Resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. In-memory storage for RDDs that are cached by user programs, through a service called the Block Manager that lives within each executor RDD Spark executors are worker processes responsible for running the individual tasks in a given Spark job. The Spark driver will look at the current set of executors and try to schedule each task in an appropriate location, based on data placement. Each stage, in turn, consists of multiple tasks. Spark performs several optimizations, such as “pipelining” map transformations together to merge them, and converts the execution graph into a set of stages. When the driver runs, it converts this logical graph into a physical execution plan. Spark program implicitly creates a logical DAG of operations. It is the process running the user code that creates a SparkContext, creates RDDs and performs transformations and actions. Spark also works with Hadoop YARN and Apache Mesos.Ī driver is the process where the main() method of your program runs. Spark is packaged with a built-in cluster manager called the Standalone cluster manager. Spark current version - 2.4.2 (as of April'19) It's actually easier to write code in spark Dataset df = session.read().json("logs.json") ĭf.where("age > 21").select("name.first").show()
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