Scenario: A colleague is facing memory-related issues with their Apache Spark job. What strategies would you suggest to optimize memory usage and improve job performance?
- Increase executor memory
- Repartition data
- Tune the garbage collection settings
- Use broadcast variables
Tuning the garbage collection settings in Apache Spark involves configuring parameters such as heap size, garbage collection algorithms, and frequency to optimize memory usage and reduce the likelihood of memory-related issues. By fine-tuning garbage collection settings, you can minimize memory overhead, improve memory management, and enhance overall job performance in Apache Spark applications.
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