excel 2010 barcode generator Sharding defined: a parallel database architecture for massive scaling in VS .NET

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Sharding defined: a parallel database architecture for massive scaling
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The term sharding was coined by Google engineers and popularized through their publication of the BigTable architecture. But the concept of shared-nothing database
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Designing and architecting for internet scale: sharding
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partitioning on which sharding is based has been around for a decade or more. There have been many implementations over this period, particularly high-profile, in-housebuilt solutions by internet leaders such as eBay, Amazon, Digg, Flickr, Skype, YouTube, Facebook, Friendster, and even Wikipedia.
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SHARDING A decomposition of a database into multiple smaller units (called
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shards) that can handle requests individually. It s related to a scalability concept called shared-nothing that removes dependencies between portions of the application such that they can run completely independently and in parallel for much higher throughput. There was a time when you scaled databases by buying bigger, faster, and more expensive machines. Whereas this arrangement is great for big iron and database vendor profit margins, it doesn t work so well for the providers of popular web-facing services that need to scale past what they can afford to spend on giant database servers (and no single database server is big enough for the likes of Google, Flickr, or eBay). This is where sharding comes in. It s a revolutionary new database architecture that, when implemented at Flickr, enabled that service to handle more than one billion transactions per day, responding to requests in less than a few seconds; and that scaled linearly at low cost. It does sound revolutionary, doesn t it Let s look at it more closely.
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SHARDING IN A NUTSHELL
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In the simplest model for partitioning, as diagrammed in figure 5.2, you can store the data for User 1 on one server and the data for User 2 on another. It s a federated model. In a system such as Flickr, you can store groups of 500,000 users together in each partition (shard). In the simple two-shard design in figure 5.2, the criteria for shard selection is odd- versus even-numbered user identification values. The more you examine sharding, the more advantages you ll discover:
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User 1 User 2 User 3 User 4
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User 2 User 4
Basic database sharding
High availability If one box goes down, the partitions the database based on who will access that part of the service. In others still operate. In the simple model, this example, the criteria are that oddwhen some users stop getting service, the numbered users go in one partition and rest of the service is unaffected. Normally, even-numbered go in the other. there s some replication; even if a box with a shard goes down, its users can still be serviced on the replicated server. Faster queries Smaller amounts of data in each user group mean faster querying. Services with a lot of experience with their particular usage pattern and database structure learn how to best partition their database onto shards to keep queries fast enough to keep users satisfied.
Designing and architecting for cloud scale
More write bandwidth With no master database serializing writes, you can write to many shards in parallel. This increases your write throughput. Writing is the major bottleneck for many websites.
The bottom line is that sharding allows your application to do more work. It provides a parallel back-end, which means your application can do more things simultaneously. It can handle higher user loads, particularly when writing data, because there are parallel paths through your system on something that normally throttles performance: database writes. You can load-balance web servers that access shards over different network paths. These paths are processed by separate CPUs that use separate caches of RAM and separate disk I/O paths. The result is that few bottlenecks limit your application s performance. Now, let s look at a real example to substantiate this further.
WHY SHARD (OR PARTITION) YOUR DATABASE
Let s take Facebook as an example. In early 2004, mostly Harvard students used the site as an online yearbook. A single beefy server handled the entire storage requirements and query load on the database. Fast-forward to 2008, when Facebook application-related page views were about 14 billion per month (more than 5,000 page views per second, each of which required multiple back-end queries to satisfy). In addition to query load, with its attendant IOPs, CPU, and memory cost, there s also storage capacity to consider. Today, Facebook stores 40 billion physical files to represent about 10 billion photos, which is over a petabyte of storage. Even though the photo files aren t likely in a relational database, their metadata, such as identifiers and locations, still requires a few terabytes of storage to represent the photos in the database. You can be sure that originally Facebook didn t have terabytes of storage available to store photo metadata. And it correctly wasn t originally designed that way, because you can t know the exact usage patterns that will drive the best sharding strategy until you observe user behaviors as they interact with your application. At some point during the development of Facebook, the company reached the physical capacity of its database server and probably suffered a lot of user dissatisfaction while it worked to rearchitect. The moot question was whether to scale vertically by buying a more expensive, beefier server with more RAM, CPU horsepower, disk I/O, and storage capacity; or to spread data across multiple relatively cheap database servers. As we discussed earlier, if your service has rapidly changing data (lots of writes) or is sporadically queried by many users in a way that causes your working set not to fit in memory (lots of reads, leading to lots of page faults and disk seeks), then your primary bottleneck will likely be I/O. This is typically the case with social media sites, such as Facebook, LinkedIn, Blogger, MySpace, and even Flickr. In such cases, it s either prohibitively expensive or physically impossible to purchase a single server to handle the load on the site. Sharding the database, although challenging to design and implement correctly, provides the best performance and highest cost savings relative to the increased complexity of the system.
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