spark-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:

Code Examples

Quick-Start Mini-Pipeline (PySpark)

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/")