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Key- or hash-based partitioning is often a synonym for user-based partitioning for Web 2.0 sites. With this approach, each entity has a value that can be used as input into a hash function whose output is used to determine which database server to use. For example, suppose you have 10 database servers, and your user IDs are numeric values that are incremented by 1 each time a new user is added. The hash function can perform a modulo operation on the user ID with the number 10 and then pick a database server based on the remainder value. This approach should ensure a uniform allocation of data to each server.
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Designing and architecting for internet scale: sharding
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The key problem with this approach is that it effectively fixes the number of database servers, because adding new servers means changing the hash function which, without downtime, is similar to being asked to change the tires on a moving car. This example illustrates the critical importance of thinking ahead when making sharding design decisions.
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Tips for avoiding unbalanced sharding
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Avoid bad hashing algorithms. You don t want to shard based on the first character of a username because our culture has many more M than Z names. Avoid the problem of users suddenly becoming unequal. The day Sarah Palin was announced as the vice presidential pick, one user became much more active than any other in any type of social networking service.
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DIRECTORY-BASED PARTITIONING
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A loosely coupled approach to this problem is to create a lookup service that knows your current partitioning scheme and abstracts it away from the database access code. This means the GetDatabaseFor() method hits a web service or a database that stores/returns the mapping between each entity key and the database server it resides on. This loosely coupled approach means you can perform tasks such as adding servers to the database pool or change your partitioning scheme without having to impact your application. Remember the previous example, where there are 10 servers and the hash function is a modulo the remainder after division of one number by another operation In spite of modulo being simple, it has a uniform distribution. Let s say you want to add five database servers to the pool without incurring downtime. You can keep the existing hash function, add these servers to the pool, and then run a script that copies data from the 10 existing servers to the 5 new servers based on a new hash function implemented by performing the modulo operation on user IDs using the new server count of 15. After the data is copied over (this is tricky because users are always updating their data), the lookup service can change to using the new hash function without any of the calling applications being any wiser that their database pool grew 50 percent and that the database they went to for John Doe s pictures 5 minutes ago is different from the one they re accessing now. Similar to any solution that creates a highly efficient layer of abstraction (or indirection), this is highly scalable. And after you write scripts to be able to migrate users to/from shards, you can tweak and rebalance to make sure all your hardware is utilized efficiently. The downside of this approach is that it s complicated. In the next section, we ll explore the challenges and problems with sharding. You need to understand that although it s extremely powerful, sharding shouldn t be used too early or too often.
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Sharding challenges and problems
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Sharding isn t perfect. It has a few problems, not the least of which is that fact that it s very complicated. When a database has been sharded, new constraints are placed on the operations that can be performed on the database. These constraints primarily center around the fact that operations across multiple tables or multiple rows in the same table no longer will run on the same server. The following sections describe some of the constraints and additional complexities introduced by sharding.
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