Production Spark Job Optimization & Tuning Suite

How do we build an optimized pipeline combining broadcast joins, partition filtering, and coalesce before writing?

Expert⏱ 3 Minutes⭐ 1060 XP

🤔 THE QUESTION

How do we build an optimized pipeline combining broadcast joins, partition filtering, and coalesce before writing?

💡 WHAT IS IT?

A complete optimization suite using broadcast joins, partition pruning, and coalesce to maximize write efficiency.

🎯 WHAT IS IT USED FOR?

Production cloud ETL pipelines requiring strict SLA runtimes and cost-optimized cluster compute.

💻 EXAMPLE

from pyspark.sql.functions import broadcast, col

fact_orders = spark.read.parquet("lakehouse/fact_orders")
dim_categories = spark.read.parquet("lakehouse/dim_categories")

optimized_pipeline = fact_orders \
    .join(broadcast(dim_categories), "category_id", "inner") \
    .filter(col("order_year") == 2026) \
    .coalesce(10)

optimized_pipeline.write.mode("overwrite").parquet("lakehouse/gold_category_sales")

🎯 Mission Objectives

Practice typing production-grade PySpark code for Production Spark Job Optimization & Tuning Suite.

✓ Combine broadcast join and partition pruning
✓ Coalesce output partitions to 10 files
✓ Execute optimized gold layer export

🚀 Start Mission

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