What is the purpose of auto-scaling in Databricks?

The purpose of auto-scaling in Databricks is to automatically change the cluster size based on workload demands. This feature enables the cluster to dynamically adjust the number of worker nodes in response to changes in the volume or complexity of the tasks being processed.

When the workload increases, auto-scaling allows additional resources to be provisioned to meet demand, which helps maintain optimal performance and minimize processing time. Conversely, when the workload decreases, the number of nodes can be reduced, which helps save costs by not maintaining unnecessary resources. This dynamic resource management is crucial in environments where data processing workloads can vary significantly, allowing users to optimize both performance and cost efficiency without manual intervention.

Other options do not provide the same level of functionality. For instance, manually adjusting cluster configurations is labor-intensive and doesn't adapt to real-time workload changes. Eliminating the need for clusters is not viable, as clusters are fundamental for running jobs. Limiting the maximum size of a cluster may help in controlling costs but does not provide the flexibility of scaling dynamically with workload changes.