Databricks Debugging Kit | Spark Error Troubleshooting Guide Cluster Config Checklist | PDF

That cryptic Spark error just killed your pipeline at 2am. Now what?

This debugging kit gives you a systematic approach to diagnosing Databricks failures — from error messages to root causes to fixes, without trial and error.

What's inside:

Error decision tree — 8 common error patterns mapped to likely causes and specific fixes. Covers SparkException, OOM, DELTA_MISSING_TRANSACTION_LOG, AnalysisException, MERGE duplicates, VACUUM race conditions, JDBC NaN errors, and checkpoint corruption.

Reading Spark executor logs — step-by-step guide to finding the actual failure in the Spark UI, driver logs, and event logs. Where to look and what to search for.

Cluster configuration checklist — recommended settings for dev, production batch, and production streaming workloads. Worker types, autoscaling, spot instances, Photon, and auto-terminate.

Quick diagnostic commands — DESCRIBE DETAIL, DESCRIBE HISTORY, partition skew detection, Spark config dump, and data freshness checks you can run immediately.

Common performance fixes — small file problem, data skew, shuffle spill, driver OOM, broadcast join timeouts, and streaming checkpoint bloat. Symptom → fix lookup table.

From PipelinePulse — no-fluff data engineering resources by a practitioner.