When running Spark on Hadoop, to what does the data node typically correspond?

In the context of Spark running on a Hadoop ecosystem, the correct correspondence of a data node is a worker node. In Hadoop, a data node is responsible for storing and managing the actual data blocks of the files in the HDFS (Hadoop Distributed File System). When Spark is executed on top of Hadoop, it utilizes these data nodes to perform computations on the data stored in HDFS.

Worker nodes are designed to execute tasks and store data. Each worker node can run multiple executor processes, which are responsible for actually running the Spark tasks on the data that is co-located with them in HDFS. This architecture allows Spark to take advantage of data locality, meaning that it can process data physically located on the same machine, leading to enhanced performance and reduced network I/O during processing.

The other choices do not align with the role of a data node: a master node serves to manage the cluster’s resources and monitors the operations of the worker nodes, the client node is used for submitting jobs to the Spark cluster, and the resource manager is responsible for allocating resources across the cluster but does not directly manage the data stored on the data nodes. Each plays a vital role in managing the resources and job execution but does not equate with the functionality of a