Oracle AI Database: Performance Management and Tuning ELS — Lesson 2: AWR, ASH, ADDM, SQL Monitoring, Execution Plans, and Workload Analysis

Learning Objectives

• Interpret the Oracle objects, services, rules, reports, interfaces, or code used in awr, ash, addm, sql monitoring, execution plans, and workload analysis.
• Perform a hands-on implementation or diagnostic sequence without bypassing approvals.
• Prove reproducibility by comparing expected and actual evidence.

Detailed Topics

• AWR
• ASH
• ADDM
• SQL Monitoring
• Execution Plans
• Workload Analysis
• ownership, prerequisites, data, and acceptance criteria for awr, ash, addm, sql monitoring, execution plans, and workload analysis
• security, control, integration, performance, recovery, and support implications

Instructor Demonstration

Demonstrate awr, ash, addm, sql monitoring, execution plans, and workload analysis within a database workload diagnosed from symptoms to root cause, tuned with measured changes, and protected against regression. Use representative data and capture ASH samples, ADDM findings so another practitioner can verify the result.

Hands-On Exercise

Complete a controlled Oracle AI Database: Performance Management and Tuning ELS activity focused on awr, ash, addm, sql monitoring, execution plans, and workload analysis. Introduce a realistic case of misread wait events, isolate the cause, apply a reversible correction, and rerun the acceptance checks.

1. Record the requirement, environment, identities, dependencies, data, controls, and expected result.
2. Perform the workflow and capture course-specific evidence before and after each material change.
3. Test normal, boundary, failure, security, reconciliation, recovery, and operational conditions.
4. Document the root cause, correction, rollback, owner, support action, and next improvement priority.

Scenario Analysis

• Map the end-to-end flow from source input through Oracle processing to the final output.
• Locate the control point most likely to prevent or expose misread wait events.
• Recommend monitoring, escalation, and handover requirements for production support.

Troubleshooting Guidance

When awr, ash, addm, sql monitoring, execution plans, and workload analysis does not produce the expected result, confirm scope, effective dates, identities, data context, dependencies, and recent changes. Review the most relevant ASH samples, ADDM findings before editing configuration. Test one reversible hypothesis at a time, verify recovery, and confirm that the fix does not create misread wait events elsewhere.

Validation Checklist

• Prerequisites, ownership, data context, settings, and expected outcomes are documented.
• The result is reproducible and supported by ASH samples, ADDM findings.
• Security, controls, data quality, integration, performance, recovery, and support conditions are reviewed.
• Exceptions, corrective action, rollback, approvals, monitoring, and handover are recorded.

Lesson Outcome

Learners can apply AWR, ASH, ADDM, SQL Monitoring, Execution Plans, and Workload Analysis in a realistic Oracle AI Database: Performance Management and Tuning ELS situation and defend the result with course-specific evidence.