JEAN-GEORGES PERRIN FOREWORD BY ROB THOMASįor online information and ordering of this and other Manning books, please visit The publisher offers discounts on this book when ordered in quantity. Your code in a JAR package Apache Spark components
#CAN I INSTALL APACHE SPARK AS ROOT DRIVER#
The driver can access its deployment mode. Job: parallel tasks triggered after an action is called Each application has its own executors.Ī parallel computation consisting of multiple tasks that gets spawned in response to a Spark action (for example, save() or collect()) check out appendix I).Įach job gets divided into smaller sets of tasks, called stages, that depend on each other (similar to the map and reduce stages in MapReduce).Ī unit of work that will be sent to one executor.Īny node that can run application code in the cluster.Īpplication processes and resources elements The executor runs tasks and keeps data in memory or in disk storage across them. Everything starts here.Ī process launched for an application on a worker node. The process running the main() function of the application and creating the SparkContext. This method returns a read-only property. You can find out which mode you are in by calling the deployMode() method. In client mode, the submitter launches the driver outside the cluster. In cluster mode, the framework launches the driver inside the cluster. More details in chapter 6.ĭistinguishes where the driver process runs.
It can be the Spark built-in cluster manager. It can be an uber JAR including all the dependencies.Īn external service for acquiring resources on the cluster. Consists of a driver program and executors on the cluster.Ī Java archive (JAR) file containing your Spark application. Your program that is built on and for Spark. Lexicon Summary of the Spark terms involved in the deployment process Term Jean-Georges Perrin Foreword by Rob Thomas
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