Spark Performance Tuning Essentials
16 Inputs
2 Hours 30 Minutes
Intermediate
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Popular10 credits
Industry
general
Skills
cloud-management
performance-tuning
data-storage
Tools
spark
databricks
Learning Objectives
Diagnose and resolve memory issues in Spark by analyzing driver/executor usage, detecting data skew, and applying caching effectively.
Choose optimal storage formats (CSV, Parquet, Delta) with proper partitioning and compression for better performance.
Interpret Spark UI metrics to identify bottlenecks like shuffle delays, memory spill, and task skew.
Optimize data schemas with explicit definitions, schema evolution handling, and type consistency.
Reduce shuffle and join overhead using coalesce/repartition, broadcast joins, and predicate pushdown.
Apply columnar optimizations through column pruning and efficient filtering using Delta Lake and Parquet.
Overview
This scenario-based learning module focuses on Spark and Delta Lake performance optimization concepts through short, thought-provoking questions. Instead of hands-on exercises, you’ll analyze real-world
scenarios that reflect common performance challenges in production data pipelines. The goal is to help you develop the analytical mindset and reasoning skills needed to diagnose and resolve performance
issues effectively.
Through these scenario questions, you’ll strengthen your understanding of key Spark optimization areas:
- Memory management — understanding driver/executor configurations, caching vs persistence, and detecting data skew.
- Schema design — avoiding inferSchema overhead, defining data types explicitly, and managing schema evolution in Delta tables.
- Data storage — choosing optimal file formats, applying effective partitioning, and leveraging compression for efficiency.
- Shuffle and join optimization — interpreting repartitio this is for your reference..Please give me in this format
Prerequisites
- Understanding of Spark architecture — driver-executor model, partitions, and transformations vs actions.
- Familiarity with Spark UI metrics like stage duration, shuffle size, memory usage, and task time.
- Basic knowledge of Delta Lake — read/write operations, schema enforcement, and table properties.
- Awareness of performance tuning — knowing the cost of collect(), repartition(), and large joins.
- Working knowledge of PySpark DataFrame API — reading, transforming, joining, and writing data to Delta or Parquet.