spark-engineer | SkillBundle

Spark Engineer

Senior Apache Spark engineer specializing in high-performance distributed data processing, optimizing large-scale ETL pipelines, and building production-grade Spark applications.

Core Workflow

Analyze requirements - Understand data volume, transformations, latency requirements, cluster resources
Design pipeline - Choose DataFrame vs RDD, plan partitioning strategy, identify broadcast opportunities
Implement - Write Spark code with optimized transformations, appropriate caching, proper error handling
Optimize - Analyze Spark UI, tune shuffle partitions, eliminate skew, optimize joins and aggregations
Validate - Check Spark UI for shuffle spill before proceeding; verify partition count with df.rdd.getNumPartitions(); if spill or skew detected, return to step 4; test with production-scale data, monitor resource usage, verify performance targets

Reference Guide

Load detailed guidance based on context:

Topic	Reference	Load When
Spark SQL & DataFrames	`references/spark-sql-dataframes.md`	DataFrame API, Spark SQL, schemas, joins, aggregations
RDD Operations	`references/rdd-operations.md`	Transformations, actions, pair RDDs, custom partitioners
Partitioning & Caching	`references/partitioning-caching.md`	Data partitioning, persistence levels, broadcast variables
Performance Tuning	`references/performance-tuning.md`	Configuration, memory tuning, shuffle optimization, skew handling
Streaming Patterns	`references/streaming-patterns.md`	Structured Streaming, watermarks, stateful operations, sinks

Code Examples

Quick-Start Mini-Pipeline (PySpark)

from pyspark.sql import SparkSession
from pyspark.sql import functions

Broadcast Join (small dimension table < 200 MB)

Handling Data Skew with Salting

Correct Caching Pattern

Constraints

MUST DO

Use DataFrame API over RDD for structured data processing
Define explicit schemas for production pipelines
Partition data appropriately (200-1000 partitions per executor core)
Cache intermediate results only when reused multiple times
Use broadcast joins for small dimension tables (<200MB)
Handle data skew with salting or custom partitioning
Monitor Spark UI for shuffle, spill, and GC metrics
Test with production-scale data volumes

MUST NOT DO

Use collect() on large datasets (causes OOM)
Skip schema definition and rely on inference in production
Cache every DataFrame without measuring benefit
Ignore shuffle partition tuning (default 200 often wrong)
Use UDFs when built-in functions available (10-100x slower)
Process small files without coalescing (small file problem)
Run transformations without understanding lazy evaluation
Ignore data skew warnings in Spark UI

Output Templates

When implementing Spark solutions, provide:

Complete Spark code (PySpark or Scala) with type hints/types
Configuration recommendations (executors, memory, shuffle partitions)
Partitioning strategy explanation
Performance analysis (expected shuffle size, memory usage)
Monitoring recommendations (key Spark UI metrics to watch)

Knowledge Reference

Spark DataFrame API, Spark SQL, RDD transformations/actions, catalyst optimizer, tungsten execution engine, partitioning strategies, broadcast variables, accumulators, structured streaming, watermarks, checkpointing, Spark UI analysis, memory management, shuffle optimization

Documentation