Introduction
DirectQuery is often used in Power BI when teams need near-real-time data or when datasets are too large to import. On paper, it sounds like the perfect solution. In reality, many Power BI reports become slow, unstable, or frustrating to use after switching to DirectQuery.
This happens not because DirectQuery is bad, but because it is frequently misunderstood and misused. This article explains common DirectQuery performance mistakes in simple words, shows what users experience in real life, and explains how these mistakes affect large Power BI reports.
1. Expecting DirectQuery to Be as Fast as Import Mode
One of the biggest mistakes is expecting DirectQuery reports to behave like Import mode reports. In DirectQuery, every click, filter, or slicer sends a query back to the data source.
In real life, this feels like visuals taking several seconds to update after every interaction. Users often think Power BI is slow, but the delay actually comes from the database responding to repeated queries.
Think of it like calling a warehouse every time you want to check stock instead of keeping a local list. Each call takes time.
2. Using DirectQuery on Poorly Optimized Databases
DirectQuery relies completely on the performance of the underlying data source. If the database is not optimized, Power BI cannot compensate.
In real-world usage, this shows up as dashboards that work fine during testing but become painfully slow in production when more users connect.
For example, missing indexes or complex joins in the database can make every Power BI interaction slow.
3. Applying Complex DAX on Top of DirectQuery
DirectQuery supports only a subset of DAX efficiently. Complex calculations force Power BI to generate heavy SQL queries.
Users experience this as visuals loading inconsistently or failing with timeout errors.
It is like asking the database to solve a complicated math problem every time you move a filter.
4. Too Many Visuals on a Single Page
Each visual triggers its own query in DirectQuery mode. When many visuals are placed on one page, Power BI sends many queries at once.
In real life, users see dashboards loading one chart at a time or freezing while data loads.
This is similar to opening too many browser tabs on a slow internet connection.
5. Ignoring Query Folding in DirectQuery
Query folding is even more critical in DirectQuery than in Import mode. When folding breaks, Power BI generates inefficient queries.
Users notice this as sudden performance drops after adding a small transformation in Power Query.
It is like asking the database for all data and sorting it yourself instead of letting the database do it efficiently.
6. Using High-Cardinality Columns in Filters and Slicers
Columns with many unique values cause expensive queries in DirectQuery.
In real usage, slicers feel slow or stop responding when users interact with them.
For example, filtering on transaction IDs instead of dates or categories significantly slows down reports.
7. Treating DirectQuery as a Shortcut for Large Data
Some teams choose DirectQuery only because the dataset is large, without redesigning the model.
This often leads to worse performance than a well-optimized Import model with Incremental Refresh.
It is like choosing a live video stream when a recorded clip would load faster and work better.
8. Not Limiting Result Sets
DirectQuery queries should return small, focused result sets. Large result sets increase query execution time and network load.
Users see this as tables taking a long time to populate or visuals timing out.
For example, showing detailed transaction-level tables instead of summarized views hurts performance.
9. Overlooking Network Latency
Even with a fast database, network latency affects DirectQuery performance.
This becomes obvious when Power BI connects to cloud databases from different regions.
In real life, users experience inconsistent performance depending on location and time of day.
10. Not Considering Composite Models
Many DirectQuery performance problems could be avoided by using Composite models.
Importing historical data and using DirectQuery only for recent data improves speed and usability.
This is like keeping frequently used files on your laptop while accessing archives from the cloud only when needed.
Summary
DirectQuery performance issues in Power BI usually come from incorrect expectations, poor database optimization, complex DAX usage, and overloaded report designs. Users experience these problems as slow visuals, delayed filters, and unresponsive dashboards. By understanding how DirectQuery works, limiting query complexity, optimizing data sources, and using Composite models where appropriate, teams can avoid most DirectQuery mistakes and build responsive Power BI reports even with large datasets.