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Add Power BI modeling skill
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Mohammad
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skills/powerbi-modeling/references/PERFORMANCE.md
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skills/powerbi-modeling/references/PERFORMANCE.md
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# Performance Optimization for Power BI Models
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## Data Reduction Techniques
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### 1. Remove Unnecessary Columns
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- Only import columns needed for reporting
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- Remove audit columns (CreatedBy, ModifiedDate) unless required
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- Remove duplicate/redundant columns
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```
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column_operations(operation: "List", filter: { tableNames: ["Sales"] })
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// Review and remove unneeded columns
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```
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### 2. Remove Unnecessary Rows
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- Filter historical data to relevant period
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- Exclude cancelled/void transactions if not needed
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- Apply filters in Power Query (not in DAX)
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### 3. Reduce Cardinality
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High cardinality (many unique values) impacts:
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- Model size
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- Refresh time
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- Query performance
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**Solutions:**
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| Column Type | Reduction Technique |
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|-------------|---------------------|
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| DateTime | Split into Date and Time columns |
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| Decimal precision | Round to needed precision |
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| Text with patterns | Extract common prefix/suffix |
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| High-precision IDs | Use surrogate integer keys |
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### 4. Optimize Data Types
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| From | To | Benefit |
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|------|-----|---------|
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| DateTime | Date (if time not needed) | 8 bytes to 4 bytes |
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| Decimal | Fixed Decimal | Better compression |
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| Text with numbers | Whole Number | Much better compression |
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| Long text | Shorter text | Reduces storage |
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### 5. Group and Summarize
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Pre-aggregate data when detail not needed:
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- Daily instead of transactional
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- Monthly instead of daily
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- Consider aggregation tables for DirectQuery
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## Column Optimization
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### Prefer Power Query Columns Over Calculated Columns
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| Approach | When to Use |
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|----------|-------------|
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| Power Query (M) | Can be computed at source, static values |
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| Calculated Column (DAX) | Needs model relationships, dynamic logic |
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Power Query columns:
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- Load faster
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- Compress better
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- Use less memory
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### Avoid Calculated Columns on Relationship Keys
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DAX calculated columns in relationships:
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- Cannot use indexes
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- Generate complex SQL for DirectQuery
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- Hurt performance significantly
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**Use COMBINEVALUES for multi-column relationships:**
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```dax
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// If you must use calculated column for composite key
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CompositeKey = COMBINEVALUES(",", [Country], [City])
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```
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### Set Appropriate Summarization
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Prevent accidental aggregation of non-additive columns:
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```
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column_operations(
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operation: "Update",
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definitions: [{
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tableName: "Product",
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name: "UnitPrice",
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summarizeBy: "None"
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}]
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)
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```
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## Relationship Optimization
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### 1. Minimize Bidirectional Relationships
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Each bidirectional relationship:
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- Increases query complexity
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- Can create ambiguous paths
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- Reduces performance
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### 2. Avoid Many-to-Many When Possible
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Many-to-many relationships:
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- Generate more complex queries
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- Require more memory
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- Can produce unexpected results
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### 3. Reduce Relationship Cardinality
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Keep relationship columns low cardinality:
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- Use integer keys over text
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- Consider higher-grain relationships
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## DAX Optimization
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### 1. Use Variables
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```dax
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// GOOD - Calculate once, use twice
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Sales Growth =
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VAR CurrentSales = [Total Sales]
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VAR PriorSales = [PY Sales]
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RETURN DIVIDE(CurrentSales - PriorSales, PriorSales)
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// BAD - Recalculates [Total Sales] and [PY Sales]
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Sales Growth =
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DIVIDE([Total Sales] - [PY Sales], [PY Sales])
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```
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### 2. Avoid FILTER with Entire Tables
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```dax
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// BAD - Iterates entire table
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Sales High Value =
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CALCULATE([Total Sales], FILTER(Sales, Sales[Amount] > 1000))
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// GOOD - Uses column reference
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Sales High Value =
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CALCULATE([Total Sales], Sales[Amount] > 1000)
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```
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### 3. Use KEEPFILTERS Appropriately
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```dax
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// Respects existing filters
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Sales with Filter =
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CALCULATE([Total Sales], KEEPFILTERS(Product[Category] = "Bikes"))
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```
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### 4. Prefer DIVIDE Over Division Operator
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```dax
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// GOOD - Handles divide by zero
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Margin % = DIVIDE([Profit], [Sales])
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// BAD - Errors on zero
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Margin % = [Profit] / [Sales]
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```
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## DirectQuery Optimization
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### 1. Minimize Columns and Tables
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DirectQuery models:
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- Query source for every visual
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- Performance depends on source
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- Minimize data retrieved
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### 2. Avoid Complex Power Query Transformations
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- Transforms become subqueries
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- Native queries are faster
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- Materialize at source when possible
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### 3. Keep Measures Simple Initially
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Complex DAX generates complex SQL:
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- Start with basic aggregations
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- Add complexity gradually
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- Monitor query performance
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### 4. Disable Auto Date/Time
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For DirectQuery models, disable auto date/time:
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- Creates hidden calculated tables
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- Increases model complexity
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- Use explicit date table instead
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## Aggregations
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### User-Defined Aggregations
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Pre-aggregate fact tables for:
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- Very large models (billions of rows)
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- Hybrid DirectQuery/Import
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- Common query patterns
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```
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table_operations(
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operation: "Create",
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definitions: [{
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name: "SalesAgg",
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mode: "Import",
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mExpression: "..."
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}]
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)
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```
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## Performance Testing
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### Use Performance Analyzer
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1. Enable in Power BI Desktop
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2. Start recording
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3. Interact with visuals
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4. Review DAX query times
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### Monitor with DAX Studio
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External tool for:
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- Query timing
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- Server timings
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- Query plans
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## Validation Checklist
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- [ ] Unnecessary columns removed
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- [ ] Appropriate data types used
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- [ ] High-cardinality columns addressed
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- [ ] Bidirectional relationships minimized
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- [ ] DAX uses variables for repeated expressions
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- [ ] No FILTER on entire tables
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- [ ] DIVIDE used instead of division operator
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- [ ] Auto date/time disabled for DirectQuery
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- [ ] Performance tested with representative data
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