@msj50, @happpy​ 

I wish I had a neat checklist of things to check for performance, but there are too many potential issues that can cause slowness. These are the most common I've seen:

• Too many files on too small of a cluster - if you have more a few thousand files and your files are not huge, consolidating them should be a performance improvement.
• How many columns does your ORC files have? If you have a ton of columns (hundreds or more), and are doing a select * against your table, even if you are only returning a small number of rows, that can be slow.
• Are you joining your tables and causing a shuffle of your data - if so, it's expected this will not be fast. Particularly, if your output files are unevenly sized, your shuffle will be bottlenecked on the slowest partition.
• Are you trying to use Spark in place of a database for production serving purposes? While Spark is meant to be fast, it's not meant to replace the need for a production database. The best architecture for your system may be to use Spark to calculate your summary statistics, but then to write these statistics into a database for serving purposes.

As you can see - it's just really intricate what issue you may be facing.

Since you both asked about support - with a professional license of Databricks, we can diagnose and work through these issues with you and even advise on architecture level decisions for using Spark. Please email sales@databricks.com to inquire further.