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Chapter 13. TMTOWTDI: Convenience and Habit Versus Performance

TMTOWTDI (sometimes pronounced "tim toady"), an acronym for "There's More Than One Way To Do It," is the main motto of Perl. In other words, you can reach the same goal (usually a working product) by coding in many different styles, using different modules and deploying the same modules in different ways.

However, when you come to the point where performance is the goal, you might have to learn what's efficient and what's not. This may mean that you will have to use an approach that you don't really like, that's less convenient, or that requires changing your coding habits.

This section is about performance trade-offs. For almost every comparison, we will provide the theoretical difference and then run benchmarks to support the theory. No matter how good the theory is, it's the numbers we get in practice that matter.

We also would like to mention that the code snippets used in the benchmarks are meant to demonstrate the points we are making and are intended to be as short and easy to understand as possible, rather than being real-world examples.

In the following benchmarks, unless stated differently, mod_perl is tested directly, and the following Apache configuration has been used:

MinSpareServers 10
MaxSpareServers 20
StartServers 10
MaxClients 20
MaxRequestsPerChild 10000

13.1. Apache::Registry PerlHandler Versus Custom PerlHandler

At some point you have to decide whether to use Apache::Registry or similar handlers and stick to writing scripts only for content generation, or to write pure Perl handlers.

Apache::Registry maps a request to a file and generates a package and the handler( )subroutine to run the code contained in that file. If you use a mod_perl handler instead of Apache::Registry, you have a direct mapping from request to subroutine, without the steps in between. The steps that Apache::Registry must go through include:

  1. Run the stat( )system call on the script's filename ($r->filename).

  2. Check that the file exists and is executable.

  3. Generate a Perl package name based on the request's URI ($r->uri).

  4. Change to the directory in which the script resides (chdir basename $r->filename).

  5. Compare the file's last-modified time to the compiled subroutine's last modified time as stored in memory (if it has already been compiled).

  6. If modified since the last compilation or not yet compiled, compile the subroutine.

  7. Change back to the previous directory (chdir $old_cwd).

If you remove these steps, you cut out some overhead, plain and simple. Do you need to cut out that overhead? Maybe yes, maybe no: it depends on your performance requirements.

You should also take a look at the sister Apache::Registry modules (e.g., Apache::RegistryBB) that don't perform all these steps, so you can still stick to using scripts to generate the content. The greatest added value of scripts is that you don't have to modify the configuration file to add the handler configuration and restart the server for each newly written content handler.

Another alternative is the Apache::Dispatch module (covered in Appendix B), which allows you to add new handlers and run them without modifying the configuration.

Now let's run some benchmarks and compare.

We want to see the overhead that Apache::Registry adds compared to a custom handler and whether it becomes insignificant when used for heavy and time-consuming code. In order to do this we will run two benchmark sets: the first, the light set, will use an almost empty script that sends only a basic header and one word of content; the second will be the heavy set, which adds some time-consuming operation to the script and handler code.

For the light set we will use the registry.pl script running under Apache::Registry (see Example 13-1).

Example 13-1. benchmarks/registry.pl

use strict;
print "Hello";

And we will use the equivalent content-generation handler, shown in Example 13-2.

Example 13-2. Benchmark/Handler.pm

package Benchmark::Handler;
use Apache::Constants qw(:common);

sub handler {
    $r = shift;
    return OK;

We will add these settings to httpd.conf:

PerlModule Benchmark::Handler
<Location /benchmark_handler>
    SetHandler perl-script
    PerlHandler Benchmark::Handler

The first directive preloads and compiles the Benchmark::Handler module. The remaining lines tell Apache to execute the subroutine Benchmark::Handler::handler when a request with the relative URI /benchmark_handler is made.

We will use the usual configuration for Apache::Registryscripts, where all the URIs starting with /perl are mapped to the files residing under the /home/httpd/perl directory:

Alias /perl /home/httpd/perl
<Location /perl>
    SetHandler perl-script
    PerlHandler +Apache::Registry
    Options ExecCGI
    PerlSendHeader On

We will use Apache::RegistryLoader to preload and compile the script at server startup as well, so the benchmark is fair and only processing time is measured. To accomplish the preloading we add the following code to the startup.pl file:

use Apache::RegistryLoader ( );

To create the heavy benchmark set, let's leave the preceding code examples unmodified but add some CPU-intensive processing operation (e.g., an I/O operation or a database query):

my $x = 100;
my $y = log ($x ** 100)  for (0..10000);

This code does lots of mathematical processing and is therefore very CPU-intensive.

Now we are ready to proceed with the benchmark. We will generate 5,000 requests with a concurrency level of 15. Here are the results:

    name        | avtime   rps
light handler   |     15   911
light registry  |     21   680
heavy handler   |    183    81
heavy registry  |    191    77

First let's compare the results from the light set. We can see that the average overhead added by Apache::Registry (compared to the custom handler) is about:

21 - 15 = 6 milliseconds

per request.

The difference in speed is about 40% (15 ms versus 21 ms). Note that this doesn't mean that the difference in real-world applications would be so big. The results of the heavy set confirm this.

In the heavy set the average processing time is almost the same for Apache::Registry and the custom handler. You can clearly see that the difference between the two is almost the same as in the light set's results—it has grown from 6 ms to 8 ms (191 ms - 183 ms). This means that the identical heavy code that has been added was running for about 168 ms (183 ms - 15 ms). However, this doesn't mean that the added code itself ran for 168 ms; it means that it took 168 ms for this code to be completed in a multiprocess environment where each process gets a time slice to use the CPU. The more processes that are running, the more time the process will have to wait to get the next time slice when it can use the CPU.

We have answered the second question as well (whether the overhead of Apache::Registry is significant when used for heavy code). You can see that when the code is not just the hello script, the overhead added by Apache::Registry is almost insignificant. It's not zero, though. Depending on your requirements, this 5-10 ms overhead may be tolerable. If that's the case, you may choose to use Apache::Registry.

An interesting observation is that when the server being tested runs on a very slow machine the results are completely different:

    name        | avtime   rps
light handler   |     50   196
light registry  |    160    61
heavy handler   |    149    67
heavy registry  |    822    12

First of all, the 6-ms difference in average processing time we saw on the fast machine when running the light set has now grown to 110 ms. This means that the few extra operations that Apache::Registry performs turn out to be very expensive on a slow machine.

Secondly, you can see that when the heavy set is used, the time difference is no longer close to that found in the light set, as we saw on the fast machine. We expected that the added code would take about the same time to execute in the handler and the script. Instead, we see a difference of 673 ms (822 ms - 149 ms).

The explanation lies in the fact that the difference between the machines isn't merely in the CPU speed. It's possible that there are many other things that are different—for example, the size of the processor cache. If one machine has a processor cache large enough to hold the whole handler and the other doesn't, this can be very significant, given that in our heavy benchmark set, 99.9% of the CPU activity was dedicated to running the calculation code.

This demonstrates that none of the results and conclusions made here should be taken for granted. Most likely you will see similar behavior on your machine; however, only after you have run the benchmarks and analyzed the results can you be sure of what is best for your situation. If you later happen to use a different machine, make sure you run the tests again, as they may lead to a completely different decision (as we found when we tried the same benchmark on different machines).

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