The rate of rows sent by the server to the clients.

The rate of rows read from all relational databases. If you are connected to SQL Server, you should be reading at least 80,000 rows per sec per table being processed.

The Processor performance object consists of counters that measure aspects of processor activity. The processor is the part of the computer that performs arithmetic and logical computations, initiates operations on peripherals, and runs the threads of processes.  A computer can have multiple processors.  The processor object represents each processor as an instance of the object.

The rate of jobs through the query thread pool.

The number of jobs in the queue of the query thread pool.

The number of busy threads in the thread pool.

The rate of non IO jobs through the thread pool.

The number of jobs in the processing thread pool queue.

The average amount of time for a query to be executing in the formula engine.  

The average amount of time spent returning data from the storage engine cache.

The average amount of time spent returning non-cached data from the storage engine cache.

The average amount of time spent serializing data to disk ( as in data aggregations).

The average amount of time spent executing SQL queries from the relational database.

Displays the average processing time in milliseconds. Processing in SSAS Involves the SE, and can cause the Cache to become outdated and flushed. This causes a performance hit because when the query is run after processing completes, the SE has to retrieve the necessary data from the Windows file cache or from disk (cold cache). Non 0 values on the indicate that processing is taking place.

Processing can be improved by:

• Re-scheduling processing when the least querying activity is going on.  This helps to avoid resource contention. 
• Check the processing type that is being used; Use ProcessIndex instead of ProcessFull when possible. 
• Break the measure groups into partitions to reduce the overall amount of work that is done.

The total number of cell properties calculated. High values for Cells calc’d while the query is executing indicates that the query is doing cell by cell calculations (this is like row by agonizing row in SQL Server) instead of Subspace (block by block) execution.

The total number of evaluation nodes built by MDX execution plans including active, and cached. High values for Calc Covers while the query is executing indicates that the query is doing cell by cell calculations (this is like row by agonizing row in SQL Server) instead of Subspace (block by block) execution.

The total number of subcubes that the query optimizer generated. High values for Sonarsubcubes while the query is executing indicates that the query is doing cell by cell calculations (this is like row by agonizing row in SQL Server) instead of Subspace (block by block) execution.

The total number of cells recomputed due to error. Recomputes indicate that there were errors in the calculations during query execution. Non 0 values indicate where unnecessary recalculations are taking place and can fall back to cell by cell executions.

The total number of times unoptimized the NON EMPTY algorithm is used. NE unopt helps determine if performance degradation is occurring due to a slower code path. Using a non-empty algorithm can speed up performance.

The total number of times the NON EMPTY algorithm was looping over calculated members. NE calc memb help determine if performance degradation is occurring due to a slower code path. Using a non-empty algorithm can speed up performance.

SSAS has two general categories of memory, shrinkable and nonshrinkable. Shrinkable memory can be easily reduced and returned back to the OS. Shrinkable memory is the amount of memory in KB that's subject to purging by the background cleaner.

SSAS has two general categories of memory, shrinkable and nonshrinkable. Nonshrinkable memory is generally used for more essential system-related tasks such as memory allocators and metadata objects, and is not easily reduced. Nonshrinkable memory is the amount of memory in KB that's not subject to purging by the background cleaner.

The rate of inserts from the cache.

The rate of evictions from the cache.

Note:  This is per partition per cube per database.

The rate of memory added to the cache (KB / Sec).

The current number of client connections established to the Analysis Services server.

The current number of user sessions established to the Analysis Services server.

The rate of incoming connection requests per second.

The rate of connection failures per second.

The current number of locked objects.

The current number of clients waiting for a lock.

The ratio of cache hits from the dimensions.

The ratio for measure groups.

The percent reading to the storage engine cache.

Displays the rate of Processing rows written during processing. Processing in SSAS Involves the SE, and can cause the Cache to become outdated and flushed. This causes a performance hit because when the query is run after processing completes, the SE has to retrieve the necessary data from the Windows file cache or from disk (cold cache). Non 0 values on the indicate that processing is taking place.

These metrics can also help determine the effectiveness of any processing tuning efforts. If your tuning efforts are effective, you should see an increase in these values accordingly.

Processing can be improved by:

• Re-scheduling processing when the least querying activity is going on.  This helps to avoid resource contention. 
• Check the processing type that is being used; Use ProcessIndex instead of ProcessFull when possible. 
• Break the measure groups into partitions to reduce the overall amount of work that is done

Displays the rate of rows from MOLAP files used to create indexes. Processing in SSAS Involves the SE, and can cause the Cache to become outdated and flushed. This causes a performance hit because when the query is run after processing completes, the SE has to retrieve the necessary data from the Windows file cache or from disk (cold cache).Non 0 values on the indicate that processing is taking place. 

These metrics can also help determine the effectiveness of any processing tuning efforts. If your tuning efforts are effective, you should see an increase in these values accordingly.

Processing can be improved by:

• Re-scheduling processing when the least querying activity is going on.  This helps to avoid resource contention. 
• Check the processing type that is being used; Use ProcessIndex instead of ProcessFull when possible. 
• Break the measure groups into partitions to reduce the overall amount of work that is done.

These metrics can also help determine the effectiveness of any processing tuning efforts. If your tuning efforts are effective, you should see an increase in these values accordingly.

Processing can be improved by:

• Re-scheduling processing when the least querying activity is going on.  This helps to avoid resource contention. 
• Check the processing type that is being used; Use ProcessIndex instead of ProcessFull when possible. 
• Break the measure groups into partitions to reduce the overall amount of work that is done.

The rate of cache lookups.

The rate of Measure group queries.

The rate of calculation cache lookups, including global, session, and query scope calculation caches.

The rate of Flat Cache lookups, including global, session, and query scope flat caches.

The rate of insertions into the cache.

Note:  This is per partition per cube per database.