Diagnosing Spills with Query Profile

Spilling is one of the most common reasons for "slow" jobs that don't fail.

The Symptom: A stage that usually takes 1 minute now takes 20 minutes, or a Join operation dominates the timeline.

The Cause: The data required for the operation (e.g., the hash map for a Join, or the buffer for a Sort) is larger than the RAM allocated to the executor core.

The Evidence: In the Spark UI (Stage tab) or Databricks SQL (Query Profile), look for "Spill".

Bytes Spilled to Disk: This should ideally be 0. If it's gigabytes, your job is "thrashing" the disk.

Remediation:
* Increase Cluster Memory: Use nodes with more RAM (e.g., r5d vs c5d).
* Tune Partitions: Increase spark.sql.shuffle.partitions. Smaller partitions mean less data per task, making it more likely to fit in RAM.
* Optimize Query: Broadcast smaller tables or filter data earlier.

Topic: Monitoring & Alerting