Shahzad Bhatti Welcome to my ramblings and rants!

Phased deployment is a software deployment strategy where new software features, changes, or updates are gradually released to a subset of a product’s user base rather than to the entire user community at once. The goal is to limit the impact of any potential negative changes and to catch issues before they affect all users. It’s often a part of modern Agile and DevOps practices, allowing teams to validate software in stages—through testing environments, to specific user segments, and finally, to the entire user base. The phased deployment solves following issues with the production changes:

1. Risk Mitigation: Deploying changes all at once can be risky, especially for large and complex systems. Phase deployment helps to mitigate this risk by gradually releasing the changes and carefully monitoring their impact.
2. User Experience: With phased deployment, if something goes wrong, it affects only a subset of users. This protects the larger user base from potential issues and negative experiences.
3. Performance Bottlenecks: By deploying in phases, you can monitor how the system performs under different loads, helping to identify bottlenecks and scaling issues before they impact all users.
4. Immediate Feedback: Quick feedback loops with stakeholders and users are established. This immediate feedback helps in quick iterations and refinements.
5. Resource Utilization: Phased deployment allows for better planning and use of resources. You can allocate just the resources you need for each phase, reducing waste.

The phased deployment applies following approaches for detecting production issues early in the deployment process:

1. Incremental Validation: As each phase is a limited rollout, you can carefully monitor and validate that the software is working as expected. This enables early detection of issues before they become widespread.
2. Isolation of Issues: If an issue does arise, its impact is restricted to a smaller subset of the system or user base. This makes it easier to isolate the problem, fix it, and then proceed with the deployment.
3. Rollbacks: In the event of a problem, it’s often easier to rollback changes for a subset of users than for an entire user base. This allows for quick recovery with minimal impact.
4. Data-driven Decisions: The metrics and logs gathered during each phase can be invaluable for making informed decisions, reducing the guesswork, and thereby reducing errors.
5. User Feedback: By deploying to a limited user set first, you can collect user feedback that can be crucial for understanding how the changes are affecting user interaction and performance. This provides another opportunity for catching issues before full-scale deployment.
6. Best Practices and Automation: Phase deployment often incorporates industry best practices like blue/green deployments, canary releases, and feature flags, all of which help in minimizing errors and ensuring a smooth release.

Building CI/CD Process for Phased Deployment

Continuous Integration (CI)

Continuous Integration (CI) is a software engineering practice aimed at regularly merging all developers’ working copies of code to a shared mainline or repository, usually multiple times a day. The objective is to catch integration errors as quickly as possible and ensure that code changes by one developer are compatible with code changes made by other developers in the team. The practice defines following steps for integrating developers’ changes:

1. Code Commit: Developers write code in their local environment, ensuring it meets all coding guidelines and includes necessary unit tests.
2. Pull Request / Merge Request: When a developer believes their code is ready to be merged, they create a pull request or merge request. This action usually triggers the CI process.
3. Automated Build and Test: The CI server automatically picks up the new code changes that may be in a feature branch and initiates a build and runs all configured tests.
4. Code Review: Developers and possibly other stakeholders review the test and build reports. If errors are found, the code is sent back for modification.
5. Merge: If everything looks good, the changes are merged into main branch of the repository.
6. Automated Build: After every commit, automated build processes compile the source code, create executables, and run unit/integration/functional tests.
7. Automated Testing: This stage automatically runs a suite of tests that can include unit tests, integration tests, test coverage and more.
8. Reporting: Generate and publish reports detailing the success or failure of the build, lint/FindBugs, static analysis (Fortify), dependency analysis, and tests.
9. Notification: Developers are notified about the build and test status, usually via email, Slack, or through the CI system’s dashboard.
10. Artifact Repository: Store the build artifacts that pass all the tests for future use.

Above continuous integration process allows immediate feedback on code changes, reduces integration risk, increases confidence, encourages better collaboration and improves code quality.

Continuous Deployment (CD)

The Continuous Deployment (CD) further enhances this by automating the delivery of applications to selected infrastructure environments. Where CI deals with build, testing, and merging code, CD takes the code from CI and deploys it directly into the production environment, making changes that pass all automated tests immediately available to users. The above workflow for Continuous Integration is added with following additional steps:

1. Code Committed: Once code passes all tests during the CI phase, it moves onto CD.
2. Pre-Deployment Staging: Code may be deployed to a staging area where it undergoes additional tests that could be too time-consuming or risky to run during CI. The staging environment can be divided into multiple environments such as alpha staging for integration and sanity testing, beta staging for functional and acceptance testing, and gamma staging environment for chaos, security and performance testing.
3. Performance Bottlenecks: The staging environment may execute security, chaos, shadow and performance tests to identify bottlenecks and scaling issues before deploying code to the production.
4. Deployment to Production: If the code passes all checks, it’s automatically deployed to production.
5. Monitoring & Verification: After deployment, automated systems monitor application health and performance. Some systems use Canary Testing to continuously verify that deployed features are behaving as expected.
6. Rollback if Necessary: If an issue is detected, the CD system can automatically rollback to a previous, stable version of the application.
7. Feedback Loop: Metrics and logs from the deployed application can be used to inform future development cycles.

The Continuous Deployment process results in faster time-to-market, reduced risk, greater reliability, improved quality, and better efficiency and resource utilization.

Phased Deployment Workflow

Phased Deployment allows rolling out a change in increments rather than deploying it to all servers or users at once. This strategy fits naturally into a Continuous Integration/Continuous Deployment (CI/CD) pipeline and can significantly reduce the risks associated with releasing new software versions. The CI/CD workflow is enhanced as follows:

1. Code Commit & CI Process: Developers commit code changes, which trigger the CI pipeline for building and initial testing.
2. Initial Deployment to Dev Environment: After passing CI, the changes are deployed to a development environment for further testing.
3. Automated Tests and Manual QA: More comprehensive tests are run. This could also include security, chaos, shadow, load and performance tests.
4. Phase 1 Deployment (Canary Release): Deploy the changes to a small subset of the production environment or users and monitor closely. If you operate in multiple data centers, cellular architecture or geographical regions, consider initiating your deployment in the area with the fewest users to minimize the impact of potential issues. This approach helps in reducing the “blast radius” of any potential problems that may arise during deployment.
5. PreProd Testing: In the initial phase, you may optionally first deploy to a special pre-prod environment where you only execute canary testing simulating user requests without actually user-traffic with the production infrastructure so that you can further reduce blast radius for impacting customer experience.
6. Baking Period: To make informed decisions about the efficacy and reliability of your code changes, it’s crucial to have a ‘baking period’ where the new code is monitored and tested. During this time, you’ll gather essential metrics and data that help in confidently determining whether or not to proceed with broader deployments.
7. Monitoring and Metrics Collection: Use real-time monitoring tools to track system performance, error rates, and other KPIs.
8. Review and Approval: If everything looks good, approve the changes for the next phase. If issues are found, roll back and diagnose.
9. Subsequent Phases: Roll out the changes to larger subsets of the production environment or user base, monitoring closely at each phase. The subsequent phases may use a simple static scheme by adding X servers or user-segments at a time or geometric scheme by exponentially doubling the number of servers or user-segments after each phase. For instance, you can employ mathematical formulas like 2^N or 1.5^N, where N represents the phase number, to calculate the scope of the next deployment phase. This could pertain to the number of servers, geographic regions, or user segments that will be included.
10. Subsequent Baking Periods: As confidence in the code increases through successful earlier phases, the duration of subsequent ‘baking periods’ can be progressively shortened. This allows for an acceleration of the phased deployment process until the changes are rolled out to all regions or user segments.
11. Final Rollout: After all phases are successfully completed, deploy the changes to all servers and users.
12. Continuous Monitoring: Even after full deployment, keep running Canary Tests for validation and monitoring to ensure everything is working as expected.

Thus, phase deployment further mitigates risk, improves user experience, monitoring and resource utilization. If a problem is identified, it’s much easier to roll back changes for a subset of users, reducing negative impact.

Criteria for Selecting Targets for Phased Deployment

When choosing targets for phased deployment, you have multiple options, including cells within a Cellular Architecture, distinct Geographical Regions, individual Servers within a data center, or specific User Segments. Here are some key factors to consider while making your selection:

1. Risk Assessment: The first step in selecting cells, regions, or user-segments is to conduct a thorough risk assessment. The idea is to understand which areas are most sensitive to changes and which are relatively insulated from potential issues.
2. User Activity: Regions with lower user activity can be ideal candidates for the initial phases, thereby minimizing the impact if something goes wrong.
3. Technical Constraints: Factors such as server capacity, load balancing, and network latency may also influence the selection process.
4. Business Importance: Some user-segments or regions may be more business-critical than others. Starting deployment in less critical areas can serve as a safe first step.
5. Gradual Scale-up: Mathematical formulas like 2^N or 1.5^N where N is the phase number can be used to gradually increase the size of the deployment target in subsequent phases.
6. Performance Metrics: Utilize performance metrics like latency, error rates, etc., to decide on next steps after each phase.

Always start with the least risky cells, regions, or user-segments in the initial phases and then use metrics and KPIs to gain confidence in the deployed changes. After gaining confidence from initial phases, you may initiate parallel deployments cross multiple environments, perhaps even in multiple regions simultaneously. However, you should ensure that each environment has its independent monitoring to quickly identify and isolate issues. The rollback strategy should be tested ahead of time to ensure it works as expected before parallel deployment. You should keep detailed logs and documentation for each deployment phase and environment.

Cellular Architecture

Phased deployment can work particularly well with a cellular architecture, offering a systematic approach to gradually release new code changes while ensuring system reliability. In cellular architecture, your system is divided into isolated cells, each capable of operating independently. These cells could represent different services, geographic regions, user segments, or even individual instances of a microservices-based application. For example, you can identify which cells will be the first candidates for deployment, typically those with the least user traffic or those deemed least critical.

The deployment process begins by introducing the new code to an initial cell or a small cluster of cells. This initial rollout serves as a pilot phase, during which key performance indicators such as latency, error rates, and other metrics are closely monitored. If the data gathered during this ‘baking period’ indicates issues, a rollback is triggered. If all goes well, the deployment moves on to the next set of cells. Subsequent phases follow the same procedure, gradually extending the deployment to more cells. Utilizing phased deployment within a cellular architecture helps to minimize the impact area of any potential issues, thus facilitating more effective monitoring, troubleshooting, and ultimately a more reliable software release.

Blue/Green Deployment

The phased deployment can employ the Blue/Green deployment strategy where two separate environments, often referred to as “blue” and “green,” are configured. Both are identical in terms of hardware, software, and settings. The Blue environment runs the current version of the application and serves all user traffic. The Green is a clone of the Blue environment where the new version of the application is deployed. This helps phased deployment because one environment is always live, thus allow for releasing new features without downtime. If issues are detected, traffic can be quickly rerouted back to the Blue environment, thus minimizing the risk and impact of new deployments. The Blue/Green deployment includes following steps:

1. Preparation: Initially, both Blue and Green environments run the current version of the application.
2. Initial Rollout: Deploy the new application code or changes to the Green environment.
3. Verification: Perform tests on the Green environment to make sure the new code changes are stable and performant.
4. Partial Traffic Routing: In a phased manner, start rerouting a small portion of the live traffic to the Green environment. Monitor key performance indicators like latency, error rates, etc.
5. Monitoring and Decision: If any issues are detected during this phase, roll back the traffic to the Blue environment without affecting the entire user base. If metrics are healthy, proceed to the next phase.
6. Progressive Routing: Gradually increase the percentage of user traffic being served by the Green environment, closely monitoring metrics at each stage.
7. Final Cutover: Once confident that the Green environment is stable, you can reroute 100% of the traffic from the Blue to the Green environment.
8. Fallback: Keep the Blue environment operational for a period as a rollback option in case any issues are discovered post-switch.
9. Decommission or Sync: Eventually, decommission the Blue environment or synchronize it to the Green environment’s state for future deployments.

Automated Testing

CI/CD and phased deployment strategy relies on automated testing to validate changes to a subset of infrastructure or users. The automated testing includes a variety of testing types that should be performed at different stages of the process such as:involved:

• Functional Testing: testing the new feature or change before initiating phased deployment to make sure it performs its intended function correctly.
• Security Testing: testing for vulnerabilities, threats, or risks in a software application before phased deployment.
• Performance Testing: testing how the system performs under heavy loads or large amounts of data before and during phased deployment.
• Canary Testing: involves rolling out the feature to a small, controlled group before making it broadly available. This also includes testing via synthetic transactions by simulating user requests. This is executed early in the phased deployment process, however testing via synthetic transactions is continuously performed in background.
• Shadow Testing: In this method, the new code runs alongside the existing system, processing real data requests without affecting the actual system.
• Chaos Testing: This involves intentionally introducing failures to see how the system reacts. It is usually run after other types of testing have been performed successfully, but before full deployment.
• Load Testing: test the system under the type of loads it will encounter in the real world before the phased deployment.
• Stress Testing: attempt to break the system by overwhelming its resources. It is executed late in the phased deployment process, but before full deployment.
• Penetration Testing: security testing where testers try to ‘hack’ into the system.
• Usability Testing: testing from the user’s perspective to make sure the application is easy to use in early stages of phased deployment.

Monitoring

Monitoring plays a pivotal role in validating the success of phased deployments, enabling teams to ensure that new features and updates are not just functional, but also reliable, secure, and efficient. By constantly collecting and analyzing metrics, monitoring offers real-time feedback that can inform deployment decisions. Here’s how monitoring can help with the validation of phased deployments:

• Real-Time Metrics and Feedback: collecting real-time metrics on system performance, user engagement, and error rates.
• Baking Period Analysis: using a “baking” period where the new code is run but closely monitored for any anomalies.
• Anomaly Detection: using automated monitoring tools to flag anomalies in real-time, such as a spike in error rates or a drop in user engagement.
• Benchmarking: establishing performance benchmarks based on historical data.
• Compliance and Security Monitoring: monitoring for unauthorized data access or other security-related incidents.
• Log Analysis: using aggregated logs to show granular details about system behavior.
• User Experience Monitoring: tracking metrics related to user interactions, such as page load times or click-through rates.
• Load Distribution: monitoring how well the new code handles different volumes of load, especially during peak usage times.
• Rollback Metrics: tracking of the metrics related to rollback procedures.
• Feedback Loops: using monitoring data for continuous feedback into the development cycle.

Feature Flags

Feature flags, also known as feature toggles, are a powerful tool in the context of phased deployments. They provide developers and operations teams the ability to turn features on or off without requiring a code deployment. This capability synergizes well with phased deployments by offering even finer control over the feature release process. The benefits of feature flags include:

• Gradual Rollout: Gradually releasing a new feature to a subset of your user base.
• Targeted Exposure: Enable targeted exposure of features to specific user segments based on different attributes like geography, user role, etc.
• Real-world Testing: With feature flags, you can perform canary releases, blue/green deployments, and A/B tests in a live environment without affecting the entire user base.
• Risk Mitigation: If an issue arises during a phased deployment, a feature can be turned off immediately via its feature flag, preventing any further impact.
• Easy Rollback: Since feature flags allow for features to be toggled on and off, rolling back a feature that turns out to be problematic is straightforward and doesn’t require a new deployment cycle.
• Simplified Troubleshooting: Feature flags simplify the troubleshooting process since you can easily isolate problems and understand their impact.
• CICD Compatibility: Feature flags are often used in conjunction with CI/CD pipelines, allowing for features to be integrated into the main codebase even if they are not yet ready for public release.
• Conditional Logic: Advanced feature flags can include conditional logic, allowing you to automate the criteria under which features are exposed to users.

A/B Testing

A/B testing, also known as split testing, is an experimental approach used to compare two or more versions of a web page, feature, or other variables to determine which one performs better. In the context of software deployment, A/B testing involves rolling out different variations (A, B, etc.) of a feature or application component to different subsets of users. Metrics like user engagement, conversion rates, or performance indicators are then collected to statistically validate which version is more effective or meets the desired goals better. Phased deployment and A/B testing can complement each other in a number of ways:

• Both approaches aim to reduce risk but do so in different ways.
• Both methodologies are user-focused but in different respects.
• A/B tests offer a more structured way to collect user-related metrics, which can be particularly valuable during phased deployments.
• Feature flags, often used in both A/B testing and phased deployment, give teams the ability to toggle features on or off for specific user segments or phases.
• If an A/B test shows one version to be far more resource-intensive than another, this information could be invaluable for phased deployment planning.
• The feedback from A/B testing can feed into the phased deployment process to make real-time adjustments.
• A/B testing can be included as a step within a phase of a phased deployment, allowing for user experience quality checks.
• In a more complex scenario, you could perform A/B testing within each phase of a phased deployment.

Safe Rollback

Safe rollback is a critical aspect of a robust CI/CD pipeline, especially when implementing phased deployments. Here’s how safe rollback can be implemented:

• Maintain versioned releases of your application so that you can easily identify which version to rollback to.
• Always have backward-compatible database changes so that rolling back the application won’t have compatibility issues with the database.
• Utilize feature flags so that you can disable problematic features without needing to rollback the entire deployment.
• Implement comprehensive monitoring and logging to quickly identify issues that might necessitate a rollback.
• Automate rollback procedures.
• Keep the old version (Blue) running as you deploy the new version (Green). If something goes wrong, switch the load balancer back to the old version.
• Use Canaray releases to roll out the new version to a subset of your infrastructure. If errors occur, halt the rollout and revert the canary servers to the old version.

Following steps should be applied on rollback:

1. Immediate Rollback: As soon as an issue is detected that can’t be quickly fixed, trigger the rollback procedure.
2. Switch Load Balancer: In a Blue/Green setup, switch the load balancer back to route traffic to the old version.
3. Database Rollback: If needed and possible, rollback the database changes. Be very cautious with this step, as it can be risky.
4. Feature Flag Disablement: If the issue is isolated to a particular feature that’s behind a feature flag, consider disabling that feature.
5. Validation: After rollback, validate that the system is stable. This should include health checks and possibly smoke tests.
6. Postmortem Analysis: Once the rollback is complete and the system is stable, conduct a thorough analysis to understand what went wrong.

One critical consideration to keep in mind is ensuring both backward and forward compatibility, especially when altering communication protocols or serialization formats. For instance, if you update the serialization format and the new code writes data in this new format, the old code may become incompatible and unable to read the data if a rollback is needed. To mitigate this risk, you can deploy an intermediate version that is capable of reading the new format without actually writing in it.

Here’s how it works:

1. Phase 1: Release an intermediate version of the code that can read the new serialization format like JSON, but continues to write in the old format. This ensures that even if you have to roll back after advancing further, the “old” version is still able to read the newly-formatted data.
2. Phase 2: Once the intermediate version is fully deployed and stable, you can then roll out the new code that writes data in the new format.

By following this two-phase approach, you create a safety net, making it possible to rollback to the previous version without encountering issues related to data format incompatibility.

Sample CI/CD Pipeline

Following is a sample GitHub Actions workflow .yml file that includes elements for build, test and deployment. You can create a new file in your repository under .github/workflows/ called ci-cd.yml:

name: CI/CD Pipeline with Phased Deployment

on:
  push:
    branches:
      - main

env:
  IMAGE_NAME: my-java-app

jobs:
  
  unit-test:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout Code
        uses: actions/checkout@v2

      - name: Run Unit Tests
        run: mvn test

  integration-test:
    needs: unit-test
    runs-on: ubuntu-latest
    steps:
      - name: Run Integration Tests
        run: mvn integration-test

  functional-test:
    needs: integration-test
    runs-on: ubuntu-latest
    steps:
      - name: Run Functional Tests
        run: ./run-functional-tests.sh  # Assuming you have a script for functional tests

  load-test:
    needs: functional-test
    runs-on: ubuntu-latest
    steps:
      - name: Run Load Tests
        run: ./run-load-tests.sh  # Assuming you have a script for load tests

  security-test:
    needs: load-test
    runs-on: ubuntu-latest
    steps:
      - name: Run Security Tests
        run: ./run-security-tests.sh  # Assuming you have a script for security tests

  build:
    needs: security-test
    runs-on: ubuntu-latest
    steps:
      - name: Build and Package
        run: |
          mvn clean package
          docker build -t ${{ env.IMAGE_NAME }} .

  phase_one:
    needs: build
    runs-on: ubuntu-latest
    steps:
      - name: Deploy to Phase One Cells
        run: ./deploy-to-phase-one.sh # Your custom deploy script for Phase One

      - name: Canary Testing
        run: ./canary-test-phase-one.sh # Your custom canary testing script for Phase One

      - name: Monitoring
        run: ./monitor-phase-one.sh # Your custom monitoring script for Phase One

      - name: Rollback if Needed
        run: ./rollback-phase-one.sh # Your custom rollback script for Phase One
        if: failure()

  phase_two:
    needs: phase_one
    # Repeat the same steps as phase_one but for phase_two
    # ...

  phase_three:
    needs: phase_two
    # Repeat the same steps as previous phases but for phase_three
    # ...

  phase_four:
    needs: phase_three
    # Repeat the same steps as previous phases but for phase_four
    # ...

  phase_five:
    needs: phase_four
    # Repeat the same steps as previous phases but for phase_five
    # ...

run-functional-tests.sh, run-load-tests.sh, and run-security-tests.sh would contain the logic for running functional, load, and security tests, respectively. You might use tools like Selenium for functional tests, JMeter for load tests, and OWASP ZAP for security tests.

Conclusion

Phased deployment, when coupled with effective monitoring, testing, and feature flags, offers numerous benefits that enhance the reliability, security, and overall quality of software releases. Here’s a summary of the advantages:

1. Reduced Risk: By deploying changes in smaller increments, you minimize the impact of any single failure, thereby reducing the “blast radius” of issues.
2. Real-Time Validation: Continuous monitoring provides instant feedback on system performance, enabling immediate detection and resolution of issues.
3. Enhanced User Experience: Phased deployment allows for real-time user experience monitoring, ensuring that new features or changes meet user expectations and don’t negatively impact engagement.
4. Data-Driven Decision Making: Metrics collected during the “baking” period and subsequent phases allow for data-driven decisions on whether to proceed with the deployment, roll back, or make adjustments.
5. Security & Compliance: Monitoring for compliance and security ensures that new code doesn’t introduce vulnerabilities, keeping the system secure throughout the deployment process.
6. Efficient Resource Utilization: The gradual rollout allows teams to assess how the new changes affect system resources, enabling better capacity planning and resource allocation.
7. Flexible Rollbacks: In the event of a failure, the phased approach makes it easier to roll back changes, minimizing disruption and maintaining system stability.
8. Iterative Improvement: Metrics and feedback collected can be looped back into the development cycle for ongoing improvements, making future deployments more efficient and reliable.
9. Optimized Testing: Various forms of testing like functional, security, performance, and canary can be better focused and validated against real-world scenarios in each phase.
10. Strategic Rollout: Feature flags allow for even more granular control over who sees what changes, enabling targeted deployments and A/B testing.
11. Enhanced Troubleshooting: With fewer changes deployed at a time, identifying the root cause of any issues becomes simpler, making for faster resolution.
12. Streamlined Deployment Pipeline: Incorporating phased deployment into CI/CD practices ensures a smoother, more controlled transition from development to production.

By strategically implementing these approaches, phased deployment enhances the resilience and adaptability of the software development lifecycle, ensuring a more robust, secure, and user-friendly product.