that adds counters to every branch, loop, and call site.
Next, we run it against a representative training workload so these counters can record which code paths are actually hot versus cold.
We then recompile the program using that data to drive the compiler’s decisions.

With this profiling …

Benchmarking MyRocks vs. InnoDB in Memory-Constrained Environments It is a well-known fact in the database world that InnoDB is incredibly fast when the entire database fits into memory. But what happens when your data grows beyond your available RAM? MyRocks, built on RocksDB, is frequently recommended as a superior choice for environments constrained by memory, […]

To be honest, the comparison between the two MySQL distributions is not something that excited me a lot. Mainly because from my MySQL memories, I knew that there is not a real difference between the two distributions when talking about the code base.To my knowledge the differences in the enterprise version are in the additional […]

In this blog, I will cover important aspects which you need to test when benchmarking replication setup. MySQL has great tools that could be used to test its performance. They include:

sysbench – https://github.com/akopytov/sysbench

BMK-kit – http://dimitrik.free.fr/blog/posts/mysql-perf-bmk-kit.html

mysqlslap – https://dev.mysql.com/doc/refman/8.0/en/mysqlslap.html

LinkBench – https://github.com/facebookarchive/linkbench

I will not describe how to use them here, as you can find instructions on the provided links or in the Percona blog by browsing tags …

While it's easier to measure the impact of Dynimize if you are running a MySQL benchmark with clear metrics, it can sometimes be a challenge on a production workload where you don't have precise performance analytics or metrics available. There are many great MySQL performance analysis tools out there, however they can often take time and effort to setup.

The simple measureDyniMysql script was created for this exact reason, and does not incur any MySQL downtime. You can find it at /opt/dynimize/measureDyniMysql after installing Dynimize. It will report the change in MySQL queries per second and mysqld CPU usage after applying Dynimize. Here is how to use it.

1. Install and start dynimize, and get your mysqld process into the dynimized state. For example, the …

In MySQL 8.0.17, we made an observation in the well-known TPC-H benchmark for one particular query. The query was executing 20% faster than in MySQL 8.0.16. This improvement is because of the “antijoin” optimization which I implemented. Here is its short mention in the release notes:

“The optimizer now transforms a WHERE condition having NOT IN (subquery), NOT EXISTS (subquery), IN (subquery) IS NOT TRUE, or EXISTS (subquery) IS NOT TRUE internally into an antijoin, thus removing the subquery.”…

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Docker presents new levels of portability and ease of use when it comes to deploying systems. We have for some time now released Dockerfiles and scripts for MySQL products, and are not surprised by it steadily gaining traction in the development community.…

Some time ago, I published the article on AWS Aurora Benchmarking (AWS Aurora Benchmarking – Blast or Splash?), in which I analyzed the behavior of different solutions using synchronous replication in AWS environment. This blog follows up with some of the comments and suggestions I received regarding that post from the community and Amazon engineers.

I decided to perform another round of tests, keeping in mind comments and suggestions received.

I presented some of the results during the Percona conference in Santa Clara last April 2016. The following is the transposition that presentation, with more details.

Not interested in the preliminary descriptions? Go to the results section

Why new tests?