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July 12, 2011 This is part 2 in a series about the architecture of. For a general overview, see. It worked just fine... In retrospect, if I ever had real traffic, this schema would not have performed. It requires 3-4 queries just to fetch a single profile. Rendering 50 match results might have required hundreds of queries. Enough caching, database replication, and clever query optimization might have made this work at scale... This is, I think, what is driving the development of NoSQL databases. The load requirements of a modern mass-consumer web application are so severe that traditional database modeling falls apart; there's so much denormalization and hackery required that your system stops looking like an RDBMS. Lo and behold, this is almost exactly what NoSQL databases like MongoDB and the Google App Engine datastore are like. The terrific flexibility of having a schemaless, blob-like data structure means you can elegantly denormalize your data into chunks that are optimal for your queries. For example, when Similarity renders a set of 50 match results, it needs almost all the stored information for 51 users the matches + you. The sequence diagram for every match request looks like this: Since the match engine simply returns IDs, the Core must fetch Person objects for each of the 50 match results from the datastore. This is fast because the data is often retrieved from memcache and cache misses are parallelized in the datastore. Thus we arrive of what the Similarity schema for a user actually looks like. Here is an excerpt of the Person class. Unindexed Cached public class Person { Id long id; String firstName; String name; String email; Date firstLogin; Date lastLogin; java. There is no canonical schema anymore. Instead you should ask: What high-volume queries will I need to serve with my data? Then work backwards from there. We have tried to maintain a consistent set of annotations where appropriate.
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