Optimizing Apache Spark™ on Databricks
In this course, you will explore the five key problems that represent the vast majority of performance issues in an Apache Spark application: skew, spill, shuffle, storage, and serialization. With examples based on 100 GB to 1+ TB datasets, you will investigate and diagnose sources of bottlenecks with the Spark UI and learn effective mitigation strategies. You will also discover new features introduced in Spark 3 that can automatically address common performance problems. Lastly, you learn how to design and configure clusters for optimal performance based on specific team needs and concerns.
Outline
Day 1
- Review of Spark architecture and Spark UI
- Skew
- Spill
- Shuffle
- Storage
- Serialization
Day 2
- Ingestion basics
- Predicate push downs
- Disk partitioning
- Z-ordering
- Bucketing
- Optimization with Adaptive Query Execution (AQE)
- Designing and configuring clusters for high performance
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Upcoming Public Classes
Career Workshop/
March 20
