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This document walks you through the process of testing your app on Jira using the Data Center App Performance Toolkit. These instructions focus on producing the required performance and scale benchmarks for your Data Center app.
Data Center App Performance Toolkit is focused on applications performance testing for Marketplace approval process. For Jira DataCenter functional testing consider JPT.
In this document, we cover the use of the Data Center App Performance Toolkit on two types of environments:
Development environment: Jira Data Center environment for a test run of Data Center App Performance Toolkit and development of app-specific actions.
Enterprise-scale environment: Jira Data Center environment used to generate Data Center App Performance Toolkit test results for the Marketplace approval process.
Running the tests in a development environment helps familiarize you with the toolkit. It'll also provide you with a lightweight and less expensive environment for developing app-specific actions. Once you're ready to generate test results for the Marketplace Data Center Apps Approval process, run the toolkit in an enterprise-scale environment.
DCAPT has fully transitioned to Terraform deployment. If you still wish to use CloudFormation deployment, refer to the Jira Data Center app testing [CloudFormation]
We recommend that you use the official documentation how to deploy a Jira Data Center environment and AWS on k8s.
You are responsible for the cost of AWS services used while running this Terraform deployment. See Amazon EC2 pricing for more detail.
To reduce costs, we recommend you to keep your deployment up and running only during the performance runs. AWS Jira Data Center development environment infrastructure costs about 20 - 40$ per working week depending on such factors like region, instance type, deployment type of DB, and other.
Jira Data Center development environment is good for app-specific actions development. But not powerful enough for performance testing at scale. See Set up an enterprise-scale environment Jira Data Center on AWS for more details.
Below process describes how to install low-tier Jira DC with "small" dataset included:
Read requirements section of the official documentation.
Set up environment.
Set up AWS security credentials.
Do not use root
user credentials for cluster creation. Instead, create an admin user.
Clone the project repo:
1 2git clone -b 2.4.0 https://github.com/atlassian-labs/data-center-terraform.git && cd data-center-terraform
Copy dcapt-small.tfvars
file to the data-center-terraform
folder.
1 2wget https://raw.githubusercontent.com/atlassian/dc-app-performance-toolkit/master/app/util/k8s/dcapt-small.tfvars
Set required variables in dcapt-small.tfvars
file:
environment_name
- any name for you environment, e.g. dcapt-jira-small
products
- jira
jira_license
- one-liner of valid jira license without spaces and new line symbolsregion
- AWS region for deployment. Do not change default region (us-east-2
). If specific region is required, contact support.Optional variables to override:
jira_version_tag
- Jira version to deploy. Supported versions see in README.md.From local terminal (Git bash terminal for Windows) start the installation (~20 min):
1 2./install.sh -c dcapt-small.tfvars
Re-index:
Copy product URL from the console output. Product url should look like http://a1234-54321.us-east-2.elb.amazonaws.com/jira
.
New trial license could be generated on my atlassian.
Use BX02-9YO1-IN86-LO5G
Server ID for generation.
All the datasets use the standard admin
/admin
credentials.
Make sure English (United States) language is selected as a default language on the > System > General configuration page. Other languages are not supported by the toolkit.
Clone Data Center App Performance Toolkit locally.
Follow the README.md instructions to set up toolkit locally.
Navigate to dc-app-performance-toolkit/app
folder.
Open the jira.yml
file and fill in the following variables:
application_hostname
: your_dc_jira_instance_hostname without protocol.application_protocol
: http or https.application_port
: for HTTP - 80, for HTTPS - 443, 8080, 2990 or your instance-specific port.secure
: True or False. Default value is True. Set False to allow insecure connections, e.g. when using self-signed SSL certificate.application_postfix
: /jira
# e.g. /jira for TerraForm deployment url like http://a1234-54321.us-east-2.elb.amazonaws.com/jira
. Leave this value blank for url without postfix.admin_login
: admin user username.admin_password
: admin user password.load_executor
: executor for load tests. Valid options are jmeter (default) or locust.concurrency
: 2
- number of concurrent JMeter/Locust users.test_duration
: 5m
- duration of the performance run.ramp-up
: 3s
- amount of time it will take JMeter or Locust to add all test users to test execution.total_actions_per_hour
: 5450
- number of total JMeter/Locust actions per hour.WEBDRIVER_VISIBLE
: visibility of Chrome browser during selenium execution (False is by default).Run bzt.
1 2bzt jira.yml
Review the resulting table in the console log. All JMeter/Locust and Selenium actions should have 95+% success rate.
In case some actions does not have 95+% success rate refer to the following logs in dc-app-performance-toolkit/app/results/jira/YY-MM-DD-hh-mm-ss
folder:
results_summary.log
: detailed run summaryresults.csv
: aggregated .csv file with all actions and timingsbzt.log
: logs of the Taurus tool executionjmeter.*
: logs of the JMeter tool executionlocust.*
: logs of the Locust tool execution (in case you use Locust as load_executor in jira.yml)pytest.*
: logs of Pytest-Selenium executionDo not proceed with the next step until you have all actions 95+% success rate. Ask support if above logs analysis did not help.
Data Center App Performance Toolkit has its own set of default test actions for Jira Data Center: JMeter/Locust and Selenium for load and UI tests respectively.
App-specific action - action (performance test) you have to develop to cover main use cases of your application. Performance test should focus on the common usage of your application and not to cover all possible functionality of your app. For example, application setup screen or other one-time use cases are out of scope of performance testing.
We strongly recommend developing your app-specific actions on the development environment to reduce AWS infrastructure costs.
You can filter your own app-specific issues for your app-specific actions.
JIRA_URL/issues/?jql=
and select Advanced
.summary ~ 'AppIssue*'
.dc-app-performance-toolkit/app/jira.yml
:
custom_dataset_query:
JQL from step 3.Next time when you run toolkit, custom dataset issues will be stored to the dc-app-performance-toolkit/app/datasets/jira/custom-issues.csv
with columns: issue_key
, issue_id
, project_key
.
You develop an app that adds some additional fields to specific types of Jira issues. In this case, you should develop Selenium app-specific action:
JIRA_URL/issues/?jql=
and check if JQL is correct: summary ~ 'AppIssue*'
.dc-app-performance-toolkit/app/jira.yml
configuration file and set custom_dataset_query: summary ~ 'AppIssue*'
.dc-app-performance-toolkit/app/extension/jira/extension_ui.py
.app_speicifc_action
as specific user uncomment app_specific_user_login
function in code example. Note, that in this case test_1_selenium_custom_action
should follow just before test_2_selenium_z_log_out
action.dc-app-performance-toolkit/app/selenium_ui/jira_ui.py
, review and uncomment the following block of code to make newly created app-specific actions executed:1 2# def test_1_selenium_custom_action(webdriver, datasets, screen_shots): # app_specific_action(webdriver, datasets)
bzt jira.yml
command to ensure that all Selenium actions including app_specific_action
are successful.You develop an app that introduces new GET and POST endpoints in Jira Data Center. In this case, you should develop Locust or JMeter app-specific action.
Locust app-specific action development example
dc-app-performance-toolkit/app/extension/jira/extension_locust.py
, so that test will call the endpoint with GET request, parse response use these data to call another endpoint with POST request and measure response time.dc-app-performance-toolkit/app/jira.yml
set load_executor: locust
to make locust
as load executor.standalone_extension
. Default value is 0
, which means that standalone_extension
action will not be executed. Locust uses actions percentage as relative weights, so if some_action: 10
and standalone_extension: 20
that means that standalone_extension
will be called twice more.standalone_extension
weight in accordance with the expected frequency of your app use case compared with other base actions.@run_as_specific_user(username='specific_user_username', password='specific_user_password')
decorator for that.bzt jira.yml
command to ensure that all Locust actions including app_specific_action
are successful.JMeter app-specific action development example
Check that jira.yml
file has correct settings of application_hostname
, application_protocol
, application_port
, application_postfix
, etc.
Set desired execution percentage for standalone_extension
. Default value is 0
, which means that standalone_extension
action will not be executed.
For example, for app-specific action development you could set percentage of standalone_extension
to 100 and for all other actions to 0 - this way only login_and_view_dashboard
and standalone_extension
actions would be executed.
Navigate to dc-app-performance-toolkit/app
folder and run from virtualenv(as described in dc-app-performance-toolkit/README.md
):
python util/jmeter/start_jmeter_ui.py --app jira
Open Jira
thread group > actions per login
and navigate to standalone_extension
Add GET HTTP Request
: right-click to standalone_extension
> Add
> Sampler
HTTP Request
, chose method GET and set endpoint in Path.
Add Regular Expression Extractor
: right-click to to newly created HTTP Request
> Add
> Post processor
> Regular Expression Extractor
Add Response Assertion
: right-click to newly created HTTP Request
> Add
> Assertions
> Response Assertion
and add assertion with Contains
, Matches
, Equals
, etc types.
Add POST HTTP Request
: right-click to standalone_extension
> Add
> Sampler
HTTP Request
, chose method POST, set endpoint in Path and add Parameters or Body Data if needed.
Right-click on View Results Tree
and enable this controller.
Click Start button and make sure that login_and_view_dashboard
and standalone_extension
are successful.
Right-click on View Results Tree
and disable this controller. It is important to disable View Results Tree
controller before full-scale results generation.
Click Save button.
To make standalone_extension
executable during toolkit run edit dc-app-performance-toolkit/app/jira.yml
and set execution percentage of standalone_extension
accordingly to your use case frequency.
App-specific tests could be run (if needed) as a specific user. In the standalone_extension
uncomment login_as_specific_user
controller. Navigate to the username:password
config element and update values for app_specific_username
and app_specific_password
names with your specific user credentials. Also make sure that you located your app-specific tests between login_as_specific_user
and login_as_default_user_if_specific_user_was_loggedin
controllers.
Run toolkit to ensure that all JMeter actions including standalone_extension
are successful.
Use or access the following variables in your standalone_extension
script if needed.
${issue_key}
- issue key being viewed or modified (e.g. ABC-123)${issue_id}
- issue id being viewed or modified (e.g. 693484)${project_key}
- project key being viewed or modified (e.g. ABC)${project_id}
- project id being viewed or modified (e.g. 3423)${scrum_board_id}
- scrum board id being viewed (e.g. 328)${kanban_board_id}
- kanban board id being viewed (e.g. 100)${jql}
- jql query being used (e.g. text ~ "qrk*" order by key)${username}
- the logged in username (e.g. admin)App-specific actions are required. Do not proceed with the next step until you have completed app-specific actions development and got successful results from toolkit run.
After adding your custom app-specific actions, you should now be ready to run the required tests for the Marketplace Data Center Apps Approval process. To do this, you'll need an enterprise-scale environment.
We recommend that you use the official documentation how to deploy a Jira Data Center environment and AWS on k8s.
AWS Pricing Calculator provides an estimate of usage charges for AWS services based on certain information you provide. Monthly charges will be based on your actual usage of AWS services and may vary from the estimates the Calculator has provided.
*The prices below are approximate and may vary depending on such factors like region, instance type, deployment type of DB, and other.
Stack | Estimated hourly cost ($) |
---|---|
One Node Jira DC | 0.8 - 1.1 |
Two Nodes Jira DC | 1.2 - 1.7 |
Four Nodes Jira DC | 2.0 - 3.0 |
Data dimensions and values for an enterprise-scale dataset are listed and described in the following table.
Data dimensions | Value for an enterprise-scale dataset |
---|---|
Attachments | ~2 000 000 |
Comments | ~6 000 000 |
Components | ~2 500 |
Custom fields | ~800 |
Groups | ~1 000 |
Issue security levels | 10 |
Issue types | ~300 |
Issues | ~1 000 000 |
Priorities | 5 |
Projects | 500 |
Resolutions | 34 |
Screen schemes | ~200 |
Screens | ~200 |
Statuses | ~400 |
Users | ~21 000 |
Versions | ~20 000 |
Workflows | 50 |
All the datasets use the standard admin
/admin
credentials.
It is recommended to terminate a development environment before creating an enterprise-scale environment. Follow Uninstallation and Cleanup instructions. If you want to keep a development environment up, read How do I deal with a pre-existing state in multiple environments?
Below process describes how to install enterprise-scale Jira DC with "large" dataset included:
Read requirements section of the official documentation.
Set up environment.
Set up AWS security credentials.
Do not use root
user credentials for cluster creation. Instead, create an admin user.
Clone the project repo:
1 2git clone -b 2.4.0 https://github.com/atlassian-labs/data-center-terraform.git && cd data-center-terraform
Copy dcapt.tfvars
file to the data-center-terraform
folder.
1 2wget https://raw.githubusercontent.com/atlassian/dc-app-performance-toolkit/master/app/util/k8s/dcapt.tfvars
Set required variables in dcapt.tfvars
file:
environment_name
- any name for you environment, e.g. dcapt-jira-large
products
- jira
jira_license
- one-liner of valid jira license without spaces and new line symbolsregion
- AWS region for deployment. Do not change default region (us-east-2
). If specific region is required, contact support.Optional variables to override:
jira_version_tag
- Jira version to deploy. Supported versions see in README.md.From local terminal (Git bash terminal for Windows) start the installation (~40min):
1 2./install.sh -c dcapt.tfvars
Copy product URL from the console output. Product url should look like http://a1234-54321.us-east-2.elb.amazonaws.com/jira
.
New trial license could be generated on my atlassian.
Use this server id for generation BX02-9YO1-IN86-LO5G
.
All the datasets use the standard admin
/admin
credentials.
It's recommended to change default password from UI account page for security reasons.
Terminate cluster when it is not used for performance results generation.
For generating performance results suitable for Marketplace approval process use dedicated execution environment. This is a separate AWS EC2 instance to run the toolkit from. Running the toolkit from a dedicated instance but not from a local machine eliminates network fluctuations and guarantees stable CPU and memory performance.
jira.yml
configuration file. Set enterprise-scale Jira Data Center parameters:Do not push to the fork real application_hostname
, admin_login
and admin_password
values for security reasons.
Instead, set those values directly in .yml
file on execution environment instance.
1 2application_hostname: test_jira_instance.atlassian.com # Jira DC hostname without protocol and port e.g. test-jira.atlassian.com or localhost application_protocol: http # http or https application_port: 80 # 80, 443, 8080, 2990, etc secure: True # Set False to allow insecure connections, e.g. when using self-signed SSL certificate application_postfix: /jira # e.g. /jira for TerraForm deployment url like `http://a1234-54321.us-east-2.elb.amazonaws.com/jira`. Leave this value blank for url without postfix. admin_login: admin admin_password: admin load_executor: jmeter # jmeter and locust are supported. jmeter by default. concurrency: 200 # number of concurrent virtual users for jmeter or locust scenario test_duration: 45m ramp-up: 3m # time to spin all concurrent users total_actions_per_hour: 54500 # number of total JMeter/Locust actions per hour
Push your changes to the forked repository.
Ubuntu Server 20.04 LTS
.c5.2xlarge
30
GiBConnect to the instance using SSH or the AWS Systems Manager Sessions Manager.
1 2ssh -i path_to_pem_file ubuntu@INSTANCE_PUBLIC_IP
Install Docker. Setup manage Docker as a non-root user.
Clone forked repository.
At this stage app-specific actions are not needed yet. Use code from master
branch with your jira.yml
changes.
You'll need to run the toolkit for each test scenario in the next section.
Using the Data Center App Performance Toolkit for Performance and scale testing your Data Center app involves two test scenarios:
Each scenario will involve multiple test runs. The following subsections explain both in greater detail.
This scenario helps to identify basic performance issues without a need to spin up a multi-node Jira DC. Make sure the app does not have any performance impact when it is not exercised.
To receive performance baseline results without an app installed:
Use SSH to connect to execution environment.
Run toolkit with docker from the execution environment instance:
1 2cd dc-app-performance-toolkit docker pull atlassian/dcapt docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt jira.yml
View the following main results of the run in the dc-app-performance-toolkit/app/results/jira/YY-MM-DD-hh-mm-ss
folder:
results_summary.log
: detailed run summaryresults.csv
: aggregated .csv file with all actions and timingsbzt.log
: logs of the Taurus tool executionjmeter.*
: logs of the JMeter tool executionpytest.*
: logs of Pytest-Selenium executionReview results_summary.log
file under artifacts dir location. Make sure that overall status is OK
before moving to the next steps. For an enterprise-scale environment run, the acceptable success rate for actions is 95% and above.
If you are submitting a Jira app, you are required to conduct a Lucene Index timing test. This involves conducting a foreground re-index on a single-node Data Center deployment (with your app installed) and a dataset that has 1M issues.
The re-index time for Jira 8.20.x is about ~30-50 minutes, while for Jira 9.4.x it can take significantly longer at around 110-130 minutes. This increase in re-index time is due to a known issue which affects Jira 9.4.x, and you can find more information about it in this ticket: Re-Index: Jira 9.4.x.
Benchmark your re-index time with your app installed:
Jira will be temporarily unavailable during the re-indexing process. Once the process is complete, the system will be fully accessible and operational once again.
Performance results generation with the app installed:
Run toolkit with docker from the execution environment instance:
1 2cd dc-app-performance-toolkit docker pull atlassian/dcapt docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt jira.yml
Review results_summary.log
file under artifacts dir location. Make sure that overall status is OK
before moving to the next steps. For an enterprise-scale environment run, the acceptable success rate for actions is 95% and above.
To generate a performance regression report:
virtualenv
as described in dc-app-performance-toolkit/README.md
ubuntu
) to access Docker generated reports:
1 2sudo chown -R ubuntu:ubuntu /home/ubuntu/dc-app-performance-toolkit/app/results
dc-app-performance-toolkit/app/reports_generation
folder.performance_profile.yml
file:
1 2python csv_chart_generator.py performance_profile.yml
dc-app-performance-toolkit/app/results/reports/YY-MM-DD-hh-mm-ss
folder, view the .csv
file (with consolidated scenario results), the .png
chart file and performance scenario summary report.Use scp command to copy report artifacts from execution env to local drive:
1 2export EXEC_ENV_PUBLIC_IP=execution_environment_ec2_instance_public_ip scp -r -i path_to_exec_env_pem ubuntu@$EXEC_ENV_PUBLIC_IP:/home/ubuntu/dc-app-performance-toolkit/app/results/reports ./reports
./reports
folder you will be able to review the action timings with and without your app to see its impact on the performance of the instance. If you see an impact (>20%) on any action timing, we recommend taking a look into the app implementation to understand the root cause of this delta.The purpose of scalability testing is to reflect the impact on the customer experience when operating across multiple nodes. For this, you have to run scale testing on your app.
For many apps and extensions to Atlassian products, there should not be a significant performance difference between operating on a single node or across many nodes in Jira DC deployment. To demonstrate performance impacts of operating your app at scale, we recommend testing your Jira DC app in a cluster.
To receive scalability benchmark results for one-node Jira DC with app-specific actions:
Apply app-specific code changes to a new branch of forked repo.
Use SSH to connect to execution environment.
Pull cloned fork repo branch with app-specific actions.
Run toolkit with docker from the execution environment instance:
1 2cd dc-app-performance-toolkit docker pull atlassian/dcapt docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt jira.yml
Review results_summary.log
file under artifacts dir location. Make sure that overall status is OK
before moving to the next steps. For an enterprise-scale environment run, the acceptable success rate for actions is 95% and above.
Before scaling your DC make sure that AWS vCPU limit is not lower than needed number. Use vCPU limits calculator to see current limit. The same article has instructions on how to increase limit if needed.
To receive scalability benchmark results for two-node Jira DC with app-specific actions:
Navigate to data-center-terraform
folder.
Open dcapt.tfvars
file and set jira_replica_count
value to 2
.
From local terminal (Git bash terminal for Windows) start scaling (~20 min):
1 2./install.sh -c dcapt.tfvars
Use SSH to connect to execution environment.
Run toolkit with docker from the execution environment instance:
1 2cd dc-app-performance-toolkit docker pull atlassian/dcapt docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt jira.yml
Review results_summary.log
file under artifacts dir location. Make sure that overall status is OK
before moving to the next steps. For an enterprise-scale environment run, the acceptable success rate for actions is 95% and above.
Before scaling your DC make sure that AWS vCPU limit is not lower than needed number. Use vCPU limits calculator to see current limit. The same article has instructions on how to increase limit if needed.
To receive scalability benchmark results for four-node Jira DC with app-specific actions:
Scale your Jira Data Center deployment to 4 nodes as described in Run 4.
Run toolkit with docker from the execution environment instance:
1 2cd dc-app-performance-toolkit docker pull atlassian/dcapt docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt jira.yml
Review results_summary.log
file under artifacts dir location. Make sure that overall status is OK
before moving to the next steps. For an enterprise-scale environment run, the acceptable success rate for actions is 95% and above.
To generate a scalability report:
ubuntu
) to access Docker generated reports:
1 2sudo chown -R ubuntu:ubuntu /home/ubuntu/dc-app-performance-toolkit/app/results
dc-app-performance-toolkit/app/reports_generation
folder.scale_profile.yml
file:
virtualenv
(as described in dc-app-performance-toolkit/README.md
):
1 2python csv_chart_generator.py scale_profile.yml
dc-app-performance-toolkit/app/results/reports/YY-MM-DD-hh-mm-ss
folder, view the .csv
file (with consolidated scenario results), the .png
chart file and summary report.Use scp command to copy report artifacts from execution env to local drive:
1 2export EXEC_ENV_PUBLIC_IP=execution_environment_ec2_instance_public_ip scp -r -i path_to_exec_env_pem ubuntu@$EXEC_ENV_PUBLIC_IP:/home/ubuntu/dc-app-performance-toolkit/app/results/reports ./reports
./reports
folder you will be able to review action timings on Jira Data Center with different numbers of nodes. If you see a significant variation in any action timings between configurations, we recommend taking a look into the app implementation to understand the root cause of this delta.It is recommended to terminate an enterprise-scale environment after completing all tests. Follow Uninstallation and Cleanup instructions.
Do not forget to attach performance testing results to your ECOHELP ticket.
profile.csv
, profile.png
, profile_summary.log
and profile run result archives. Archives
should contain all raw data created during the run: bzt.log
, selenium/jmeter/locust logs, .csv and .yml files, etc.For Terraform deploy related questions see Troubleshooting tipspage.
If the installation script fails on installing Helm release or any other reason, collect the logs, zip and share to community Slack #data-center-app-performance-toolkit channel.
For instructions on how to do this, see How to troubleshoot a failed Helm release installation?.
In case of the above problem or any other technical questions, issues with DC Apps Performance Toolkit, contact us for support in the community Slack #data-center-app-performance-toolkit channel.
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