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Employee MySQL 5.7 improves CPU scaling further
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As shown in previous blogs and technical papers about MySQL 5.6, MySQL 5.6 improved scalability from 36 CPU threads sockets all the way to 60 CPU threads on a machine where each socket have 6 cores and 12 CPU threads.

Now that we have released the MySQL 5.7.2 DMR it's interesting to note that we have improved scaling yet one more step. I performed a number of simple test runs to see how our scalability have improved from MySQL 5.6.15 to MySQL 5.7.2 DMR. What the numbers clearly shows is that we have increased our scalability from 60 CPU threads to 72 CPU threads. Where we previously leveled off going from 48 CPU threads to 60 CPU threads, we're now leveling off going from 60 CPU threads to 72 CPU threads and the maximum performance is now found at 72 CPU threads compared to 60 CPU threads in MySQL 5.6.

Here we have the graph for scalability improvements of Sysbench RO.
Here is the graph for scalability improvements of Sysbench RW.
The reason is a series of improvements, both in the MySQL Server and in the InnoDB parts. One important thing is the improvement of the index locking which improves write scalability since updating indexes now is done with less concurrency problems. For read only workloads and primary key reads in particular there have been great improvements in the area of MDL locking and in this area we have almost doubled the throughput which is possible for MySQL 5.6 compared to MySQL 5.7.

In my work on scalability I have started using the perf tool found in modern Linux releases. It's an awesome tool, to get a complete performance analysis I can simply start perf record and specify the CPU and PID that I want to track. I can track on timed events, on first-level cache misses, last-level cache misses, various forms of TLB misses, branch prediction misses and much more. This should prove useful also in finding improvements also for single-threaded workloads. I have already done such analysis and improvements of the MySQL Cluster data node code and seen some stunning results. It has taken some time to get to understand the perf tool however.

The main obstacle with this tool is that the reporting sometimes points to assembler instructions which are not where the real issues are. The problem here is that the reporting is very inaccurate, the reported instruction can sometimes be as much as several dozens of instructions away from the real instruction where the bottleneck resides. In the literature this problem is referred to as skid, if an event occurs then the reported instruction is the next instruction to restart execution from after handling the event. Since a processor can have hundreds of instructions in flight at one point in time, this means that the skid (the number of instructions between the instruction that caused the event and the reported instruction) can be hundreds of instructions. This means that it becomes much more difficult to use the results from the perf tool. I read some Linux discussions about this tool and it seems not to be a priority of Linux kernel developers to do something about this skid problem. So one needs to remember that the perf output is mostly an indication of where the problem resides and no more.

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