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https://issues.apache.org/jira/browse/ARIES-1357?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Bas updated ARIES-1357:
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Attachment:
nl.capesystemsintegration.emagiz.flow.emagiz.mikrotik.process_1.1.0.esa
This is an example of one of our flows. The upper subsystem is always a
feature, the inner subsystem is always an application. The manifest shows how
many bundles can be expected in a flow like this.
So when we talk about 200 subsystems we have 100 of these files installed.
Because of some jars being closed source I can't give you all jars. But this
can give you an idea on how we use subsystems.
Maybe you have some testcase that can simulate this type of subsystem.
> BasicSubsystem can be used by the subsystem install process a factor 6 times
> faster
> -----------------------------------------------------------------------------------
>
> Key: ARIES-1357
> URL: https://issues.apache.org/jira/browse/ARIES-1357
> Project: Aries
> Issue Type: Improvement
> Components: Subsystem
> Affects Versions: subsystem-core-1.2.0, subsystem-2.0.1
> Environment: linux windows java
> Reporter: Bas
> Assignee: John Ross
> Labels: patch, performance
> Fix For: subsystem-core-1.2.0, subsystem-2.0.1
>
> Attachments: ManyFeaturesWithSharedBundlesTest.java,
> nl.capesystemsintegration.emagiz.flow.emagiz.mikrotik.process_1.1.0.esa,
> patch-basicsubsystem.patch, performance-105-afterrestart.nps,
> subsystem-basicsubsystem-patch.txt
>
>
> The problem was poor performance while installing a subsystem on a container
> with 200+ subsystems installed.
> While doing performance analyses on the subsystem install process we noticed
> the BasicSubsystem is working a lot with its manifest instead of
> pre-calculating data that is used a lot.
> While using the pre-patch version of BasicSubsystem a subsystem install
> (while having 200+ running subsystems) could take more than a minute. While
> using the patched version where we pre-calculate highly used data when a new
> subsystem manifest or deployment manifest is set we could bring it down to 10
> seconds. We only pre-calculated the data that was highly used.
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