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In many large applications, data is divided into ‘partitions’ that can be managed separately. Partitioning can optimize performance and improve scalability, which is why database administrators choose it. However, the partition strategy must be chosen carefully to maximize the benefits and reduce the risk for adverse effects. 

Let’s learn more about what it means to partition data and the different design strategies to choose from. 

Why Partition Data? 

Partitioning data is the technique of distributing data across multiple tables. Here are some of the reasons why you might want to partition your own data. 

  • Improve scalability. When you scale up your database, you’ll eventually reach your hardware limit. By partitioning data, you can scale out your system almost indefinitely because each partition is hosted on a separate server.
  • Enhance performance. Data spread across partitions covers a smaller volume of data. This can make your system more efficient. Even operations that affect multiple partitions can be run simultaneously. 
  • Increase security. Partitions give you the option to separate sensitive and non-sensitive data into different sections. You can then apply different security controls to each.
  • Greater flexibility. You have a lot more options when you partition your data. You can maximize administrative efficiency, minimize cost and define strategies for backup and restoration. 
  • Improve availability. Separating data across multiple servers gives you greater availability. If one partition is unavailable, you can still access data on the other partitions. 

What are the Options for Designing Partitions 

When it comes to partitioning data, there are three main methods: 

  • Horizontal partitioning. With horizontal partitioning, also known as sharding, each partition is a separate data store, but they all have the same schema. 
  • Vertical partitioning. In this strategy, each partition holds a subset of the fields for items in the data store. The fields are separated based on their pattern of use. 
  • Functional partitioning. This strategy aggregates data based on how it is used by each bounded context in the system. For example, an online shop might store invoice data in one partition and customer information in another. 

Keep in mind that you don’t have to choose just one partition strategy – you can combine them all. To determine which partitioning strategy is right for you, contact Arkware today. We can help you answer this question and consider all options when designing a partitioning scheme.