Hi @jamson ,
Here's a good article that answers your question. I think author did a pretty good job - many of his advice I apply on everyday job.
How I Tuned Databricks Query Performance from Power BI Desktop: A Personal Journey. | by Brahmareddy...
Step 1: Optimize Queries at the Source
The first thing I learned was the importance of optimizing queries directly in Databricks. Hereās what I did:
Filter Early: Initially, I was pulling entire datasets into Power BI and then applying filters. This was a big mistake. Instead, I started applying filters directly in my Databricks SQL queries. For example, instead of pulling all sales data and then filtering by region in Power BI, I added a WHERE clause in my Databricks query to filter by region before the data even left the server. This simple change drastically reduced the amount of data being transferred and sped up my reports.
SELECT * FROM sales_data WHERE region = āNorth Americaā
Aggregate Data: I also realized I didnāt always need granular data in Power BI. For instance, instead of pulling every transaction, I aggregated data at the monthly level directly in Databricks. This not only reduced the data size but also made the subsequent analysis much quicker.
SELECT region, MONTH(transaction_date) as month, SUM(sales) as total_sales FROM sales_data GROUP BY region, MONTH(transaction_date)
Step 2: Use DirectQuery Wisely
Power BIās DirectQuery mode allows you to work with large datasets without importing them into Power BI, which sounds great on paper. However, I quickly learned that it comes with its own set of challenges.
Minimize Data in DirectQuery: At first, I was pulling in large tables, thinking I might need all the data. But this led to painfully slow reports. I started being more selective about the data I queried. For example, instead of querying a full yearās worth of data, I limited it to the last three months, which was sufficient for most of my analyses.
Optimize Relationships: Another lesson was to simplify relationships in my Power BI data model. Initially, I had multiple tables with complex joins, which significantly slowed down performance. I restructured my model to reduce the number of relationships and avoid many-to-many relationships whenever possible. This made a noticeable difference in query times.
Step 3: Simplify Power BI Reports
Another area that needed attention was the complexity of my Power BI reports.
Reduce the Number of Visuals: I realized that each visual in Power BI sends its own query to Databricks. My reports were overloaded with visuals, many of which were redundant or could be combined. By reducing the number of visuals and focusing only on the most essential ones, I reduced the query load significantly.
Simplify Measures: I also found that complex measures were dragging down performance. Initially, I had measures that calculated results on the fly with intricate DAX formulas. By simplifying these measures and doing more of the heavy lifting in Databricks, I was able to speed up my reports.
For example, instead of calculating a rolling average in Power BI, I calculated it directly in Databricks and passed the results to Power BI.
SELECT region, sales, AVG(sales) OVER (ORDER BY transaction_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) as rolling_avg FROM sales_data WHERE region = āNorth Americaā
Step 4: Monitor Performance Continuously
To fine-tune everything, I used Power BI Desktopās Performance Analyzer. This tool became my best friend.
Using Performance Analyzer: I ran the Performance Analyzer on my reports to see exactly where the slowdowns were happening. It allowed me to pinpoint which visuals or queries were the bottlenecks. Armed with this information, I could go back and optimize those specific areas, leading to faster overall performance.
Step 5: Optimize Databricks Configuration
Improving query performance wasnāt just about tweaking Power BI; I also needed to make sure my Databricks environment was up to the task.
Right-Size the Cluster: I realized that my Databricks cluster wasnāt properly configured for the size of the datasets I was working with. After upgrading to a larger cluster with more resources, query processing sped up considerably.
Use Delta Tables: I switched to using Delta Tables in Databricks, which are specifically optimized for performance. Delta Tables handle large datasets efficiently, and I noticed an immediate improvement in query times.
Step 6: Regular Maintenance for Long-Term Performance
Finally, I learned the importance of regularly maintaining my data environment.
Optimize Tables: I made it a routine to run the OPTIMIZE command on my Delta Tables. This ensures that data is stored in the most efficient way possible, which helps keep queries running smoothly.
OPTIMIZE sales_data
Vacuum Old Data: I also used the VACUUM command to clean up old files that were no longer needed. This reduced storage clutter and further improved performance.
VACUUM sales_data
The Results: Faster, Smoother Reports
After implementing these strategies, the difference was night and day. My Power BI reports now load much faster, and the overall experience is far smoother. While it took some time to figure out the right combination of optimizations, the effort was well worth it.
If youāre struggling with slow query performance when using Databricks with Power BI, I highly recommend trying out these tips. They made a huge difference for me, and Iām confident they can help you too. Happy reporting!
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