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npx skills add https://github.com/wshobson/agents --skill spark-optimization시작 전 확인
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원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
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
python
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()
