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https://issues.apache.org/jira/browse/BEAM-4430?focusedWorklogId=112790&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-112790
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ASF GitHub Bot logged work on BEAM-4430:
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Author: ASF GitHub Bot
Created on: 18/Jun/18 15:49
Start Date: 18/Jun/18 15:49
Worklog Time Spent: 10m
Work Description: chamikaramj commented on a change in pull request #465:
[BEAM-4430] Improve Performance Testing Documentation
URL: https://github.com/apache/beam-site/pull/465#discussion_r196127291
##########
File path: src/documentation/io/testing.md
##########
@@ -282,12 +466,11 @@ As discussed in [Integration tests, data stores, and
Kubernetes](#integration-te
If you would like help with this or have other questions, contact the Beam
dev@ mailing list and the community may be able to assist you.
Guidelines for creating a Beam data store Kubernetes script:
-1. **You must only provide access to the data store instance via a `NodePort`
service.**
- * This is a requirement for security, since it means that only the local
network has access to the data store. This is particularly important since many
data stores don't have security on by default, and even if they do, their
passwords will be checked in to our public Github repo.
Review comment:
Is this not required any more ?
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Issue Time Tracking
-------------------
Worklog Id: (was: 112790)
> Improve Performance Testing Documentation
> -----------------------------------------
>
> Key: BEAM-4430
> URL: https://issues.apache.org/jira/browse/BEAM-4430
> Project: Beam
> Issue Type: Wish
> Components: testing
> Reporter: Łukasz Gajowy
> Assignee: Łukasz Gajowy
> Priority: Critical
> Time Spent: 1h 20m
> Remaining Estimate: 0h
>
> Currently, the only documentation regarding IO Performance Testing can be
> found here:
> [https://beam.apache.org/documentation/io/testing/#i-o-transform-integration-tests].
> This is certainly not enough given that the performance testing framework
> currently allows to run tests:
> - on local or hdfs filesystems
> - on direct or dataflow runners
> - manually using integrationTest task
> - automatically using performanceTest task
> - using pkb.py tool directly (PerfKitBenchmarker)
> - on demand from pending Pull Requests
> - detecting anomalies
> - gathering results in dashboards
> All the above bullets (and maybe others - to be investigated) need more
> explanation in the docs to make the Performance Testing Framework usable by
> the broader community.
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