[
https://issues.apache.org/jira/browse/BEAM-4430?focusedWorklogId=112788&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-112788
]
ASF GitHub Bot logged work on BEAM-4430:
----------------------------------------
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_r196125174
##########
File path: src/documentation/io/testing.md
##########
@@ -147,21 +147,30 @@ However, **PerfKit Benchmarker is not required for
running integration tests**.
Prerequisites:
1. [Install PerfKit
Benchmarker](https://github.com/GoogleCloudPlatform/PerfKitBenchmarker)
-1. Have a running Kubernetes cluster you can connect to locally using kubectl
+1. Have a running Kubernetes cluster you can connect to locally using
kubectl. A cluster hosted on Google Kubernetes Engine might be the best fit as
it is used to run the tests on Beam's Jenkins.
-You won’t need to invoke PerfKit Benchmarker directly. Run `./gradlew
performanceTest` in project's root directory, passing appropriate kubernetes
scripts depending on the network you're using (local network or remote one).
+You won’t need to invoke PerfKit Benchmarker directly. Run `./gradlew
performanceTest` task in project's root directory, passing kubernetes scripts
of your choice (located in .test_infra/kubernetes directory). It will setup
PerfKitBenchmarker for you.
-Example run with the direct runner:
+Example run with the Direct runner:
```
./gradlew performanceTest -DpkbLocation="/Users/me/PerfKitBenchmarker/pkb.py"
-DintegrationTestPipelineOptions='["--numberOfRecords=1000"]'
-DitModule=sdks/java/io/jdbc/
-DintegrationTest=org.apache.beam.sdk.io.jdbc.JdbcIOIT
-DkubernetesScripts="/Users/me/beam/.test-infra/kubernetes/postgres/postgres-service-for-local-dev.yml"
-DbeamITOptions="/Users/me/beam/.test-infra/kubernetes/postgres/pkb-config-local.yml"
-DintegrationTestRunner=direct
```
Example run with the Cloud Dataflow runner:
```
-/gradlew performanceTest -DpkbLocation="/Users/me/PerfKitBenchmarker/pkb.py"
-DintegrationTestPipelineOptions='["--numberOfRecords=1000",
"--project=GOOGLE_CLOUD_PROJECT", "--tempRoot=GOOGLE_STORAGE_BUCKET"]'
-DitModule=sdks/java/io/jdbc/
-DintegrationTest=org.apache.beam.sdk.io.jdbc.JdbcIOIT
-DkubernetesScripts="/Users/me/beam/.test-infra/kubernetes/postgres/postgres-service-for-local-dev.yml"
-DbeamITOptions="/Users/me/beam/.test-infra/kubernetes/postgres/pkb-config-local.yml"
-DintegrationTestRunner=dataflow
+./gradlew performanceTest -DpkbLocation="/Users/me/PerfKitBenchmarker/pkb.py"
-DintegrationTestPipelineOptions='["--numberOfRecords=1000",
"--project=GOOGLE_CLOUD_PROJECT", "--tempRoot=GOOGLE_STORAGE_BUCKET"]'
-DitModule=sdks/java/io/jdbc/
-DintegrationTest=org.apache.beam.sdk.io.jdbc.JdbcIOIT
-DkubernetesScripts="/Users/me/beam/.test-infra/kubernetes/postgres/postgres-service-for-local-dev.yml"
-DbeamITOptions="/Users/me/beam/.test-infra/kubernetes/postgres/pkb-config-local.yml"
-DintegrationTestRunner=dataflow
```
+Example run with the HDFS filesystem and Cloud Dataflow runner:
Review comment:
DataflowRunner (with a link to
https://beam.apache.org/documentation/runners/dataflow/).
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Issue Time Tracking
-------------------
Worklog Id: (was: 112788)
> 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 10m
> 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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