Slides for “Writing OOP Modules for Drupal 7″ Posted

November 19th, 2014

The slides for “Writing OOP Modules for Drupal 7″, my first talk at last week’s php[world], have been posted to Speaker Deck.

Slides for “Stupid PHP Tricks” Posted

November 18th, 2014

The slides for “Stupid PHP Tricks”, my second talk at last week’s php[world], have been posted to Speaker Deck.

Automatic Build Versioning in Xcode with Subversion

November 17th, 2010

For awhile, I had wanted to include the svn revision number in my iOS app, and when I came across a blog post by Daniel Jalkut from a few years ago, I thought I had found an answer. No sooner than I implemented his script, though, did I discover that Apple doesn’t allow build numbers in iOS app packages.

I still really wanted this information in my dev builds so it would be easier to keep track of what version I was working with, so wound up I made a number of modifications to the script to make it suit my needs:
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GPSTrack 2.0 released

November 17th, 2010

Today, the first major update to GPSTrack, my iOS GPS tracking application, made its way to the App Store, now with support for the iPad (WiFi + 3G).

I didn’t make too big a fuss about the 1.0 version, knowing it would have the sort of annoying bugs that every 1.0 release has, and while 2.0 still doesn’t have all the features I wanted — I severely underestimated how much time adding iPad support was going to take, and some of which I hope to blog about soon — I’m pretty happy with the improvements I’ve made so far, but now that 2.0’s released, I can actually get back to adding new functionality.

Slides for CodeWorks talk posted

October 3rd, 2009

This morning, in the lovely “Sauna” room at CodeWorks DC, I presented my talk on “What Happens When a Website Crashes: A Case Study” on how The Bivings Group scaled the Pickens Plan website after it stopped working following a national advertising campaign after the first 2008 presidential debate drew thousands of people to the website.

The slides are now available from my talks page.

If you were in the audience, please feel feel free to rate the talk at I welcome your comments.

Twitterslurp open source release

June 30th, 2009

Last month, I wrote about Twitterslurp, the twitter searching tool I developed at The Bivings Group, which displays a constantly-updating stream of tweets, as well as a leaderboard and stats graphs.

Today, we are very happy to release it as open source. You can download Twitterslurp from its Google Code project page at

Since last month, I’ve made a lot of changes to improve the quality (and ease of configuration) of the Twitterslurp code. Twitterslurp’s error handling has been improved, and I added the ability to start and stop the tweet stream and show more than the most recent 20 tweets. Our graphics team also created a spiffy logo.

Yesterday and today, Twitterslurp has been driving a video wall of tweets at the Personal Democracy Forum conference in NYC. The conference, which just ended, had over 17,000 tweets in the last two days.

Previously, we ran test versions of Twitterslurp during mysqlconf and php|tek, and officially on behalf of the Dutch PHP Conference. Twitterslurp started as a project for a client to allow them to track tweets, and give members of their website rewards for tweeting with a particular hashtag.

We’ve also set up a copy of Twitterslurp tracking itself.

We’d love for you to check out Twitterslurp, and we’re open to any and all feedback.

Tracking php|tek Tweets With Twitterslurp

May 20th, 2009

For a client at work a few months ago, I created a Twitter search tool, Twitterslurp, that put all the tweets related to the client’s project on their webpage, updated in (close to) real-time via AJAX.

I’ve since added a lot of features, including a set of graphs, and we’ve set up a version of Twitterslurp for php|tek 2009.
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Pickens Plan Scaling Talk Slides Are Available

May 15th, 2009

On Wednesday night, I gave a talk at the DC PHP developer’s group regarding how The Bivings Group scaled the Pickens Plan website after it stopped working following a national advertising campaign after the first 2008 presidential debate drew thousands of people to the website.

The slides are now available for download.

Also, I want to thank everyone who showed up. It was great talking to all of you!

MySQL Performance Benefits of Storing Integer IP Addresses

March 9th, 2009

On ##php on freenode over the weekend, there was a brief discussion regarding the performance benefits of storing IP addresses in a database as an integer, rather than as a varchar. One commenter was making the argument that the numeric-to-string and string-to-numeric conversions cost involved in storing IPs as integers was more significant than the space savings.

While the space savings are easily apparent, and I’ve seen demonstrations of how much faster search operations are, I could not recall anyone ever doing an analysis of how long it takes to insert IPs as strings or integers into the database. Naturally, I had to determine an answer to this so I’d know for future reference.

My findings show that, beyond the disk space savings and the much faster index queries possible with integer-based IP addresses, even with the database doing the string-to-numeric conversion, it is 9-12% faster to store IP addresses into the database as integers, rather than strings.


There are two ways you can store an IPv4 address in a database. In its native format, an IP address is a four-byte long integer, and can be represented as an int unsigned.

In its human-readable format, an IP address is a string, with a minimum length of 7 characters ( and a maximum length of 15 ( This gives it an average length (assuming uniform random distribution) of 13.28 characters. Accordingly, one would store it in a database field of type varchar(15). In order for the database to keep track of exactly how much data is in the column, an additional byte of data must be added to store the length of the string. This brings the actual data-storage costs of an IP represented as a string to an average of 14.28 bytes (assuming the characters can be represented by one byte per character, as with latin1 or utf8).

This means that storing an IP address as a string requires, on average, about 10 bytes of extra data. In an application that saves access logs, that 10 bytes of data will eventually add up, and that should be reason enough to store IPs as an integer, rather than a string.

Disk Space Is Cheap, So No Big Deal, Right?

There are other costs associated with having larger data fields. If the column is indexed, the index will be larger as well. Larger indexes tend to perform slower than smaller indexes. Additionally, while disk space is plentiful and cheap, RAM is considerably more limited, so more memory will be used to cache the data or indexes, potentially pushing other, more valuable content out of the cache.

Further, while disks have been gradually getting faster, it still takes a relatively long time to read data from (and even longer to write to) a disk, and CPUs have gotten faster much more quickly than disks. The more data that has to be moved around, the more time the CPU wastes moving that data instead of performing more interesting work.

So, What’s the Actual Cost?

I wrote a PHP script that generates and inserts 1,000,000 random IP addresses into four mysql tables and timed the results. All tests were done with PHP 5.3alpha2 and MySQL 5.0.67 on a 2.16 GHz MacBook Pro (Intel Core Duo), with MyISAM tables.

The test uses four tables as described below. Each table has three columns: an id int unsigned not null auto_increment primary key column, and ip and s as described below:

table ip s description
inetbench_long int unsigned not null char(1) not null fixed row length
inetbench_long_dynamic int unsigned not null varchar(1) not null dynamic row length
inetbench_varchar varchar(15) not null varchar(1) not null dynamic row length; IP stored as string
inetbench_varchar_utf8 varchar(15) not null varchar(1) not null same as inetbench_varchar, but charset = utf8

The PHP benchmark script generates 1 million random IP addresses, and then inserts that data into MySQL To make MySQL do as much work as possible, for the inetbench_long tables, the sql query string uses INET_ATON() to convert the IP to an integer, rather than attempting to do it in PHP.

The results:

table insert time avg row length data length index length total length
inetbench_long 132.35 sec 10 bytes 10,000,000 22,300,672 32,300,627
inetbench_long_dynamic 132.71 sec 20 bytes 20,000,000 22,300,672 42,300,627
inetbench_varchar 144.44 sec 24 bytes 24,504,148 36,341,760 60,845,908
inetbench_varchar_utf8 148.86 sec 24 bytes 24,504,148 36,341,760 60,845,908

MySQL is adding 1 byte of overhead per row to inetbench_long, 10 bytes/row to inetbench_long_dynamic, and an average of 5 bytes/row of overhead to inetbench_varchar and inetbench_varchar_utf8.

The s column was added to test the performance difference of fixed vs. dynamic row storage, but it turns out there’s not much difference.

You can download the code used to generate these results here.


Storing IPs as a string, besides requiring more disk space, takes 9% longer than storing them as integers, even with the overhead of converting the IP from a string to an integer. If the table uses utf8 encoding, it’s 12% slower. (This should not be surprising: UTF-8 is inherently slower to process than a strictly 8-bit encoding.) Storing data as an integer in a table with a dynamic row length is not appreciably slower. The indexes on the string tables are 63% larger.

For good measure, I tested a few select queries against the tables. The results were actually somewhat interesting.

Doing a search where you’re looking for a specific IP, or a range of IPs that can be satisfied with a like clause resulted in no significant difference between the integer and varchar storage. (This was somewhat surprising to me.)

select benchmark(10000000, (select count(*) from inetbench_long where ip between inet_aton('') and inet_aton('')));
1 row in set (0.41 sec)

select benchmark(10000000, (select count(*) from inetbench_varchar where ip like '172.%'));
1 row in set (0.42 sec)

However, as expected, when the search range can’t be represented with a simple like query, the speed difference between numeric and string indexes really show:

select benchmark(35000000, (select count(*) from inetbench_long where ip between inet_aton('') and inet_aton('')));
1 row in set (1.43 sec)

select count(*) from inetbench_varchar where inet_aton(ip) between inet_aton('') and inet_aton('');
1 row in set (1.47 sec)

select count(*) from inetbench_varchar_utf8 where inet_aton(ip) between inet_aton('') and inet_aton('');
1 row in set (1.72 sec)

This results in an integer search that’s about 35 million times faster than the string search. Also, the utf8 table is about 17% slower than the latin1 table (which isn’t too surprising, given UTF-8’s overhead).

In Conclusion…

With “only” a difference of 12 microseconds per insert query, it may not make sense to change an existing database if you’re not doing many queries against stored IPs. If you’re doing IP range queries, though, you probably want to convert your tables. Any new development should be storing IP addresses in the database as integers by default. The space and time savings are worth it.

Once IPv6 becomes more prevalent, the savings will only become larger: a 128-bit (16 byte) IPv6 address can be up to 39 characters long when represented in a “human readable” format. (Storing IPv6 addresses in the database is going to be a bit more difficult, as MySQL doesn’t have a native 16-byte-wide data type.)

The load on MySQL when inserting integer IPs could likely be slightly reduced by doing that conversion in your application, rather than using MySQL’s INET_ATON() function.

Aggregate Map Tools, Part 1: GlobalMapTiles

February 26th, 2009

My fourth open source release this week comes from work I’ve done for my employer, The Bivings Group. Today, we are releasing a set of code that assists with aggregating markers on a Google Map. Our clients wanted to be able to display markers on a map reflecting the locations of people who provided their location (city, state, zip, and in some cases, street address), but with tens of thousands of expected sign-ups, it’s not feasible to display all the points on the map at once.

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