Data Center App Performance Toolkit User Guide For Crowd

This document walks you through the process of testing your app on Crowd 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 Enterprise-scale environment.

Enterprise-scale environment: Crowd Data Center environment used to generate Data Center App Performance Toolkit test results for the Marketplace approval process. Preferably, use the parameters prescribed below.

1. Set up an enterprise-scale environment Crowd Data Center on AWS.
2. App-specific actions development.
3. Setting up load configuration for Enterprise-scale runs.
4. Running the test scenarios from execution environment against enterprise-scale Crowd Data Center.

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1. Set up an enterprise-scale environment Crowd Data Center on k8s

EC2 CPU Limit

#### Setup Crowd Data Center with an enterprise-scale dataset on k8s

Below process describes how to install Crowd DC with an enterprise-scale dataset included. This configuration was created specifically for performance testing during the DC app review process.

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-crowd

   • products - crowd

   • crowd_license - one-liner of valid crowd license without spaces and new line symbols

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

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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
   

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

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

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

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2. App-specific actions development

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

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.

JMeter app-specific actions development

1. Set up local environment for toolkit using the README.

2. Check that crowd.yml file has correct settings of application_hostname, application_protocol, application_port, application_postfix, etc.

3. 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 crowd

4. Open Crowd thread group and add new transaction controller.

5. Open newly added transaction controller, and add new HTTP requests (based on your app use cases) into it.

6. Run toolkit locally from dc-app-performance-toolkit/app folder with the command
   bzt crowd.yml
   Make sure that execution is successful.

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3. 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 crowd.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_crowd_instance.atlassian.com    # Crowd DC hostname without protocol and port e.g. test-crowd.atlassian.com or localhost
    application_protocol: http      # http or https
    application_port: 80            # 80, 443, 8080, 4990, etc
    secure: True                    # Set False to allow insecure connections, e.g. when using self-signed SSL certificate
    application_postfix: /crowd     # Default postfix value for TerraForm deployment url like `http://a1234-54321.us-east-2.elb.amazonaws.com/crowd`
    admin_login: admin
    admin_password: admin
    application_name: crowd
    application_password: 1111
    load_executor: jmeter            
    concurrency: 1000               # number of concurrent threads to authenticate random users
    test_duration: 45m
   

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

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4. Running the test scenarios from execution environment against enterprise-scale Crowd 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 Crowd DC. Make sure the app does not have any performance impact when it is not exercised.

Run 1 (~50 min)

To receive performance baseline results without an app installed and without app-specific actions (use code from master branch):

1. Before run:

   • Make sure crowd.yml and toolkit code base has default configuration from the master branch. No app-specific actions code applied.
   • 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 crowd.yml
   

3. View the following main results of the run in the dc-app-performance-toolkit/app/results/crowd/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

Run 2 (~50 min)

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 crowd.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.

Scenario 2: Scalability testing

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 Crowd DC deployment. To demonstrate performance impacts of operating your app at scale, we recommend testing your Crowd DC app in a cluster.

Run 3 (~50 min)

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

1. Before run:

   • Make sure crowd.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.
   • 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 crowd.yml
   

Run 4 (~50 min)

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

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

2. Open dcapt.tfvars file and set crowd_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. Edit run parameters for 2 nodes run. To do it, left uncommented only 2 nodes scenario parameters in crowd.yml file.

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   # 1 node scenario parameters
   # ramp-up: 20s                    # time to spin all concurrent threads
   # total_actions_per_hour: 180000  # number of total JMeter actions per hour
   
   # 2 nodes scenario parameters
     ramp-up: 10s                    # time to spin all concurrent threads
     total_actions_per_hour: 360000  # number of total JMeter actions per hour
   
   # 4 nodes scenario parameters
   # ramp-up: 5s                     # time to spin all concurrent threads
   # total_actions_per_hour: 720000  # number of total JMeter actions per hour
   

5. 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 crowd.yml
   

Run 5 (~50 min)

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

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

2. Edit run parameters for 4 nodes run. To do it, left uncommented only 4 nodes scenario parameters crowd.yml file.

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   # 1 node scenario parameters
   # ramp-up: 20s                    # time to spin all concurrent threads
   # total_actions_per_hour: 180000  # number of total JMeter actions per hour
   
   # 2 nodes scenario parameters
   # ramp-up: 10s                    # time to spin all concurrent threads
   # total_actions_per_hour: 360000  # number of total JMeter actions per hour
   
   # 4 nodes scenario parameters
   ramp-up: 5s                     # time to spin all concurrent threads
   total_actions_per_hour: 720000  # number of total JMeter actions per hour
   

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 crowd.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.