When working with SQL Server, it is crucial to consider the impact of table design choices on performance. The choice of columns and their data types can greatly affect the efficiency of your queries. With the growing list of available data types, it can be challenging to make the right design decisions. In addition, real-world data is often not optimized for performance, requiring methods of transforming the information to ensure efficient storage.

One important factor to consider is the human readability of the data. SQL Server is often used in conjunction with external applications or services responsible for presenting the table data. Deciding where to implement the human readability factor can have a significant impact on performance. Implementing the transformation at the table level eliminates the need for data conversion when displaying results but increases query costs for comparison, filtering, and sorting. On the other hand, implementing the transformation at the application level can negate any performance benefits achieved in SQL Server if large amounts of data need to be displayed simultaneously.

IPv4 addresses, for example, require little human readability but are often stored in databases for security or data mining purposes. Storing IPv4 addresses efficiently can greatly improve performance. The most efficient data type for storing IPv4 addresses in SQL Server is the 32-bit INT. Storing the value as an integer only requires 4 bytes of storage, compared to 8 bytes for BIGINT or 17 bytes for VARCHAR(15). The conversion from standard notation to an integer can be done at the application level, using bit shifting operations.

There are different methods for converting a string IPv4 address to an integer IPv4 address. One common approach is to use SUBSTRING and CHARINDEX functions to extract the octets and perform the necessary bit shifting. Another approach is to use the PARSENAME function, which was not designed for this purpose but can be used due to SQL Server’s dot-delimited path naming. Performance testing of these methods shows that PARSENAME is faster than multiple SUBSTRING and CHARINDEX calls.

Converting an integer IPv4 address back to a string can be done using division and remainder operations or bit masking and division. Performance testing shows that the bit masking and division method is more efficient.

When it comes to table design, storing the IPv4 address as an INT offers the best performance. However, the conversion to a human-readable string can affect performance. Adding an additional VARCHAR(15) column to the table and populating it at the same time as the INT column eliminates the need for conversions when querying the data. On the other hand, using a separate table for lookups can introduce additional overhead.

Performance testing of different table designs shows that using the INT data type and performing the conversion on the fly using a scalar function is the most efficient approach. The overhead caused by additional columns or tables outweighs the benefits of normalization.

In conclusion, optimizing performance in SQL Server involves making careful design choices. Storing IPv4 addresses as INT provides the best performance and storage efficiency. The choice of where to implement the human readability factor depends on the specific requirements of your application. By considering these factors and conducting performance testing, you can ensure that your SQL Server database performs at its best.