Finding a slow workload is only the start. In SAP HANA environments, teams still need to decide what to change first and how to reduce risk. Query tuning and blocking analysis help make that decision. Together, they provide evidence for safer performance improvements.
Start With the Highest-Impact Problem
SolarWinds® Database Performance Analyzer (DPA) uses wait-based analysis to help teams focus on issues that can deliver the greatest performance improvements. Rather than starting with health metrics alone, DPA shows where the longest waits occur. It also identifies periods when waits rise above normal levels. From there, teams can drill in to investigate root cause and review recommended next steps.
For SAP HANA teams, this matters because change windows can be limited. Application sensitivity also raises the stakes. Teams need to improve performance without creating new risk. DPA highlights top SQL statements by cumulative wait time. As a result, teams can identify high-impact tuning candidates and organize optimization work more effectively.
Use Top SQL and Plan History to See What Changed
Not every slow-looking query deserves equal attention. Statements that accumulate the most wait time are usually strong optimization candidates. However, queries with less total wait time can still affect critical business functions. In those cases, teams need to identify the queries affecting those processes. They also need to spot concurrent queries that may be slowing critical workloads.
DPA’s drill-down context helps teams move beyond a generic list of slow statements. By filtering by program or user, they can focus on the queries that directly affect important business functions. Execution plans add another layer of context. They show the steps a query takes, including steps that may be slow. When the optimizer selects a new plan, performance can change sharply. Sometimes, it gets worse. DPA shows plan usage over time, helping teams see when a new plan was adopted and how it affected performance.
Add Advisors and Context Before You Tune
Tuning decisions require more than a list of slow statements. Teams need to understand whether an issue relates to physical design, workload shape, blocking, or another factor. DPA’s built-in query, table, and index advisors add context around query behavior. They also provide actionable design recommendations. For example, advisors can show that a query runs more frequently during certain hours. They can also reveal when a query spends significant time blocked. This context gives DBAs a better starting point for deciding what to address first.
Use DPA Evidence to Guide Tuning
For SAP HANA, effective tuning starts with evidence. Use wait analysis to identify where time is being spent. Then examine SQL details, execution-plan history, blocking activity, and advisor recommendations. Together, these views help teams determine whether the issue is caused by query behavior, a plan change, lock contention, or a wider workload constraint.
Resolve Blocking at the Source
Not every slowdown is a traditional query-tuning problem. DPA includes blocking as part of its correlated investigation path. Blocking creates cascading delay. One session holds a lock longer than expected. Downstream sessions wait, and the resulting slowdown can affect multiple business functions.
DPA exposes blocking chains and aggregates this information to show impactful root blockers and affected blocking victims. Teams can use the blocking correlation chart to determine whether lock contention is contributing significantly to instance-wide performance problems.From there, the Blocking tab can help identify root blocker sessions creating the largest traffic jams. Tuning the root blocker’s query or queries can reduce lock duration and relieve downstream sessions. That is different from tuning the blocked query, which may be a victim rather than the cause.
In other cases, teams may need to shift workload timing. Where possible, this can reduce competition for shared data and alleviate lock contention.
Validate the Root Cause With System Context
Performance work is not complete when a change is applied. DPA helps teams drill into root cause, correlate query wait times with related events, and focus on waits most likely to improve performance. Teams should confirm that a change improves the relevant waits rather than shifting pressure elsewhere. Reviewing waits alongside statistics, plans, blocking, and metrics makes that validation more defensible. Baselines add useful context by showing when a metric falls outside its normal range. Teams should continue monitoring and tuning until performance is satisfactory and stable.
Why the Operational Model Matters
The agentless architecture uses less than one percent of database resources. As a result, teams can monitor performance with minimal impact on production systems. SolarWinds Database Performance Analyzer connects to SAP HANA through JDBC. It uses a read-only monitoring user and polls native monitoring views.
This low-overhead model can make it easier to expand visibility while preserving the evidence teams need for performance analysis.
Closing Thought
Tuning and blocking belong in the same performance analysis workflow. Tuning helps teams improve lasting performance. Blocking analysis helps them resolve contention that can damage responsiveness right now. For SAP HANA teams, disciplined optimization starts with impact. Identify the waits that matter most. Then determine whether the issue involves SQL behavior, blocking, a plan change, or another workload constraint. Use available advisors and data correlations to confirm the performance story. Choose the safest mitigation, then measure the outcome. Continue monitoring and tuning until performance is satisfactory and stable.
FAQ
What should teams tune first?
Start with statements that accumulate the most cumulative wait time. However, teams should also prioritize queries that directly affect critical business functions.
Why does execution plan history matter in SAP HANA root cause analysis?
Plan history helps teams see when a new plan was adopted. It can also show whether that change contributed to a slowdown.
When should blocking analysis come before tuning?
Investigate blocking when the wait pattern shows sessions waiting on a blocking condition. First, identify the root blocker. Then determine whether tuning its query, adjusting workload timing, or another mitigation will reduce lock contention.
How do advisors help with query tuning in an SAP HANA monitoring tool?
DPA advisors add context around execution patterns and blocking. They also provide actionable design recommendations that can support mitigation decisions.
Where do memory, CPU, disk, and connection context fit into troubleshooting?
DPA combines wait-based investigation with resource trends, including memory, CPU, disk usage, and connection counts. This helps teams determine whether an issue is isolated or part of a broader workload pattern.
Further resources
If you want to go deeper on SAP HANA performance and DPA’s wait-based approach, these SolarWinds resources are a good next step:
- SAP HANA Monitoring With SolarWinds Database Performance Analyzer — the launch blog covering why DPA extended wait-based analysis to SAP HANA, including memory visibility, plan cache data, and plan drift.
- SAP HANA Monitoring Use Case — a deeper look at how DPA tracks HANA memory, CPU, disk, and connection trends alongside wait-time analytics, plus how to set up monitoring without adding agents.
- The DPA Approach to Investigating Performance Issues — official documentation on wait-based monitoring, anomaly detection, and how DPA guides root-cause investigation across every supported platform.
- About Anomaly Detection in DPA — a closer look at how DPA’s anomaly detection algorithm learns normal wait-time patterns and flags unexpected increases.
- DPA 2026.2 Release Announcement — details on the release that brought native SAP HANA support to DPA, alongside AI Query Assist expansion and other platform updates.