Optimizing Apache Spark™ on Databricks
Summary
This 2-day course aims to deepen the knowledge of key “problem” areas in Apache Spark, how to mitigate those problems, and even explores new features in Spark 3 that further help to push the envelope in terms of application performance.
Note: This class is available as a one-day version in Private Only Training.
Description
In this course, students will explore five key problems that represent the vast majority of performance problems in an Apache Spark application: skew, spill, shuffle, storage, and serialization. With each of these topics, we explore coding examples based on 100 GB to 1+ TB datasets that demonstrate how these problems are introduced, how to diagnose these problems with tools like the Spark UI, and conclude by discussing mitigation strategies for each of these problems.
We continue the conversation by looking at a series of key ingestion concepts that promote strategies for processing terabytes of data including managing Spark partition sizes, disk-partitioning, bucketing, z-ordering, and more. With each of these topics, we explore when and how each of these techniques should be implemented, new challenges that productionalizing these solutions might provide along with corresponding mitigation strategies.
Finally, we introduce a couple of other key topics such as issues with data locality, IO caching and spark caching, pitfalls of broadcast Joins, and new features like Spark 3’s Adaptive Query Execution and Dynamic Partition Pruning. We then conclude the course with discussions and exercises on designing and configuring clusters for optimal performance given specific use cases, personas, the divergent needs of various teams, and cross-team security concerns.
Prerequisites
- Intermediate to advanced programming experience in Python or Scala
- Hands-on experience developing Apache Spark applications
Outline
Day 1 Morning: Introduction and the 5 Most Common Performance Problems
Day 1 Afternoon: The 5 Most Common Performance Problems Continued
Day 2 Morning: Key Ingestion Concepts
Day 2 Afternoon: Optimizing with AQE and High Performance; Designing Clusters for High Performance
Upcoming Classes
Central European Summer Time
Australian Eastern Standard Time (Victoria)
Pacific Daylight Time
Central European Summer Time
Central European Summer Time
Pacific Daylight Time
Eastern Standard Time
Onsite Training
Public Training
Virtual - EMEA
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9:00 AM - 5:00 PM CEST$ 1500.00 USD -
9:00 AM - 5:00 PM CEST$ 1500.00 USD
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9:00 AM - 5:00 PM CEST$ 1500.00 USD
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9:00 AM - 5:00 PM CET$ 1500.00 USD
Virtual - Australia AEST
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8:00 AM - 12:00 PM AEST$ 1500.00 USD
Virtual - Americas (half-day schedule)
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9:00 AM - 1:00 PM PDT$ 1500.00 USD
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9:00 AM - 1:00 PM PDT$ 1500.00 USD
Virtual - US Eastern
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9:00 AM - 5:00 PM EST$ 1500.00 USD
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