SQL Server Partitioning is a technique used to divide large tables into smaller, more manageable parts. It can improve query performance, reduce maintenance costs, and make data management easier. There are several types of SQL Server Partitioning, including Range, Hash, and List Partitioning. Each type has its own advantages and disadvantages, and the choice of which one to use depends on the specific needs of the application.
SQL Server TABLE Partitioning is a method of dividing a large table into smaller, more manageable parts, based on a specified partitioning key. Each partition is stored separately, allowing for faster data retrieval and improved query performance. Partitioned Views, on the other hand, are a way to present data from multiple tables as a single table. They are useful for combining data from different sources, but do not provide the same performance benefits as SQL Server TABLE Partitioning.
When comparing SQL Server TABLE Partitioning versus Partitioned Views, there are several factors to consider. SQL Server TABLE Partitioning is generally faster and more efficient than Partitioned Views, especially when dealing with large tables. It also allows for more granular control over data storage and retrieval. Partitioned Views, on the other hand, are more flexible and can be used to combine data from multiple sources, making them a better choice for certain types of applications.
In conclusion, SQL Server Partitioning is an important technique for managing large tables in SQL Server. When deciding between SQL Server TABLE Partitioning and Partitioned Views, it is important to consider the specific needs of the application and choose the method that provides the best performance and flexibility.
There is a sweet-spot when it comes to the rules determining the physical partitioning of the data in a table. Strategies regarding retention focus detracts from search centric focus. Storage versus fast resulting query performance. These two aspects tend to clash more often than they overlap. A retention based strategy, with a focus on storage optimization leads to an excellent opportunity for switching new partitions in and switching old partitions out. A bit like fridge management. Query performance then has to be tailored to fit with that, within partitions and sometimes span partitions. On the other hand, if query performance is king, then this can be achieved, but at the cost of more heavy workload when it comes to adding new and deleting old partitions. New data may have to go into any partition and old data may have to come out of any partition. This puts obstacles in the way for simple and fast switching in and out partitions. In my experience the best approach is to start with retention in mind and then see how well searches can be accommodated.