Spark Grocery Store: 5 Tips for Effective Data Processing
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Spark is like a grocery store - data partitions are groceries, tasks are customers, cores are cashiers. This analogy helps explain concepts like scaling, skew, etc.
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Leverage lazy evaluation properly - don't over-collect or you'll recompute upstream transformations.
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Optimize pipelines effectively - meet SLAs, then optimize further only if needed. Don't over-engineer.
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Prevent disk spill to avoid slow jobs - process less data per task, increase RAM to core ratio.
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Use SQL syntax where possible - it's less verbose than Python or Scala for many operations.