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Behavioral · Core Behavioral

Improved a slow or failing pipeline

Mediumbehavior-03
performancedebuggingSLASTAR

Question

Describe a time you improved a slow or failing data pipeline. What was the root cause and what changed?

Solution

Walk through debugging discipline: measure → hypothesize → fix → verify.

STAR outline

Situation: Nightly Spark/SQL job ran 3 hours and often missed the 8 AM SLA.

Task: Reduce runtime and stabilize success rate.

Action (pick what fits your story):

  • Checked logs for OOM, skew, or full scans
  • Profiled stages / EXPLAIN plan
  • Fixed partition pruning, broadcast small dimension, or reduced shuffle
  • Added retries with backoff and clearer failure alerts
  • Split heavy transform from load

Result: Runtime down (e.g. 3h → 45m) or failure rate down; SLA met for N days.

Example talking points

> The join to a large fact was shuffling on an unpartitioned key. I filtered early, selected only needed columns, and broadcast the small lookup. I also added a freshness check so we fail loudly before dashboards go stale.

Fresher-honest version

College or personal project: "CSV ingest took 20 minutes locally because I loaded everything then filtered. I pushed filters earlier and chunked reads; it finished in under 3 minutes."

Interview tip: Name one root cause and one primary fix. Don't list ten micro-optimizations.

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