All posts by moore - 85. page

If you’re going to use an apache proxy to fix some of the issues with XMLHttpRequest, be aware that mod_alias sends a redirect to the browser. That is, it sends one of the 3XX HTTP status codes to the XMLHttpRequest object. The XMLHttpRequest object then issues the GET itself (well, some do, check out these XMLHttpRequest tests for more). That’s fine if you’re doing a GET, but if you’re doing a POST, then some redirects will require user interaction. I found that mod_alias, which was sending a 301 (redirect permanent) just turned the POST request into a GET. (mod_alias doesn’t always come to the rescue, apparently.)

The solution? Well, since you are really proxying the XMLHttpRequest’s request, use mod_proxy.

I’ve done some recent work with Google Maps for a client and thought I’d share some lessons learned. (It seems I’ve been posting a lot about Google lately–I don’t know why.)

First off, like many other folks on the web, I think Google Maps are great. I’ve been a long time MapQuest user and the fact is that Google’s image panes just produces a better, slicker user experience than MapQuest’s dynamic image generation. Not to mention the fact that Google’s map API is free (as in beer, not in speech). Now, I haven’t had a chance to compare Yahoo’s map offering (as Michael Yuan did), though I have played around with Yahoo! MapMaker for Excel, but more mapping options can only be better. On to the issues I had with Google Maps.

* Geocoding is not provided with Google Maps, which means that you need to set up your own geocoding engine. However, the Tiger/Line dataset has some holes in it. Especially for rural regions, I was seeing many addresses that could not be geocoded. Even for an urban area (like Boulder, CO) around ten percent of the addresses were not geocodable. As for accuracy of the geocoding itself, I don’t even know how to test it on a large scale, but my client said he saw at least some instances where an address was geocoded incorrectly (a small number, but still significant). Well, what can you say? If you want precision, much less accuracy, pay for it. I investigated using Yahoo’s geocoding service, which is free and based on higher quality commercial data. Since my client is a commercial site (even though the maps are available for free) Yahoo said that they would require Yahoo maps on the site if it were to use their geocoding service. Fair enough. (As an aside, this was an interesting podcast of a speech by an executive of Navteq outlining some of the issues surrounding procuring good geodata.)

* PNGs are the default image type for the map pinpoints on Google Maps. These images () let you mark certain locations. However, if you display them in a list alongside the map, you’ll quickly find that transparent PNGs don’t work with Internet Explorer. They show up, but are surrounded by a black text box (Update Feb 8: transparent PNGs are outlined by a box in Internet Explorer. I’ve seen black boxes and blue boxes; regardless of the color, it looks bad). Luckily, the transparent PNG/Internet Explorer problem has already been solved.

* Each pinpoint/marker is added using an overlay. If you have significant numbers of overlays, map rendering becomes quite slow. Now, for Firefox users, it’s not as big an issue, because Firefox renders the rest of the page before the map. But for IE users, the table containing both the list and the map is not rendered until the map is displayable. On a reasonably fast box, I was seeing times of 80 seconds until the page was rendering. Clearly, this was unacceptable. Even on Firefox, some of the rendering times were just too slow. I searched the Google Maps Discussion Group and most everyone was saying that if you have a large number of markers, you should cluster them into a few representative markers until the user has zoomed sufficiently. (If I recall correctly, the maximum number of markers most folks recommended was around 20.) Such clustering can happen on the server side, or on the client side.

* Data retrieval architecture affects performance. For the first revision of the mapping application, I sent the data for each pinpoint at the same time as the map and the listing. This was the wrong way to do it–this method makes perceived rendering time much longer. The correct way to go is documented in ‘Hacking Maps with the Google Maps API’ XML.com article (linked below), where you use an XMLHttpRequest to pull in the pinpoint data asynchronously. I am in the midst of developing revision two of the interface and have noticed an appreciable speed up in rendering, so I’d recommend heading down that path at the start.

Finally, some resources. The first article you should read is Hacking Maps with the Google Maps API. This tutorial steps you through everything you need to know. The API documentation is adequate. I found some interesting discussions happening on the API discussion group. And, finally, the GoogleMapki was useful in looking at performance concerns. I haven’t read Scott Davis’ Google Maps API article, but keep meaning to spend the $8.50 and do so.

All in all, I enjoyed learning this new technology. The API is simple and easy to use, even for someone who’s no javascript expert. I look forward to adding maps to some of my other pages; my cross country skiing resources page in particular would benefit. Google has kickstarted a whole new area of web development and I think there’s plenty more to do.

I am using Apache JMeter to load test a web application. JMeter has an XML file format for storing load test configuration information. I wanted to system test as well, and needed to generate a large number of unique URL hits. Rather than using the clunky UI to add them, and getting carpal tunnel from it, I analyzed the XML file format and split it up. Then I put tokens (XXXTIMETOWAITXXX) in the appropriate places, and used an Excel generated CSV file to drive perl to assemble the pieces of text into a valid JMeter config file.

Well, what happened next? I needed some way to generate a larger number URLs than Excel would be pleasant to handle? Again, perl came to the rescue, making it easy to generate umpteen lines of correctly formatted CSV.