Hey guys! 
I’m currently wrestling with optimizing query performance for some massive datasets (think billions of rows!). While Dremio’s been amazing so far, I’m hitting a snag with speeding up my queries. They’re taking longer than I’d like, and I have a feeling there’s room for some serious optimization.
Here is a quick rundown of my setup:
- I am using Dremio with a bunch of Parquet data chilling on S3.
- Most of my queries involve joining these hefty tables together.
- I have been trying reflections to give things a boost, but I’m not too sure I am using them effectively for these large datasets.
My Dremio cluster has 5 nodes, each with 64GB of RAM. So, I have a few burning questions for the Dremio gurus out there:
- How can I best set up and manage reflections in Dremio, especially when dealing with these big data beasts?
- Any tips or tricks for optimizing my queries to cut down on execution time? Are there specific strategies for handling massive joins?
- What tweaks can I make to my cluster configuration to squeeze out some extra performance?
- Anyone else faced similar performance challenges? If so, what strategies or adjustments did you find most helpful?
I also check this resource: https://community.dremio.com/t/how-to-get-large-queries-from-drerubymio/7383 But I have not found any solution. could anyone guide me about this?
Thanks in advance!