This repository is not currently maintained. We encourage you to explore it, fork it, or otherwise use it as inspiration for your own metrics instrumentation.

Through six years of research, the DevOps Research and Assessment (DORA) team has identified four key metrics that indicate the performance of software delivery. Four Keys allows you to collect data from your development environment (such as GitHub or GitLab) and compiles it into a dashboard displaying these key metrics.

These four key metrics are:

• Deployment Frequency
• Lead Time for Changes
• Time to Restore Services
• Change Failure Rate

Use Four Keys if:

• You want to measure your team's software delivery performance. For example, you may want to track the impact of new tooling or more automated test coverage, or you may want a baseline of your team's performance.
• You have a project in GitHub or GitLab.
• Your project has deployments.

Four Keys works well with projects that have deployments. Projects with releases and no deployments, for example, libraries, do not work well because of how GitHub and GitLab present their data about releases.

For a quick baseline of your team's software delivery performance, you can also use the DORA DevOps Quick Check. The quick check also suggests DevOps capabilities you can work on to improve your performance. The Four Keys project itself can help you improve the following DevOps capabilities:

• Monitoring and observability
• Monitoring systems to inform business decisions
• Visual management capabilities

1. Events are sent to a webhook target hosted on Cloud Run. Events are any occurrence in your development environment (for example, GitHub or GitLab) that can be measured, such as a pull request or new issue. Four Keys defines events to measure, and you can add others that are relevant to your project.
2. The Cloud Run target publishes all events to Pub/Sub.
3. A Cloud Run instance is subscribed to the Pub/Sub topics, does some light data transformation, and inputs the data into BigQuery.
4. The BigQuery view to complete the data transformations and feed into the dashboard.

This diagram shows the design of the Four Keys system:

• bq-workers/
  • Contains the code for the individual BigQuery workers. Each data source has its own worker service with the logic for parsing the data from the Pub/Sub message. For example, GitHub has its own worker which only looks at events pushed to the GitHub-Hookshot Pub/Sub topic
• dashboard/
  • Contains the code for the Grafana dashboard displaying the Four Keys metrics
• data-generator/
  • Contains a Python script for generating mock GitHub or Gitlab data.
• event-handler/
  • Contains the code for the event-handler, which is the public service that accepts incoming webhooks.
• queries/
  • Contains the SQL queries for creating the derived tables.
• setup/
  • Contains the code for setting up and tearing down the Four Keys pipeline. Also contains a script for extending the data sources.
• shared/
  • Contains a shared module for inserting data into BigQuery, which is used by the bq-workers
• terraform/
  • Contains Terraform modules and submodules, and examples for deploying Four Keys using Terraform.

The project uses Python 3 and supports data extraction for Cloud Build and GitHub events.

1. Fork this project.
2. Run the automation scripts, which does the following (See the setup README for more details):
   1. Create and deploy the Cloud Run webhook target and ETL workers.
   2. Create the Pub/Sub topics and subscriptions.
   3. Enable the Google Secret Manager and create a secret for your GitHub repo.
   4. Create a BigQuery dataset, tables and views.
   5. Output a URL for the newly generated Grafana dashboard.
3. Set up your development environment to send events to the webhook created in the second step.
   1. Add the secret to your GitHub webhook.

NOTE: Make sure you don't use "Squash Merging" in Git when merging back into trunk. This breaks the link between the commit into trunk and the commits from the branch you developed on and as thus it is not possible to measure "Time to Change" on these commits. It is possible to disable this feature in the settings of your repo

The setup script includes an option to generate mock data. Generate mock data to play with and test the Four Keys project.

The data generator creates mocked GitHub events, which are ingested into the table with the source “githubmock.” It creates following events:

• 5 mock commits with timestamps no earlier than a week ago
  • Note: Number can be adjusted
• 1 associated deployment
• Associated mock incidents
  • Note: By default, less than 15% of deployments create a mock incident. This threshold can be adjusted in the script.

To run outside of the setup script:

1. Ensure that you’ve saved your webhook URL and secret in your environment variables:

   export WEBHOOK={your event handler URL}
   export SECRET={your event-handler secret}

2. Run the following command:

   python3 data-generator/generate_data.py --vc_system=github

   You can see these events being run through the pipeline:

   • The event handler logs show successful requests
   • The Pub/Sub topic show messages posted
   • The BigQuery GitHub parser show successful requests

3. You can query the events_raw table directly in BigQuery:

   SELECT * FROM four_keys.events_raw WHERE source = 'githubmock';

The scripts consider some events to be “changes”, “deploys”, and “incidents.” You may want to reclassify one or more of these events, for example, if you want to use a label for your incidents other than “incident.” To reclassify one of the events in the table, no changes are required on the architecture or code of the project.

1. Update the view in BigQuery for the following tables:

   • four_keys.changes
   • four_keys.deployments
   • four_keys.incidents

   To update the view, we recommend that you update the sql files in the queries folder, rather than in the BigQuery UI.

2. Once you've edited the SQL, run terraform apply to update the view that populates the table:

   cd ./setup && terraform apply

Notes:

• To feed into the dashboard, the table name should be one of changes, deployments, incidents.

To add other event sources:

1. Add to the AUTHORIZED_SOURCES in sources.py.
   1. If create a verification function, add the function to the file as well.
2. Run the new_source.sh script in the setup directory. This script creates a Pub/Sub topic, a Pub/Sub subscription, and the new service using the new_source_template .
   1. Update the main.py in the new service to parse the data properly.
3. Update the BigQuery script to classify the data properly.

If you add a common data source, please submit a pull request so that others may benefit from the functionality.

This project uses nox to manage tests. The noxfile defines what tests run on the project. It’s set up to run all the pytest files in all the directories, as well as run a linter on all directories.

To run nox:

1. Ensure that nox is installed:

   pip install nox

2. Use the following command to run nox:

   python3 -m nox

To list all the test sessions in the noxfile, use the following command:

python3 -m nox -l

Once you have the list of test sessions, you can run a specific session with:

python3 -m nox -s "{name_of_session}" 

The "name_of_session" will be something like "py-3.6(folder='.....').

*indicates that the ID is generated by the original system, such as GitHub.

This table will be used to create the following three derived tables:

Note: Deployments and changes have a many to one relationship. Table only contains successful deployments.

The dashboard displays all four metrics with daily systems data, as well as a current snapshot of the last 90 days. The key metric definitions and description of the color coding are below.

For a deeper understanding of the metrics and intent of the dashboard, see the 2019 State of DevOps Report.

For details about how Four Keys calculates each metric in this dashboard, see the Four Keys Metrics calculation doc.

This Four Keys project defines the key metrics as follows:

Deployment Frequency

• How frequently a team successfully releases to production, e.g., daily, weekly, monthly, yearly.

Lead Time for Changes

• The median amount of time for a commit to be deployed into production.

Time to Restore Services

• For a failure, the median amount of time between the deployment which caused the failure and the remediation. The remediation is measured by closing an associated bug / incident report.

Change Failure Rate

• The number of failures per the number of deployments. For example, if there are four deployments in a day and one causes a failure, that is a 25% change failure rate.

For more information on the calculation of the metrics, see the METRICS.md

The dashboard has color coding to show the performance of each metric. Green is strong performance, yellow is moderate performance, and red is poor performance. Below is the description of the data that corresponds to the color for each metric.

The data ranges used for this color coding roughly follows the ranges for elite, high, medium, and low performers that are described in the 2019 State of DevOps Report.

Deployment Frequency

• Purple: On-Demand (multiple deploys per day)
• Green: Daily, Weekly
• Yellow: Monthly
• Red: Between once per month and once every 6 months.
  • This is expressed as “Yearly.”

Lead Time to Change

• Purple: Less than one day
• Green: Less than one week
• Yellow: Between one week and one month
• Red: Between one month and 6 months.
• Red: Anything greater than 6 months
  • This is expressed as “One year.”

Time to Restore Service

• Purple: Less than one hour
• Green: Less than one day
• Yellow: Less than one week
• Red: Between one week and a month
  • This is expressed as “One month”
• Red: Anything greater than a month
  • This is expressed as “One year”

Change Failure Rate

• Green: Less than 15%
• Yellow: 16% - 45%
• Red: Anything greater than 45%

The following chart is from the 2019 State of DevOps Report, and shows the ranges of each key metric for the different category of performers.

Disclaimer: This is not an officially supported Google product