Debugging Spark at Scale: Slow to Shipped
Last Updated on October 13, 2025 by Editorial Team
Author(s): Diogo Santos
Originally published on Towards AI.
A stepwise playbook to locate the true bottleneck — I/O, shuffle, Python, or memory — and fix it with minimal changes and hard measurements.
If you’re here, you’ve got a Spark job that should finish in minutes but keeps dragging into hours. You’ve toggled the usual knobs (spark.sql.shuffle.partitions, cache here, broadcast there) and it still crawls.
This article provides a detailed approach for debugging slow Spark jobs by identifying bottlenecks in I/O, shuffle operations, Python performance, and memory management. It outlines a systematic process that includes measuring performance, localizing issues using Spark UI, and implementing targeted fixes such as optimizing shuffles, pruning data, and reducing the overhead of Python UDFs. Additionally, it emphasizes the importance of maintaining a debug diary to track changes and improvements over time.
Read the full blog for free on Medium.
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