How I Fixed a Critical Spark Production Performance Issue (and Cut Runtime by 70%)

""The job didn't fail. It just... never finished." That was the worst part. No errors.No stack traces.Just a Spark job running forever in production - blocking downstream pipelines, delaying reports, and waking up on-call engineers at 2 AM. This is the story of how I diagnosed a real Spark performance issue in production and fixed it drastically, not by adding more machines - but by understanding Spark properly."

"Not on Medium? Here is a friend link so you can read the full blog on:How I Fixed a Critical Spark Production Performance Issue (and Cut Runtime by 70%) The Problem: A Job That Suddenly Became 10x Slower We had a Spark job that had been running fine for months. One day, after a routine data increase, the job started taking 4+ hours. No code changes.No cluster changes.Just data growth."

A production Spark job silently ran indefinitely after a routine data increase, blocking downstream pipelines, delaying reports, and waking on-call engineers at 2 AM. The job produced no errors or stack traces; it simply never finished. Runtime rose to over four hours despite no code or cluster changes, representing roughly a tenfold slowdown driven by data growth. The performance issue was diagnosed in production and resolved by understanding and optimizing Spark behavior rather than provisioning more machines. The applied optimizations cut runtime by about 70%, restoring pipeline throughput and reducing operational burden.

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