1. Designing Aggregations

2. Performance Fundamentals - Aggregations Pre-calculated summaries of data Intersections of levels from each dimension Tradeoff between processing (disk space) and query times Number of Aggregations Time Processing Time Query Time

3. Aggregation Generalities Are created when a cube is processed Include all measures Cannot generally be designed for specific members Are built and maintained automatically

4. Contents of an Aggregation

5. Time – 33 Members All (1) Year (2) Quarter (8) Month (22) Total Number of Aggregations = 4 Total Aggregated Values = 33

6. Contents of an Aggregation Time – 33 Total Members All (1) Year (2) Quarter (8) Month (22) State - 14 Total Members All (1) Country (3) Region (4) State (6) Total Number of Aggregations = 16 Total Aggregated Values = 462 (Theoretical)

7. Data Storage – Minimal Data Explosion Data Explosion Historical weakness of OLAP 100% Dense No heaters sold in Phoenix in July; no storage allocated Intelligent Aggregation Design Pre-aggregate only a subset of the data Compression Algorithm

8. Fact Table Show all sales for all products for all... Most detailed aggregations Highest level of aggregation Partial Aggregation

9. Fact Table MonthProducts Quarter Pro. Family QuarterProduct Month Partial Aggregation

10. Analysis Server Cube Storage

11. MOLAP Storage Mode Details and Aggregations Stored in Multidimensional Format Fastest Storage Option for Queries Often the Most Efficient in Terms of Disk Storage, Due to Compression

12. ROLAP Storage Mode Details and Aggregations Stored in RDBMS Slowest Query Performance Most Often the Slowest to Process Analysis Server Can Create Indexed Views Useful for Large Data Sources Provides Real-Time OLAP Solution

13. HOLAP Storage Mode Details Maintained in RDBMS Aggregations Created in Multidimensional Format Good Option where Disk Consumption Is a Concern Good Compromise if Details Are Accessed Infrequently

14. Cube Aggregations Full Aggregation Not Necessary Effects on Cube Size and Processing Time Cube size and processing times increase as aggregations are added to a cube Tools for Implementing Aggregations Storage Design Wizard Usage-Based Optimization Wizard

15. Why Usage-Based Optimization? Limitations of Storage Design Wizard Driven by structural factors Does not take Into account user behavior Benefits of Usage Based Optimization Collects data about user queries Allows aggregations to be built based on usage patterns

16. Using the Query Log Populating the Usage Log The server logs one out of ten queries by default You can change the query sampling rate Accessing the Usage Log You can view reports in Analysis Manager You can open the file msmdqlog.mdb Modifying the Cube Structure If you update the cube structure, query log entries may be invalid You can clear or filter the contents of the query log in Analysis Manager

17. Partitioning Subsets of the data within a cube May come from different tables or data sources Role in data management Varying storage modes and aggregations Adding new and removing obsolete data

18. Current Year History Prior Year MOLAP 15% Agg ROLAP 25% Agg HOLAP 20% Agg History Prior Year Current Year Partitioning Strategy

19. Month 1 Partitioning Data Management Example Month 61 Month 24 Month 2 Month 25 Month 60 Month 26 Month 25 Most recent 3 years of data in MOLAP storage Prior 2 years of data in ROLAP storage New partition for new month data

20. Partitioning Guidelines Benefits for processing Parallelism Particularly in an initial load Increased selectivity when querying Must set slice information for the system! Beware! Changing the aggregations for a partition will not alter other partitions