In the context of shared clusters, where multiple jobs can run concurrently, the behavior regarding driver containers differs from that of single clusters.
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Single Clusters:
- In a single cluster, there is indeed a single driver node responsible for managing the Spark application.
- This driver node runs in a single driver container.
- All tasks and computations are coordinated by this single driver.
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Shared Clusters:
- In shared clusters, multiple jobs can indeed run concurrently.
- However, each job still has its own driver.
- Therefore, multiple driver containers can run concurrently, one for each Spark application.
- Each driver manages its specific job’s execution, task scheduling, and communication with the cluster.
In summary, shared clusters allow multiple jobs to coexist, but each job still operates with its dedicated driver container. This design ensures isolation and proper management of resources for each application. 🚀
