![]() ![]() ![]() ![]() Each partition is known as a shard and holds a specific subset of the data, such as all the orders for a specific set of customers. In this strategy, each partition is a separate data store, but all partitions have the same schema. ![]() Horizontal partitioning (often called sharding). There are three typical strategies for partitioning data: For managed PaaS data stores, this consideration is less relevant, because these services are designed with built-in redundancy. Operations on other partitions can continue. If one instance fails, only the data in that partition is unavailable. Separating data across multiple servers avoids a single point of failure. For example, large binary data can be stored in blob storage, while more structured data can be held in a document database. Partitioning allows each partition to be deployed on a different type of data store, based on cost and the built-in features that data store offers. Match the data store to the pattern of use. For example, you can define different strategies for management, monitoring, backup and restore, and other administrative tasks based on the importance of the data in each partition. Partitioning offers many opportunities for fine-tuning operations, maximizing administrative efficiency, and minimizing cost. In some cases, you can separate sensitive and nonsensitive data into different partitions and apply different security controls to the sensitive data. Operations that affect more than one partition can run in parallel. Correctly done, partitioning can make your system more efficient. Data access operations on each partition take place over a smaller volume of data. If you divide data across multiple partitions, each hosted on a separate server, you can scale out the system almost indefinitely. When you scale up a single database system, it will eventually reach a physical hardware limit. It is not the same as SQL Server table partitioning. In this article, the term partitioning means the process of physically dividing data into separate data stores. ![]()
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