performance-optimization（性能优化）

Performance Optimization

Overview

Power BI performance depends on data model design, DAX efficiency, visual configuration, and infrastructure. This skill covers diagnostic tools, optimization techniques, and best practices for achieving fast, responsive reports.

Diagnostic Tools

Performance Analyzer (Built-in)

Enable in Power BI Desktop: View > Performance Analyzer > Start recording

Workflow:

1. Start recording
2. Clear visual cache (click "Refresh visuals")
3. Interact with the report (change slicers, navigate pages)
4. Copy DAX query from slow visuals
5. Paste into DAX Studio for deeper analysis

DAX Studio

Free external tool for deep DAX performance analysis:

Key features:

• Execute DAX queries with timing
• Server Timings: shows Storage Engine (SE) vs Formula Engine (FE) time
• Query Plan: view logical and physical query plans
• VertiPaq Analyzer: model size and compression analysis
• All Queries trace: capture all queries sent by a report

Server Timings breakdown:

Ideal ratio: SE should be 80-90% of total time. High FE % means DAX is doing too much computation.

Common DAX Studio workflow:

1. Connect to Power BI Desktop (or XMLA endpoint)
2. Enable Server Timings (Query > Server Timings)
3. Paste the DAX query from Performance Analyzer
4. Execute and analyze timing breakdown
5. Look for:

- Many SE queries (indicates materialization issues) - CallbackDataID in SE queries (data sent to FE for processing -- avoid) - High FE time (DAX too complex) - Large SE row counts (too much data scanned)

VertiPaq Analyzer

Analyze model size and compression in DAX Studio: Advanced > View Metrics

Data Model Optimization

Column Optimization

Relationship Optimization

• Use single-direction cross-filtering (avoid bidirectional)
• Enable "Assume Referential Integrity" for DirectQuery relationships
• Remove unused or redundant relationships
• Use integer key columns for relationships

Partition Strategy

For large tables, partition by date range:

• Historical partitions (yearly/quarterly) -- refresh rarely
• Recent partition (current month/week) -- refresh frequently
• Use incremental refresh to automate partition management

DAX Optimization

High-Impact Patterns

Use variables to avoid repeated calculations:

// BAD: Calculates [Total Sales] three times
Margin % = DIVIDE([Total Sales] - [Total Cost], [Total Sales])

// GOOD: Single calculation, reuse via variable
Margin % =
VAR Sales = [Total Sales]
VAR Cost = [Total Cost]
RETURN DIVIDE(Sales - Cost, Sales)

Avoid FILTER with large tables in CALCULATE:

// BAD: Scans entire table
CALCULATE([Sales], FILTER(ALL(Products), Products[Category] = "Electronics"))

// GOOD: Column filter (optimized)
CALCULATE([Sales], Products[Category] = "Electronics")

Avoid nested iterators:

// BAD: O(n^2) complexity
SUMX(Products,
    SUMX(FILTER(Sales, Sales[ProductID] = Products[ProductID]),
        Sales[Amount]))

// GOOD: Use relationship + simple aggregation
SUMX(Products, [Total Sales])

Use DISTINCTCOUNT instead of COUNTROWS(DISTINCT(...)):

// BAD
COUNTROWS(DISTINCT(Sales[CustomerID]))

// GOOD
DISTINCTCOUNT(Sales[CustomerID])

Avoid FORMAT() in measures (returns text, kills sort):

// BAD: Returns text, cannot sort
MonthLabel = FORMAT([Date], "MMMM yyyy")

// GOOD: Use a pre-computed column in the Date table for display
// And a numeric sort column for ordering

Measure Complexity Guidelines

Visual Optimization

Reduce Visual Count

Query Reduction

Enable query reduction features:

1. Report settings > Query reduction > Add Apply button to slicers -- users click "Apply" after all slicer changes
2. Reduce number of queries sent by > Disable cross-highlighting by default -- reduces inter-visual queries

Conditional Formatting

Avoid complex DAX-based conditional formatting on large tables. Use simple column references or measures with limited computation.

Aggregations

Pre-aggregated tables that Power BI queries instead of the detail table:

Setup

1. Create an aggregation table (Import) with pre-computed aggregates:

SELECT
    ProductCategory,
    CAST(OrderDate AS DATE) AS OrderDate,
    SUM(Amount) AS TotalAmount,
    COUNT(*) AS OrderCount
FROM Sales
GROUP BY ProductCategory, CAST(OrderDate AS DATE)

1. In Power BI, set up aggregation mappings:

- Table > Manage aggregations - Map: TotalAmount summarization Sum to detail column Sales[Amount] - Map: OrderCount summarization Count to detail table Sales - Map: ProductCategory group-by to Sales[ProductCategory] - Map: OrderDate group-by to Sales[OrderDate]

1. Hide the aggregation table from report view

Power BI automatically routes queries:

• Queries at aggregation grain -> hit the small Import table (fast)
• Queries at detail grain -> hit the DirectQuery detail table (slower but accurate)

Automatic Aggregations (Premium/Fabric)

Premium and Fabric capacities support automatic aggregation training:

• System analyzes query patterns
• Automatically creates and maintains agg tables
• No manual configuration required
• Enable in dataset settings

Composite Models

Mix Import and DirectQuery tables in one model:

Dual mode: Table exists as both Import and DirectQuery. Engine chooses based on query context:

• If all tables in query are Import, uses Import mode (VertiPaq)
• If any table requires DirectQuery, uses DirectQuery for dual tables too

Set storage mode: Model view > select table > Properties > Storage mode

Composite models with Direct Lake (2025 Preview):

• Mix Direct Lake tables with Import tables in a single model
• Direct Lake tables load from OneLake delta files; Import tables from traditional sources
• Enables extending a Fabric lakehouse model with additional reference data
• Monitor fallback behavior -- DL/SQL tables may fall back to DirectQuery under load

Direct Lake Performance

Direct Lake provides near-Import query speed without data duplication:

Large Dataset Optimization (10GB+ Models)

Power BI Desktop Performance Settings

Power BI Report Server Performance Tuning

Report Server has different performance characteristics from the cloud service:

Bookmark and Filter Optimization

Performance Checklist

Data Model

• Star schema design (fact + dimension tables)
• Auto date/time disabled
• No unused columns
• Integer keys for relationships
• Single-direction cross-filtering
• Text columns only in dimension tables
• Calculated columns converted to measures where possible
• High-cardinality columns addressed

DAX

• Variables used for repeated expressions
• No FILTER on large tables in CALCULATE
• No nested iterators
• DISTINCTCOUNT preferred over COUNTROWS(DISTINCT(...))
• No FORMAT in measures used for sorting
• Measures return numeric types (not text)

Visuals

• 8-12 visuals per page maximum
• Apply button on slicers
• Top N applied on large tables
• Cross-highlighting minimized for heavy pages
• Conditional formatting uses simple expressions

Infrastructure

• Correct capacity size for workload
• Premium/Fabric for large models (>1GB)
• Gateway optimized (sufficient RAM, SSD, close to data source)
• Incremental refresh for large tables
• Aggregations for DirectQuery heavy queries

Direct Lake (if applicable)

• V-Order enabled on delta table writes
• Framing scheduled at appropriate frequency
• File/row-group counts within capacity guardrails
• Fallback behavior configured and monitored
• Calculated columns tested for fallback impact

Report Server (if applicable)

• Report server DB isolated from PBIRS process
• Sufficient CPU cores for peak concurrent users
• SSD storage with high IOPS for DB
• Report caching configured for popular reports
• Scale-out with NLB if >50 concurrent users

Additional Resources

Reference Files

• references/dax-studio-walkthrough.md -- Step-by-step DAX Studio analysis guide with query plan interpretation and latest DAX Studio features