Databricks utilizes Apache Spark for processing, which has its own memory management system. Clearing memory in Databricks typically involves these steps:

1. Clearing the Spark Cache:

• SQL: Use the CLEAR CACHE command within a SQL cell to remove cached tables and views.
• Scala/Python:
  • spark.catalog.clearCache() to clear the entire catalog cache.
  • df.unpersist() to remove a specific DataFrame from memory.

2. Clearing Object Storage:

• Delete any temporary files or intermediate results you’ve stored in object storage (e.g., DBFS, S3, ADLS).

3. Advanced Memory Management:

• Garbage Collection: If you’re using Scala or Python, ensure your code properly releases references to objects so the garbage collector can reclaim memory. You can manually trigger garbage collection in Python using gc.collect().
• Cluster Configuration: Monitor memory usage in the Spark UI. If you consistently run out of memory, consider increasing the cluster size or adjusting Spark memory settings (e.g., spark.executor.memory, spark.driver.memory).

Additional Tips:

• Restarting the Cluster: This is a last resort, but restarting the cluster will clear all memory and caches.
• Custom Function: For more comprehensive clearing, refer to this custom function shared on the Databricks Community

Important Considerations:

• Clearing the cache can impact performance if you frequently reuse the same data.
• Memory management is a complex topic. If you’re facing frequent memory issues, consider consulting with a Databricks expert.