Active and Passive Nodes in Spark for Distributed Computing

In Spark, there are two types of nodes: active nodes and passive nodes. Active nodes are nodes that process data and run tasks, while passive nodes are nodes that are not actively processing data.

Spark uses a master-slave architecture to manage the distribution of tasks and data among the slave nodes. The master node assigns tasks to active nodes and ensures that the processing is done efficiently and effectively.

Passive nodes provide fault tolerance and high availability. If an active node fails, another node can take its place and continue processing the data. Passive nodes can also store replicas of data to ensure that data is not lost if an active node fails.

Using a combination of active and passive nodes in Spark enables efficient and reliable processing of large data sets in distributed computing environments.

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