Code Analysis

The given code is a DAX expression used in Power BI or Power Pivot to calculate modified sales summary based on the original sales data.

Time Complexity

• The time complexity of the code depends on the size of the Sales table and the number of distinct products in the table.
• The most time-consuming part of the code is the SUMMARIZE function, which calculates the summary statistics for each distinct product.
• The time complexity of the SUMMARIZE function is O(n * m), where n is the number of rows in the filtered table and m is the number of aggregated columns.
• The SUM function used inside the SUMMARIZE function has a time complexity of O(n), where n is the number of rows in the filtered table.
• The FILTER function filters the rows based on the condition [Total Sales] <> [Discount Sales]. The time complexity of the FILTER function is O(n), where n is the number of rows in the Sales table.

Therefore, the overall time complexity of the code can be estimated as O(n * m), where n is the number of rows in the Sales table and m is the number of distinct products in the Sales table.

Space Complexity

• The space complexity of the code depends on the size of the Sales table and the number of distinct products in the table.
• The SUMMARIZE function creates a new table with summarized statistics for each distinct product. The size of this new table depends on the number of distinct products.
• The ADDCOLUMNS function adds two additional columns to the filtered table, increasing the memory usage.
• The FILTER function creates a new table with filtered rows based on the condition [Total Sales] <> [Discount Sales]. The size of this new table depends on the number of rows in the Sales table.
• The ALL function creates a new table with all rows from the Sales table. The size of this new table is the same as the original Sales table.

Therefore, the overall space complexity of the code can be estimated as O(n * m), where n is the number of rows in the Sales table and m is the number of distinct products in the Sales table.

Potential Bottlenecks and Optimization

• The code can potentially be optimized by reducing the number of distinct products in the Sales table. This can be achieved by pre-aggregating the data at a higher granularity before applying the SUMMARIZE function.
• Additionally, if the Sales table is very large, it may be worth considering partitioning or indexing the table to improve query performance.
• It's also important to ensure that the data model is properly optimized for query performance, including appropriate indexing and relationships between tables.

Advice for Improvement

• To improve your understanding of DAX expressions and optimization techniques, you can explore the courses offered by Enterprise DNA's Data Mentor platform. The platform provides comprehensive training on DAX and Power BI optimization strategies to enhance your skills as a developer.

Remember to keep optimizing and refining your code to ensure optimal performance!