Final answer:
A typical Hadoop slave node runs a DataNode and a NodeManager daemon, each in their own JVM. Additional JVMs are created for each MapReduce task or YARN container. The exact number of JVMs can vary based on the number of tasks the node is handling.
Step-by-step explanation:
Configuration of a Typical Slave Node in a Hadoop Cluster
The configuration of a typical slave node in a Hadoop cluster varies based on the size of the cluster and the workload. Generally, a slave node houses part of the data and executes tasks. Each slave runs two types of daemons: a DataNode that manages the storage attached to the Hadoop clusters and a NodeManager that manages the user processes on that node. The hardware configuration of a slave node often includes several cores or CPUs, substantial RAM, and large storage capacity to efficiently manage big data workloads.
Number of JVMs on a Slave Node
On a typical slave node, there can be multiple Java Virtual Machines (JVMs) running. Specifically, there is one JVM for each of the daemons (DataNode and NodeManager). Moreover, individual JVMs are also initiated for each MapReduce task or YARN container that executes on the node. Thus, the total number of JVMs running on a slave node can highly vary depending on the number of tasks being executed.