awesome-copilot/skills/powerbi-modeling/references/PERFORMANCE.md

Performance Optimization for Power BI Models

Data Reduction Techniques

1. Remove Unnecessary Columns

• Only import columns needed for reporting
• Remove audit columns (CreatedBy, ModifiedDate) unless required
• Remove duplicate/redundant columns

column_operations(operation: "List", filter: { tableNames: ["Sales"] })
// Review and remove unneeded columns

2. Remove Unnecessary Rows

• Filter historical data to relevant period
• Exclude cancelled/void transactions if not needed
• Apply filters in Power Query (not in DAX)

3. Reduce Cardinality

High cardinality (many unique values) impacts:

• Model size
• Refresh time
• Query performance

Solutions:

Column Type	Reduction Technique
DateTime	Split into Date and Time columns
Decimal precision	Round to needed precision
Text with patterns	Extract common prefix/suffix
High-precision IDs	Use surrogate integer keys

4. Optimize Data Types

From	To	Benefit
DateTime	Date (if time not needed)	8 bytes to 4 bytes
Decimal	Fixed Decimal	Better compression
Text with numbers	Whole Number	Much better compression
Long text	Shorter text	Reduces storage

5. Group and Summarize

Pre-aggregate data when detail not needed:

• Daily instead of transactional
• Monthly instead of daily
• Consider aggregation tables for DirectQuery

Column Optimization

Prefer Power Query Columns Over Calculated Columns

Approach	When to Use
Power Query (M)	Can be computed at source, static values
Calculated Column (DAX)	Needs model relationships, dynamic logic

Power Query columns:

• Load faster
• Compress better
• Use less memory

Avoid Calculated Columns on Relationship Keys

DAX calculated columns in relationships:

• Cannot use indexes
• Generate complex SQL for DirectQuery
• Hurt performance significantly

Use COMBINEVALUES for multi-column relationships:

// If you must use calculated column for composite key
CompositeKey = COMBINEVALUES(",", [Country], [City])

Set Appropriate Summarization

Prevent accidental aggregation of non-additive columns:

column_operations(
  operation: "Update",
  definitions: [{
    tableName: "Product",
    name: "UnitPrice",
    summarizeBy: "None"
  }]
)

Relationship Optimization

1. Minimize Bidirectional Relationships

Each bidirectional relationship:

• Increases query complexity
• Can create ambiguous paths
• Reduces performance

2. Avoid Many-to-Many When Possible

Many-to-many relationships:

• Generate more complex queries
• Require more memory
• Can produce unexpected results

3. Reduce Relationship Cardinality

Keep relationship columns low cardinality:

• Use integer keys over text
• Consider higher-grain relationships

DAX Optimization

1. Use Variables

// GOOD - Calculate once, use twice
Sales Growth = 
VAR CurrentSales = [Total Sales]
VAR PriorSales = [PY Sales]
RETURN DIVIDE(CurrentSales - PriorSales, PriorSales)

// BAD - Recalculates [Total Sales] and [PY Sales]
Sales Growth = 
DIVIDE([Total Sales] - [PY Sales], [PY Sales])

2. Avoid FILTER with Entire Tables

// BAD - Iterates entire table
Sales High Value = 
CALCULATE([Total Sales], FILTER(Sales, Sales[Amount] > 1000))

// GOOD - Uses column reference
Sales High Value = 
CALCULATE([Total Sales], Sales[Amount] > 1000)

3. Use KEEPFILTERS Appropriately

// Respects existing filters
Sales with Filter = 
CALCULATE([Total Sales], KEEPFILTERS(Product[Category] = "Bikes"))

4. Prefer DIVIDE Over Division Operator

// GOOD - Handles divide by zero
Margin % = DIVIDE([Profit], [Sales])

// BAD - Errors on zero
Margin % = [Profit] / [Sales]

DirectQuery Optimization

1. Minimize Columns and Tables

DirectQuery models:

• Query source for every visual
• Performance depends on source
• Minimize data retrieved

2. Avoid Complex Power Query Transformations

• Transforms become subqueries
• Native queries are faster
• Materialize at source when possible

3. Keep Measures Simple Initially

Complex DAX generates complex SQL:

• Start with basic aggregations
• Add complexity gradually
• Monitor query performance

4. Disable Auto Date/Time

For DirectQuery models, disable auto date/time:

• Creates hidden calculated tables
• Increases model complexity
• Use explicit date table instead

Aggregations

User-Defined Aggregations

Pre-aggregate fact tables for:

• Very large models (billions of rows)
• Hybrid DirectQuery/Import
• Common query patterns

table_operations(
  operation: "Create",
  definitions: [{
    name: "SalesAgg",
    mode: "Import",
    mExpression: "..."
  }]
)

Performance Testing

Use Performance Analyzer

1. Enable in Power BI Desktop
2. Start recording
3. Interact with visuals
4. Review DAX query times

Monitor with DAX Studio

External tool for:

• Query timing
• Server timings
• Query plans

Validation Checklist

• Unnecessary columns removed
• Appropriate data types used
• High-cardinality columns addressed
• Bidirectional relationships minimized
• DAX uses variables for repeated expressions
• No FILTER on entire tables
• DIVIDE used instead of division operator
• Auto date/time disabled for DirectQuery
• Performance tested with representative data