spark-development

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Mis à jour10 juin 2026 à 12:49

Write and optimize PySpark and Spark SQL. Use for Spark DataFrames, joins, windows, UDF decisions, partitioning, caching, AQE, explain plans, OOM, shuffle, skew, Delta writes, and Spark pipeline performance.

Installation

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Source

datacoolie

datacoolie/dekit

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

SKILL.md

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name	spark-development
description	Write and optimize PySpark and Spark SQL. Use for Spark DataFrames, joins, windows, UDF decisions, partitioning, caching, AQE, explain plans, OOM, shuffle, skew, Delta writes, and Spark pipeline performance.

Spark Development

Use this skill for Spark implementation choices and performance risk checks.

Rules

Define schemas explicitly at production read boundaries.
Prefer built-in functions over pandas UDFs; pandas UDFs over Python UDFs.
Keep transformations set-based; avoid driver-side loops.
Use idempotent writes: MERGE/upsert, replaceWhere, or partition overwrite.
Partition by query/lifecycle pattern, not convenience.
Cache only DataFrames reused across multiple actions; unpersist after use.
Inspect plans before optimizing: explain("formatted") or platform equivalent.
Tune from observed data size, skew, shuffle, and SLA, not guesses.

Join Checklist

Expected cardinality is known before joining.
Join keys have compatible types and null behavior.
Broadcast only genuinely small sides.
No cartesian product unless deliberately bounded.
For skew, validate key distribution before salting or repartitioning.

Red Flags

collect(), toPandas(), or repartition(1) on large data.
inferSchema=True in production.
Python UDF for simple expressions.
Repeated .count() actions just for logging.
Writes without atomicity or retry/idempotency plan.
Partition columns missing from common filters.

Verification

Before reporting Spark work complete:

Run affected transformation or representative local/unit test.
Validate output schema and row counts.
Confirm rerun behavior is idempotent.
Inspect explain plan for unexpected cartesian joins, excessive exchanges, or missing pruning.
Reconcile critical measures against source or prior layer.

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name	spark-development
description	Write and optimize PySpark and Spark SQL. Use for Spark DataFrames, joins, windows, UDF decisions, partitioning, caching, AQE, explain plans, OOM, shuffle, skew, Delta writes, and Spark pipeline performance.

Spark Development

Use this skill for Spark implementation choices and performance risk checks.

Rules

Define schemas explicitly at production read boundaries.
Prefer built-in functions over pandas UDFs; pandas UDFs over Python UDFs.
Keep transformations set-based; avoid driver-side loops.
Use idempotent writes: MERGE/upsert, replaceWhere, or partition overwrite.
Partition by query/lifecycle pattern, not convenience.
Cache only DataFrames reused across multiple actions; unpersist after use.
Inspect plans before optimizing: explain("formatted") or platform equivalent.
Tune from observed data size, skew, shuffle, and SLA, not guesses.

Join Checklist

Expected cardinality is known before joining.
Join keys have compatible types and null behavior.
Broadcast only genuinely small sides.
No cartesian product unless deliberately bounded.
For skew, validate key distribution before salting or repartitioning.

Red Flags

collect(), toPandas(), or repartition(1) on large data.
inferSchema=True in production.
Python UDF for simple expressions.
Repeated .count() actions just for logging.
Writes without atomicity or retry/idempotency plan.
Partition columns missing from common filters.

Verification

Before reporting Spark work complete:

Run affected transformation or representative local/unit test.
Validate output schema and row counts.
Confirm rerun behavior is idempotent.
Inspect explain plan for unexpected cartesian joins, excessive exchanges, or missing pruning.
Reconcile critical measures against source or prior layer.