Distributed relational databases are becoming increasingly popular in the era of cloud computing and distributed cloud infrastructure. They offer numerous benefits such as scalability, high availability, geo-location of data, multi-tenancy, and more. However, to fully leverage these benefits, it is crucial to have a well-defined data distribution policy. In this blog post, we will explore the concept of data distribution policy and its importance in SQL Server.
What is a Data Distribution Policy?
A data distribution policy defines the rules for distributing data across a set of smaller databases that together form a distributed database. The goal is to evenly distribute workloads across the database clusters, ensuring optimal performance and scalability. A well-designed data distribution policy aligns with the unique usage patterns and requirements of your application.
The Challenges of Sharding
In the past, developers had to manually program data distribution logic into their applications, a process known as sharding. This approach required custom-built code to determine where data should be placed or retrieved. While sharding allowed for distributed databases, it came with several challenges. Operational tasks like backups, index changes, and schema modifications became more complex. Additionally, queries had to collect data from multiple databases, resulting in increased workload and decreased performance.
The Benefits of a Good Data Distribution Policy
A well-designed data distribution policy offers several benefits:
- Endless scalability: The ability to handle more concurrent users, higher transaction throughput, and larger volumes of data.
- High availability: Ensuring that a specific transaction or query is completed within a specific database.
- Geo-location of data: Distributing data based on the location of users, improving response times.
- Multi-tenancy: Supporting multiple applications or tenants on the same distributed database.
- Archiving data: Efficiently storing and retrieving historical data.
- Data “tiering”: Distributing data based on its importance or access frequency.
Creating the Best Data Distribution Policy
When creating a data distribution policy, there are two broad approaches:
- Arbitrary Distribution: Data is distributed across database instances without considering specific application requirements. This approach is commonly used in NoSQL databases.
- Declarative, Policy-Based Distribution: Data is distributed in a way that aligns with application requirements, data relationships, and usage patterns. This approach is recommended for relational databases like SQL Server.
While arbitrary distribution may be simpler, it can lead to excessive use of database nodes and network resources. Declarative, policy-based distribution, on the other hand, anticipates future requirements and growth assumptions, resulting in more efficient and scalable data distribution.
Conclusion
Understanding data distribution and creating an effective data distribution policy is crucial for maximizing the benefits of a distributed relational database like SQL Server. By distributing data intelligently and aligning it with your application’s unique requirements, you can achieve optimal scalability, performance, and availability.
Stay tuned for our next blog post, where we will discuss key observations and measurements for scaling existing applications in a distributed database environment.