Multidimensional Expressions (MDX) is a powerful query language for Online Analytical Processing (OLAP) cubes. Writing efficient and maintainable MDX queries is crucial for delivering performant business intelligence solutions. This article outlines several best practices to help you write better MDX.

1. Understand Your Data Model

Before writing any MDX, thoroughly understand the structure of your Analysis Services cube. This includes dimensions, hierarchies, levels, measures, and their relationships. Knowing your data model will prevent common pitfalls and lead to more targeted queries.

• Familiarize yourself with the business logic behind the measures and dimensions.
• Identify the grain of your fact tables and how dimensions relate to them.
• Document your understanding for future reference and collaboration.

2. Selectivity and Performance

The core of MDX performance lies in selectivity. The more you can limit the data returned, the faster your queries will execute.

Use `NON EMPTY` Judiciously

The `NON EMPTY` keyword is essential for returning only rows and columns that contain data. However, overuse or incorrect placement can hinder performance. Apply it to the axes where you expect sparse data.

SELECT
    {[Measures].[Sales Amount]} ON COLUMNS,
    NON EMPTY {[Dim Geography].[City].MEMBERS} ON ROWS
FROM [Adventure Works DW]`

Leverage Subselects and `CALCULATE`

When performing complex calculations or filtering, use subselects within the `SELECT` statement. The `CALCULATE` function is implicitly invoked for measures, but explicitly using it can sometimes clarify intent or improve performance in specific scenarios.

SELECT
    {[Measures].[Reseller Sales Amount]} ON COLUMNS,
    NON EMPTY {[Dim Product].[Category].MEMBERS} ON ROWS
FROM [Adventure Works DW]
WHERE (
    CALCULATE(
        [Measures].[Reseller Sales Amount],
        [Dim Date].[Calendar Year].&[2003]
    )
)`

Filter Early and Often

Apply filters in the `WHERE` clause or through `SET` operations that reduce the scope of calculations as early as possible.

SELECT
    {[Measures].[Internet Sales Amount]} ON 0
FROM [Adventure Works DW]
WHERE ([Dim Product].[Subcategory].&[12], [Dim Date].[Calendar Year].&[2004])`

3. Hierarchy Navigation and Slicing

MDX offers robust ways to navigate hierarchies. Understand how to use `Parent`, `Children`, `Level`, `Descendants`, and `Ancestor` effectively.

Using `Children` vs. `MEMBERS`

`Children` returns the immediate child members of a given member, while `MEMBERS` returns all members within a specified set or hierarchy.

-- Gets direct children of 'United States'
SELECT
    {[Measures].[Sales Amount]} ON COLUMNS,
    [Dim Geography].[Country].&[United States].Children ON ROWS
FROM [Adventure Works DW]`

-- Gets all members of the City level
SELECT
    {[Measures].[Sales Amount]} ON COLUMNS,
    [Dim Geography].[City].MEMBERS ON ROWS
FROM [Adventure Works DW]`

Efficiently Using `Descendants`

`Descendants` is a powerful function for traversing hierarchies. Be mindful of the `POST` and `PRE` flags for performance.

-- All descendants, including self, at a specific level
SELECT
    {[Measures].[Sales Amount]} ON 0,
    Descendants([Dim Date].[Calendar Year].&[2003], [Dim Date].[Month], AFTER) ON 1
FROM [Adventure Works DW]`

4. Measure Usage and Calculation

Measures are the heart of your analysis. Write them clearly and ensure their calculation logic is sound.

Avoid Repeating Complex Calculations

If a complex calculation is used in multiple queries, consider defining it as a calculated member within the cube itself. This promotes reusability and consistency.

Understand `AGGREGATE` and `SUM`

While `SUM` is common, `AGGREGATE` can be more performant as it uses the cube's aggregation logic. For basic summation over a set, `SUM` is often sufficient.

5. Formatting and Readability

Well-formatted MDX is easier to debug, maintain, and share.

• Use consistent indentation.
• Use meaningful variable names for `WITH` members.
• Add comments where complex logic is employed.

6. Performance Tuning Tools

Utilize tools provided by SQL Server Management Studio (SSMS) and SQL Server Profiler to diagnose performance issues. The MDX query execution plan can reveal bottlenecks.

"Premature optimization is the root of all evil." - Donald Knuth. Focus on writing logically correct queries first, then optimize based on profiling data.

Conclusion

Mastering MDX requires practice and a deep understanding of your cube. By adhering to these best practices, you can write more efficient, readable, and maintainable MDX queries, leading to a better overall BI experience.