yarn

Cluster vs Client: Execution modes for a Spark application

Reading Time: 3 minutes Whenever we submit a Spark application to the cluster, the Driver or the Spark App Master should get started. And the Driver will be starting N number of workers. Spark driver will be managing spark context object to share the data and coordinates with the workers and cluster manager across the cluster. Cluster Manager can be Spark Standalone or Hadoop YARN or Mesos. Workers will Continue Reading

Understanding the working of Spark Driver and Executor

Reading Time: 4 minutes This blog pertains to Apache SPARK, where we will understand how Spark’s Driver and Executors communicate with each other to process a given job. So let’s get started. First, let’s see what Apache Spark is. The official definition of Apache Spark says that “Apache Spark™ is a unified analytics engine for large-scale data processing.” It is an in-memory computation processing engine where the data is Continue Reading

Understanding how Spark runs on YARN with HDFS

Reading Time: 6 minutes This blog pertains to Apache SPARK and YARN (Yet Another Resource Negotiator), where we will understand how Spark runs on YARN with HDFS. So let’s get started. First, let’s see what Apache Spark is. The official definition of Apache Spark says that “Apache Spark™ is a unified analytics engine for large-scale data processing.” It is an in-memory computation processing engine where the data is kept Continue Reading

Introduction to Mesos

Reading Time: 4 minutes What is Mesos ? In layman’s term, Imagine a busy airport. Airplanes are constantly taking off and landing. There are multiple runways, and an airport dispatcher is assigning time-slots to airplanes to land or takeoff. So Mesos is the airport dispatcher, runways are compute nodes, airplanes are compute tasks, and frameworks like Hadoop, Spark and Google Kubernetes are airlines companies. In technical terms, Apache Mesos Continue Reading