Understanding SAP HANA's Architecture

In-Memory Columnar Store

SAP HANA stores data in columnar format in-memory, which benefits analytics but can strain memory and CPU during frequent write or transactional operations if not optimized. Real-time performance depends heavily on compression, partitioning, and execution plan stability.

Persistent Storage and Savepoints

HANA uses delta storage and performs regular savepoints to persist changes. Improper delta merge frequencies or large unmerged deltas can cause memory bloat and slow queries.

Common Performance Bottlenecks and Root Causes

1. Large Unmerged Delta Stores

Frequent inserts/updates accumulate in delta storage. If delta merges are not triggered promptly, query performance degrades due to scanning both main and delta stores.

-- Monitor delta merge stats
SELECT * FROM M_DELTA_MERGE_STATISTICS
WHERE TABLE_NAME = 'SALES_FACTS';

2. Inefficient Calculation Views

Calculation views with excessive nested projections, script-based nodes, or unrestricted joins degrade performance significantly. Performance suffers more when using row-based operations in otherwise columnar paths.

3. Missing or Misused Partitioning

Tables without partitions become bottlenecks under concurrent access. Hash or range partitioning must align with access patterns to avoid NUMA node memory contention and CPU imbalance.

-- Check partitioning of large tables
SELECT TABLE_NAME, PARTITION_METHOD FROM M_CS_PARTITIONS;

4. SQL Plan Instability

Queries behave inconsistently under similar loads due to plan cache thrashing, especially with literals instead of parameters. Parameterized queries stabilize plan reuse.

-- Check for plan cache issues
SELECT * FROM M_SQL_PLAN_CACHE WHERE STATEMENT_STRING LIKE 'SELECT%SALES%';

Diagnostics and Tools

Monitoring SQL Performance

Use SAP HANA Studio or Cockpit to analyze expensive statements and their execution plans. Focus on:

  • Expensive Statements trace
  • Visual Plan Analysis
  • Plan Cache hit ratios

Memory Usage Auditing

Track memory usage at schema and table level:

SELECT SCHEMA_NAME, TABLE_NAME, MEMORY_SIZE_IN_TOTAL
FROM M_CS_TABLES
ORDER BY MEMORY_SIZE_IN_TOTAL DESC;

Delta Merge Scheduling

Manually triggering delta merges can alleviate memory pressure during off-peak windows.

MERGE DELTA OF "SALES_FACTS";

Remediation and Optimization Strategies

Enforce Proper Data Modeling

  • Avoid multi-level calculation views when not needed
  • Use analytic or attribute views where simpler modeling suffices
  • Push filters down into base views to enable pruning

Stabilize Execution Plans

Use parameterized queries or stored procedures to reduce plan volatility. Monitor plan aging and evictions via M_SQL_PLAN_CACHE.

Automate Delta Merge Management

Configure merge policies based on workload characteristics. Use MERGE DELTA in background jobs or via XS Engine triggers.

Partition Large Fact Tables

Align partition keys with query filters or join keys. Avoid round-robin unless access is evenly distributed.

CREATE COLUMN TABLE SALES_FACTS ( ... )
PARTITION BY RANGE (SALES_DATE)
(PARTITION 'P1' VALUES LESS THAN (DATE '2020-01-01'),
 PARTITION 'P2' VALUES LESS THAN (DATE '2021-01-01'));

Best Practices

  • Regularly monitor delta merge stats and memory growth
  • Favor columnar storage unless row operations are frequent
  • Use table-level compression and unload infrequently accessed data
  • Test query plans under representative data volumes
  • Leverage SAP HANA Performance Tuning Workbench for systematic reviews

Conclusion

Troubleshooting SAP HANA performance requires a multi-dimensional view of memory, query execution, data modeling, and runtime resource usage. Common issues stem not from system failures but suboptimal modeling, partitioning, or misuse of calculation views. Effective diagnostics using SAP's system views and tuning tools, combined with proactive data architecture planning, can resolve and prevent recurring slowdowns and bottlenecks in enterprise environments.

FAQs

1. Why is my HANA query slow despite indexes?

HANA relies more on columnar scans and vectorized execution than traditional indexing. Poor modeling, large deltas, or nested views often cause slowdowns.

2. How do I know when to trigger delta merges?

Monitor M_DELTA_MERGE_STATISTICS and set policies for merge triggers based on delta size and read/write ratios. Regular merges during low-load times help maintain performance.

3. What causes plan cache thrashing in HANA?

Frequent queries with literal values instead of parameters prevent plan reuse and fill the cache with near-duplicate plans, reducing overall efficiency.

4. Should I always partition large tables in SAP HANA?

Not necessarily. Partitioning helps when access patterns are predictable and align with the partition key. Poor partitioning can worsen performance.

5. How can I debug performance of calculation views?

Use the Plan Visualizer to analyze execution steps, and validate that filters are pushed down. Avoid unnecessary script nodes or nested joins.