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.

To use the Data Center App Performance Toolkit, you'll need to:

1. Set up Bitbucket Data Center on AWS.
2. Load an enterprise-scale dataset on your Bitbucket Data Center deployment.
3. Set up an execution environment for the toolkit.
4. Run all the testing scenarios in the toolkit.

1. Setting up Bitbucket Data Center

We recommend that you use the AWS Quick Start for Bitbucket Data Center (How to deploy tab) to deploy a Bitbucket Data Center testing environment. This Quick Start will allow you to deploy Bitbucket Data Center with a new Atlassian Standard Infrastructure (ASI) or into an existing one.

The ASI is a Virtual Private Cloud (VPC) consisting of subnets, NAT gateways, security groups, bastion hosts, and other infrastructure components required by all Atlassian applications, and then deploys Bitbucket into this new VPC. Deploying Bitbucket with a new ASI takes around 50 minutes. With an existing one, it'll take around 30 minutes.

Using the AWS Quick Start for Bitbucket

If you are a new user, perform an end-to-end deployment. This involves deploying Bitbucket into a new ASI:

Navigate to AWS Quick Start for Bitbucket Data Center > How to deploy tab > Deploy into a new ASI link.

If you have already deployed the ASI separately by using the ASI Quick StartASI Quick Start or by deploying another Atlassian product (Jira, Bitbucket, or Confluence Data Center development environment) with ASI, deploy Bitbucket into your existing ASI:

Navigate to AWS Quick Start for Bitbucket Data Center > How to deploy tab > Deploy into your existing ASI link.

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

AWS cost estimation

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 factors such as (region, instance type, deployment type of DB, etc.)

Stop cluster nodes

To reduce AWS infrastructure costs you could stop cluster nodes when the cluster is standing idle.
Cluster node might be stopped by using Suspending and Resuming Scaling Processes.

To stop one node within the cluster, follow the instructions below:

1. In the AWS console, go to Services > EC2 > Auto Scaling Groups and open the necessary group to which belongs the node you want to stop.
2. Click Edit (in case you have New EC2 experience UI mode enabled, press Edit on Advanced configuration) and add HealthCheck to the Suspended Processes. Amazon EC2 Auto Scaling stops marking instances unhealthy as a result of EC2 and Elastic Load Balancing health checks.
3. Go to EC2 Instances, select instance, click Instance state > Stop instance.

To return node into a working state follow the instructions:

1. Go to EC2 Instances, select instance, click Instance state > Start instance, wait a few minutes for node to become available.
2. Go to EC2 Auto Scaling Groups and open the necessary group to which belongs the node you want to start.
3. Press Edit (in case you have New EC2 experience UI mode enabled, press Edit on Advanced configuration) and remove HealthCheck from Suspended Processes of Auto Scaling Group.

Stop database

To reduce AWS infrastructure costs database could be stopped when the cluster is standing idle. Keep in mind that database would be automatically started in 7 days.

To stop database:

1. In the AWS console, go to Services > RDS > Databases.
2. Select cluster database.
3. Click on Actions > Stop.

To start database:

1. In the AWS console, go to Services > RDS > Databases.
2. Select cluster database.
3. Click on Actions > Start.

Quick Start parameters

All important parameters are listed and described in this section. For all other remaining parameters, we recommend using the Quick Start defaults.

Bitbucket setup

Cluster nodes

We recommend c5.2xlarge to strike the balance between cost and hardware we see in the field for our enterprise customers. More info could be found in public recommendations.

The Data Center App Performance Toolkit framework is also set up for concurrency we expect on this instance size. As such, underprovisioning will likely show a larger performance impact than expected.

File server

Database

Elasticsearch

Networking (for new ASI)

Networking (for existing ASI)

Running the setup wizard

After successfully deploying Bitbucket Data Center in AWS, you'll need to configure it:

1. In the AWS console, go to Services > CloudFormation > Stack > Stack details > Select your stack.
2. On the Outputs tab, copy the value of the LoadBalancerURL key.
3. Open LoadBalancerURL in your browser. This will take you to the Bitbucket setup wizard.
4. On the Bitbucket setup page, populate the following fields:
   • Application title: any name for your Bitbucket Data Center deployment
   • Base URL: your stack's Elastic LoadBalancer URL
   • License key: select new evaluation license or existing license checkbox Click Next.
5. On the Administrator account setup page, populate the following fields:
   • Username: admin (recommended)
   • Full name: any full name of the admin user
   • Email address: email address of the admin user
   • Password: admin (recommended)
   • Confirm Password: admin (recommended) Click Go to Bitbucket.

2. Preloading your Bitbucket deployment with an enterprise-scale dataset

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

Pre-loading the dataset is a three-step process:

1. Importing the main dataset. To help you out, we provide an enterprise-scale dataset you can import either via the populate_db.sh script or restore from xml backup file.
2. Restoring attachments. We also provide attachments, which you can pre-load via an upload_attachments.sh script.

The following subsections explain each step in greater detail.

Importing the main dataset

You can load this dataset directly into the database (via a populate_db.sh script).

Loading the dataset via populate_db.sh script (~2 hours)

To populate the database with SQL:

1. In the AWS console, go to Services > EC2 > Instances.
2. On the Description tab, do the following:
   • Copy the Public IP of the Bastion instance.
   • Copy the Private IP of the Bitbucket node instance.
   • Copy the Private IP of the Bitbucket NFS Server instance.

3. Using SSH, connect to the Bitbucket node via the Bastion instance:

   For Linux or MacOS run following commands in terminal (for Windows use Git Bash terminal):

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   ssh-add path_to_your_private_key_pem
   export BASTION_IP=bastion_instance_public_ip
   export NODE_IP=node_private_ip
   export SSH_OPTS='-o ServerAliveInterval=60 -o ServerAliveCountMax=30'
   ssh ${SSH_OPTS} -o "proxycommand ssh -W %h:%p ${SSH_OPTS} ec2-user@${BASTION_IP}" ec2-user@${NODE_IP}

   For more information, go to Connecting your nodes over SSH.

4. Stop Bitbucket Server:

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   sudo systemctl stop bitbucket

5. In a new terminal session connect to the Bitbucket NFS Server over SSH:

   For Linux or MacOS run following commands in terminal (for Windows use Git Bash terminal):

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   ssh-add path_to_your_private_key_pem
   export BASTION_IP=bastion_instance_public_ip
   export NFS_SERVER_IP=nfs_server_private_ip
   export SSH_OPTS='-o ServerAliveInterval=60 -o ServerAliveCountMax=30'
   ssh ${SSH_OPTS} -o "proxycommand ssh -W %h:%p ${SSH_OPTS} ec2-user@${BASTION_IP}" ec2-user@${NFS_SERVER_IP}

   For more information, go to Connecting your nodes over SSH.

6. Download the populate_db.sh script and make it executable:

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   wget https://raw.githubusercontent.com/atlassian/dc-app-performance-toolkit/master/app/util/bitbucket/populate_db.sh && chmod +x populate_db.sh

7. Review the following Variables section of the script:

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   DB_CONFIG="/media/atl/bitbucket/shared/bitbucket.properties"
   
   # Depending on BITBUCKET installation directory
   BITBUCKET_CURRENT_DIR="/opt/atlassian/bitbucket/current/"
   BITBUCKET_VERSION_FILE="/media/atl/bitbucket/shared/bitbucket.version"
   
   # DB admin user name, password and DB name
   BITBUCKET_DB_NAME="bitbucket"
   BITBUCKET_DB_USER="postgres"
   BITBUCKET_DB_PASS="Password1!"
   
   # Datasets AWS bucket and db dump name
   DATASETS_AWS_BUCKET="https://centaurus-datasets.s3.amazonaws.com/bitbucket"
   DATASETS_SIZE="large"

8. Run the script:

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   ./populate_db.sh 2>&1 | tee -a populate_db.log

Restoring attachments (~2 hours)

After Importing the main dataset, you'll now have to pre-load an enterprise-scale set of attachments.

1. Using SSH, connect to the Bitbucket NFS Server via the Bastion instance:

   For Linux or MacOS run following commands in terminal (for Windows use Git Bash terminal):

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   ssh-add path_to_your_private_key_pem
   export BASTION_IP=bastion_instance_public_ip
   export NFS_SERVER_IP=nfs_server_private_ip
   export SSH_OPTS='-o ServerAliveInterval=60 -o ServerAliveCountMax=30'
   ssh ${SSH_OPTS} -o "proxycommand ssh -W %h:%p ${SSH_OPTS} ec2-user@$BASTION_IP" ec2-user@${NFS_SERVER_IP}

   For more information, go to Connecting your nodes over SSH.

2. Download the upload_attachments.sh script and make it executable:

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   wget https://raw.githubusercontent.com/atlassian/dc-app-performance-toolkit/master/app/util/bitbucket/upload_attachments.sh && chmod +x upload_attachments.sh

3. Review the following Variables section of the script:

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   DATASETS_AWS_BUCKET="https://centaurus-datasets.s3.amazonaws.com/bitbucket"
   ATTACHMENTS_TAR="attachments.tar.gz"
   DATASETS_SIZE="large"
   ATTACHMENTS_TAR_URL="${DATASETS_AWS_BUCKET}/${BITBUCKET_VERSION}/${DATASETS_SIZE}/${ATTACHMENTS_TAR}"
   NFS_DIR="/media/atl/bitbucket/shared"
   ATTACHMENT_DIR_DATA="data"

4. Run the script:

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   ./upload_attachments.sh 2>&1 | tee -a upload_attachments.log

Start Bitbucket DC

1. Using SSH, connect to the Bitbucket node via the Bastion instance:

   For Linux or MacOS run following commands in terminal (for Windows use Git Bash terminal):

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   ssh-add path_to_your_private_key_pem
   export BASTION_IP=bastion_instance_public_ip
   export NODE_IP=node_private_ip
   export SSH_OPTS='-o ServerAliveInterval=60 -o ServerAliveCountMax=30'
   ssh ${SSH_OPTS} -o "proxycommand ssh -W %h:%p ${SSH_OPTS} ec2-user@${BASTION_IP}" ec2-user@${NODE_IP}

   For more information, go to Connecting your nodes over SSH.

2. Start Bitbucket DC:

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   sudo systemctl start bitbucket

3. Wait 10-15 minutes until Bitbucket DC is started.

Elasticsearch Index

If your app does not use Bitbucket search functionality just skip this section.

Otherwise, if your app is depending on Bitbucket search functionality you need to wait until Elasticsearch index is finished. Bitbucket-project index and bitbucket-repository index usually take about 10 hours on a User Guide recommended configuration, bitbucket-search index (search by repositories content) could take up to a couple of days.

To check status of indexing:

1. Open LoadBalancerURL in your browser.
2. Login with admin user.

3. Navigate to LoadBalancerURL/rest/indexing/latest/status page:

   "status":"INDEXING" - index is in progress

   "status":"IDLE" - index is finished

3. Setting up an execution environment

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.

1. Go to GitHub and create a fork of dc-app-performance-toolkit.

2. Clone the fork locally, then edit the bitbucket.yml configuration file. Set enterprise-scale Jira Data Center parameters:

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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:            # e.g. /bitbucket in case of url like http://localhost:7990/bitbucket
       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

3. Push your changes to the forked repository.

4. Launch AWS EC2 instance.
   • OS: select from Quick Start Ubuntu Server 18.04 LTS.
   • Instance type: c5.2xlarge
   • Storage size: 30 GiB

5. Connect to the instance using SSH or the AWS Systems Manager Sessions Manager.

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   ssh -i path_to_pem_file ubuntu@INSTANCE_PUBLIC_IP

6. Install Docker. Setup manage Docker as a non-root user.

7. Connect to the AWS EC2 instance and clone forked repository.

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

4. Running the test scenarios on your execution environment

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. Use SSH to connect to execution environment.

2. Run toolkit with docker:

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   cd dc-app-performance-toolkit
   docker pull atlassian/dcapt
   docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt bitbucket.yml

3. 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 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:

1. Install the app you want to test.
2. Setup app license.

3. Run bzt.

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   cd dc-app-performance-toolkit
   docker pull atlassian/dcapt
   docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt bitbucket.yml

Generating a performance regression report

To generate a performance regression report:

1. Use SSH to connect to execution environment.
2. Install and activate the virtualenv as described in dc-app-performance-toolkit/README.md
3. Allow current user (for execution environment default user is ubuntu) to access Docker generated reports:

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   sudo chown -R ubuntu:ubuntu /home/ubuntu/dc-app-performance-toolkit/app/results

4. Navigate to the dc-app-performance-toolkit/app/reports_generation folder.
5. Edit the performance_profile.yml file:
   • Under runName: "without app", in the fullPath key, insert the full path to results directory of Run 1.
   • Under runName: "with app", in the fullPath key, insert the full path to results directory of Run 2.
6. Run the following command:

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   python csv_chart_generator.py performance_profile.yml

7. In the 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.

Analyzing report

Use scp command to copy report artifacts from execution env to local drive:

1. From local machine terminal (Git bash terminal for Windows) run command:

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   export 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

2. Once completed, in the ./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.

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

Extending the base action

Extension scripts, which extend the base Selenium (bitbucket-ui.py) scripts, are located in a separate folder (dc-app-performance-toolkit/extension/bitbucket). You can modify these scripts to include their app-specific actions.

Modifying Selenium

You can extend Selenium scripts to measure the end-to-end browser timings.

We use Pytest to drive Selenium tests. The bitbucket-ui.py executor script is located in the app/selenium_ui/ folder. This file contains all browser actions, defined by the test_ functions. These actions are executed one by one during the testing.

Example of app-specific Selenium action development

You develop an app that adds additional UI elements to a repository page, in this case you should edit dc-app-performance-toolkit/extension/bitbucket/extension_ui.py: Code example.

In the bitbucket-ui.py script, view the following block of code:

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

If you need to run 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_logout action.

To view more examples, see the modules.py file in the selenium_ui/bitbucket directory.

Running tests with your modification

To ensure that the test runs without errors in parallel, run your extension scripts with the base scripts as a sanity check.

Run 3 (~1 hour)

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

1. Apply app-specific code changes to a new branch of forked repo.
2. Use SSH to connect to execution environment.
3. Pull cloned fork repo branch with app-specific actions.

4. Run toolkit with docker:

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   cd dc-app-performance-toolkit
   docker pull atlassian/dcapt
   docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt bitbucket.yml

Run 4 (~1 hour)

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

1. In the AWS console, go to CloudFormation > Stack details > Select your stack.
2. On the Update tab, select Use current template, and then click Next.
3. Enter 2 in the Maximum number of cluster nodes and the Minimum number of cluster nodes fields.
4. Click Next > Next > Update stack and wait until stack is updated.

5. Run toolkit with docker:

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   cd dc-app-performance-toolkit
   docker pull atlassian/dcapt
   docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt 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 the same way as in Run 4.

2. Run toolkit with docker:

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   cd dc-app-performance-toolkit
   docker pull atlassian/dcapt
   docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt bitbucket.yml

Generating a report for scalability scenario

To generate a scalability report:

1. Use SSH to connect to execution environment.
2. Allow current user (for execution environment default user is ubuntu) to access Docker generated reports:

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   sudo chown -R ubuntu:ubuntu /home/ubuntu/dc-app-performance-toolkit/app/results

3. Navigate to the dc-app-performance-toolkit/app/reports_generation folder.
4. Edit the scale_profile.yml file:
   • For runName: "Node 1", in the fullPath key, insert the full path to results directory of Run 3.
   • For runName: "Node 2", in the fullPath key, insert the full path to results directory of Run 4.
   • For runName: "Node 4", in the fullPath key, insert the full path to results directory of Run 5.
5. Run the following command from the virtualenv (as described in dc-app-performance-toolkit/README.md):

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   python csv_chart_generator.py scale_profile.yml

6. In the 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.

Analyzing report

Use scp command to copy report artifacts from execution env to local drive:

1. From local terminal (Git bash terminal for Windows) run command:

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   export 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

2. Once completed, in the ./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.

Attaching testing results to DCHELP 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.
2. Attach two reports folders to your DCHELP ticket.

Support

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

title: "Data Center App Performance Toolkit User Guide For Bitbucket" platform: platform product: marketplace category: devguide subcategory: build

date: "2021-01-12"

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.

To use the Data Center App Performance Toolkit, you'll need to:

1. Set up Bitbucket Data Center on AWS.
2. Load an enterprise-scale dataset on your Bitbucket Data Center deployment.
3. Set up an execution environment for the toolkit.
4. Run all the testing scenarios in the toolkit.

1. Setting up Bitbucket Data Center

We recommend that you use the AWS Quick Start for Bitbucket Data Center (How to deploy tab) to deploy a Bitbucket Data Center testing environment. This Quick Start will allow you to deploy Bitbucket Data Center with a new Atlassian Standard Infrastructure (ASI) or into an existing one.

The ASI is a Virtual Private Cloud (VPC) consisting of subnets, NAT gateways, security groups, bastion hosts, and other infrastructure components required by all Atlassian applications, and then deploys Bitbucket into this new VPC. Deploying Bitbucket with a new ASI takes around 50 minutes. With an existing one, it'll take around 30 minutes.

Using the AWS Quick Start for Bitbucket

If you are a new user, perform an end-to-end deployment. This involves deploying Bitbucket into a new ASI:

Navigate to AWS Quick Start for Bitbucket Data Center > How to deploy tab > Deploy into a new ASI link.

If you have already deployed the ASI separately by using the ASI Quick StartASI Quick Start or by deploying another Atlassian product (Jira, Bitbucket, or Confluence Data Center development environment) with ASI, deploy Bitbucket into your existing ASI:

Navigate to AWS Quick Start for Bitbucket Data Center > How to deploy tab > Deploy into your existing ASI link.

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

AWS cost estimation

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 factors such as (region, instance type, deployment type of DB, etc.)

Stop cluster nodes

To reduce AWS infrastructure costs you could stop cluster nodes when the cluster is standing idle.
Cluster node might be stopped by using Suspending and Resuming Scaling Processes.

To stop one node within the cluster, follow the instructions below:

1. In the AWS console, go to Services > EC2 > Auto Scaling Groups and open the necessary group to which belongs the node you want to stop.
2. Click Edit (in case you have New EC2 experience UI mode enabled, press Edit on Advanced configuration) and add HealthCheck to the Suspended Processes. Amazon EC2 Auto Scaling stops marking instances unhealthy as a result of EC2 and Elastic Load Balancing health checks.
3. Go to EC2 Instances, select instance, click Instance state > Stop instance.

To return node into a working state follow the instructions:

1. Go to EC2 Instances, select instance, click Instance state > Start instance, wait a few minutes for node to become available.
2. Go to EC2 Auto Scaling Groups and open the necessary group to which belongs the node you want to start.
3. Press Edit (in case you have New EC2 experience UI mode enabled, press Edit on Advanced configuration) and remove HealthCheck from Suspended Processes of Auto Scaling Group.

Stop database

To reduce AWS infrastructure costs database could be stopped when the cluster is standing idle. Keep in mind that database would be automatically started in 7 days.

To stop database:

1. In the AWS console, go to Services > RDS > Databases.
2. Select cluster database.
3. Click on Actions > Stop.

To start database:

1. In the AWS console, go to Services > RDS > Databases.
2. Select cluster database.
3. Click on Actions > Start.

Quick Start parameters

All important parameters are listed and described in this section. For all other remaining parameters, we recommend using the Quick Start defaults.

Bitbucket setup

Cluster nodes

We recommend c5.2xlarge to strike the balance between cost and hardware we see in the field for our enterprise customers. More info could be found in public recommendations.

The Data Center App Performance Toolkit framework is also set up for concurrency we expect on this instance size. As such, underprovisioning will likely show a larger performance impact than expected.

File server

Database

Elasticsearch

Networking (for new ASI)

Networking (for existing ASI)

Running the setup wizard

After successfully deploying Bitbucket Data Center in AWS, you'll need to configure it:

1. In the AWS console, go to Services > CloudFormation > Stack > Stack details > Select your stack.
2. On the Outputs tab, copy the value of the LoadBalancerURL key.
3. Open LoadBalancerURL in your browser. This will take you to the Bitbucket setup wizard.
4. On the Bitbucket setup page, populate the following fields:
   • Application title: any name for your Bitbucket Data Center deployment
   • Base URL: your stack's Elastic LoadBalancer URL
   • License key: select new evaluation license or existing license checkbox Click Next.
5. On the Administrator account setup page, populate the following fields:
   • Username: admin (recommended)
   • Full name: any full name of the admin user
   • Email address: email address of the admin user
   • Password: admin (recommended)
   • Confirm Password: admin (recommended) Click Go to Bitbucket.

2. Preloading your Bitbucket deployment with an enterprise-scale dataset

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

Pre-loading the dataset is a three-step process:

1. Importing the main dataset. To help you out, we provide an enterprise-scale dataset you can import either via the populate_db.sh script or restore from xml backup file.
2. Restoring attachments. We also provide attachments, which you can pre-load via an upload_attachments.sh script.

The following subsections explain each step in greater detail.

Importing the main dataset

You can load this dataset directly into the database (via a populate_db.sh script).

Loading the dataset via populate_db.sh script (~2 hours)

To populate the database with SQL:

1. In the AWS console, go to Services > EC2 > Instances.
2. On the Description tab, do the following:
   • Copy the Public IP of the Bastion instance.
   • Copy the Private IP of the Bitbucket node instance.
   • Copy the Private IP of the Bitbucket NFS Server instance.

3. Using SSH, connect to the Bitbucket node via the Bastion instance:

   For Linux or MacOS run following commands in terminal (for Windows use Git Bash terminal):

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   ssh-add path_to_your_private_key_pem
   export BASTION_IP=bastion_instance_public_ip
   export NODE_IP=node_private_ip
   export SSH_OPTS='-o ServerAliveInterval=60 -o ServerAliveCountMax=30'
   ssh ${SSH_OPTS} -o "proxycommand ssh -W %h:%p ${SSH_OPTS} ec2-user@${BASTION_IP}" ec2-user@${NODE_IP}

   For more information, go to Connecting your nodes over SSH.

4. Stop Bitbucket Server:

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   sudo systemctl stop bitbucket

5. In a new terminal session connect to the Bitbucket NFS Server over SSH:

   For Linux or MacOS run following commands in terminal (for Windows use Git Bash terminal):

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   ssh-add path_to_your_private_key_pem
   export BASTION_IP=bastion_instance_public_ip
   export NFS_SERVER_IP=nfs_server_private_ip
   export SSH_OPTS='-o ServerAliveInterval=60 -o ServerAliveCountMax=30'
   ssh ${SSH_OPTS} -o "proxycommand ssh -W %h:%p ${SSH_OPTS} ec2-user@${BASTION_IP}" ec2-user@${NFS_SERVER_IP}

   For more information, go to Connecting your nodes over SSH.

6. Download the populate_db.sh script and make it executable:

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   wget https://raw.githubusercontent.com/atlassian/dc-app-performance-toolkit/master/app/util/bitbucket/populate_db.sh && chmod +x populate_db.sh

7. Review the following Variables section of the script:

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   DB_CONFIG="/media/atl/bitbucket/shared/bitbucket.properties"
   
   # Depending on BITBUCKET installation directory
   BITBUCKET_CURRENT_DIR="/opt/atlassian/bitbucket/current/"
   BITBUCKET_VERSION_FILE="/media/atl/bitbucket/shared/bitbucket.version"
   
   # DB admin user name, password and DB name
   BITBUCKET_DB_NAME="bitbucket"
   BITBUCKET_DB_USER="postgres"
   BITBUCKET_DB_PASS="Password1!"
   
   # Datasets AWS bucket and db dump name
   DATASETS_AWS_BUCKET="https://centaurus-datasets.s3.amazonaws.com/bitbucket"
   DATASETS_SIZE="large"

8. Run the script:

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   ./populate_db.sh 2>&1 | tee -a populate_db.log

Restoring attachments (~2 hours)

After Importing the main dataset, you'll now have to pre-load an enterprise-scale set of attachments.

1. Using SSH, connect to the Bitbucket NFS Server via the Bastion instance:

   For Linux or MacOS run following commands in terminal (for Windows use Git Bash terminal):

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   ssh-add path_to_your_private_key_pem
   export BASTION_IP=bastion_instance_public_ip
   export NFS_SERVER_IP=nfs_server_private_ip
   export SSH_OPTS='-o ServerAliveInterval=60 -o ServerAliveCountMax=30'
   ssh ${SSH_OPTS} -o "proxycommand ssh -W %h:%p ${SSH_OPTS} ec2-user@$BASTION_IP" ec2-user@${NFS_SERVER_IP}

   For more information, go to Connecting your nodes over SSH.

2. Download the upload_attachments.sh script and make it executable:

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   wget https://raw.githubusercontent.com/atlassian/dc-app-performance-toolkit/master/app/util/bitbucket/upload_attachments.sh && chmod +x upload_attachments.sh

3. Review the following Variables section of the script:

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   DATASETS_AWS_BUCKET="https://centaurus-datasets.s3.amazonaws.com/bitbucket"
   ATTACHMENTS_TAR="attachments.tar.gz"
   DATASETS_SIZE="large"
   ATTACHMENTS_TAR_URL="${DATASETS_AWS_BUCKET}/${BITBUCKET_VERSION}/${DATASETS_SIZE}/${ATTACHMENTS_TAR}"
   NFS_DIR="/media/atl/bitbucket/shared"
   ATTACHMENT_DIR_DATA="data"

4. Run the script:

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   ./upload_attachments.sh 2>&1 | tee -a upload_attachments.log

Start Bitbucket DC

1. Using SSH, connect to the Bitbucket node via the Bastion instance:

   For Linux or MacOS run following commands in terminal (for Windows use Git Bash terminal):

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   ssh-add path_to_your_private_key_pem
   export BASTION_IP=bastion_instance_public_ip
   export NODE_IP=node_private_ip
   export SSH_OPTS='-o ServerAliveInterval=60 -o ServerAliveCountMax=30'
   ssh ${SSH_OPTS} -o "proxycommand ssh -W %h:%p ${SSH_OPTS} ec2-user@${BASTION_IP}" ec2-user@${NODE_IP}

   For more information, go to Connecting your nodes over SSH.

2. Start Bitbucket DC:

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   sudo systemctl start bitbucket

3. Wait 10-15 minutes until Bitbucket DC is started.

Elasticsearch Index

If your app does not use Bitbucket search functionality just skip this section.

Otherwise, if your app is depending on Bitbucket search functionality you need to wait until Elasticsearch index is finished. Bitbucket-project index and bitbucket-repository index usually take about 10 hours on a User Guide recommended configuration, bitbucket-search index (search by repositories content) could take up to a couple of days.

To check status of indexing:

1. Open LoadBalancerURL in your browser.
2. Login with admin user.

3. Navigate to LoadBalancerURL/rest/indexing/latest/status page:

   "status":"INDEXING" - index is in progress

   "status":"IDLE" - index is finished

3. Setting up an execution environment

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.

1. Go to GitHub and create a fork of dc-app-performance-toolkit.

2. Clone the fork locally, then edit the bitbucket.yml configuration file. Set enterprise-scale Jira Data Center parameters:

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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:            # e.g. /bitbucket in case of url like http://localhost:7990/bitbucket
       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

3. Push your changes to the forked repository.

4. Launch AWS EC2 instance.
   • OS: select from Quick Start Ubuntu Server 18.04 LTS.
   • Instance type: c5.2xlarge
   • Storage size: 30 GiB

5. Connect to the instance using SSH or the AWS Systems Manager Sessions Manager.

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   ssh -i path_to_pem_file ubuntu@INSTANCE_PUBLIC_IP

6. Install Docker. Setup manage Docker as a non-root user.

7. Connect to the AWS EC2 instance and clone forked repository.

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

4. Running the test scenarios on your execution environment

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. Use SSH to connect to execution environment.

2. Run toolkit with docker:

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   cd dc-app-performance-toolkit
   docker pull atlassian/dcapt
   docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt bitbucket.yml

3. 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 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:

1. Install the app you want to test.
2. Setup app license.

3. Run bzt.

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   cd dc-app-performance-toolkit
   docker pull atlassian/dcapt
   docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt bitbucket.yml

Generating a performance regression report

To generate a performance regression report:

1. Use SSH to connect to execution environment.
2. Install and activate the virtualenv as described in dc-app-performance-toolkit/README.md
3. Allow current user (for execution environment default user is ubuntu) to access Docker generated reports:

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   sudo chown -R ubuntu:ubuntu /home/ubuntu/dc-app-performance-toolkit/app/results

4. Navigate to the dc-app-performance-toolkit/app/reports_generation folder.
5. Edit the performance_profile.yml file:
   • Under runName: "without app", in the fullPath key, insert the full path to results directory of Run 1.
   • Under runName: "with app", in the fullPath key, insert the full path to results directory of Run 2.
6. Run the following command:

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   python csv_chart_generator.py performance_profile.yml

7. In the 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.

Analyzing report

Use scp command to copy report artifacts from execution env to local drive:

1. From local machine terminal (Git bash terminal for Windows) run command:

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   export 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

2. Once completed, in the ./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.

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

Extending the base action

Extension scripts, which extend the base Selenium (bitbucket-ui.py) scripts, are located in a separate folder (dc-app-performance-toolkit/extension/bitbucket). You can modify these scripts to include their app-specific actions.

Modifying Selenium

You can extend Selenium scripts to measure the end-to-end browser timings.

We use Pytest to drive Selenium tests. The bitbucket-ui.py executor script is located in the app/selenium_ui/ folder. This file contains all browser actions, defined by the test_ functions. These actions are executed one by one during the testing.

Example of app-specific Selenium action development

You develop an app that adds additional UI elements to a repository page, in this case you should edit dc-app-performance-toolkit/extension/bitbucket/extension_ui.py: Code example.

In the bitbucket-ui.py script, view the following block of code:

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

If you need to run 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_logout action.

To view more examples, see the modules.py file in the selenium_ui/bitbucket directory.

Running tests with your modification

To ensure that the test runs without errors in parallel, run your extension scripts with the base scripts as a sanity check.

Run 3 (~1 hour)

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

1. Apply app-specific code changes to a new branch of forked repo.
2. Use SSH to connect to execution environment.
3. Pull cloned fork repo branch with app-specific actions.

4. Run toolkit with docker:

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   cd dc-app-performance-toolkit
   docker pull atlassian/dcapt
   docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt bitbucket.yml

Run 4 (~1 hour)

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

1. In the AWS console, go to CloudFormation > Stack details > Select your stack.
2. On the Update tab, select Use current template, and then click Next.
3. Enter 2 in the Maximum number of cluster nodes and the Minimum number of cluster nodes fields.
4. Click Next > Next > Update stack and wait until stack is updated.

5. Run toolkit with docker:

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   cd dc-app-performance-toolkit
   docker pull atlassian/dcapt
   docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt 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 the same way as in Run 4.

2. Run toolkit with docker:

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   cd dc-app-performance-toolkit
   docker pull atlassian/dcapt
   docker run --shm-size=4g -v "$PWD:/dc-app-performance-toolkit" atlassian/dcapt bitbucket.yml

Generating a report for scalability scenario

To generate a scalability report:

1. Use SSH to connect to execution environment.
2. Allow current user (for execution environment default user is ubuntu) to access Docker generated reports:

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   sudo chown -R ubuntu:ubuntu /home/ubuntu/dc-app-performance-toolkit/app/results

3. Navigate to the dc-app-performance-toolkit/app/reports_generation folder.
4. Edit the scale_profile.yml file:
   • For runName: "Node 1", in the fullPath key, insert the full path to results directory of Run 3.
   • For runName: "Node 2", in the fullPath key, insert the full path to results directory of Run 4.
   • For runName: "Node 4", in the fullPath key, insert the full path to results directory of Run 5.
5. Run the following command from the virtualenv (as described in dc-app-performance-toolkit/README.md):

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   python csv_chart_generator.py scale_profile.yml

6. In the 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.

Analyzing report

Use scp command to copy report artifacts from execution env to local drive:

1. From local terminal (Git bash terminal for Windows) run command:

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   export 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

2. Once completed, in the ./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.

Attaching testing results to DCHELP 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.
2. Attach two reports folders to your DCHELP ticket.

Support

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