Speeding up Database Access with mod_perl
Now that we have our test program, how can we actually test it? I wrote a short Perl program using Benchmark.pm, in Listing 3. The timethese function is imported by Benchmark.pm, which we bring in at the beginning of the program. We also bring in LWP::Simple, part of the “Library for WWW access in Perl” that makes it a snap to write a small web client. How simple? Well, the following one-liner returns the HTML contents at http://www.linuxjournal.com/:
perl -e 'use LWP::Simple; print get "http://www.linuxjournal.com";'
Perl does not format the output for you. That's the difference between a web browser and a web client; the former is meant to retrieve information for humans, while the latter is meant to retrieve information for programs. In this particular case, we just want to simulate 100 retrievals of each of our programs via the web. Any timing differences will thus be due to the program on the server side—which, since they are identical, means that the differences will be due to mod_perl and Apache::DBI.
How much faster is the Apache::DBI version than its CGI counterpart? Here are the results I got running time-db.pl:
[1086] ~% ./time-db.pl Benchmark: timing 100 iterations ... Apache::DBI: 24 secs (1.77 usr 0.67 sys = 2.44 cpu) Plain CGI: 394 secs (1.10 usr 0.61 sys = 1.71 cpu)
That's quite a difference. When I first ran this benchmark, I was convinced that the plain CGI program had somehow gotten stuck. Alas, that was not the case; the overhead associated with CGI was simply too great.
Here is a second run of the same benchmark, just for comparison.
[1099] ~% ./time-db.pl Benchmark: timing 100 iterations ... Apache::DBI: 28 secs (1.89 usr 0.61 sys = 2.50 cpu) Plain CGI: 355 secs (1.15 usr 0.62 sys = 1.77 cpu)
Yes, it looks like CGI is indeed much slower. By the way, you can see that Apache::DBI used more CPU time than plain CGI—which means that the time was spent forking the new Perl process, rather than performing our program's computations.
What if we take out the Apache::DBI directive in srm.conf and restart the server? That would give us an indication of how much overhead was being used opening the database connection. As you can see, things do indeed slow down—although admittedly not by a huge amount:
[1104] ~% ./time-db.pl Benchmark: timing 100 iterations ... Apache::DBI: 34 secs (1.97 usr 0.63 sys = 2.60 cpu) Plain CGI: 460 secs (1.19 usr 0.60 sys = 1.79 cpu)
The moral, then, seems to be that moving from CGI to mod_perl gives a huge performance boost, and that moving from DBI to Apache::DBI gives a moderate performance boost. The more database accesses your web applications do, the more useful these technologies will probably be in your work. Perl has always been known as a useful language, but rarely as one that can help you write fast software. Now, with mod_perl and Apache::DBI, you can write web applications quickly, and watch them run quickly as well.

- « first
- ‹ previous
- 1
- 2
- 3
- 4
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Sponsored by AMD
Built-in forensics, incident response, and security with Red Hat Enterprise Linux 6
Every security policy provides guidance and requirements for ensuring adequate protection of information and data, as well as high-level technical and administrative security requirements for a system in a given environment. Traditionally, providing security for a system focuses on the confidentiality of the information on it. However, protecting the data integrity and system and data availability is just as important. For example, when processing United States intelligence information, there are three attributes that require protection: confidentiality, integrity, and availability.
Learn more about catching the bad guy in this free white paper.
Sponsored by DLT Solutions
Free Webinar: Hadoop
How to Build an Optimal Hadoop Cluster to Store and Maintain Unlimited Amounts of Data Using Microservers
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Some of key questions to be discussed are:
- What is the “typical” Hadoop cluster and what should be installed on the different machine types?
- Why should you consider the typical workload patterns when making your hardware decisions?
- Are all microservers created equal for Hadoop deployments?
- How do I plan for expansion if I require more compute, memory, storage or networking?
| Designing Electronics with Linux | May 22, 2013 |
| Dynamic DNS—an Object Lesson in Problem Solving | May 21, 2013 |
| Using Salt Stack and Vagrant for Drupal Development | May 20, 2013 |
| Making Linux and Android Get Along (It's Not as Hard as It Sounds) | May 16, 2013 |
| Drupal Is a Framework: Why Everyone Needs to Understand This | May 15, 2013 |
| Home, My Backup Data Center | May 13, 2013 |
- Linux Systems Administrator
- New Products
- Senior Perl Developer
- Technical Support Rep
- UX Designer
- Web & UI Developer (JavaScript & j Query)
- Designing Electronics with Linux
- Dynamic DNS—an Object Lesson in Problem Solving
- Using Salt Stack and Vagrant for Drupal Development
- Making Linux and Android Get Along (It's Not as Hard as It Sounds)




2 hours 14 min ago
6 hours 45 min ago
6 hours 46 min ago
8 hours 46 min ago
17 hours 32 min ago
18 hours 6 min ago
19 hours 4 min ago
19 hours 54 min ago
23 hours 56 min ago
1 day 3 hours ago