To address the memory issue in your Serverless compute environment, you can consider the following strategies:

1. Optimize the Query:

   • Filter Early: Ensure that you are filtering the data as early as possible in your query to reduce the amount of data being processed. For example, if you can add more specific conditions to your WHERE clause, it will help in reducing the data size.
   • Limit Columns: Select only the necessary columns instead of using SELECT *. This reduces the amount of data being transferred and processed.

2. Use Spark DataFrame Operations:

   • Instead of converting the entire result to a Pandas DataFrame using toPandas(), try to perform as many operations as possible using Spark DataFrame operations. Spark DataFrames are distributed and can handle larger datasets more efficiently than Pandas DataFrames.

3. Use Delta Tables:

   • If you are working with large datasets, consider using Delta tables. Delta tables provide optimized storage and query performance, which can help in managing memory usage more efficiently.