Towards Data Science 12:24 am on June 3, 2024
Spark's expression generation in code optimization provided marginal gains over map_filter queries but showed significant performance improvements when explode_then_filter was used, especially for large expressions. Performance hinged on efficient execution topology rather than the refactored queries alone. Notably, this benefited from micro-batch processing and resource utilization in Spark streaming contexts.
- Code Optimization: Expression generation outperformed map_filter for large query performance.
- Execution Efficiency: Parallelism in micro-batch processing was key to optimizing resource utilization.
- Refactoring Impact: The shift from lambda functions to explode_then_filter improved performance, highlighting execution topology over code transformation.
- Stream Processing Consideration: Idle CPUs during task waiting in Spark's micro-batches boosted overall throughput and efficiency.
- Collaborative Insights: This study was part of a broader team effort at the Canadian Centre for Cybersecurity, contributing to data science research.
https://towardsdatascience.com/performance-insights-from-sigma-rule-detections-in-spark-streaming-fac8c67d37b8
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