SQL Performance for SAP Analytics
As of 2026-08-16
A calculation view that runs in 120 ms in development can take 45 seconds in production once a scalar UDF, an unpruned join, or a badly partitioned fact table pushes HANA out of its columnar fast path — and the fix is almost never a SQL rewrite, it is a structural one. This module teaches you to read a PlanViz output, isolate whether the slowdown is a push-down failure, a partitioning mismatch, a missed join prune, or stale optimizer statistics, and prescribe the calculation-view or partitioning change that removes it for good. Consultants who can do this on a live production HANA system — not just tune a SQL string — are the ones clients retain for performance remediation work billed at €1,200–1,800/day, well past the original migration scope.
What you will learn
- Diagnose push-down failures in HANA calculation views using PlanViz and M_EXPENSIVE_STATEMENTS, and prescribe structural fixes that keep aggregation and filtering in the columnar engine
- Design HANA table partitioning strategies (range, hash, range-hash sub-partitioning) matched to BW/4HANA and Datasphere query patterns, including delta merge tuning for real-time InfoProvider scenarios
- Apply join pruning conditions — join type, uniqueness constraints, cardinality annotations — in both graphical calculation views and Datasphere Analytic Models to eliminate unnecessary dimension joins at runtime
- Maintain the HANA cost-based optimizer through column statistics refresh discipline, and identify memory-pressure-driven performance degradation patterns distinct from plan-quality issues
Why SQL Performance Matters Differently in SAP Analytics
SAP analytics workloads sit at an unusual intersection: columnar in-memory engines, semantic layer abstractions, and mixed OLTP/OLAP traffic that shifts throughout the day. A query that runs in 120 ms on an isolated development tenant can take 45 seconds when SAP HANA is under concurrent load from SAC dashboards, BW/4HANA InfoProviders, and Datasphere replication flows all hitting the same tables. The difference is rarely the query itself — it is almost always push-down failures, missing partition pruning, or unmaterialised calculation view layers that force row-by-row evaluation.
This module teaches you to diagnose and fix those problems at the level of the HANA SQL engine, not at the level of SAP documentation abstractions.
Reading the HANA Explain Plan and PlanViz
Prerequisites
- Intermediate hands-on experience on SAP analytics projects
- Review core concepts first: C040, C004, C087
Outcomes
- Understand the core concepts behind sql performance for sap analytics
- Apply SQL in a typical SAP analytics engagement
- Explain the core architecture and decision points for SQL Performance for SAP Analytics
- Apply a repeatable implementation pattern in a 15-minute lab format
Full module available to members. The full module adds: the decision framework · the end-to-end scenario walkthrough · the KPI scorecard · the anti-patterns · the code blocks · the knowledge check · the diagrams.