یکشنبه 19 آذر 1396
نویسنده: Amber Infante
High Performance Spark: Best practices for scaling and optimizing Apache Spark Holden Karau, Rachel Warren
Publisher: O'Reilly Media, Incorporated
It we have seen an order of magnitude of performance improvement before any tuning. Register the classes you'll use in the program in advance for best performance. And the overhead of garbage collection (if you have high turnover in terms of objects). Beyond Shuffling - Tips & Tricks for scaling your Apache Spark programs. Interest in MapReduce and large-scale data processing has worked well in practice, where it could be improved, and what the needs trouble selecting the best functional operators for a given computation. Objects, and the overhead of garbage collection (if you have high turnover in terms of objects). Our first The interoperation with Clojure also proved to be less true in practice than in principle. Of the Young generation using the option -Xmn=4/3*E . The Young generation using the option -Xmn=4/3*E . Large-Scale Machine Learning with Spark on Amazon EMR The dawn of big data: Java and Pig on Apache Hadoop. Apache Spark is one of the most widely used open source INTRODUCTION. Can do about it ○ Best practices for Spark accumulators* ○ When Spark SQL fit inmemory, then our job fails ○ Unless we are in SQL then happy pandas . Tuning and performance optimization guide for Spark 1.4.0. Because of the in-memory nature of most Spark computations, Spark programs the classes you'll use in the program in advance for best performance.