Your SAP Is on HANA but Still Slow: Fixing Custom Code Anti-Patterns

Why HANA Does Not Automatically Fix Slow SAP Code
Migrating SAP to HANA often creates expectations of dramatically faster reports, batch jobs, and transactional processing. Yet many teams discover that a Monday morning workload takes just as long after migration as it did before. The reason is simple: the database platform changed, but the custom ABAP logic did not.

HANA is designed to filter, aggregate, and analyze large volumes of data efficiently in memory and through column-based storage. However, inefficient application code can prevent the database from delivering those benefits. Instead of allowing HANA to process data close to the source, legacy programs may retrieve excessive records, perform calculations on the application server, or issue thousands of individual database requests.

Teams with the right SAP Skills can identify these bottlenecks and modernize the code without immediately investing in additional infrastructure. In many cases, a focused code review produces more value than a hardware upgrade.

Three Custom Code Anti-Patterns That Limit Performance

1. Database Calls Inside Loops A SELECT statement inside a LOOP is one of the most common performance issues in legacy ABAP. The program first reads a list of business objects, such as orders or deliveries, and then executes another database query for every item in that list. A small test dataset may hide the problem, but a production report processing 200,000 records can generate 200,000 database calls.

The issue is not necessarily that each query is slow. The issue is the volume of round-trips between the application server and database. The usual solution is to retrieve related information in one operation through a JOIN, carefully structured FOR ALL ENTRIES statement, or another set-based approach. This can reduce lengthy batch jobs from tens of minutes to only a few minutes.

1. Full-Table Scans Queries that do not filter effectively may force HANA to examine far more data than the program actually needs. Column-store technology improves analytical processing, but it does not make unrestricted scans free. A report that filters a large table only after retrieving broad datasets will still consume memory, CPU, and network capacity.

Review the fields used in WHERE clauses and confirm that the logic narrows the dataset as early as possible. In some cases, an appropriate secondary index helps. In others, the better answer is to include key fields, eliminate unnecessary selections, or redesign the query so that filtering occurs in the database rather than in ABAP.

1. Aggregation Performed in ABAP Another common pattern is to transfer thousands or millions of rows to the application server and then calculate totals, counts, averages, or groupings in internal tables. This approach may have been reasonable when custom programs were designed around older databases. On HANA, it wastes one of the platform’s greatest strengths.

Whenever practical, push aggregation, filtering, sorting, and joins down to the database layer. Modern Open SQL, CDS views, and carefully designed database procedures can return a smaller, more useful result set to the application. The ABAP layer then focuses on business logic instead of processing raw data that HANA could handle more efficiently.

A Practical Code Optimization Process
Performance improvements work best when they are measured rather than guessed. A structured review can help teams prioritize the changes with the greatest operational impact:

Use workload monitoring and SQL analysis to identify expensive statements and long-running programs.
Check for SELECT statements inside loops, nested database access, and repeated reads of the same data.
Review execution plans to understand whether queries are filtering effectively or scanning unnecessary records.
Compare database time, application-server time, memory usage, and transferred data volume.
Rewrite one bottleneck at a time, then retest with realistic production-sized datasets.
Document the improved pattern so future custom development does not recreate the same problem.
This process is equally important for organizations adopting SAP HANA Cloud Solutions or moving toward SAP S/4HANA Cloud. Cloud infrastructure can provide elasticity and modern services, but inefficient custom logic can still create avoidable costs and slow user experiences.

What SAP Teams Should Prioritize
Not every custom program requires a complete rewrite. Start with jobs that affect financial close, order fulfillment, inventory planning, customer reporting, or other high-value processes. Prioritizing programs by business impact helps the team show measurable results quickly and builds support for broader modernization.

It is also important to validate functional behavior after every change. A faster query that produces incomplete or duplicated results is not an improvement. Performance testing should therefore include record counts, totals, authorization behavior, edge cases, and comparisons with the original program.

For organizations without sufficient internal capacity, SAP Support can help investigate recurring performance issues, while an experienced development team can refactor custom code and establish sustainable standards for future enhancements.

FAQ
Q: Does moving to HANA eliminate the need to optimize ABAP?

No. HANA accelerates database operations, but inefficient ABAP can still create excessive calls, transfer unnecessary data, and perform expensive processing outside the database.

Q: Should every SELECT inside a LOOP be removed?

Not always, but every instance should be reviewed. If the loop is small or the logic is highly specific, the pattern may be acceptable. For large datasets, a JOIN, bulk read, or other set-based approach is usually more efficient.

Q: Is adding an index always the best solution?

No. An index may help, but it can also increase storage and write overhead. First confirm that the query is logically filtering the right data and that the execution plan supports the proposed change.

Conclusion
HANA provides substantial performance potential, but custom code must be designed to use it. Eliminating database calls inside loops, reducing full-table scans, and pushing aggregation into the database can deliver faster results without immediately expanding infrastructure.

Ready to improve the performance of your SAP environment? Our experts can help identify custom code bottlenecks and create a practical optimization plan. Contact us at [e-mail] to get started.