Low level benchmarking on Mac OS X and LinuxLmbench 3.0 provides a suite of micro-benchmarks that measure the bottlenecks at the Unix operating system and CPU level. We were not able to install Yellow Dog Linux on the 2.7 GHz Apple machine, although the paper specs (excluding the CPU) were exactly the same as our 2.5 GHz PowerMac. Some small chipset tweak or firmware change is probably the cause of our YDL installation failure on the newest and latest Apple PowerMac.
So, we'll give Mac OS X a small advantage by running it on the 2.7 GHz and Linux on the 2.5 GHz machine. Frankly, I don't care much about an 8% clock difference, as the main goal is to find out why MySQL runs between 5 and 8 times slower on Mac OS X!
The Unix process/thread creation is called "forking" as a copy of the calling process is made. lmbench "fork" measures simple process creation by creating a process and immediately exiting the child process. The parent process waits for the child process to exit. lmbench "exec" measures the time to create a completely new process, while "sh" measures the time to start a new process and run a little program via /bin/sh (complicated new process creation).
Everything is expressed in micro seconds, lower is better.
|G5 2.7 GHz||Darwin||2700||659||2308||4960|
|G5 2.5 GHz||Linux||2500||182||748||2259|
|Xeon 3.6 GHz||Linux||3585||158||467||2688|
In the previous article, I wrote: "Mac OS X is incredibly slow - between 2 and 5(!) times slower - in creating new threads, as it doesn't use kernelthreads, and has to go through extra layers (wrappers)"
Readers pointed out that there were two errors in this sentence. The first one is that Mac OS X does use kernelthreads, and this is completely true. My confusion came from the fact that FreeBSD 4.x and older - which was part of the OS X kernel until Tiger came along - did not implement kernelthreads; rather, only userthreads. It was one of the reasons why MySQL ran badly on FreeBSD 4.x and older. In the case of userthreads, it is not the kernel that manages the threads, but an application running on top of the kernel in userspace.
However, this is not the case of Mac OS X. Pthreads, available to the programmer, map directly to a Mach thread, and thread handling is the very heart of the Mach kernel inside the OS X kernel.
Mac OS X thread layering hierarchy (Courtesy: Apple)
"POSIX thread (commonly referred to as a "pthread") is a lightweight wrapper around a Mach thread that enables it to be used by user-level processes. POSIX threads are the basis for all of the application-level threads."Readers also pointed out that LMBench uses "fork", which is the way to create a process and not threads in all Unix variants, including Mac OS X and Linux. I fully agree, but does this mean that the benchmark tells us nothing about the way that the OS handles threading? The relation between a low number in this particular Lmbench benchmark and a slow creating of threads may or may not be the answer, but it does give us some indication of a performance issue. Allow me to explain.
In the Unix world, threads are often described as lightweight processes. A thread is a sequential flow of control within a program, whereas a process is one or more threads plus its own (virtual) address space. Threads share the same address space and thus, share memory and can exchange information very quickly. However, it is important to understand that threads and processes are not completely different, but they are related to each other.
In the case of Linux, creating a thread is very similar to creating a process. In fact, you use the same procedure, only with different flags or parameters. Linux implements all threads as if they were standard processes. You create a new thread with the clone() call, and the necessary flags, which describe the resources (memory) to be shared. The process creation is done with fork(), but fork() is nothing less than a clone() without the flags that describe the resources that must be shared. So, if you test fork() on Linux, you also get a rough idea of how fast threads are created.
What about Mac OS X? Well, when the Mach kernel is asked to create a Unix process (fork()), the mach kernel creates a task (which describes the resources available) and one thread. So, thread creation time is included in the fork () benchmark of Lmbench.
What can we conclude from this? First, the above tables demonstrate clearly that the creation of UNIX processes is much slower on MAC OS X, and the G5, the CPU, is not to blame. In the first test, the G5 2.5 GHz running Linux is only slightly slower than a Pentium 4 at 3.6 GHz. The third test shows that the G5 is even capable of outperforming the other CPUs, which points towards Mac OS X being the problem here. Even with a faster CPU, the OS X scores are all slower than the Linux scores on the G5.
Does this give us an idea of why MySQL performs so badly? Unfortunately, it makes us suspect that not only process, but thread creation is also slow. We can suspect it, since the process creation, which includes the creation of one thread, takes up to 5 times longer. We can't prove it, as the thread creation time is a small part of the total benchmark time, and we are not sure that the time to create a thread compared to the total time is the same proportionally on both Mac OS X and Linux. LMBench gives us a rough indication that we might be right, but it doesn't give us cold hard facts. We need to look elsewhere for those.
Interprocess Communication (IPC) and SignalingSignals allow processes (and thus threads) to interrupt other processes. Although MySQL is only one process, it must cooperate with other process via IPC. Benchmarking signal and interprocess communication latency allows to us to understand how quickly the MySQL process can cooperate with other processes and the Operating System. Contrary to workstation and gaming applications, access to the operating system and other processes is critical for database server performance. For example, our client in our database testing setup sends the queries via a Gigabit Ethernet connection (hardware - Layer 1) and via TCP-IP (Network stack Layer 2-4) to the server.
Larry McVoy (SGI) and Carl Staelin (HP) on signaling:
"Lmbench measures both signal installation and signal dispatching in two separate loops, within the context of one process. It measures signal handling by installing a signal handler and then repeatedly sending itself the signal."All numbers are expressed in micro seconds; thus, lower is better.
|G5 2.7 GHz||Darwin||2700||1.13||1.91||4.64||8.6||21.9||1.67||6.2|
|G5 2.5 GHz||Linux||2500||0.14||0.26||3.41||4.16||18.9||0.38||1.9|
|Xeon 3.6 GHz||Linux||3585||0.19||0.25||2.3||2.88||9.0||0.28||2.7|
Signaling needs significantly more time in Mac OS X (Darwin) than on Linux. The processor plays a minor role: the Opteron at 2.4 GHz is a bit faster than the Xeon 3.6 GHz running exactly the same (x86) code. However, it is clear that the operating system plays a much bigger role: a 2.5 GHz G5 running Linux easily beats the identical system with a 2.7 GHz G5 running Mac OS X. Despite the FreeBSD heritage, the TCP signals are very slow (4 times slower!) on Mac OS X.
The slower signaling results likely contribute to the overall unimpressive MySQL performance. There are still other factors that also play a part. Let us check out Inter Process Communication (IPC).
|G5 2.7 GHz||Darwin||9.496||13.1||34.8||44.5||61|
|G5 2.5 GHz||Linux||11.6||16.4||19.1||19.6||34|
|Xeon 3.6 GHz||Linux||9.909||19.0||16.0||19.3||40|
As TCP is connection based, you get Synchronize (SYN) and Acknowledgement (ACK) messages to establish a reliable connection, before any data can be transferred. Lmbench measures this startup time (TCP conn). Notice how the G5 performs this task quite quickly with Linux, but much slower with Mac OS X. The latency to connect to a TCP server is also measured (TCP) and Mac OS X is measured to be more than twice as slow compared to the Linux based machines, including the same G5 machine. So, although network bandwidth might not be a problem for our benchmark, network latency might have an influence.
Some studies show that there is a direct relationship between these TCP benchmarks and some aspects of Database performance. For example, it was reported that "The TCP latency benchmark is an accurate predictor of the Oracle distributed lock manager's performance."