_using-apache-spark-to-tune-spark-adrian-popescu-unravel-data-systems-and-shivnath-babu-unravel-data-systems-duke-univ.jpg "Using Apache Spark to Tune Spark Adrian Popescu (Unravel Data Systems) and Shivnath Babu (Unravel Data Systems Duke University) from executor Video Screenshot Preview")
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⏲ Duration: 15 min 42 sec
✓ Published: 11-Jun-2018
✓ Published: 11-Jun-2018
Description:
We have developed a workload-aware performance tuning framework for Spark that collects and analyzes telemetry information about all the Spark applications in a cluster. Based on this analysis—which uses batch processing, real-time streaming, and ML analysis that Spark excels at—the framework can identify many ways to improve the overall performance of the Spark workload: (i) by identifying datasets with skewed distributions that are causing significant performance degradation, (ii) by ident
We have developed a workload-aware performance tuning framework for Spark that collects and analyzes telemetry information about all the Spark applications in a cluster. Based on this analysis—which uses batch processing, real-time streaming, and ML analysis that Spark excels at—the framework can identify many ways to improve the overall performance of the Spark workload: (i) by identifying datasets with skewed distributions that are causing significant performance degradation, (ii) by ident
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