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Starter system: Small server + small database
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Expanded system: Large server + large database
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Figure 5.3 The traditional approach to scaling a database. Bigger servers drive bigger databases. But the system is throttled by how fast the server and the disk subsystem can handle writes, and it quickly reaches its limits when dealing with internet scale.
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Designing and architecting for internet scale: sharding
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Internet scaled system: Many commodity servers + many parallel databases
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Figure 5.4 The sharding approach to database scaling uses more modest servers (usually based on cheap commodity hardware) with modest databases, where each server and its associated database takes on a portion of the database load. A good partitioning scheme balances the load and allows continued expansion as the application continues to scale. DATA ARE MORE HIGHLY AVAILABLE
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Because shards are independent, a failure in one doesn t cause a failure in another. And if you make each shard operate at 50 percent capacity, it s much easier to upgrade a shard in place. Keeping multiple data copies within a shard also helps with redundancy, making the data more parallelized so more work can be done on the data. You can also set up a shard to have master-slave (where the master database is the authoritative source and the slave databases are synchronized to it) or dual master (where each server functions as both a master and a slave to the other server) replication to avoid a single point of failure within the shard. If one server goes down, the other can take over.
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DATA AREN T REPLICATED
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Replicating data from a master server to slave servers is a traditional approach to scaling. Data are written to a master server and then replicated to one or more slave servers. At that point, read operations can be handled by the slaves, but all writes happen on the master. Obviously, the master becomes the write bottleneck and a single point of failure. And as load increases, the cost of replication increases. Replication costs in CPU, network bandwidth, and disk I/O. The slaves fall behind and have stale data. Now that you ve learned about the sharding concept and its attributes, let s explore the various common approaches to partitioning databases into shards.
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Sharding in practice: the most common database partitioning schemes
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Continuing to peel the onion, we ll discuss the most common types of sharding. The way the database is partitioned needs to match the characteristics of the application and its usage patterns. Do you separate out features, each to its own database Should you divide segments of users to each have a separate database Or is it best to use an even more sophisticated scheme, because your system may need to be repartitioned over and over as it grows You have to make these choices early. To make an informed decision, you need to understand how your application is used.
VERTICAL PARTITIONING
A simple way to segment your application database is to move tables related to specific features to their own server. For example, placing user profile information on
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Videos User profiles Blogs
one database server, putting friend lists on another, and using a third for usergenerated content, such as photos and blogs, may make sense. Figure 5.5 shows a hypothetical social networking site that employs vertical database partitioning (the real social networks guard their inner architectures like state secrets). The key benefit of this approach is that it s straightforward to implement and has low impact on the application as a whole. The drawback here is, if the site experiences additional growth, it may be necessary to further shard a feature-specific database across multiple servers (for example, handling metadata queries for 10 billion photos by 400 million users may be more than a single server can handle; but not many services will see Facebook s growth profile).
RANGE-BASED PARTITIONING
MySocialNetworkingSite.com
Friend lists Photos
Fan pages
Figure 5.5 A hypothetical social networking site that employed vertical (feature-based) partitioning when it implemented sharding to help it scale its application to ever-larger numbers of users
When the entire data set for a single feature or table needs to be further subdivided across multiple servers, it s important to ensure that the data is split up in a predictable manner. One approach to ensuring this predictability is to split the data based on value ranges that occur within each entity. For example, you can split sales transactions by what year they were created or assign users to servers based on the first digit of their zip code. The main problem with this approach is that if the value whose range is used for partitioning isn t chosen carefully, then the sharding scheme leads to unbalanced servers. In the previous example, splitting up transactions by date means that the server with the current year gets a disproportionate amount of read and write traffic. Similarly, partitioning users based on their zip code assumes that your user base is evenly distributed across the different zip codes. But this fails to account for situations where an application is popular in a particular region and the fact that human populations vary across zip codes.
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