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 previously stable Spark job suddenly became ten times slower after routine data growth, increasing runtime to over four hours. There were no code changes, no cluster changes, and no error messages or stack traces to indicate the cause. The job ran indefinitely in production, blocking downstream pipelines, delaying reports, and waking on-call engineers at 2 AM. The performance regression stemmed from data scaling rather than infrastructure or code modifications. Diagnosis focused on understanding Spark behavior and internals instead of adding machines. A targeted fix based on that understanding reduced runtime dramatically, by approximately 70%.

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