Data and Performance

Switch on this option to stop the automatic update that happens when changes are made in a chart or table. This reduces the load on the system and improves performance.

Switch on this option to optimize performance by consuming a limited set of metadata for live models based on SAP BW connections.

The application normally works on a limited amount of set of attributes, but still have to process additional attributes transferred to the metadata. Per query, these additional attributes can reach up to multiple thousands, largely increasing the size of the metadata. This option limits the number of attributes to the ones that are explicitly defined at the query level.

This option can be enabled at the model level, and has impacts in stories, App Design and Data Analyzer.

This option optimizes performance in a table widget by consuming a reduced set of metadata. This limits the number of attributes to the ones that are explicitly defined at the query level.

When this option is activated, the only attributes that will be available in an SAP Analytics Cloud story are the attributes that were marked as visible in the query.

Working with limits in private version offers better control over a model performance. One benefit of working with limits is that, as a planner, you get notified whenever you create a data snapshot that is above the configured limit. When you get notified, you can either proceed or create a private version with more filters. For more details, please check out Creating Versions.

Every planning version has a default limit set to 5 million facts that can be adjusted, as planning performance depends on multiple factors, such as the number and complexity of the dimensions, calculations, concurrent users, and more. Make sure you adjust that limit accordingly if necessary in the Model Preferences, under Data & Performance  Size Limits for Planning Performance, with the Overwrite Default Limit option.

You can think of the planning area as a version’s data used for all planning actions, and a basis for creating private versions and editing public versions. By optimizing the planning area, you keep the data size manageable and can work on data that’s the most relevant to you.

Optimizing the planning area is especially useful if you’re trying to limit the data size of a model with a large public version that’s not limited. When you edit a public version, or create a private one, the application stores the recommended planning area as a reduced data snapshot but still shows locked data outside of that snapshot.

You can limit the size of the recommended planning area either using data access control, data locking, or both. Data access restrictions give you access to the data for which you have write access, while data locking restrictions give you access to the data regions that are unlocked.

You can optimize the size of the planning area in the Model Preferences, under the Data and Performance tab. Click the toggle under the Optimize Recommended Planning Area section, and using the dedicated options, select whether you want to limit the planning area based on data locking, data access control and model privacy, or both. Note that for both options to be effective, data access control and data locking must be enabled and configured. For more information, see Configuring Data Locking and Set Up Data Access Control.

For more information about the planning area, see Optimize Planning Models Using the Planning Area. For more information about working with the recommended planning area when editing or creating versions, see Planning on Public Versions and Creating Versions.

The SAP HANA processing engine used by SAP Analytics Cloud has been optimized and made available for models created from version 2021.02+ to provide better supportability, performance and precision. As a result, you might notice different data results between models created before and after version 2021.02. These differences are expected and solely due to the update.

However, if you notice unexpected results in new models or errors that didn’t occur before, you can decide to switch back to the legacy processing engine as opposed to the optimized processing engine. For more information, please refer to 2994816 .

For performance reasons, after a system upgrade, models are upgraded consecutively over time. You can use the Redeploy Model option if you want to make sure that a specific model is upgraded after a system upgrade.

You can also use that option if you have incorrect data or exceptions errors in widgets in stories and analytic applications. This usually means that the model is corrupted. The Redeploy Model option regenerates all runtime artefacts and fixes these errors.