Google AppEngine Disappointment

I was eager to try out Google’s AppEngine for Java, but I was soon disappointed to find out that the AppEngine is just a partial implementation of the Java APIs. The biggest problem with the missing APIs is that a lot of 3rd party software and libraries simply won’t work, which means that you loose all the benefits of the mature Java ecosystem. Porting an existing Java application to AppEngine is simply out of question.

Another major problem is persistence. You cannot deploy your favourite DBMS on AppEngine, you have to use Google’s Datastore, which is not a common relational DB. Google makes available JDO and JPA interfaces to access the Datastore. However, these are also partial implementations which provide just a familiar syntax for a substantially different persistence mechanism and create a false sense of familiarity. This is particularly true when you try to refer to the JDO documentation for troubleshooting, but you discover that the semantics of Google’s JDO implementation are quite different. It would have been much better to stick with a proprietary persisntece API and not pretend to fill the gap to reach JDO or JPA semantics.

Another important missing feature are Threads, which makes AppEngine a poor choice for massive computations since paralellization of subtasks is not possible, at least until Google implement the AppEngine Map/Reduce service.

Lastly the overall sense of a finished product is not there. Things that one would expect to work, fail for some obscure reason. Error messages are often cryptic. Apart from an introductionary tutorial, documentation is quite scarce. I guess this is justifiable since AppEngine is still a beta release, but I would rather consider it a pre-alpha. I was expecting much more considerate choices from the smart Google engineers, who have amazed us many times so far.

There were a few things I did like about the app engine. For example, the Eclipse plugin  simplifies configuration, testing and deployment. Integration with other Google services are a promissing factor. All in all, Google AppEngine is an interesting toy, but compared to other solutions it’s remains just a toy. If you want to play with it, I suggest you should read this.

Webapp stacks comparison

Ever wondered what is the best technology stack for building web applications? I’m sure you have.

Last week my company went on a workshop, during which we tried to answer that question. I wrote a requirements specification for a simple web application. Everyone had to choose the technology stack they’re most familiar with and implement the application. Everyone received a benchmark script for the application and at the end of the day we compared the performance results. The application was just enough complex to require at least a day to implement. In the end we multiplied the performance by the level of functionality implemented.

The Application

The webapp I chose is the same one for the couldspeed project. It is based on the model of social networking applicatins. Once registered and logged in, users can add other users as friends and submit posts. The homepage will display the 20 most recent posts from all user’s friends including the user himself.

There are 2 entities each with 2 attributes:

• users(email, password)
• post(date, content)

and there are 2 relationships

• users are friends with other users (n to n)
• each post is written by a user (1 to n)

there are 2 web pages: login and home.

From the login page, users can register themselves or log in.

The home page displays the number of friends, the 20 most recent posts (for each post, the date, author and content) and allows users to add posts, friends or logout.

Information has to be persisted somehow.

The Benchmark

Each developer got a copy of JMeter and 3 scripts. The 3 scripts excercized 3 different levels of functionality:

1. Register Users – registered a number of users (100)
2. Make Friends – created around 10 friends per user
3. Post Stuff – posted messages

For the first level of functionality, the developer had to implement the functionality behind the Register button. Registered users had to be persisted. This level had a multiplier of 1.

For the second level of functionality, developers had to implement logging in, keeping session information, and the friendship relationship. The homepage should have shown the number of friends. This level had a multiplier of 3.

For the third level of functionality, developers had to implement everything: posting messages and showing the messages from all friends. Since this functionality included a complex query that ate a lot of processing power, the multiplier was 50.

The Contenders

Ben: Erlang + CouchDB

Team 2: Java + Spring + Hibernate + Postgres

Team 3: Java + Spring + Hibernate

Team 4: Ruby On Rails + MySQL

Team 5: ASP with Visual Basic + SQLServer

Immo: Ruby + Ramaze + MySQL

The Results

Only three of the contenders implemented level 3 of functionality, but we were able to measure the performance only for the erlang implementation. The other 2 that made it to the 3rd level were ASP+SQLServer and Ramaze. We will soon publish the results of the two missing level 3 implementations. 

The winner was Ben with 1111.1 points. Erlang + CouchDB proved to be quite fast and productive in the right hands.

Conclusions

Allthough just one day of development is not nearly enough to measure productivity, we noticed that choosing a particular language that promised a high level of productivity wasn’t as important as choosing the language you’re most familiar with.

Alltough Level 1 of functionality does not provide a meaningful scenario to measure performance, we can see that most of the implementations had the same performance, including Ruby, which has been measured to be 100 times slower than C++, but in our scenario it actually showed the best performance.

Allthough we must point out that both ruby implementations and the ASP implementation showed instability. Errors ranging from 15% to 30% were reported during the benchmarks.

As a comparison, I have done some java implementations before the competition. They all provide level 3 functionality. They all use Servlets in the web tier, but the DAO tier changes: we have JDBC with MySQL, EJB3 with MySQL and pure in memory.

Compared to the Erlang implementation wich serves 15 req/s I have similar results for the JDBC (15 req/s). A “heavyweigth” stack such as EJBs actually manages to produce 38 req/s, but by far the fastest solution is keeping everything in memory that brings us to an impressive 2000 req/s. Obviously the in memory solution cannot be compared to the others because it doesn’t satisfy the persistence requirement. But can still be used as a comparison to see how much performance do we loose on the persistence layer.

Future Work

We have had requests from other people that were not taking part of the competition to submit their implementations. Since we have no way to measure productivity, all submissions now require level 3 of functionality. All implementations will be published on the cloudspeed website. If you wish to contribute by improving an implementation or submitting your own just send me an email or leave a comment here.

Currently the benchmark is adapted to quickly give a very aproximative measure. 100 users is not a realistic number even for a small website. Our next goal is to create a benchmark that will register up to millions of users, have an average of 100 friends per user and post several million messages.

The final goal is to port these implementations to the cloud and measure their scalability. We don’t only want to see the performance on a single machine, but also how will the performance be when we reach the limits of a single server. Ideally we want to try to run these applications on clusters as big as we can get.

Stay tuned for updates…

foreach in Java

Here’s a cool but unfortunately useless example of java generics, combined with reflection, combined with dynamic proxies.

Eclipse Action with Generics

I wrote this code a couple of weeks ago. It’s a nice idea on how to use generics in order to reduce the pain of using the Eclipse API.

Concurrency with fork/join in JDK 7

A few days ago, Brian Goetz came to visit a colleague of mine in our offices and I had the opportunity to hear some of the new concurrency features coming in JDK 7. We talked about the fork/join framework and Brian kindly showed us the presentation he gave at JavaOne.