Wshobson spark optimization

Skill ItamarZand88/awesome-agent-conventions/conventions/skill-md/examples/data-analysis/wshobson-spark-optimization

A curated guide to convention files AI agents read, write, and act on: AGENTS.md, CLAUDE.md, SKILL.md, llms.txt, MCP configs, rules, and examples.

Install

npx -y skills add ItamarZand88/awesome-agent-conventions --skill wshobson-spark-optimization

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SKILL.md

3.2 KB, 768 tokens by cl100k_base, as published. Nobody here has run it

name: spark-optimization description: Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.

Apache Spark Optimization

Production patterns for optimizing Apache Spark jobs including partitioning strategies, memory management, shuffle optimization, and performance tuning.

When to Use This Skill

Optimizing slow Spark jobs
Tuning memory and executor configuration
Implementing efficient partitioning strategies
Debugging Spark performance issues
Scaling Spark pipelines for large datasets
Reducing shuffle and data skew

Core Concepts

1. Spark Execution Model

Driver Program
    ↓
Job (triggered by action)
    ↓
Stages (separated by shuffles)
    ↓
Tasks (one per partition)

2. Key Performance Factors

Factor	Impact	Solution
Shuffle	Network I/O, disk I/O	Minimize wide transformations
Data Skew	Uneven task duration	Salting, broadcast joins
Serialization	CPU overhead	Use Kryo, columnar formats
Memory	GC pressure, spills	Tune executor memory
Partitions	Parallelism	Right-size partitions

Quick Start

from pyspark.sql import SparkSession
from pyspark.sql import functions as F

# Create optimized Spark session
spark = (SparkSession.builder
    .appName("OptimizedJob")
    .config("spark.sql.adaptive.enabled", "true")
    .config("spark.sql.adaptive.coalescePartitions.enabled", "true")
    .config("spark.sql.adaptive.skewJoin.enabled", "true")
    .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    .config("spark.sql.shuffle.partitions", "200")
    .getOrCreate())

# Read with optimized settings
df = (spark.read
    .format("parquet")
    .option("mergeSchema", "false")
    .load("s3://bucket/data/"))

# Efficient transformations
result = (df
    .filter(F.col("date") >= "2024-01-01")
    .select("id", "amount", "category")
    .groupBy("category")
    .agg(F.sum("amount").alias("total")))

result.write.mode("overwrite").parquet("s3://bucket/output/")

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Best Practices

Do's

Enable AQE - Adaptive query execution handles many issues
Use Parquet/Delta - Columnar formats with compression
Broadcast small tables - Avoid shuffle for small joins
Monitor Spark UI - Check for skew, spills, GC
Right-size partitions - 128MB - 256MB per partition

Don'ts

Don't collect large data - Keep data distributed
Don't use UDFs unnecessarily - Use built-in functions
Don't over-cache - Memory is limited
Don't ignore data skew - It dominates job time
Don't use .count() for existence - Use .take(1) or .isEmpty()

Gives 0 of the 12 instructions most performance cost skills give in 768 tokens

Counted across 803 of the 1,058 authors here whose files we hold, read 2026-08-07

keep skill files under 500 linesin 82 of 803, across 16 files
use imperative form in instructionsin 80 of 803, across 9 files
draft assertions while test runs are in progressin 75 of 803, across 9 files
create two to three realistic test promptsin 74 of 803, across 9 files
write skill descriptions to be pushyin 72 of 803, across 7 files
save test cases to evals jsonin 72 of 803, across 6 files
ask questions about edge cases and input formatsin 72 of 803, across 7 files
save timing data immediately when runs completein 70 of 803, across 5 files
include all trigger conditions in the skill descriptionin 69 of 803, across 3 files
launch all test runs in a single turnin 69 of 803, across 3 files
capture intent before writing a skillin 67 of 803, across 1 file
import directly instead of barrel filesin 52 of 803, across 15 files

Said here and by no other author read

minimize wide transformations
use columnar formats with compression
broadcast small tables to avoid shuffle
monitor the Spark UI for performance issues
right-size partitions between 128MB and 256MB
use Kryo serializer to reduce CPU overhead

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.