Peter Wayner
Contributing Writer

Test spin: Google App Engine

news
May 14, 20085 mins

Google App Engine simplifies the problem of deploying and scaling Web applications, but not without a few wrinkles and question marks

Credit: Shutterstock / Jay Fog

One of the joys of being a Web programmer is heading to a dinner party, a haircut, or a reunion and fielding the pitches for everyone’s dream for a brilliant Web application. Everyone is always happy to cut you in for 5, 10, maybe even 15 percent of the equity if you just build out the Web site that’s sort of like a combination of Twitter, AltaVista, Eliza, TurboTax, and the corner pharmacy, but cooler.

Google App Engine is meant for dreams like these. You write a bit of code in Python, customize some HTML, and bingo, you’ve got your database-backed dynamic Web site up and running in a few short minutes. The magic comes when the world starts flocking to your Web application, and Google’s cloud of computers quickly adapts to the load, handling everything the public demands. There’s no need for you to buy servers, load balancers, or special DNS tables. Google’s application cloud handles all of the grungy deployment headaches.

[ See also ” What cloud computing really means” and “Early experiments in cloud computing.” ]

I played around with the App Engine SDK and, sure enough, developed and deployed applications on my desktop with just a few minutes of work. I didn’t upload them to the cloud because I didn’t make it into the beta program, but I was able to simulate the experience on my office server. The billions of hits haven’t shown up yet, but it has only been a few hours now. It works and it is quite simple.

Google me this

A trickier question is deciding whether this is really what a future Web application really needs. There is little doubt that App Engine makes it simple to get incoming data, make some decisions, store it in a database, and then move on. The more complicated questions are often political, technical, and almost aesthetic. There will be a number of programmers who look at App Engine and melt with excitement, and there will be many who tilt their head like a dog that can’t understand his master.

Being a Python lover certainly helps, but it isn’t necessary because the language isn’t that much different from the other scripting languages. A good programmer should be able to shift gears quickly and easily. There are rumors that Google has a number of other languages waiting around the corner, but there are equally good arguments that this may not be happening as soon as some devotees would like.

Java programmers, in particular, are used to being known as providing the most scalable and flexible applications because the language and the API are some of the most sophisticated ensembles around. The J2EE standard nurtured tools that simplified some of these problems, even though it never really turned out to be as simple as the sales literature promised. Today, Java’s sophistication is probably hurting the language as much as helping it. A quick survey of Web hosting services shows that shared hosting for JSP applications begins around $10 a month, while Python shared services can cost as little as $2 a month. The JVM may speed things up and provide better service, but it comes with a hefty memory footprint. If the brutally competitive Web hosting business can support five Python sites for every Java site, then perhaps Google is more interested in the long tail, the niche Web sites, than the big iron.

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Peter Wayner

Peter Wayner is a contributing writer to InfoWorld. He has written extensively about programming languages (including Java, JavaScript, SQL, WebAssembly, and experimental languages), databases (SQL and NoSQL), cloud computing, cloud-native computing, artificial intelligence, open-source software, prompt engineering, programming habits (both good and bad), and countless other topics of keen interest to software developers. Peter also has written for mainstream publications including The New York Times and Wired, and he is the author of more than 20 books, mainly on technology. His work on mimic functions, a camouflaging technique for encoding data so that it takes on the statistical characteristics of other information (an example of steganography), was the basis of his book, Disappearing Cryptography. Peter’s book Free for All covered the cultural, legal, political, and technical roots of the open-source movement. His book Translucent Databases offered practical techniques for scrambling data so that it is inscrutable but still available to make important decisions. This included some of the first homomorphic encryption. In his book Digital Cash, Peter illustrates how techniques like a blockchain can be used establish an efficient digital economy. And in Policing Online Games, Peter lays out the philosophical and mathematical foundations for building a strong, safe, and cheater-free virtual world.

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