Details
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Improvement
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Status: In Progress
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Major
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Resolution: Unresolved
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3.2.0
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None
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None
Description
Currently, Spark allows users to set scalability within a Spark application using dynamic allocation. spark.dynamicAllocation.minExecutors & spark.dynamicAllocation.maxExecutors are used for scaling up and down. Within an application，Spark tactfully use them to request executors from cluster manager according to the real-time workload. Once set, the range is fixed through the whole application lifecycle. This is not very convenient for long-running application when the range should be changeable for some cases, such as:
1. the cluster manager itself or the queue will scale up and down, which looks very likely to happen in modern cloud platforms
2. the application is long-running, but the timeliness, priority, e.t.c are not only determined by the workload with the application, but also by the traffic across the cluster manager or just different moments
3. e.t.c.
