What is the fastest MDX?
There isn't a universal “fastest” MDX; performance depends on the OLAP engine, cube design, and query patterns. In practice, the fastest MDX is typically achieved with a well-tuned MOLAP cube on SQL Server Analysis Services (SSAS) that uses pre-aggregations, proper dimension design, and efficient MDX patterns.
Understanding MDX performance in context
MDX (Multi-Dimensional Expressions) queries run against OLAP cubes. The speed of an MDX query is influenced by the storage model (MOLAP, HOLAP, ROLAP), the complexity of the cube's dimensions and measures, the presence of calculated members, and how aggressively the engine caches and parallelizes work. Some engines are optimized for MDX better than others; Microsoft SSAS is widely used and often fastest for enterprise cubes when properly configured.
Key factors that shape MDX speed
In practice, several technical choices determine query performance. The following list highlights the most impactful ones in typical enterprise deployments.
- Storage mode and aggregations: MOLAP storage with pre-aggregations usually yields fastest reads; HOLAP/ROLAP may be slower but can be more flexible for large volumes of data.
- Cube design: clean dimension hierarchies, natural keys, and minimized cross-joins help MDX processors prune results early.
- Partitioning and processing: small, well-partitioned cubes allow parallel processing and faster query response, especially for large data sets.
- Calculated measures vs stored measures: rely on stored measures when possible; calculated members add computation at query time and can slow queries.
- Non-empty behavior and optimization tricks: using NonEmpty and slicing reduces the number of cells the engine evaluates.
- Query patterns: avoid unnecessary crossjoins; filter early with where clauses; prefer set-based expressions over row-by-row logic.
These design choices, along with server hardware and caching behavior, typically determine whether a given MDX query returns in sub-second time or takes longer.
Best practices to speed up MDX queries
If you want to achieve the fastest possible MDX performance, apply a disciplined set of optimization practices that align with your specific cube and workload.
- Limit the cube scope with slicers or WHERE clauses so the engine processes only the relevant portion of data.
- Use NonEmpty and NonEmptyCrossJoin to prune empty cells and reduce result sets.
- Favor stored measures and pre-built aggregations; minimize on-the-fly calculated members.
- Avoid complex cross-joins across large dimensions; leverage hierarchies to navigate data instead.
- Partition large fact data, and keep aggregations up to date; schedule processing to warm caches.
- Test queries with representative workloads and use profiling tools (Extended Events, SQL Server Profiler) to identify bottlenecks.
- Keep MDX expressions simple where possible and push work into the cube design rather than into the query itself.
Applying these practices can significantly improve response times for common MDX workloads and help you derive consistent performance across reporting environments.
Real-world considerations for fastest MDX
In production, the "fastest" MDX is often the one that returns the needed results reliably within service-level targets, rather than the one that is fastest in isolation. Organizations typically measure MDX performance by end-to-end report latency, cache warm-up, and the ability to scale under concurrent users. This means brute force speed isn't the sole objective; stability, predictability, and maintainability matter as well.
What to consider when choosing an MDX engine
Choosing an engine matters for MDX speed. The most common enterprise choices are:
- Microsoft SQL Server Analysis Services (SSAS) Multidimensional with MOLAP storage for fast MDX on curated cubes.
- Mondrian (Java) in open-source stacks, which can be tuned for performance but requires careful design and hardware allocation.
- Other OLAP servers that offer MDX compatibility or MDX-compatible layers; performance varies by version and configuration.
In modern practice, many teams also weigh the benefits of moving to DAX-based models (for tabular cubes) for new workloads, as DAX queries can be faster and simpler for certain scenarios, while MDX remains essential for legacy multidimensional cubes.
Summary
There is no single fastest MDX. The speed of MDX queries depends on the engine (SSAS, Mondrian, or other), the storage model, and how the cube is designed and queried. The best path to speed is a combination of MOLAP storage with pre-aggregations, careful dimension design, and query patterns that prune results early. For most enterprise workloads, a well-tuned SSAS MOLAP cube with aggregations and optimized MDX patterns yields the best performance, while staying mindful of the ongoing shift toward DAX for newer tabular models.
