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https://issues.apache.org/jira/browse/SPARK-54596?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dongjoon Hyun updated SPARK-54596:
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Affects Version/s: 4.2.0
(was: 4.2)
> Burst-aware memoryOverhead Allocation Algorithm for Spark@K8S
> -------------------------------------------------------------
>
> Key: SPARK-54596
> URL: https://issues.apache.org/jira/browse/SPARK-54596
> Project: Spark
> Issue Type: Improvement
> Components: Kubernetes, Spark Core
> Affects Versions: 4.2.0
> Reporter: Nan Zhu
> Priority: Major
>
> {{memoryOverhead}} is one of the largest contributors to memory consumption
> in Spark workloads. To avoid being killed by the OOM Killer in cluster
> environments, users typically reserve a large {{memoryOverhead}} budget.
> However, the actual usage pattern of this space—covering
> serialization/deserialization buffers, compression/decompression workspaces,
> direct memory allocations, etc.—is highly bursty. As a result, users must
> provision for peak {{memoryOverhead}} even though most Spark tasks utilize
> only a small fraction of it for the majority of their lifespan.
> To address this inefficiency in {*}Spark on Kubernetes{*}, we implemented the
> algorithm proposed in the paper _“Towards Resource Efficiency: Practical
> Insights into Large-Scale Spark Workloads at ByteDance”_ (VLDB 2024). Our
> approach introduces a configurable parameter that splits a user’s requested
> {{memoryOverhead}} into two segments:
> * *Guaranteed memoryOverhead* — exclusive to each Spark pod, ensuring
> baseline reliability.
> * *Shared memoryOverhead* — pooled and time-shared across multiple pods to
> absorb bursty usage.
> In the Kubernetes environment, this translates to the following resource
> settings for each Spark executor pod:
> * *memory request* = user-requested on-heap memory + guaranteed
> {{memoryOverhead}}
> * *memory limit* = user-requested on-heap memory + guaranteed
> {{memoryOverhead}} + shared {{memoryOverhead}}
> This design preserves reliability while substantially improving cluster-level
> memory utilization by avoiding the over-provisioning traditionally required
> for worst-case {{memoryOverhead}} peaks.
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