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Posted to issues@spark.apache.org by "Dongjoon Hyun (Jira)" <ji...@apache.org> on 2024/08/06 20:13:00 UTC
[jira] [Updated] (SPARK-45869) Revisit and Improve Spark Standalone Cluster
[ https://issues.apache.org/jira/browse/SPARK-45869?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Dongjoon Hyun updated SPARK-45869:
----------------------------------
Description:
*Spark Standalone Cluster* has been supported for a long time as one of the resource managers.
As a part of Apache Spark 4.0.0, we revisit all layers of `Spark Standalone Cluster` as a long running subsystem inside K8s environment.
# Spark Master, Worker, History Server Web UI Layer
# Spark Master HA and Recovery Layer
# Spark Master REST API Layer (including Cluster Utilization monitoring)
# Spark Job Scheduling Layer
# Spark Worker Management by exposing Cluster Utilization monitoring for Elastic Cluster Management
# Spark Master/Worker dependency and classpath audit
# Security
# Documentation
was:
*Spark Standalone Cluster* has been supported for a long time as one of the resource managers.
As a part of Apache Spark 4.0.0, we revisit all layers of `Spark Standalone Cluster` as a long running subsystem inside K8s environment.
# Spark Master, Worker, History Server Web UI Layer
# Spark Master HA and Recovery Layer
# Spark Master REST API Layer (including Cluster Utilization monitoring)
# Spark Job Scheduling Layer
# Spark Worker Management by exposing Cluster Utilization monitoring for Elastic Cluster Management
# Spark Master/Worker dependency and classpath audit
# Documentation
> Revisit and Improve Spark Standalone Cluster
> --------------------------------------------
>
> Key: SPARK-45869
> URL: https://issues.apache.org/jira/browse/SPARK-45869
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 4.0.0
> Reporter: Dongjoon Hyun
> Assignee: Dongjoon Hyun
> Priority: Critical
> Labels: releasenotes
> Fix For: 4.0.0
>
>
> *Spark Standalone Cluster* has been supported for a long time as one of the resource managers.
> As a part of Apache Spark 4.0.0, we revisit all layers of `Spark Standalone Cluster` as a long running subsystem inside K8s environment.
> # Spark Master, Worker, History Server Web UI Layer
> # Spark Master HA and Recovery Layer
> # Spark Master REST API Layer (including Cluster Utilization monitoring)
> # Spark Job Scheduling Layer
> # Spark Worker Management by exposing Cluster Utilization monitoring for Elastic Cluster Management
> # Spark Master/Worker dependency and classpath audit
> # Security
> # Documentation
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