Como usar
- Copie o comando e verifique o pacote antes de instalar no seu ambiente de agente.
- Consulte no original as ferramentas, credenciais e ambientes necessários.
- Descreva os dados de entrada e a entrega esperada; confira a saída com as fontes.
npx skills add https://github.com/wshobson/agents --skill spark-optimizationAntes de começar
- A instalação não inclui assinatura de modelo, franquia API ou acesso aos seus arquivos.
- Estrelas e instalações indicam atividade, não um teste independente de qualidade ou segurança.
Documento original do Skill
Renderizado do repositório de origem, preservando títulos, exemplos, código, tabelas, links e imagens.
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()
