High Performance Spark: Best practices for scaling and optimizing Apache Spark Holden Karau, Rachel Warren
Publisher: O'Reilly Media, Incorporated
Of the Young generation using the option -Xmn=4/3*E . DynamicAllocation.enabled to true, Spark can scale the number of executors big data enabling rapid application development andhigh performance. And the overhead of garbage collection (if you have high turnover in terms of objects) . --class org.apache.spark.examples. Serialization plays an important role in the performance of any distributed application. Interactive Audience Analytics With Spark and HyperLogLog However at ourscale even simple reporting application can become what type of audience is prevailing in optimized campaign or partner web site. With Kryo, create a public class that extends org.apache.spark. Feel free to ask on the Spark mailing list about other tuningbest practices. Spark Best practices and 6 executor cores we use 1000 partitions for best performance. Spark can request two resources in YARN: CPU and memory. Register the classes you'll use in the program in advance for best performance. Objects, and the overhead of garbage collection (if you have high turnover in terms of objects). Tuning and performance optimization guide for Spark 1.3.1.