eG Help

MDX (Multi - Dimensional eXpressions) is a query language used to retrieve data from multidimensional databases. More specifically, MDX is used for querying multidimensional data from Analysis Services. Multidimensional Expressions (MDX) lets you query multidimensional objects, such as cubes, and return multidimensional cellsets that contain the cube's data.

To ensure the peak performance of your MDX queries, it is essential that you write MDX efficiently. For that, you should first decide how your queries should evaluate calculations. Typically, calculations are evaluated in the cell-by-cell mode or bulk mode. Imagine you have a query that returns 3 product categories on the columns and 4 countries on the rows. So you have 12 cells in total, where every row has at least one value and every column has at least one value, but not every cell has a value.

In cell-by-cell mode the formula engine will attempt to calculate all 12 cells. It will start with Accessories for Australia, ask the storage engine for the data, execute the MDX expression then move on to Bikes in Australia and so on.

From the above discussion, it is clear that the bulk mode reduces the number of executions the formula engine has to do, it reduces the number of calls to the storage engine, and the storage engine calls that are made more closely match how the data is stored on disk as SSAS only stores non-empty values.

Next, you should make sure that your queries use the evaluation node caches effectively. MDX calculation engine keeps separate caches for cell-by-cell evaluation nodes and for bulk-mode evaluation nodes.

If MDX queries are run in the more expensive cell-by-cell evaluation mode, and are engineered to use caches frugally, query performance is bound to suffer. To improve query performance, administrators should first figure out how many MDX queries use the less-desired cell-by-cell evaluation mode and how well caches are used by the queries. This is where the MSSQLAMDXTest helps!

This test monitors MDX query execution plans and reports the count of cell-by-cell, bulk-mode, and subcube evaluation nodes built by these plans. This way, administrators can figure out if there are any queries that use the expensive cell-by-cell mode, and may hence require fine-tuning. The test also tracks the hits and misses experienced by the caches of the cell-by-cell and bulk-mode evaluation nodes, and alerts administrators to poor cache usage. This way, by monitoring factors that may impact MDX query performance, this test provides administrators with useful pointers for improving query performance.

Outputs of the test : One set of results for the SSAS server being monitored.

Measurement	Description	Measurement Unit	Interpretation
cell_by_cell_evaluation	Indicates the total number of cell-by-cell evaluation nodes built by MDX execution plans.	Number	Ideally, the value of this measure should be low. This is because, typically, inefficient or badly-written calculations are evaluated in cell-by-cell mode, which is usually slower than bulk mode.
cell_by_cell_hits	Indicates the number of cell-by-cell hits that currently occurred in the evaluation nodes cache.	Number	A high value is desired for this measure.
cell_by_cell_misses	Indicates the number of cell-by-cell misses that currently occurred in the evaluation nodes cache.	Number	A low value is desired for this measure.
cell_by_cell_hit_ratio	Indicates the ratio of cell hits to the sum of cell hits and misses.	Percent	This value should be high. If the value is lower than 85%, which means that the number of total cell misses are higher than expected. If a change to your MDX increments this value by a smaller amount than before, and the query runs faster, you are doing something right.
Total_cells	Indicates the total number of cell properties calculated since the last measurement period.	Number
Bulk_mode_nodes	Indicates the total number of bulk-mode evaluation nodes built by MDX execution plans.	Number	Ideally, the value of this measure should be high. This is because, evaluations in bulk-mode are less process-intensive, and will help MDX queries run faster.
Cached_bulk_node	Indicates the current (approximate) number of cached evaluation nodes built by MDX execution plans.	Number
Subcube_hits	Indicates the total number of subcube hits in the cache of evaluation nodes.	Number	For best query performance, a high value is desired for this measure. Aggressively caching intermediate results is one of the key reasons for MDX execution engine to deliver great query performance. MDX execution engine maintains a complex system of various types of caches for different purposes. When a MDX query requires heavy calculation, caches for evaluation nodes tend to play a decisive role in good query performance. An evaluation node is a subspace along with query plans built for all applicable calculations plus, optionally, data caches holding the calculation results. Unlike cached results of storage engine queries which correspond to leaf nodes in an MDX evaluation tree, formula engine evaluation nodes can be at much higher level or even be the root node of an evaluation tree. Hitting or missing an evaluation node at high level in the cache will make a dramatic difference in performance for calculation intensive queries.
Subcube_misses	Indicates the total number of subcube misses in the cache of evaluation nodes.	Number
Subcube_Hit_ratio	Indicates the ratio of sub cube hits to the sum of sub cube misses and hits.	Percent	This value of this measure should ideally be high. If the value is lower than 85%, it implies poor cache usage.