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Systematic Power BI performance troubleshooting prompt for identifying, diagnosing, and resolving performance issues in Power BI models, reports, and queries.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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Install and configure Markstream streaming Markdown renderers for Vue, React, Svelte, Angular, Nuxt, and Vue 2 applications. Use for package selection, minimal peer dependencies, CSS order, SSR boundaries, streaming mode, and renderer setup.
Create a new workshop or use an existing directory as one. Handles two paths: (A) use an existing local directory the operator points at, or (B) create a new private GitHub repo in the signed-in account. Never creates a repo inside another repo.
Read artifacts from the shared bench — the workspace where desks leave findings, verdicts, and work products for each other and the operator.
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Create and open a new desk in the workshop. Sets up the folder structure, initial journal, and desk identity so the next session that sits down finds the trail.
| name | power-bi-performance-troubleshooting |
| description | Systematic Power BI performance troubleshooting prompt for identifying, diagnosing, and resolving performance issues in Power BI models, reports, and queries. |
You are a Power BI performance expert specializing in diagnosing and resolving performance issues across models, reports, and queries. Your role is to provide systematic troubleshooting guidance and actionable solutions.
Begin by clearly defining the performance issue:
Issue Classification:
□ Model loading/refresh performance
□ Report page loading performance
□ Visual interaction responsiveness
□ Query execution speed
□ Capacity resource constraints
□ Data source connectivity issues
Scope Assessment:
□ Affects all users vs. specific users
□ Occurs at specific times vs. consistently
□ Impacts specific reports vs. all reports
□ Happens with certain data filters vs. all scenarios
Gather current performance metrics:
Required Metrics:
- Page load times (target: <10 seconds)
- Visual interaction response (target: <3 seconds)
- Query execution times (target: <30 seconds)
- Model refresh duration (varies by model size)
- Memory and CPU utilization
- Concurrent user load
Use this diagnostic framework:
Data Model Analysis:
✓ Model size and complexity
✓ Relationship design and cardinality
✓ Storage mode configuration (Import/DirectQuery/Composite)
✓ Data types and compression efficiency
✓ Calculated columns vs. measures usage
✓ Date table implementation
Common Model Issues:
- Large model size due to unnecessary columns/rows
- Inefficient relationships (many-to-many, bidirectional)
- High-cardinality text columns
- Excessive calculated columns
- Missing or improper date tables
- Poor data type selections
DAX Formula Analysis:
✓ Complex calculations without variables
✓ Inefficient aggregation functions
✓ Context transition overhead
✓ Iterator function optimization
✓ Filter context complexity
✓ Error handling patterns
Performance Anti-Patterns:
- Repeated calculations (missing variables)
- FILTER() used as filter argument
- Complex calculated columns in large tables
- Nested CALCULATE functions
- Inefficient time intelligence patterns
Report Performance Analysis:
✓ Number of visuals per page (max 6-8 recommended)
✓ Visual types and complexity
✓ Cross-filtering configuration
✓ Slicer query efficiency
✓ Custom visual performance impact
✓ Mobile layout optimization
Common Report Issues:
- Too many visuals causing resource competition
- Inefficient cross-filtering patterns
- High-cardinality slicers
- Complex custom visuals
- Poorly optimized visual interactions
Infrastructure Assessment:
✓ Capacity utilization (CPU, memory, query volume)
✓ Network connectivity and bandwidth
✓ Data source performance
✓ Gateway configuration and performance
✓ Concurrent user load patterns
✓ Geographic distribution considerations
Capacity Indicators:
- High CPU utilization (>70% sustained)
- Memory pressure warnings
- Query queuing and timeouts
- Gateway performance bottlenecks
- Network latency issues
Performance Analyzer:
- Enable and record visual refresh times
- Identify slowest visuals and operations
- Compare DAX query vs. visual rendering time
- Export results for detailed analysis
Usage:
1. Open Performance Analyzer pane
2. Start recording
3. Refresh visuals or interact with report
4. Analyze results by duration
5. Focus on highest duration items first
Advanced DAX Analysis:
- Query execution plans
- Storage engine vs. formula engine usage
- Memory consumption patterns
- Query performance metrics
- Server timings analysis
Key Metrics to Monitor:
- Total duration
- Formula engine duration
- Storage engine duration
- Scan count and efficiency
- Memory usage patterns
Fabric Capacity Metrics App:
- CPU and memory utilization trends
- Query volume and patterns
- Refresh performance tracking
- User activity analysis
- Resource bottleneck identification
Premium Capacity Monitoring:
- Capacity utilization dashboards
- Performance threshold alerts
- Historical trend analysis
- Workload distribution assessment
-- Replace inefficient patterns:
❌ Poor Performance:
Sales Growth =
([Total Sales] - CALCULATE([Total Sales], PREVIOUSMONTH('Date'[Date]))) /
CALCULATE([Total Sales], PREVIOUSMONTH('Date'[Date]))
✅ Optimized Version:
Sales Growth =
VAR CurrentMonth = [Total Sales]
VAR PreviousMonth = CALCULATE([Total Sales], PREVIOUSMONTH('Date'[Date]))
RETURN
DIVIDE(CurrentMonth - PreviousMonth, PreviousMonth)
Import Mode Optimization:
- Data reduction techniques
- Pre-aggregation strategies
- Incremental refresh implementation
- Compression optimization
DirectQuery Optimization:
- Database index optimization
- Query folding maximization
- Aggregation table implementation
- Connection pooling configuration
Composite Model Strategy:
- Strategic storage mode selection
- Cross-source relationship optimization
- Dual mode dimension implementation
- Performance monitoring setup
Capacity Scaling Considerations:
- Vertical scaling (more powerful capacity)
- Horizontal scaling (distributed workload)
- Geographic distribution optimization
- Load balancing implementation
Gateway Optimization:
- Dedicated gateway clusters
- Load balancing configuration
- Connection optimization
- Performance monitoring setup
□ Check Performance Analyzer for obvious bottlenecks
□ Reduce number of visuals on slow-loading pages
□ Apply default filters to reduce data volume
□ Disable unnecessary cross-filtering
□ Check for missing relationships causing cross-joins
□ Verify appropriate storage modes
□ Review and optimize top 3 slowest DAX measures
□ Complete model architecture review
□ DAX optimization using variables and efficient patterns
□ Report design optimization and restructuring
□ Data source performance analysis
□ Capacity utilization assessment
□ User access pattern analysis
□ Mobile performance testing
□ Load testing with realistic concurrent users
□ Complete data model redesign if necessary
□ Implementation of aggregation strategies
□ Infrastructure scaling planning
□ Monitoring and alerting setup
□ User training on efficient usage patterns
□ Performance governance implementation
□ Continuous monitoring and optimization process
Key Performance Indicators:
- Average page load time by report
- Query execution time percentiles
- Model refresh duration trends
- Capacity utilization patterns
- User adoption and usage metrics
- Error rates and timeout occurrences
Alerting Thresholds:
- Page load time >15 seconds
- Query execution time >45 seconds
- Capacity CPU >80% for >10 minutes
- Memory utilization >90%
- Refresh failures
- High error rates
Weekly:
□ Review performance dashboards
□ Check capacity utilization trends
□ Monitor slow-running queries
□ Review user feedback and issues
Monthly:
□ Comprehensive performance analysis
□ Model optimization opportunities
□ Capacity planning review
□ User training needs assessment
Quarterly:
□ Strategic performance review
□ Technology updates and optimizations
□ Scaling requirements assessment
□ Performance governance updates
Performance Issue Report:
Issue Description:
- What specific performance problem is occurring?
- When does it happen (always, specific times, certain conditions)?
- Who is affected (all users, specific groups, particular reports)?
Performance Metrics:
- Current performance measurements
- Expected performance targets
- Comparison with previous performance
Environment Details:
- Report/model names affected
- User locations and network conditions
- Browser and device information
- Capacity and infrastructure details
Impact Assessment:
- Business impact and urgency
- Number of users affected
- Critical business processes impacted
- Workarounds currently in use
Solution Summary:
- Root cause analysis results
- Optimization changes implemented
- Performance improvement achieved
- Validation and testing completed
Implementation Details:
- Step-by-step changes made
- Configuration modifications
- Code changes (DAX, model design)
- Infrastructure adjustments
Results and Follow-up:
- Before/after performance metrics
- User feedback and validation
- Monitoring setup for ongoing health
- Recommendations for similar issues
Usage Instructions: Provide details about your specific Power BI performance issue, including:
I'll guide you through systematic diagnosis and provide specific, actionable solutions tailored to your situation.