This is a fascinating read by those of you who, like myself, are particularly interested in approaches to scaling applications at the database level. According to this post, FriendFeed has implemented a very interesting schema-less model for storing data within MySQL.
From the article:
As our database has grown, we have tried to iteratively deal with the scaling issues that come with rapid growth. We did the typical things, like using read slaves and memcache to increase read throughput and sharding our database to improve write throughput. However, as we grew, scaling our existing features to accomodate more traffic turned out to be much less of an issue than adding new features.
[snip]
Our datastore stores schema-less bags of properties (e.g., JSON objects or Python dictionaries). The only required property of stored entities is id, a 16-byte UUID. The rest of the entity is opaque as far as the datastore is concerned. We can change the “schema” simply by storing new properties.
We index data in these entities by storing indexes in separate MySQL tables. If we want to index three properties in each entity, we will have three MySQL tables - one for each index. If we want to stop using an index, we stop writing to that table from our code and, optionally, drop the table from MySQL. If we want a new index, we make a new MySQL table for that index and run a process to asynchronously populate the index without disrupting our live service.
As a result, we end up having more tables than we had before, but adding and removing indexes is easy. We have heavily optimized the process that populates new indexes (which we call “The Cleaner”) so that it fills new indexes rapidly without disrupting the site. We can store new properties and index them in a day’s time rather than a week’s time, and we don’t need to swap MySQL masters and slaves or do any other scary operational work to make it happen.
Make sure to read the article for a lot of design details, it is a very interesting approach to scaling.
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