Introduction

Power Query simplifies data transformation, but inefficiently structured queries, overuse of computed columns, and lack of query folding can degrade performance significantly. Common pitfalls include using non-foldable transformations that force in-memory computations, loading unnecessary columns leading to large dataset processing, excessive merge operations slowing down execution, unoptimized parameterized queries causing redundant recalculations, and missing indexes leading to inefficient joins. These issues become particularly problematic when working with large data sources where query optimization is critical for responsiveness. This article explores common causes of slow Power Query performance, debugging techniques, and best practices for optimizing query transformations and execution.

Common Causes of Slow Query Performance and High Memory Usage

1. Lack of Query Folding Causing In-Memory Computations

When Power Query does not push transformations to the data source, computations happen in memory, slowing down performance.

Problematic Scenario

let
  Source = Sql.Database("server", "database"),
  FilteredRows = Table.SelectRows(Source, each [Year] = 2023),
  AddedColumn = Table.AddColumn(FilteredRows, "NewCol", each [Sales] * 1.1)
 in
  AddedColumn

Since `Table.AddColumn` is not foldable, all filtering and calculations happen in memory.

Solution: Perform Transformations in the Data Source

let
  Source = Sql.Database("server", "database", [Query="SELECT Year, Sales, Sales * 1.1 AS NewCol FROM SalesTable WHERE Year = 2023"])
 in
  Source

Pushing transformations to SQL ensures that filtering and calculations occur at the source.

2. Loading Unnecessary Columns Increasing Data Size

Retrieving all columns from a dataset significantly increases memory usage and processing time.

Problematic Scenario

let
  Source = Sql.Database("server", "database"),
  SelectedTable = Source{[Schema="dbo", Item="SalesTable"]}[Data]
in
  SelectedTable

This loads all columns, even if only a subset is needed.

Solution: Select Only Required Columns

let
  Source = Sql.Database("server", "database"),
  SelectedTable = Table.SelectColumns(Source{[Schema="dbo", Item="SalesTable"]}[Data], {"Year", "Sales"})
in
  SelectedTable

Reducing the number of columns significantly improves query performance.

3. Excessive Merge Operations Slowing Execution

Joining large tables inefficiently increases query execution time.

Problematic Scenario

let
  Sales = Sql.Database("server", "database", [Query="SELECT * FROM Sales"]),
  Customers = Sql.Database("server", "database", [Query="SELECT * FROM Customers"]),
  MergedTable = Table.NestedJoin(Sales, "CustomerID", Customers, "CustomerID", "NewColumn")
in
  MergedTable

Merging large tables in Power Query forces in-memory operations.

Solution: Perform Joins in SQL or Enable Query Folding

let
  Source = Sql.Database("server", "database", [Query="SELECT s.*, c.CustomerName FROM Sales s INNER JOIN Customers c ON s.CustomerID = c.CustomerID"])
in
  Source

Performing joins at the database level reduces processing time in Power Query.

4. Unoptimized Parameterized Queries Causing Repeated Execution

Querying data dynamically without optimization leads to repeated and slow query execution.

Problematic Scenario

let
  Parameter = Excel.CurrentWorkbook(){[Name="Year"]}[Content]{0}[Value],
  Source = Sql.Database("server", "database"),
  FilteredRows = Table.SelectRows(Source, each [Year] = Parameter)
in
  FilteredRows

Each query execution retrieves all data before filtering, slowing performance.

Solution: Use Parameterized SQL Queries

let
  Parameter = Excel.CurrentWorkbook(){[Name="Year"]}[Content]{0}[Value],
  Source = Sql.Database("server", "database", [Query="SELECT * FROM Sales WHERE Year = " & Number.ToText(Parameter)])
in
  Source

Using SQL-side filtering improves query execution speed.

5. Missing Indexes Leading to Inefficient Filtering

Filtering unindexed columns results in full table scans, slowing down queries.

Problematic Scenario

let
  Source = Sql.Database("server", "database", [Query="SELECT * FROM Sales WHERE Region = 'North'"])
in
  Source

If `Region` is not indexed, the query performs a full table scan.

Solution: Ensure Indexed Columns for Filtering

CREATE INDEX idx_sales_region ON Sales (Region);

Adding indexes speeds up filtering operations in Power Query.

Best Practices for Optimizing Power Query Performance

1. Ensure Query Folding for Efficient Execution

Perform transformations at the data source whenever possible.

Example:

SELECT Year, Sales FROM SalesTable WHERE Year = 2023

2. Load Only Necessary Columns

Avoid loading unnecessary data to reduce memory usage.

Example:

Table.SelectColumns(Source, {"Year", "Sales"})

3. Perform Joins in SQL Instead of Power Query

Reduce in-memory operations by pre-joining tables.

Example:

SELECT s.*, c.CustomerName FROM Sales s INNER JOIN Customers c ON s.CustomerID = c.CustomerID

4. Optimize Parameterized Queries

Filter data at the source rather than in Power Query.

Example:

SELECT * FROM Sales WHERE Year = @Parameter

5. Use Indexing for Fast Filtering

Ensure filtering columns are indexed for optimal query execution.

Example:

CREATE INDEX idx_sales_region ON Sales (Region);

Conclusion

Slow query performance and memory overhead in Power Query often result from missing query folding opportunities, excessive data loading, inefficient joins, unoptimized parameterized queries, and missing database indexes. By ensuring query folding, limiting loaded columns, optimizing joins, using efficient parameterized queries, and indexing filtering columns, developers can significantly improve Power Query performance. Regular monitoring using Power Query diagnostics and query dependency views helps detect and resolve performance bottlenecks before they impact data refresh times.