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.

Setting up Bitbucket Data Center

We recommend that you use the AWS Quick Start for Bitbucket Data Center 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.

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

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

AWS cost estimation

SIMPLE MONTHLY 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.)

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

The Data Center App Performance Toolkit officially supports:

• Bitbucket Platform release version: 7.0.0
• Bitbucket Long Term Support releases: 6.10.0

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.

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 Windows, use Putty to connect to the Bitbucket node over SSH. For Linux or MacOS:

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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 Windows, use Putty to connect to the Bitbucket node over SSH. For Linux or MacOS:

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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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   INSTALL_PSQL_CMD="amazon-linux-extras install -y postgresql10"
   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 | 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 Windows, use Putty to connect to the Bitbucket node over SSH. For Linux or MacOS:

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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 | tee -a upload_attachments.log

Start Bitbucket Server

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

   For Windows, use Putty to connect to the Bitbucket node over SSH. For Linux or MacOS:

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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 Server:

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   sudo systemctl start bitbucket

3. Wait 10-15 minutes until Bitbucket Server 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.

Setting up an execution environment

If you're using the DC App Performance Toolkit to produce the required performance and scale benchmarks for your Data Center app, we recommend that you set up your execution environment on AWS:

1. Launch AWS EC2 instance. Instance type: c5.2xlarge, OS: select from Quick Start Ubuntu Server 18.04 LTS.

2. 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

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

4. Go to GitHub and create a fork of dc-app-performance-toolkit.
5. Clone the fork locally, then edit the bitbucket.yml configuration file and other files as needed.
6. Push your changes to the forked repository.
7. Connect to the AWS EC2 instance and clone forked repository.

Once your environment is set up, you can run the DC App Performance Toolkit:

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

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

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. On the computer where you cloned the Data Center App Performance Toolkit, navigate to dc-app-performance-toolkit/app folder.
2. 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, or your instance-specific port. The self-signed certificate is not supported.
   • admin_login: admin user username
   • admin_password: admin user password
   • concurrency: number of concurrent users for JMeter scenario - we recommend you use the defaults to generate full-scale results.
   • test_duration: duration of the performance run - we recommend you use the defaults to generate full-scale results.
   • ramp-up: amount of time it will take JMeter to add all test users to test execution - we recommend you use the defaults to generate full-scale results.

3. Run bzt.

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   bzt bitbucket.yml

4. 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:

1. Install the app you want to test.

2. Run bzt.

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   bzt bitbucket.yml

Generating a performance regression report

To generate a performance regression report:

1. Navigate to the dc-app-performance-toolkit/app/reports_generation folder.
2. 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.

3. Run the following command:

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   python csv_chart_generator.py performance_profile.yml

4. 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

Once completed, 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 a significant impact (>10%) 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.

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)

This is a placeholder to add an extension action. The custom action can be moved to a different line, depending on the required workflow, as long as it is between the login (test_0_selenium_a_login) and logout (test_2_selenium_z_log_out) actions.

To implement the app_specific_action function, modify the extension_ui.py file in the extension/bitbucket/ directory. The following is an example of the app_specific_action function, where Selenium navigates to a URL, clicks on an element, and waits until an element is visible.

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, run bzt:

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bzt 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 bzt.

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   bzt 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 bzt.

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   bzt bitbucket.yml

Generating a report for scalability scenario

To generate a scalability report:

1. Navigate to the dc-app-performance-toolkit/app/reports_generation folder.
2. 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.

3. Run the following command:

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   python csv_chart_generator.py scale_profile.yml

4. 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

Once completed, you will be able to review action timings on Bitbucket 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.

After completing all your tests, delete your Bitbucket Data Center stacks.

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.