Diagnosing Long-Running Spark Tasks in Databricks: A Deep Dive

In the world of big data processing, few things are more frustrating than a Spark job that’s running much longer than expected or appears to be stuck. As a seasoned data engineer working with Databricks, I’ve encountered and resolved numerous such scenarios. In this comprehensive guide, I’ll share advanced techniques for diagnosing long-running Spark tasks specifically in the Databricks environment.

Understanding the Databricks Environment

Before diving into diagnosis, it’s crucial to understand how Databricks abstracts and enhances Apache Spark’s native capabilities:

• Databricks Runtime (DBR): Includes optimized versions of Spark with additional features
• Web Terminal: Provides direct access to cluster nodes
• Ganglia Metrics: Offers cluster-level performance visualization
• Spark UI: Enhanced version with Databricks-specific features

Key Areas for Investigation

Databricks Spark UI Deep Dive

The Spark UI in Databricks provides several critical views for diagnosis:

Jobs Tab

-- Example query causing long-running tasks
SELECT…