FLICKR S DATABASE PARTITIONING SCHEME in .NET

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FLICKR S DATABASE PARTITIONING SCHEME
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Flickr s equivalent to the GetDatabaseFor() method assigns a random number for new accounts and uses this number as an index into the correct shard for this new user:
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ShardToUse = RandomNumber mod NumberofShards
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Designing and architecting for cloud scale
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Figure 5.7 The profile of the Flickr photo-sharing site, showcasing its large number of users and data
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My data is stored on my shard, but the record of me performing an action on your comment is stored on your shard. Shards contain a slice of the main database. The main database employs 100 percent replication in a dual-master architecture. Migration of certain users is done manually from time to time. A minor percentage of extreme power users destroy the nice balance between shards, and it s important to restore that balance by migrating these types of users off to a different area of the database. Even on a site as big and active as Flickr, migration can be done manually. Each shard is designed to hold approximately 400,000+ users data (apart from the photos themselves). Interestingly, a lot of data is stored twice. For example, a comment is part of the relation between the commenter and the commentee. Where is the comment stored Both places. This is a good tradeoff between performance and disk use. Certain operations, such as clicking a favorite, access several shards. First, the photo owner s account is pulled from cache to get the shard location for this user. Then, it pulls my information from cache to get my shard location. Next, a distributed transaction is started to answer a question like, Who favorited my photo
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FLICKR S RELIABILITY STRATEGY
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To get rid of replication lag, on every page load, the user is assigned to a bucket. If a host is down, Flickr goes to the next host in the list; if all hosts are down, it displays an error page. Flickr doesn t use persistent connections; it builds connections and tears them down. Every page load tests the connection. You can see the architecture that evolved to handle this level of demand in figure 5.8. Each server in a shard is 50 percent loaded. Flickr can shut down half the servers in each shard. It s designed such that one server in the shard can take the full load if a server of that shard is down or in maintenance mode. To upgrade, all the company has to do is shut down half the shards, upgrade that half, and then repeat the process. As you ve seen, sharding is not only a powerful strategy for building high-scalability applications but also a common one. Google, Yahoo!, Flickr, Facebook, and many other sites that deal with huge user communities have found common ground that database
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Designing for on-demand capacity: cloudbursting
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Squid caches
PHP App servers
NetApps
Storage managers
Dual-master shards Dual-tree central database Memcached cluster
Big Search Engine
Figure 5.8 Flickr s architecture includes many levels. First is load balancing as incoming connection requests arrive. Squid caches are open-source web-delivery systems that operate as reverse proxies for HTML pages and images. The PHP App Servers connect to the shards and keep the data consistent. The storage managers do the actual mapping from an index to the correct shard. NetApps are for mass storage of photos. Their fundamental database architecture uses dual masters for each shard. This gives them resilience in the case of failure. The dual-tree structure is a custom set of changes to MySQL that allows scaling by incrementally adding masters without a ring architecture. The central database includes data, such as the users table, which includes primary user keys (a few different IDs) and a pointer to which shard a user s data can be found on. The Big Search Engine is a replication of the database Flickr wants to search.
partitioning in this way is a must to enable a good user experience. Next, let s look at how to design for scale that isn t necessarily based on a large user community it may be compute-based and that isn t consistent over time demand ebbs and flows. For these scenarios, cloudbursting is proving to be the standard solution that s commonly employed.
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