Software Deployment and It's Strategies
Overview
In this article, we will discuss about software deployment and different strategies to deploy any software on production
Software Deployment
Software deployment refers to the process of making a software application or system available for use. It involves a series of tasks and activities that aim to ensure the configuration, testing, and release of software. The deployment process can vary depending on factors such as the type of software, the target platform, and the specific requirements of the organization. Following are the some strategies by using this we can release software on production
- Big Bang
- Ramped / Rolling Update
- Blue Green Deployment
- Canary Deployment
- AB Testing
- Shadow
Deployment Strategies
Deployment strategies define how software changes are released and made available to users. The choice of a deployment strategy depends on factors such as the nature of the application, user impact tolerance, and the organization's release cycle. Here are some common deployment strategies:
Big Bang Deployment
The Big Bang deployment strategy refers to an all-at-once approach. It means the new software version is made live simultaneously, replacing the older version. While this approach can be simple, it also carries risks. If there's a problem, the entire system can be affected.
In some cases, it is not feasible to slowly rollout updates of release, particularly when creating a new version of a product or release. In such cases, the only logical solution is a deployment that occurs instantly, like flipping a switch. This deployment method is often referred to as big bang adoption, which requires that all users of a system are moved to the new system at a specific date and time.
Example: Big Bang deployment might be suitable for small systems or non-critical applications.
For instance, deploying a small internal tool within a company that won't significantly impact operations if something goes wrong or any task scheduler which runs on particular time.
Ramped / Rolling Update Deployment
The "ramped" or "rolling update" deployment strategy involves gradually deploying updates to different parts of the system or user base. Instead of releasing changes all at once, updates are incrementally applied to avoid potential widespread issues and to ensure a smoother transition. This strategy allows for continuous delivery of updates with minimal downtime for the entire system. It involves careful coordination to monitor and manage the deployment process as changes are rolled out progressively.
Example: Rolling update deployment suitable for large scale system, high availabilty requirements, user facing application, complex dependent systems.
Lets take a example of large e-commerce platform with millions of users plans to introduce a new feature that affects the user interface and checkout process. By using a rolling update strategy, the platform can gradually release the new feature to a subset of users, ensuring a smoother transition and addressing any issues before a full release
Blue-Green Deployment
Blue-Green Deployment is a deployment strategy that involves having two identical environments, referred to as "Blue" (the current version) and "Green" (the new version). Only one environment is active and serves production traffic at a time.
Here's an overview of the Blue-Green Deployment strategy:
"Blue" environment represents the currently active production environment. "Green" environment represents the new version or changes that will be deployed.
The "Blue" environment is serving live production traffic, and users are interacting with this version. The new version or changes are deployed to the "Green" environment in isolation from the production environment.
The "Green" environment undergoes thorough testing, including functional, performance, and acceptance testing. This allows for a comprehensive validation of the new version before it is exposed to end-users.
Once testing is successful, the traffic is gradually switched from the "Blue" environment to the "Green" environment. This can be done using load balancers or DNS changes. Users are now interacting with the new version in the "Green" environment.
The system is closely monitored to ensure that the new version in the "Green" environment performs as expected. If issues are detected, traffic can be quickly switched back to the "Blue" environment, effectively rolling back the deployment.
Once the "Green" environment has proven to be stable and successful, it becomes the new production environment. The "Blue" environment, which now represents the previous version, can be retained for a period as a backup or eventually decommissioned.
Example: Blue green deployment suitable for high availabilty requirements, frequently released applications, user facing application, mission critical systems, geographically distributed systems, large and complex applications and requirement of production testing before live. Lets take sample of e commerce platform where we require high availiability and frequent releases of features.
Canary Deployment
Canary deployment is a deployment strategy that involves releasing a new version of an application or service to a small subset of users before a full rollout. A small subset of users receives the new version while the majority continues to use the old one. If the new version performs well, it is gradually rolled out to the entire user base.
Here's an overview of the Canary Deployment strategy:
The existing version of the application (let's call it Version 1.0) is currently in production and serving all users.
A new version of the application (Version 2.0) is ready for release. Rather than deploying it to all users at once, Canary Deployment involves releasing it to a limited, carefully selected subset of users.
A small percentage of users, often chosen based on specific criteria (e.g., geographic location, user behavior, or user roles), are designated as the canary group. These users will be the first to experience the new version.
Version 2.0 is deployed to the canary group while the majority of users continue to interact with Version 1.0.
The system is closely monitored during the Canary Deployment phase to detect any issues, such as performance issues, errors, or unexpected behavior. Automated monitoring tools and real-time analytics are often used to assess the impact on the canary group.
The canary users provide feedback on their experience with Version 2.0. Based on this feedback and the monitoring results, developers may iterate on the new version to address any identified issues or make improvements.
If Version 2.0 performs well with the canary group and no critical issues are detected, the deployment is gradually expanded to a larger percentage of users. This process continues iteratively, with incremental increases in the user base.
Once the new version has been successfully deployed to the entire user base and has proven to be stable, the deployment is considered complete. If issues are discovered at any point, deployment can be halted, rolled back, or adjustments can be made before proceeding.
Example: Canary Deployment is a deployment strategy that is particularly useful in various scenarios where organizations want to minimize risk, gather real-world feedback, and ensure a smooth transition to a new version or features of their application. Here are cases where Canary Deployment is commonly beneficial: new feaure introductions, performance optimizations, User interface changes, Infrastructure and configuration changes, large scale systems and Geographically distributed users.
Shadow Deployment
Shadow Deployment is a deployment strategy in which a new version of an application is deployed alongside the existing production version, but the shadow version does not serve live user traffic. Instead, it runs in the background, allowing organizations to observe and evaluate its performance, behavior, and interactions with the existing system without impacting end-users.
Here's an overview of the Shadow Deployment strategy:
The current production version (Version 1.0) is actively serving user traffic.
The new version of the application (Version 2.0) is deployed in parallel to the existing production environment. However, Version 2.0 does not handle live user requests or affect the production system.
Version 2.0 runs in the background, and organizations monitor its behavior, performance, and interactions with the existing infrastructure. Automated tools and manual observation help identify potential issues, errors, or unexpected behavior.
Shadow Deployment allows for real-world testing without exposing users to the new version. It helps validate the new version's compatibility with the existing infrastructure and provides insights into its impact on system resources.
Organizations analyze the performance metrics of Version 2.0, such as response times, resource utilization, and error rates, to ensure it meets performance expectations.
Data interactions in the shadow environment are monitored to ensure that data integrity is maintained, especially in scenarios involving database schema changes or data migrations.
After successful monitoring and validation, organizations can proceed with an incremental rollout of the new version to a subset of users or specific environments.
Depending on the results of the shadow deployment, organizations can make an informed decision to either fully roll out the new version to all users or roll back if unexpected issues are identified.
Example: Shadow Deployment is particularly useful for organizations seeking a low-risk strategy to introduce changes, validate performance, and ensure a seamless transition to a new version or set of features in their applications. It aligns well with the principles of risk reduction and gradual adoption of changes in a production environment. We can use this strategy in database schema changes, infrastructure upgrade, rollout of new endpoints and integration with external systems.
A/B Testing Deployment
A/B Testing, also known as split testing, is a deployment strategy used to compare two or more versions of a product or feature to determine which one performs better. It involves dividing users into groups and exposing each group to a different variant, then analyzing the results to make data-driven decisions.
Here's an overview of the A/B Testing Deployment Strategy:
The current version of the product or feature (Version 1.0 and Version 2.0) is in place and serving users.
Users are divided into segments, with each segment assigned to one of the version. The segmentation can be random or based on specific criteria, such as geographic location, user behavior, demographics or device type.
The deployment may involve changes to the user interface, features, content, or any other aspect being tested.
Both versions are monitored, and relevant data is collected, such as user interactions, conversion rates, click-through rates, and other performance metrics. The goal is to assess how each version performs against the defined metrics.
Statistical analysis is performed on the collected data to determine the statistical significance of the differences between interfaces. This analysis helps identify whether the observed variations are statistically meaningful or simply due to chance.
Based on the statistical analysis, organizations can determine which version performs better in terms of the defined metrics. The results guide decision-making, and organizations may choose to implement the version that demonstrates superior performance.
Depending on the results, the preferred version may be rolled out to the entire user base. Alternatively, organizations may choose to iterate on the design, features, or content based on the insights gained from the A/B test.
Example: This deployment stratefy is widely used to test conversion of a given feature and only roll-out the version that converts the most. We can use this strategy website design changes, email marketing campaigns, mobile app features, content marketing and ad campaigns.
Strategies Comparision
| Strategies/ Parameters | Zero Downtime | Real Traffic Testing | Targeted User | Infra Cost | Rollback Duration | Negative User Impact | Setup Complexity |
| Big Bang | No | No | No | Low | High | High | Low |
| Rolling Update | Yes | No | No | Low | High | Low | Low |
| Blue Green | Yes | No | No | High | Low | Medium | Medium |
| Canary | Yes | Yes | No | Low | Medium | Low | Medium |
| Shadow | Yes | Yes | No | High | Low | Low | High |
| A/B Testing | Yes | Yes | Yes | Low | Medium | Low | High |
Conclusion
Deployment strategies play a crucial role in the software development lifecycle, influencing how changes are introduced and managed in a production environment. Choosing the right deployment strategy involves considering factors such as the impact on users, the criticality of the application, the complexity of the changes, and the organization's tolerance for risk. Many modern deployment strategies also leverage continuous integration and continuous deployment (CI/CD) practices to automate and streamline the release process
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