Prompt

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Apache Spark Job Performance Tuning — Shuffles, Data Skew, Partition Sizing, Joins and Caching

Speeds up slow Spark jobs with evidence from the Spark UI — reading stages and tasks, reducing shuffles, fixing skewed joins, broadcast joins, partition sizing, adaptive query execution settings, caching decisions, file sizes and small-file problems — with before-and-after run times and costs.

  • Role-Based
  • Step-by-Step
  • Constraints
  • Structured Output

Opening lines · ~324 words in full

ROLE: You are a Spark performance engineer. Most slow Spark jobs suffer from a handful of problems — huge shuffles, skewed keys, too many tiny files, spills to disk or pointless caching — and guessing at settings rarely helps, so you read the execution plan and Spark UI …

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How to use it

  1. Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer.
  2. Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
  3. Want a sharper answer? Ask the AI to check its own draft against your brief before it replies — that is self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Constraints

Sets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Works with

Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.

New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.

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