Performance
A variety of functions are provided to help you improve the performance of your BI system. The main functions are:
● SAP NetWeaver Business Intelligence Accelerator
This tool will help you to achieve significant performance improvements when reading queries from an InfoCube. It is available with installed and preconfigured software on specific hardware. The data in an InfoCube is provided in compressed form as a BI accelerator index. SAP NetWeaver BI Accelerator thus provides you with rapid access to any data in the InfoCube, while keeping the administration effort to a minimum. It can be used for complex scenarios with unpredictable request types, high data volume and request frequency.
● Aggregates
Relational aggregates are another way in which you can improve the read performance of queries when reading data from an InfoCube. The data in an InfoCube is saved in relational aggregates in aggregated form. Relational aggregates are useful if you want to improve the performance of one or more specific queries, or make specific improvements to reporting with characteristic hierarchies.
● OLAP Cache
A global and local cache are both available for buffering query the results and navigation states calculated using the OLAP processor:
The global cache is a cross-transaction application buffer, in which the query navigation states and query results calculated using the OLAP processor are stored on the application server instance. With similar query requests, the OLAP processor can access the data stored in the cache.
Queries can be executed much faster if the OLAP processor can read data from the cache. This is because the cache can be accessed far faster than InfoProviders since it is not necessary to access the database.
In the local OLAP processor cache, the results calculated by the OLAP processor are stored in a special storage type in the SAP Memory Management System (roll area) for each session.
A global and local cache are both available for buffering query the results and navigation states calculated using the OLAP processor:
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