High Scalability Building bigger, faster, more reliable websites.

I've done some XMPP development so when I read Facebook was making a Jabber chat client I was really curious how they would make it work. While core XMPP is straightforward, a number of protocol extensions like discovery, forms, chat states, pubsub, multi user chat, and privacy lists really up the implementation complexity. Some real engineering challenges were involved to make this puppy scale and perform. It's not clear what extensions they've implemented, but a blog entry by Facebook's Eugene Letuchy hits some of the architectural challenges they faced and how they overcame them.

High Performance Multithreaded Access to Amazon SimpleDB is a great follow up to the idea in How SimpleDB Differs from a RDBMS that more programming is the price paid for performance in SimpleDB. It shows how much work and infrastructure is required to batter better performance out of SimpleDB.

Remember, in SimpleDB you get keys to records from queries so if you want to get all the fields for records you need to make separate requests. Since SimpleDB isn't exactly a speed daemon the obvious strategy is to parallelize. Even if a job takes a 100 msecs you can get a lot done in a little time if you can execute enough jobs in parallel.

Parallelization is the approach taken by Haakon@AWS in his Java code example of how to get the most out of SimpleDB. You can find the code at Indexing and Querying Amazon S3 Metadata with Amazon SimpleDB. We'll also consider how a back-end service architecture built on Erlang may be a better fit with cloud computing.