Apache Spark is a user-friendly toolkit that allows you to write applications quickly in Java, Scala, Python, R, and SQL. It offers over 80 high-level operators that make it easy to build parallel applications. But Spark comes with its challenges, and learning the basics of Spark performance tuning is a must.

In the How to Overcome the Five Most Common Spark Challenges webinar, Alexander Pierce, a Pepperdata Field Engineer, explores issues observed in a cluster environment with Apache Spark and offers a range of Spark tuning tips.

The Problem with DAG Management

It’s always a good idea to keep an eye on the complexity of the execution plan. Use the DAG (direct acyclic graph) Visualization tool that comes with SparkUI for one possible visual map. If something that you think should be straightforward (a basic join, for example) is taking 10 stages, look at your query or code to reduce it to 2 to 3 stages if possible.

In the webinar, Alex offers this recommendation for Spark performance tuning: “Look at each of the stages in the parallelization. Keep an eye on your DAG, not just on the overall complexity. Make sure each stage in your code is actually running in parallel.” If you have a (non-parallel) stage that is using less than 60% of the available executors, the questions to keep in mind are: Should that compute be rolled into another stage? Is there a separate partitioning issue? Is there an issue in my code preventing tasks from processing in parallel?

Spark Performance Tuning Tips

Watch the  How to Overcome the Five Most Common Spark Challenges webinar with Alex Pierce to learn more about tackling the many challenges with Spark, and what your Spark tuning checklist could look like. Find out more about how to improve the supportability and usability of Spark in their projects and successfully overcome common challenges aside from DAG management.

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