Hi @h_h_ak ,

Thank you for your follow-up questions!

While Databricks’ autoscaling implementation is proprietary and functions as a black box, we can gain a clearer understanding by examining Apache Spark’s open-source dynamic resource allocation mechanism.

Here are the files to investigate:

ExecutorAllocationManager: ExecutorAllocationManager.scala

ExecutorAllocationClient: ExecutorAllocationClient.scala

ExecutorMonitor:ExecutorMonitor.scala

1. Number of Pending Tasks as the Primary Trigger
Dynamic resource allocation in Spark primarily relies on the number of pending tasks in the scheduler’s queue. If the number of tasks waiting to be assigned exceeds the current executor capacity (i.e., more tasks than available cores), Spark considers adding new executor nodes. This mechanism ensures that the cluster can handle increased workloads by scaling out when necessary.

2. Scheduler Backlog Timeout (spark.dynamicAllocation.schedulerBacklogTimeout)
The key configuration parameter here is parameter: spark.dynamicAllocation.schedulerBacklogTimeout. This parameter defines how long Spark should wait while there is a sustained backlog of pending tasks before deciding to add new executors. 

3. Absence of Direct CPU Utilization Thresholds
Spark’s dynamic allocation does not directly use CPU utilization metrics as triggers for scaling. Instead, it focuses on task backlog and executor idle times. While high CPU usage can indirectly lead to a task backlog (since tasks may take longer to complete), there are no explicit CPU utilization thresholds that Spark monitors to decide on scaling actions

Summary
Scaling Triggers: Primarily based on a sustained backlog of pending tasks rather than direct CPU or memory utilization metrics.
Key Parameter: spark.dynamicAllocation.schedulerBacklogTimeout defines how long Spark waits with a sustained backlog before scaling up.
Open-Source Insight: While Databricks’ implementation may add proprietary enhancements, understanding Spark’s dynamic allocation provides a solid foundation for anticipating and configuring autoscaling behavior

Hope it helps