spark-engineer

---
name: spark-engineer
description: >-
license: MIT
---

# 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 = 50

# Add salt to the skewed key on both sides
skewed_df = skewed_df.withColumn("salt", (F.rand() * SALT_BUCKETS).cast("int")) \
    .withColumn("salted_key", F.concat(F.col("skewed_key"), F.lit("_"), F.col("salt")))

other_df = other_df.withColumn("salt", F.explode(F.array([F.lit(i) for i in range(SALT_BUCKETS)]))) \
    .withColumn("salted_key", F.concat(F.col("skewed_key"), F.lit("_"), F.col("salt")))

result = skewed_df.join(other_df, on="salted_key", how="inner") \
    .drop("salt", "salted_key")
```

### Correct Caching Pattern

```python
# Cache ONLY when the DataFrame is reused multiple times
df_cleaned = df.filter(...).withColumn(...).cache()
df_cleaned.count()  # Materialize immediately; check Spark UI for spill

report_a = df_cleaned.groupBy("region").agg(...)
report_b = df_cleaned.groupBy("product").agg(...)

df_cleaned.unpersist()  # Release when done
```

## 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:
1. Complete Spark code (PySpark or Scala) with type hints/types
2. Configuration recommendations (executors, memory, shuffle partitions)
3. Partitioning strategy explanation
4. Performance analysis (expected shuffle size, memory usage)
5. 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](https://jeffallan.github.io/claude-skills/skills/data-ml/spark-engineer/)