What are the differences between the master node and worker nodes in Spark?
In Spark, the master node and worker nodes are different types of nodes in the cluster, each playing a distinct role.
- Main Node
- The master node serves as the central control unit for the entire Spark cluster, responsible for coordinating and managing all the worker nodes in the cluster.
- The master node typically runs a Spark cluster manager, such as Spark Standalone, YARN, or Mesos, to allocate resources and schedule tasks.
- The master node is responsible for monitoring the health status of worker nodes, managing task allocation and scheduling, and maintaining the overall state of the cluster.
- The master node typically does not participate in actual data processing and computation tasks; its main responsibility is to manage and coordinate the work of worker nodes.
- Worker Node: Job node
- The worker node is the actual node within a cluster that executes tasks and processes data, responsible for computing and storing data.
- The working nodes receive task assignments from the master node and carry out specific computational tasks.
- Typically, working nodes will run Spark Executor processes to execute tasks and acquire resources from the master node as needed.
- The number of work nodes can be dynamically expanded or reduced to accommodate different computing workloads and demands.
In general, the master node serves as the manager and coordinator of the cluster, while the worker nodes are the ones responsible for carrying out tasks and computations. They communicate and collaborate through the cluster manager to create an efficient Spark computing cluster.