The tradeoff is the additional data access overhead required in determining the location of each data item as it's retrieved. It also enables data to migrate between shards without reworking the business logic of an application if the data in the shards need to be redistributed later (for example, if the shards become unbalanced).
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This sharding logic can be implemented as part of the data access code in the application, or it could be implemented by the data storage system if it transparently supports sharding.Ībstracting the physical location of the data in the sharding logic provides a high level of control over which shards contain which data. When an application stores and retrieves data, the sharding logic directs the application to the appropriate shard. It shouldn't be based on data that might change. These attributes form the shard key (sometimes referred to as the partition key). A shard typically contains items that fall within a specified range determined by one or more attributes of the data. When dividing a data store up into shards, decide which data should be placed in each shard. In the cloud, shards can be located physically close to the users that'll access the data. You can reduce contention and improve performance by balancing the workload across shards. You can scale the system out by adding further shards running on additional storage nodes.Ī system can use off-the-shelf hardware rather than specialized and expensive computers for each storage node. A shard is a data store in its own right (it can contain the data for many entities of different types), running on a server acting as a storage node. Each shard has the same schema, but holds its own distinct subset of the data. Solutionĭivide the data store into horizontal partitions or shards. A commercial cloud application capable of supporting large numbers of users and high volumes of data must be able to scale almost indefinitely, so vertical scaling isn't necessarily the best solution.
Scaling vertically by adding more disk capacity, processing power, memory, and network connections can postpone the effects of some of these limitations, but it's likely to only be a temporary solution. If the users are dispersed across different countries or regions, it might not be possible to store the entire data for the application in a single data store. It might be necessary to store data generated by specific users in the same region as those users for legal, compliance, or performance reasons, or to reduce latency of data access. It's possible that the volume of network traffic might exceed the capacity of the network used to connect to the server, resulting in failed requests. Ultimately, the performance of a data store running on a single server is governed by the rate the server can receive requests and send replies.
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It might be possible to add memory or upgrade processors, but the system will reach a limit when it isn't possible to increase the compute resources any further. A single server hosting the data store might not be able to provide the necessary computing power to support this load, resulting in extended response times for users and frequent failures as applications attempting to store and retrieve data time out. A cloud application is required to support a large number of concurrent users, each of which run queries that retrieve information from the data store. However, the system will eventually reach a limit where it isn't possible to easily increase the storage capacity on a given server.Ĭomputing resources. A server typically provides only a finite amount of disk storage, but you can replace existing disks with larger ones, or add further disks to a machine as data volumes grow. A data store for a large-scale cloud application is expected to contain a huge volume of data that could increase significantly over time.
Context and problemĪ data store hosted by a single server might be subject to the following limitations:
This can improve scalability when storing and accessing large volumes of data. Divide a data store into a set of horizontal partitions or shards.
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