Step-by-Step Guide on Converting SAP HANA CDS Views into Database Tables

In SAP HANA, Core Data Services (CDS) views provide a flexible and virtual approach to accessing and combining data from multiple sources on the fly without persisting it physically. These views enable analytical and transactional applications to dynamically compose data in real-time based on their needs. However, there can be scenarios where materializing a CDS view as a persistent database table may deliver performance, storage, or other architectural benefits. This comprehensive guide walks through the process of converting an existing CDS view into a fully persisted HANA database table.

?

Identifying the CDS View for Conversion

The first crucial step is to determine which specific CDS view needs to be materialized into a physical table format. This decision may stem from performance bottlenecks caused by frequent, complex view calculations or large data volumes involved. Alternatively, there could be architectural reasons, such as a need for persistent storage, better control over data lifecycle management, or integrating with external systems that prefer working with database tables overviews.

?

Once the target CDS view has been identified, it is essential to thoroughly review and understand its definition, including the data sources it accesses, the logic it encapsulates, and the structure of the resulting data set. This analysis will inform the subsequent steps and any potential challenges or considerations during the conversion process.

?

Creating a Table with an Identical Structure

With the CDS view selected for conversion, the next phase involves generating a CREATE TABLE statement that mirrors the view's structure. This can be achieved by extracting the Data Definition Language (DDL) code from the view definition and modifying the root annotation from @Analytics.view to @Table. Tools like the SAP Web IDE facilitate this process by allowing developers to copy the full-view code and swap this annotation easily.

?

At this stage, any additional table properties, such as indexes, partitioning strategies, compression settings, or other storage optimizations, can also be specified based on the anticipated usage patterns and performance requirements of the persisted table.

?

Once the modified CREATE TABLE statement is ready, it can be executed in the HANA database to provision a brand new, empty table with an identical column structure as the original CDS view.

?

Populating the New Table with Data

With the new table created, the next critical step is to populate it with the result data from the CDS view. This is typically accomplished using an Insert.Select statement, which queries the view and inserts all output rows into the newly created persisted table.

?

However, depending on the complexity of the CDS view and the volume of data involved, this population step may require special considerations and optimizations. For instance, if the view involves complex calculations or joins over large data sets, the INSERT operation could be a long-running and resource-intensive process. In such cases, techniques like disabling constraints temporarily, using the NVARCHAR data type instead of VARCHAR for better optimization, or splitting the operation across multiple INSERT statements for manageability may become necessary.

?

Furthermore, if the CDS view incorporates any complex logic, calculated measures, or data transformations, these may need to be replicated within the Insert.Select statement or handle separately to ensure data integrity and consistency between the view and the persisted table.

?

Indexing and Optimization

Once the table has been populated with data from the CDS view, the next step involves analyzing the anticipated usage patterns and queries that will be executed against the persisted table. Based on this analysis, appropriate indexes can be defined and created to optimize query performance for common filter conditions, join operations, or other access patterns.

?

While indexes improve query speeds, they also introduce overhead for insert, update, and delete operations, as well as increased storage requirements. Therefore, a careful balance must be struck between indexing needs and the associated costs, based on the specific application requirements and workloads.

?

Updating Existing Applications and Queries

With the persisted table now populated and optimized, the next critical step is to update any existing applications, reports, analytical models, queries, or other artifacts that previously consumed data from the original CDS view. These artifacts must be modified to instead read from the new database table going forward.

?

Depending on the complexity and number of applications involved, this update process may require changes across multiple layers, including the application code, data models, reporting tools, and any other components that referenced the original CDS view.

?

Proper testing and validation are crucial during this phase to ensure that all applications continue to function correctly and produce the expected results when reading from the persisted table instead of the view.

?

Handling View Dependencies (Optional)

In some cases, the CDS view being converted may be referenced or consumed by other dependent views or artifacts within the HANA environment. If these dependencies exist, they must be identified and addressed appropriately during the conversion process.

?

One approach is to update the dependent views or artifacts to reference the new persisted table instead of the original CDS view. Alternatively, if the dependencies are no longer required or can be consolidated, they may be candidates for removal or refactoring.

?

Proper communication, impact analysis, and coordination are essential when dealing with view dependencies to avoid unintended application breaks or data inconsistencies.

?

Decommissioning the Original CDS View (Optional)

As a final step, once all applications and dependencies have been successfully updated to leverage the new persisted table, the original CDS view definition can be considered for decommissioning and removal from the HANA environment. However, this step should be approached with caution and thorough testing to ensure, that, no unintended consequences or application breaks occur due to the view removal.

?

In some cases, organizations may choose to retain the original CDS view for a period, especially if there are concerns about potential future requirements or the need to temporarily revert to the view-based approach.

?

Benefits and Trade-offs

By converting a CDS view into a fully persisted and optimized database table, applications can benefit from faster query response times, as they no longer need to perform real-time view calculations on the fly. This can be particularly advantageous for complex views, large data volumes, or scenarios where query performance is critical.

?

However, this conversion also comes with trade-offs. Persisted tables require more storage space than virtual views and lack the flexibility and dynamic data composition capabilities that CDS views offer. An update to the underlying data sources may necessitate refreshing or rebuilding the persisted table, introducing additional overhead and complexity.

?

The decision to convert a CDS view to a persisted table should be carefully evaluated based on specific application requirements, performance needs, data volumes, and the desired balance between querying flexibility and persisted storage needs.

?

By following this comprehensive guide, organizations can navigate the conversion process successfully, unlocking potential performance gains while maintaining data integrity and ensuring a smooth transition for existing applications and queries.