spark-engineer

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---
name: spark-engineer
description: Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines, tune shuffle operations, configure executor memory, process .parquet files, handle data partitioning, or build structured streaming analytics.
license: MIT
metadata:
  author: https://github.com/Jeffallan
  company: https://synergetic.solutions
  version: "1.1.0"
  domain: data-ml
  triggers: Apache Spark, PySpark, Spark SQL, distributed computing, big data, DataFrame API, RDD, Spark Streaming, structured streaming, data partitioning, Spark performance, cluster computing, data processing pipeline
  role: expert
  scope: implementation
  output-format: code
  related-skills: python-pro, sql-pro, devops-engineer
---

# 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

1. **Analyze requirements** - Understand data volume, transformations, latency requirements, cluster resources
2. **Design pipeline** - Choose DataFrame vs RDD, plan partitioning strategy, identify broadcast opportunities
3. **Implement** - Write Spark code with optimized transformations, appropriate caching, proper error handling
4. **Optimize** - Analyze Spark UI, tune shuffle partitions, eliminate skew, optimize joins and aggregations
5. **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)

```python
from pyspark.sql import SparkSession
from pyspark.sql import functions as F
from pyspark.sql.types import StructType, StructField, StringType, LongType, DoubleType

spark = SparkSession.builder \
    .appName("example-pipeline") \
    .config("spark.sql.shuffle.partitions", "400") \
    .config("spark.sql.adaptive.enabled", "true") \
    .getOrCreate()

# Always define explicit schemas in production
schema = StructType([
    StructField("user_id", StringType(), False),
    StructField("event_ts", LongType(), False),
    StructField("amount", DoubleType(), True),
])

df = spark.read.schema(schema).parquet("s3://bucket/events/")

result = df \
    .filter(F.col("amount").isNotNull()) \
    .groupBy("user_id") \
    .agg(F.sum("amount").alias("total_amount"), F.count("*").alias("event_count"))

# Verify partition count before writing
print(f"Partition count: {result.rdd.getNumPartitions()}")

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

### Broadcast Join (small dimension table < 200 MB)

```python
from pyspark.sql.functions import broadcast

# Spark will automatically broadcast dim_table; hint makes intent explicit
enriched = large_fact_df.join(broadcast(dim_df), on="product_id", how="left")
```

### Handling Data Skew with Salting

```python
import pyspark.sql.functions as F

SALT_BUCKETS =