Optimize Apache Spark jobs in HDInsight
This article provides an overview of strategies to optimize Apache Spark jobs on Azure HDInsight.
The performance of your Apache Spark jobs depends on multiple factors. These performance factors include: how your data is stored, how the cluster is configured, and the operations that are used when processing the data.
Common challenges you might face include: memory constraints due to improperly sized executors, long-running operations, and tasks that result in cartesian operations.
There are also many optimizations that can help you overcome these challenges, such as caching, and allowing for data skew.
In each of the following articles, you can find information on different aspects of Spark optimization.
- Optimize data storage for Apache Spark
- Optimize data processing for Apache Spark
- Optimize memory usage for Apache Spark
- Optimize HDInsight cluster configuration for Apache Spark