How to Optimize Query Performance in SAP BW/4HANA

How to Optimize Query Performance in SAP BW/4HANA

How to Optimize Query Performance in SAP BW/4HANA

SAP BW/4HANA is a next-generation data warehousing solution that enables you to optimize processes and enable innovation with one trusted source for all insights. One of the key challenges of SAP BW/4HANA is the query performance, which determines how fast and efficient you can access and analyze data from different perspectives and levels of detail. In this blog post, we will explain what factors affect the query performance, how you can measure and monitor it, and how you can optimize it with some examples.

What factors affect the query performance?

The query performance in SAP BW/4HANA depends on various factors, such as:

  • The data volume and complexity of the underlying InfoProviders, which are the data stores in SAP BW/4HANA. The more data and joins involved, the longer it takes to retrieve and process the data.
  • The query design and structure, which define the dimensions, measures, filters, variables and properties of the query. The more calculations, aggregations, restrictions and exceptions involved, the longer it takes to format and display the data.
  • The query execution mode, which determines how the query is processed by the SAP HANA database and the Analytic Manager. The query execution mode can be either A (all data at once), E (exception aggregation), L (local calculation) or S (SAP HANA calculation). The optimal mode depends on the query type and characteristics.
  • The query output format, which determines how the query result is delivered to the BI client. The query output format can be either MDX (Multidimensional Expressions), BICS (Business Intelligence Consumer Services), OData (Open Data Protocol) or SQL. The optimal format depends on the BI client type and capabilities.
  • The system configuration and settings, which affect the query performance at different levels, such as database, server, application and client. The system configuration and settings include parameters such as memory allocation, parallel processing, caching, compression, partitioning, indexing, etc.

How to measure and monitor the query performance?

You can measure and monitor the query performance in SAP BW/4HANA using various tools and methods, such as:

  • The Eclipse-based BW Query tool, which allows you to create and execute queries on SAP BW/4HANA. You can use it to preview and test your queries directly from the tool without switching to another application. You can also use it to access different aspects of your queries, such as general properties, sheet definition, filters, variables and jump targets.
  • The authorization check tool (transaction code RSRT), which allows you to execute queries in different modes and formats and check their runtime statistics. You can use it to compare different query execution modes (A/E/L/S) and output formats (MDX/BICS/OData/SQL) and see their impact on the query performance. You can also use it to check the authorization objects and fields for your queries.
  • The BW statistics service (transaction code RSDDSTAT), which allows you to collect and analyze statistics data for your queries. You can use it to see various metrics for your queries, such as runtime, memory consumption, database time, OLAP time, etc. You can also use it to see various details for your queries, such as SQL statements, cache hits/misses, parallel processes, etc.
  • The BW administration cockpit (transaction code RSTCO_ADMIN), which allows you to monitor and manage various aspects of your SAP BW/4HANA system. You can use it to see various indicators for your system performance, such as CPU utilization, memory consumption, disk space usage, etc. You can also use it to perform various tasks for your system administration, such as backup/restore, delta merge jobs, process chains execution, etc.

How to optimize the query performance with examples?

You can optimize the query performance in SAP BW/4HANA using various techniques and approaches, such as:

  • Optimal use of SAP HANA functionality: Make sure that the tables have the correct storage type for the respective requirement, especially for self-defined models. For small tables, especially if you change entire records, the row-based storage approach is optimal. However, if you use tables for reporting and you aggregate individual columns, the column store format is better. If the access times for the column store format are increasing, ensure that the jobs that re-sort and condense the column store (delta merge jobs) are executed often enough and are successful. For programs, make sure that as much logic as possible is processed in the database, for example with native calculation views. Read only the columns needed. Choose performance-optimized join types in calculation views: if the referential integrity of data is guaranteed, use referential joins instead of inner joins.
  • Additional saving of values: If highly aggregated data is required (for example, thousands of rows are aggregated to one average) and if large amounts of data are read frequently, storing the aggregated data results makes sense, especially if key figure filters are executed on the aggregated values. Since joins on calculated values are much slower than joins on stored column contents, the performance of such joins can be increased if the calculated values are physically stored. With SAP HANA, it is also useful to buffer data that is to be read repeatedly in an application cache.
  • Additional options for performance optimization: Large SAP HANA database tables are partitioned by default using a hash function, i.e. divided into parts that are read in parallel. But even with medium-sized tables, manually defined partitioning can make sense. For example, you can partition a table by calendar year or month to improve the query performance for time-based queries. You can also use indexes to speed up the access to specific columns or combinations of columns. However, indexes should be used sparingly, as they consume additional memory and disk space and require maintenance.

Here are some examples of how you can optimize the query performance for different scenarios:

Example 1: Optimizing a query with many calculations and aggregations

To optimize a query with many calculations and aggregations, follow these steps:

  • Check the query execution mode and output format using the authorization check tool or the Eclipse-based BW Query tool. Compare different modes and formats and see their impact on the query performance. Choose the optimal mode and format for your query type and characteristics.
  • Check the query design and structure using the Eclipse-based BW Query tool. Avoid unnecessary calculations, aggregations, restrictions and exceptions in your query. Use variables or fixed values instead of formulas or conditions whenever possible. Use exception aggregation mode (E) or SAP HANA calculation mode (S) if your query contains many exceptions or calculations.
  • Check the underlying InfoProvider design and structure using the BW Modeling tools or the Data Warehousing Workbench. Avoid unnecessary joins and unions in your InfoProvider. Use referential joins instead of inner joins if the referential integrity of data is guaranteed. Use native calculation views instead of BEx queries or HANA views if possible.

Example 2: Optimizing a query with large data volume and complexity

To optimize a query with large data volume and complexity, follow these steps:

  • Check the data volume and complexity of the underlying InfoProvider using the BW Modeling tools or the Data Warehousing Workbench. Reduce the data volume and complexity by applying filters, aggregations or transformations at the InfoProvider level. Use advanced DSOs or composite providers instead of classic DSOs or multi-providers if possible.
  • Check the table storage type and partitioning using the SAP HANA studio or the BW Modeling tools. Choose the optimal storage type (row-based or column-based) for your table requirement. Use manual partitioning by relevant criteria (such as calendar year or month) to improve the parallel processing of your table.
  • Check the table indexes using the SAP HANA studio or the BW Modeling tools. Create indexes on specific columns or combinations of columns that are frequently used in your queries. Use indexes sparingly, as they consume additional memory and disk space and require maintenance.

Example 3: Optimizing a query with frequent data access and high aggregation

To optimize a query with frequent data access and high aggregation, follow these steps:

  • Check the data access frequency and aggregation level using the BW statistics service or the authorization check tool. Identify the queries that are executed frequently and require high aggregation of data.
  • Store the aggregated data results using the BW Modeling tools or the Data Warehousing Workbench. Create an InfoProvider that stores the aggregated data results based on your query criteria. Use this InfoProvider as a source for your query instead of accessing the original data.
  • Buffer the data that is to be read repeatedly using the SAP HANA studio or the BW Modeling tools. Create an application cache that stores the data that is to be read repeatedly by your queries. Use this application cache as a source for your query instead of accessing the database.

Conclusion

The query performance in SAP BW/4HANA is a crucial factor that determines how fast and efficient you can access and analyze data from different perspectives and levels of detail. The query performance depends on various factors, such as data volume and complexity, query design and structure, query execution mode and output format, system configuration and settings. You can measure and monitor the query performance using various tools and methods, such as Eclipse-based BW Query tool, authorization check tool, BW statistics service, BW administration cockpit. You can optimize the query performance using various techniques and approaches, such as optimal use of SAP HANA functionality, additional saving of values, additional options for performance optimization.

We hope this blog post has helped you to understand what factors affect the query performance, how you can measure and monitor it, and how you can optimize it with some examples. If you have any questions or feedback, please leave a comment below.

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