Data Center App Performance Toolkit User Guide For Bitbucket

This document walks you through the process of testing your app on Bitbucket using the Data Center App Performance Toolkit. These instructions focus on producing the required performance and scale benchmarks for your Data Center app.

In this document, we cover the use of the Data Center App Performance Toolkit on two types of environments:

Development environment: Bitbucket Data Center environment for a test run of Data Center App Performance Toolkit and development of app-specific actions.

1. Set up a development environment Bitbucket Data Center on AWS.
2. Run toolkit on the development environment locally.
3. Develop and test app-specific actions locally.

Enterprise-scale environment: Bitbucket Data Center environment used to generate Data Center App Performance Toolkit test results for the Marketplace approval process.

4. Set up an enterprise-scale environment Bitbucket Data Center on AWS.
5. Setting up load configuration for Enterprise-scale runs.
6. Running the test scenarios from execution environment against enterprise-scale Bitbucket Data Center.

---

Development environment

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.

1. Setting up Bitbucket Data Center development environment

AWS cost estimation for the development environment

To reduce costs, we recommend you to keep your deployment up and running only during the performance runs. AWS Bitbucket 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.

Setup Bitbucket Data Center development environment on k8s.

Below process describes how to install low-tier Bitbucket DC with "small" dataset included:

1. Create Access keys for AWS CLI:

   Do not use root user credentials for cluster creation.

   Option 1 (simple): create admin user with AdministratorAccess permissions.

   Option 2 (complex): create granular permission policies with policy1 and policy2.

   The specific configuration relies on how you manage permissions within AWS.

   Example Option 1 with Admin user:

   1. Go to AWS Console -> IAM service -> Users
   2. Create new user -> attach policies directly -> AdministratorAccess
   3. Open newly created user -> Security credentials tab -> Access keys -> Create access key -> Command Line Interface (CLI) -> Create access key
   4. Use Access key and Secret access key in aws_envs file

   Example Option 2 with granular Policies:

   1. Go to AWS Console -> IAM service -> Policies

   2. Create policy1 with json content of the policy1 file

      Important: change all occurrences of 123456789012 to your real AWS Account ID.

   3. Create policy2 with json content of the policy2 file

      Important: change all occurrences of 123456789012 to your real AWS Account ID.

   4. Go to User -> Create user -> Attach policies directly -> Attach policy1 and policy2-> Click on Create user button

   5. Open newly created user -> Security credentials tab -> Access keys -> Create access key -> Command Line Interface (CLI) -> Create access key

   6. Use Access key and Secret access key in aws_envs file

2. Clone Data Center App Performance Toolkit locally.

   For annual review, always get the latest version of the DCAPT code from the master branch.

   DCAPT supported versions: three latest minor version releases.

3. Navigate to dc-app-performance-toolkit/app/util/k8s folder.

4. Set AWS access keys created in step1 in aws_envs file:

   • AWS_ACCESS_KEY_ID
   • AWS_SECRET_ACCESS_KEY
   • AWS_SESSION_TOKEN (only for temporary creds)

5. Set required variables in dcapt-small.tfvars file:

   • environment_name - any name for you environment, e.g. dcapt-bitbucket-small

   • products - bitbucket

   • bitbucket_license - one-liner of valid bitbucket license without spaces and new line symbols

   • region - AWS region for deployment. Do not change default region (us-east-2). If specific region is required, contact support.

   New trial license could be generated on my atlassian. Use BX02-9YO1-IN86-LO5G Server ID for generation.

6. Optional variables to override:

   • bitbucket_version_tag - Bitbucket version to deploy. Supported versions see in README.md.

7. From local terminal (Git Bash for Windows users) start the installation (~20 min):

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   docker run --env-file aws_envs \
   -v "/$PWD/dcapt-small.tfvars:/data-center-terraform/conf.tfvars" \
   -v "/$PWD/dcapt-snapshots.json:/data-center-terraform/dcapt-snapshots.json" \
   -v "/$PWD/logs:/data-center-terraform/logs" \
   -it atlassianlabs/terraform:2.9.3 ./install.sh -c conf.tfvars
   

8. Copy product URL from the console output. Product url should look like http://a1234-54321.us-east-2.elb.amazonaws.com/bitbucket.

---

2. Run toolkit on the development environment locally

1. Clone Data Center App Performance Toolkit locally.

   For annual review, always get the latest version of the DCAPT code from the master branch.

   DCAPT supported versions: three latest minor version releases.

2. Follow the README.md instructions to set up toolkit locally.

3. Navigate to dc-app-performance-toolkit/app folder.

4. Open the bitbucket.yml file and fill in the following variables:

   • application_hostname: your_dc_bitbucket_instance_hostname without protocol.
   • application_protocol: http or https.
   • application_port: for HTTP - 80, for HTTPS - 443, 8080, 1990 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: /bitbucket - default postfix value for TerraForm deployment url like http://a1234-54321.us-east-2.elb.amazonaws.com/bitbucket
   • admin_login: admin user username.
   • admin_password: admin user password.
   • load_executor: executor for load tests - jmeter
   • concurrency: 1 - number of concurrent JMeter users.
   • test_duration: 5m - duration of the performance run.
   • ramp-up: 1s - amount of time it will take JMeter to add all test users to test execution.
   • total_actions_per_hour: 3270 - number of total JMeter actions per hour.
   • WEBDRIVER_VISIBLE: visibility of Chrome browser during selenium execution (False is by default).

5. Run bzt.

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   bzt bitbucket.yml
   

6. Review the resulting table in the console log. All JMeter and Selenium actions should have 0+% success rate.
   In case some actions have 0% success rate refer to the following logs in dc-app-performance-toolkit/app/results/bitbucket/YY-MM-DD-hh-mm-ss folder:

   • results_summary.log: detailed run summary
   • results.csv: aggregated .csv file with all actions and timings
   • bzt.log: logs of the Taurus tool execution
   • jmeter.*: logs of the JMeter tool execution
   • pytest.*: logs of Pytest-Selenium execution

---

3. Develop and test app-specific action locally

Data Center App Performance Toolkit has its own set of default test actions for Bitbucket Data Center: JMeter 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.

1. Define main use case of your app. Usually it is one or two main app use cases.
2. Your app adds new UI elements in Bitbucket Data Center - Selenium app-specific action has to be developed.
3. Your app introduces new endpoint or extensively calls existing Bitbucket Data Center API - JMeter app-specific actions has to be developed.

Example of app-specific Selenium action development

You develop an app that adds some additional fields to specific types of Bitbucket issues. In this case, you should develop Selenium app-specific action:

1. Extend example of app-specific action in dc-app-performance-toolkit/app/extension/bitbucket/extension_ui.py.
   Code example. So, our test has to open app-specific issues and measure time to load of this app-specific issues.
2. If you need to run app_specific_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.
3. In dc-app-performance-toolkit/app/selenium_ui/bitbucket_ui.py, review and uncomment the following block of code to make newly created app-specific actions executed:

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# def test_1_selenium_custom_action(webdriver, datasets, screen_shots):
#     app_specific_action(webdriver, datasets)

4. Run toolkit with bzt bitbucket.yml command to ensure that all Selenium actions including app_specific_action are successful.

---

Enterprise-scale environment

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.

4. Setting up Bitbucket Data Center enterprise-scale environment with "large" dataset

EC2 CPU Limit

### AWS cost estimation ### [AWS Pricing Calculator](https://calculator.aws/) 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 factors such as (region, instance type, deployment type of DB, etc.)

Setup Bitbucket Data Center enterprise-scale environment on k8s.

Data dimensions and values for an enterprise-scale dataset are listed and described in the following table.

Below process describes how to install enterprise-scale Bitbucket DC with "large" dataset included:

1. Create Access keys for AWS CLI:

   Do not use root user credentials for cluster creation.

   Option 1 (simple): create admin user with AdministratorAccess permissions.

   Option 2 (complex): create granular permission policies with policy1 and policy2.

   The specific configuration relies on how you manage permissions within AWS.

   Example Option 1 with Admin user:

   1. Go to AWS Console -> IAM service -> Users
   2. Create new user -> attach policies directly -> AdministratorAccess
   3. Open newly created user -> Security credentials tab -> Access keys -> Create access key -> Command Line Interface (CLI) -> Create access key
   4. Use Access key and Secret access key in aws_envs file

   Example Option 2 with granular Policies:

   1. Go to AWS Console -> IAM service -> Policies

   2. Create policy1 with json content of the policy1 file

      Important: change all occurrences of 123456789012 to your real AWS Account ID.

   3. Create policy2 with json content of the policy2 file

      Important: change all occurrences of 123456789012 to your real AWS Account ID.

   4. Go to User -> Create user -> Attach policies directly -> Attach policy1 and policy2-> Click on Create user button

   5. Open newly created user -> Security credentials tab -> Access keys -> Create access key -> Command Line Interface (CLI) -> Create access key

   6. Use Access key and Secret access key in aws_envs file

2. Clone Data Center App Performance Toolkit locally.

   For annual review, always get the latest version of the DCAPT code from the master branch.

   DCAPT supported versions: three latest minor version releases.

3. Navigate to dc-app-performance-toolkit/app/util/k8s folder.

4. Set AWS access keys created in step1 in aws_envs file:

   • AWS_ACCESS_KEY_ID
   • AWS_SECRET_ACCESS_KEY
   • AWS_SESSION_TOKEN (only for temporary creds)

5. Set required variables in dcapt.tfvars file:

   • environment_name - any name for you environment, e.g. dcapt-bitbucket-large

   • products - bitbucket

   • bitbucket_license - one-liner of valid bitbucket license without spaces and new line symbols

   • region - AWS region for deployment. Do not change default region (us-east-2). If specific region is required, contact support.

   New trial license could be generated on my atlassian. Use this server id for generation BX02-9YO1-IN86-LO5G.

6. Optional variables to override:

   • bitbucket_version_tag - Bitbucket version to deploy. Supported versions see in README.md.

7. From local terminal (Git Bash for Windows users) start the installation (~40min):

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   docker run --env-file aws_envs \
   -v "/$PWD/dcapt.tfvars:/data-center-terraform/conf.tfvars" \
   -v "/$PWD/dcapt-snapshots.json:/data-center-terraform/dcapt-snapshots.json" \
   -v "/$PWD/logs:/data-center-terraform/logs" \
   -it atlassianlabs/terraform:2.9.3 ./install.sh -c conf.tfvars
   

8. Copy product URL from the console output. Product url should look like http://a1234-54321.us-east-2.elb.amazonaws.com/bitbucket.

---

5. Setting up load configuration for Enterprise-scale runs

Default TerraForm deployment configuration already has a dedicated execution environment pod to run tests from. For more details see Execution Environment Settings section in dcapt.tfvars file.

1. Check the bitbucket.yml configuration file. If load configuration settings were changed for dev runs, make sure parameters were changed back to the defaults:

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       application_hostname: test_bitbucket_instance.atlassian.com   # Bitbucket DC hostname without protocol and port e.g. test-bitbucket.atlassian.com or localhost
       application_protocol: http        # http or https
       application_port: 80              # 80, 443, 8080, 7990 etc
       secure: True                      # Set False to allow insecure connections, e.g. when using self-signed SSL certificate
       application_postfix:  /bitbucket  # e.g. /bitbucket for TerraForm deployment url like `http://a1234-54321.us-east-2.elb.amazonaws.com/bitbucket`. Leave this value blank for url without postfix.
       admin_login: admin
       admin_password: admin
       load_executor: jmeter             # only jmeter executor is supported
       concurrency: 20                   # number of concurrent virtual users for jmeter scenario
       test_duration: 50m
       ramp-up: 10m                      # time to spin all concurrent users
       total_actions_per_hour: 32700     # number of total JMeter actions per hour
   

You'll need to run the toolkit for each test scenario in the next section.

---

6. Running the test scenarios from execution environment against enterprise-scale Bitbucket Data Center

Using the Data Center App Performance Toolkit for Performance and scale testing your Data Center app involves two test scenarios:

• Performance regression
• Scalability testing

Each scenario will involve multiple test runs. The following subsections explain both in greater detail.

Scenario 1: Performance regression

This scenario helps to identify basic performance issues without a need to spin up a multi-node Bitbucket DC. Make sure the app does not have any performance impact when it is not exercised.

Run 1 (~1 hour)

To receive performance baseline results without an app installed:

1. Before run:

   • Make sure bitbucket.yml and toolkit code base has default configuration from the master branch.
   • App-specific actions code base is not needed for Run1 and Run2.
   • Check load configuration parameters needed for enterprise-scale run: Setting up load configuration for Enterprise-scale runs.
   • Check correctness of application_hostname, application_protocol, application_port and application_postfix in .yml file.
   • AWS access keys set in ./dc-app-performance-toolkit/app/util/k8s/aws_envs file:
     • AWS_ACCESS_KEY_ID
     • AWS_SECRET_ACCESS_KEY
     • AWS_SESSION_TOKEN (only for temporary creds)

2. Navigate to dc-app-performance-toolkit folder and start tests execution:

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   export ENVIRONMENT_NAME=your_environment_name
   

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   docker run --pull=always --env-file ./app/util/k8s/aws_envs \
   -e REGION=us-east-2 \
   -e ENVIRONMENT_NAME=$ENVIRONMENT_NAME \
   -v "/$PWD:/data-center-terraform/dc-app-performance-toolkit" \
   -v "/$PWD/app/util/k8s/bzt_on_pod.sh:/data-center-terraform/bzt_on_pod.sh" \
   -it atlassianlabs/terraform:2.9.3 bash bzt_on_pod.sh bitbucket.yml
   

3. View the following main results of the run in the dc-app-performance-toolkit/app/results/bitbucket/YY-MM-DD-hh-mm-ss folder:

   • results_summary.log: detailed run summary
   • results.csv: aggregated .csv file with all actions and timings
   • bzt.log: logs of the Taurus tool execution
   • jmeter.*: logs of the JMeter tool execution
   • pytest.*: logs of Pytest-Selenium execution

Run 2 (~1 hour)

To receive performance results with an app installed (still use master branch):

1. Install the app you want to test.

2. Setup app license.

3. Navigate to dc-app-performance-toolkit folder and start tests execution:

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   export ENVIRONMENT_NAME=your_environment_name
   

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   docker run --pull=always --env-file ./app/util/k8s/aws_envs \
   -e REGION=us-east-2 \
   -e ENVIRONMENT_NAME=$ENVIRONMENT_NAME \
   -v "/$PWD:/data-center-terraform/dc-app-performance-toolkit" \
   -v "/$PWD/app/util/k8s/bzt_on_pod.sh:/data-center-terraform/bzt_on_pod.sh" \
   -it atlassianlabs/terraform:2.9.3 bash bzt_on_pod.sh bitbucket.yml
   

Generating a performance regression report

To generate a performance regression report:

1. Edit the ./app/reports_generation/performance_profile.yml file:
   • For runName: "without app", in the relativePath key, insert the relative path to results directory of Run 1.
   • For runName: "with app", in the relativePath key, insert the relative path to results directory of Run 2.
2. Navigate locally to dc-app-performance-toolkit folder and run the following command from local terminal (Git Bash for Windows users) to generate reports:

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   docker run --pull=always \
   -v "/$PWD:/dc-app-performance-toolkit" \
   --workdir="//dc-app-performance-toolkit/app/reports_generation" \
   --entrypoint="python" \
   -it atlassian/dcapt csv_chart_generator.py performance_profile.yml
   

3. In the ./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. 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.

Run 3 (~1 hour)

To receive scalability benchmark results for one-node Bitbucket DC with app-specific actions:

1. Before run:

   • Make sure bitbucket.yml and toolkit code base has code base with your developed app-specific actions.
   • Check correctness of application_hostname, application_protocol, application_port and application_postfix in .yml file.
   • Check load configuration parameters needed for enterprise-scale run: Setting up load configuration for Enterprise-scale runs.
   • test_1_selenium_custom_action is uncommented and has implementation in case of Selenium app-specific actions.
   • AWS access keys set in ./dc-app-performance-toolkit/app/util/k8s/aws_envs file:
     • AWS_ACCESS_KEY_ID
     • AWS_SECRET_ACCESS_KEY
     • AWS_SESSION_TOKEN (only for temporary creds)

2. Navigate to dc-app-performance-toolkit folder and start tests execution:

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   export ENVIRONMENT_NAME=your_environment_name
   

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   docker run --pull=always --env-file ./app/util/k8s/aws_envs \
   -e REGION=us-east-2 \
   -e ENVIRONMENT_NAME=$ENVIRONMENT_NAME \
   -v "/$PWD:/data-center-terraform/dc-app-performance-toolkit" \
   -v "/$PWD/app/util/k8s/bzt_on_pod.sh:/data-center-terraform/bzt_on_pod.sh" \
   -it atlassianlabs/terraform:2.9.3 bash bzt_on_pod.sh bitbucket.yml
   

Run 4 (~1 hour)

To receive scalability benchmark results for two-node Bitbucket DC with app-specific actions:

1. Navigate to dc-app-performance-toolkit/app/util/k8s folder.

2. Open dcapt.tfvars file and set bitbucket_replica_count value to 2.

3. From local terminal (Git Bash for Windows users) start scaling (~20 min):

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   docker run --pull=always --env-file aws_envs \
   -v "/$PWD/dcapt.tfvars:/data-center-terraform/conf.tfvars" \
   -v "/$PWD/dcapt-snapshots.json:/data-center-terraform/dcapt-snapshots.json" \
   -v "/$PWD/logs:/data-center-terraform/logs" \
   -it atlassianlabs/terraform:2.9.3 ./install.sh -c conf.tfvars
   

4. Navigate to dc-app-performance-toolkit folder and start tests execution:

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   export ENVIRONMENT_NAME=your_environment_name
   

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   docker run --pull=always --env-file ./app/util/k8s/aws_envs \
   -e REGION=us-east-2 \
   -e ENVIRONMENT_NAME=$ENVIRONMENT_NAME \
   -v "/$PWD:/data-center-terraform/dc-app-performance-toolkit" \
   -v "/$PWD/app/util/k8s/bzt_on_pod.sh:/data-center-terraform/bzt_on_pod.sh" \
   -it atlassianlabs/terraform:2.9.3 bash bzt_on_pod.sh bitbucket.yml
   

Run 5 (~1 hour)

To receive scalability benchmark results for four-node Bitbucket DC with app-specific actions:

1. Scale your Bitbucket Data Center deployment to 4 nodes as described in Run 4.

2. Navigate to dc-app-performance-toolkit folder and start tests execution:

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   export ENVIRONMENT_NAME=your_environment_name
   

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   docker run --pull=always --env-file ./app/util/k8s/aws_envs \
   -e REGION=us-east-2 \
   -e ENVIRONMENT_NAME=$ENVIRONMENT_NAME \
   -v "/$PWD:/data-center-terraform/dc-app-performance-toolkit" \
   -v "/$PWD/app/util/k8s/bzt_on_pod.sh:/data-center-terraform/bzt_on_pod.sh" \
   -it atlassianlabs/terraform:2.9.3 bash bzt_on_pod.sh bitbucket.yml
   

Generating a report for scalability scenario

To generate a scalability report:

1. Edit the ./app/reports_generation/scale_profile.yml file:
   • For runName: "1 Node", in the relativePath key, insert the relative path to results directory of Run 3.
   • For runName: "2 Nodes", in the relativePath key, insert the relative path to results directory of Run 4.
   • For runName: "4 Nodes", in the relativePath key, insert the relative path to results directory of Run 5.
2. Navigate locally to dc-app-performance-toolkit folder and run the following command from local terminal (Git Bash for Windows users) to generate reports:

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   docker run --pull=always \
   -v "/$PWD:/dc-app-performance-toolkit" \
   --workdir="//dc-app-performance-toolkit/app/reports_generation" \
   --entrypoint="python" \
   -it atlassian/dcapt csv_chart_generator.py scale_profile.yml
   

3. In the ./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. 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.

Attaching testing results to ECOHELP ticket

1. Make sure you have two reports folders: one with performance profile and second with scale profile results. Each folder should have 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.
2. Attach two reports folders to your ECOHELP ticket.

Support

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 collect detailed logs, see Collect detailed k8s logs. For failed cluster uninstall use Force terminate command.

In case of any technical questions or issues with DC Apps Performance Toolkit, contact us for support in the community Slack #data-center-app-performance-toolkit channel.