Application deployment in 2024: Process, strategies, and examples

What is application deployment?

Application deployment is the process of releasing and installing software applications or updates. This procedure ensures the software is correctly placed within a specific environment and ready for use by its end-customers. Deployment encompasses several stages, from preparation and configuration to final installation, ensuring that both the software and the underlying system resources are ready for operation. It’s a critical aspect of the software development life cycle, aiming to deliver functional software that meets user needs.

Deployment tasks often include related activities like monitoring for issues post-installation and executing rollback procedures if necessary. These steps ensure reliability while minimizing disruptions. Modern deployment processes leverage automated tools to reduce errors and deliver software earlier. An effective deployment process enhances software reliability and agility, improves productivity for development and operations teams, and ultimately results in more satisfied users.

This is part of a series of articles about software deployment.

A brief history of application deployment

The history of application deployments is deeply entangled with changes in hosting technology, architectural trends, tooling improvements, and software delivery methods. Often, a technology created decades before takes on new relevance thanks to other developments, such as FTP, which was created in 1971 but enjoyed great popularity in the 1990s due to the rise of the World Wide Web. We started with physical on-premises machines, then moved workloads into data centers, virtualized them to make them easier to create and reconfigure, then moved to lightweight isolated containers.

With this in mind, we can view the history of application deployments across the decades:

1. Physical manual deployments (1980s and earlier):
   Applications were mainly deployed to physical servers and direct physical access was typically needed to perform a deployment. To scale a server, you would have to power it down and upgrade its components.
2. Remote deployments (1990s):
   Though FTP was created in the 1970s, it had its heyday in the 1990s thanks to the world wide web (WWW). Uploading files to servers instead of physically accessing them became one of the most popular ways to deploy applications, with individual file patches often being used to bugfix.
3. Installer files and scripts (2000s):
   Instead of transferring individual files, installer files became a popular way to capture the software artifact and ensure the installation would result in the same software version being installed on each environment. Installer files could be created as part of the Continuous Integration (CI) process. The installer would prompt for variables to allow different configuration settings to be applied to each environment. An alternative solution was to script deployments using batch files to reduce the number of error-prone manual steps.
4. Continuous Delivery (CD) (2010s):
   Continuous Delivery and DevOps encouraged the automation of deployment pipelines so deployments could be made on demand at the push of a button. Deployment automation tools are created to manage deployment-specific features like environment progression and variable management so a single deployment process can be applied to all environments, locations, and customer-specific instances of an application.
5. Modern software delivery (2020s to present):
   While the fuzzy front end of development is seen as the zone of creativity and design, the deployment pipeline is seen as the software delivery factory. CD tools provide advanced features that allow deployment and operations automation across all technology stacks to be managed in one place.

What are the benefits of streamlined application deployment?

Streamlined updates

A well-structured deployment strategy makes deploying new software application versions easier and safer. Deployments can happen on demand without manual intervention, significant downtime, or disruption for the software’s users.

Streamlined deployments let you get feedback earlier and respond to market demands. The ability to deliver timely enhancements and bug fixes makes your software more competitive and increases user satisfaction. Modern deployments use automation to increase throughput and stability, which were traditionally seen as trade-offs.

Stronger security

When you deploy infrequently, dependencies remain out of date for longer, which exposes you to threats based on known vulnerabilities. When you deploy often, you can address vulnerabilities sooner, quickly promote changes to mitigate cyber threats, and swiftly address security concerns.

You can include security testing as part of your deployment pipeline, using static and dynamic security testing tools to automate the process. Including security earlier in your software delivery process reduces the potential for attacks and keeps your data safe.

Enhanced visibility

Modern application deployments bring a new level of visibility into the state and history of environments and changes to the deployment process. Dashboards provide an overview of which versions are deployed to each environment and reports track key deployment metrics to help teams improve their deployment pipeline and software delivery performance.

CD tools provide audit trails for actions and changes to simplify audits and reduce the regulatory and compliance burden on teams.

The application deployment process

1. Strategic planning

Strategic planning involves defining objectives, identifying resources, and creating a roadmap for deployment. This phase ensures that all stakeholders are aligned on the goals and timelines, reducing the risk of misunderstandings and delays. Effective strategic planning considers the target environment, potential risks, and mitigation strategies, setting the stage for a smooth deployment.

Planning includes selecting appropriate tools and processes, enabling a tailored approach to deployment. By anticipating challenges and preparing contingencies, organizations can confidently navigate complexities, ensuring the deployment process is orderly and efficient. Strategic planning lays the groundwork for a successful deployment and enhances collaboration and communication among the deployment team.

2. Development and testing

Developers should build and refine the application in small batches, with early feedback from a fast automated test suite. Changes should be committed to version control several times a day, with tests to validate that the application remains deployable at all times.

A test environment reflective of production should be used for acceptance testing with the same artifact and deployment process used for pre-production and production deployments. This ensures the deployment process is tested as frequently as the application.

3. Automated builds

Automated builds are critical to modern deployment strategies, compiling, linking, and packaging software code. Continuous Integration (CI) tools like Jenkins or GitHub Actions ensure all code changes are automatically built and tested.

Automated builds reduce manual labor, minimize errors, and facilitate frequent releases, allowing for rapid iteration and feedback. Automation mitigates the risk of human error and ensures that every build is done the same way, enhancing overall stability.

4. Testing configurations and scripts

Your configuration management and deployment process should be tested as regularly as your application. This involves using the same deployment process or configuration scripts for all environments and checking that environment-specific settings are applied correctly.

You can increase the value of your validation steps by making sure your staging environment mirrors your production setup. For example, failing to load balance requests in staging will hide bugs that only happen when different instances serve a sequence of requests.

5. Rollout and validation

Rollout and validation are the phases where the application is deployed to the production environment and its performance is assessed in real time. During rollout, teams follow a predefined plan to deploy the application efficiently, often using tools to manage and automate the process. Validation involves monitoring the application to verify that it functions correctly and meets performance expectations. Any issues are promptly addressed to ensure minimal impact on users.

Effective rollout strategies, such as phased or canary deployments, can minimize risk by gradually introducing changes and observing their effects. Once the rollout is complete, validation through testing and monitoring ensures the application is stable and performs under real-world conditions. This approach ensures that post-deployment issues are quickly identified and resolved, maintaining high service quality and reliability.

6. Ongoing performance monitoring

Performance monitoring is crucial for maintaining the health and efficiency of the deployed application. It involves continuous tracking of application metrics such as response times, error rates, and resource usage. Monitoring tools like Datadog, Nagios, or Prometheus help teams gain real-time insights into the application’s performance, quickly identifying and addressing potential issues before they impact users.

You can use performance data to help with capacity planning and make decisions about scaling. You can analyze trends and patterns in application usage to make more informed decisions about resource allocation. Effective monitoring will help you keep applications stable, responsive, and reliable. When you have a system problem, finding out before it impacts users can help reduce its impact.

Common application deployment strategies

[suggest adding a visual listing the different strategies, with an icon or illustration for each one]

1. Basic deployment

Basic deployment, also known as direct or simple deployment, is a straightforward approach where the new version of an application replaces the old one immediately. This method is easy to implement and suitable for small-scale applications with minimal user impact. It involves copying new files, updating configurations, and restarting services to apply changes.

However, basic deployment comes with risks, such as potential downtime and rollback challenges if issues arise post-deployment. It’s not ideal for complex or highly available systems where even brief downtime is unacceptable. Despite these drawbacks, basic deployment remains viable for scenarios where simplicity and speed outweigh the need for more elaborate deployment strategies.

2. Rolling deployment

Rolling deployment is a strategy where updates are progressively applied to different infrastructure segments. This method ensures that only a portion of the environment is updated at any given time, reducing the impact of potential failures. It works by gradually replacing application instances with new ones while keeping the old versions running until the process completes. This approach allows for smoother transitions and continuous service availability.

The primary advantage of rolling deployment is its ability to minimize disruptions. If issues occur, the rollback process is simpler, affecting only a subset of instances. Rolling deployment is particularly beneficial for large-scale applications with high user traffic, ensuring that most users can continue accessing the service during updates. This method balances risk and continuity.

3. Multi-service deployment

Multi-service deployment targets complex applications consisting of multiple interconnected services or microservices. Instead of deploying the entire application at once, individual services are deployed separately, allowing for more granular control and reduced risk. This strategy often uses containerization technologies like Docker and orchestration tools like Kubernetes, facilitating isolated updates and efficient resource management. Multi-service deployment ensures that changes to one service do not impact the entire application.

This deployment strategy supports scalability and flexibility, making updating, monitoring, and troubleshooting specific components easier. Organizations can implement targeted updates by decoupling services, which minimizes downtime and dependencies. Multi-service deployment is well-suited for modern, scalable architectures, promoting resilience and adaptability in complex application environments.

4. Blue/green deployment

Blue/green deployment involves maintaining two identical production environments, called “blue” and “green”. One environment (blue) serves live production traffic, while the other (green) remains inactive. When new updates are ready, they are deployed to the inactive environment. After thorough testing and validation in the green environment, traffic is switched from blue to green, ensuring a seamless transition with minimal downtime.

This deployment strategy provides significant advantages in terms of risk mitigation and rollback capabilities. If issues arise in the Green environment, traffic can quickly revert to the Blue environment, ensuring uninterrupted service. Blue/green deployment is highly effective for critical applications where availability and reliability are paramount, offering a safe and efficient method to perform updates without affecting end-users.

5. Canary deployment

Canary deployment introduces new software versions to a small subset of users before rolling it out to the entire user base. This strategy reduces risk by exposing potential issues to a limited audience, allowing teams to gather feedback and address problems before broader deployment. If the new version performs well, the deployment gradually expands to more users. If issues are detected, the deployment can be halted, and the impact is contained.

The canary deployment approach is beneficial for testing in varied real-world conditions without affecting most users. It provides a balance between rapid deployment and risk management, enabling organizations to deliver updates confidently. This method ensures that performance issues and bugs are identified early, enhancing the quality and stability of the application.

6. A/B testing

A/B testing, or split testing, deploys two or more versions of an application feature to different user segments simultaneously. This strategy allows a comparison of user interactions with each version to determine which performs better. By analyzing metrics such as engagement, conversion rates, and user feedback, organizations can make data-driven decisions about which version to roll out more broadly.

This deployment strategy is effective for optimizing user experience and improving application features. It enables iterative testing and refinement, ensuring the best-performing version reaches the entire user base. A/B testing minimizes the risk of widespread issues and provides valuable insights into user preferences and behavior, helping to enhance application functionality and user satisfaction.

7. Shadow deployment

Shadow deployment involves deploying a new version of an application alongside the current version without exposing it to end users. The new version runs in the background, receiving live traffic and processing data, but its outputs are not visible to users. This strategy allows teams to validate new features and performance under real-world conditions without affecting the user experience.

The main advantage of shadow deployment is risk-free testing. It enables the detection of performance bottlenecks, bugs, and compatibility issues in a live environment without impacting users. Once the new version proves stable and reliable, the transition can be made smoothly. Shadow deployment is particularly useful for major updates and structural changes.

Application deployment examples

Deploying a web application using Docker

To deploy a web application with Docker:

1. Set up Dockerfile: Begin by creating a Dockerfile in the root directory of your application. This file defines the environment and steps to build the application image.

   FROM node:14-alpine
   WORKDIR /app
   COPY . .
   RUN npm install
   EXPOSE 3000
   CMD ["npm", "start"]

2. Build Docker image: Once the Dockerfile is created, use Docker to build the image. Navigate to the directory containing the Dockerfile and run:

   docker build -t my-web-app .

3. Run the Docker container: After the image is built, you can start the container with the following command:

   docker run -d -p 3000:3000 my-web-app

   This command runs the container in detached mode and maps port 3000 of the container to port 3000 on your host machine.

4. Push to Docker Hub (optional): To share or deploy your Docker image to other environments, you can push it to Docker Hub:

   docker tag my-web-app my_username/my-web-app
   docker push my_username/my-web-app

Deploying a Kubernetes application with Helm

To deploy a Kubernetes app using Helm:

1. Create a Helm chart: Start by creating a Helm chart for your application. Use the following command to create a new chart:

   helm create my-app-chart

2. Modify deployment files: In the templates/ directory, customize the deployment.yaml file to define how the application should be deployed in Kubernetes.

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: my-app
   spec:
     replicas: 3
     selector:
       matchLabels:
         app: my-app
     template:
       metadata:
         labels:
           app: my-app
       spec:
         containers:
           - name: my-app
             image: my-docker-repo/my-app:latest
             ports:
               - containerPort: 80

3. Deploy with Helm: Once the chart is set up, you can deploy it to your Kubernetes cluster using Helm:

   helm install my-app-release ./my-app-chart

   This command deploys the application and creates the necessary Kubernetes resources such as Pods, Services, and ConfigMaps.

4. Monitor deployment: You can monitor the status of your deployment with:

   kubectl get pods

5. To view the logs of a specific pod:

   kubectl logs <pod-name>

Canary deployment using Kubernetes

To implement a canary deployment with Kubernetes:

1. Create two deployments: Set up two deployments for your application. One will serve the majority of the traffic, while the canary version serves a smaller portion.

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: my-app-stable
   spec:
     replicas: 5
     template:
       spec:
         containers:
           - name: my-app
             image: my-app:stable
   ---
   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: my-app-canary
   spec:
     replicas: 1
     template:
       spec:
         containers:
           - name: my-app
             image: my-app:canary

2. Configure a service: Set up a single Kubernetes Service to load balance traffic between the stable and canary deployments.

   apiVersion: v1
   kind: Service
   metadata:
     name: my-app-service
   spec:
     selector:
       app: my-app
     ports:
       - protocol: TCP
         port: 80
         targetPort: 80

3. Adjust traffic splitting: Use an ingress controller or service mesh (such as Istio) to split traffic between the stable and canary versions. For example, in Istio, you can define a VirtualService to direct 90% of traffic to the stable deployment and 10% to the canary deployment.

   apiVersion: networking.istio.io/v1alpha3
   kind: VirtualService
   metadata:
     name: my-app
   spec:
     hosts:
       - "my-app.example.com"
     http:
       - route:
           - destination:
               host: my-app-stable
             weight: 90
           - destination:
               host: my-app-canary
             weight: 10

4. Monitor canary performance: Monitor the performance of the canary version using Kubernetes monitoring tools like Prometheus or Grafana. If the canary performs well, gradually increase the traffic it receives. If issues arise, scale down or remove the canary deployment to maintain system stability.

Application deployment challenges

Application deployment was traditionally one of the most challenging parts of the software delivery lifecycle. Automation can make this easier, though you may still have gaps in your toolchain.

Deploying to many physical locations

Many applications must be deployed to multiple physical locations. Restaurants, retail chains, hospitals, and other point-of-use businesses often deploy applications to multiple locations and sometimes multiple devices at each location.

Without appropriate tooling, managing the deployment process, applying the correct configuration settings, and managing release rings can create a high operations burden. Automation tools that support tenanted deployments let you share a single automated process across hundreds or even thousands of deployments. They will substitute configuration settings and manage client preferences for early feature access or stable releases.

Deploying to customer-owned infrastructure

Deploying software to customer-owned infrastructure, also known as on-premises deployment, requires navigating various environments that are not directly controlled by the development team. This scenario presents challenges related to compatibility, security, and customer-specific configurations. Deployment strategies must be flexible enough to accommodate diverse hardware, operating systems, and network setups.

Customer infrastructure deployments are often handled by providing packages and installation instructions for the client to self-install, with support on hand to resolve installation issues. A better way to manage these deployments is through secure automated deployments issued by the application provider.

Multi-region deployments

Multi-region deployments involve deploying applications across multiple geographic regions to improve availability, reduce latency, and ensure compliance with local regulations. This approach is essential for global applications serving users in different time zones and adhering to various data residency laws.

Multi-region deployments require different maintenance periods and specific configuration settings as the application often needs to connect to other region-local resources, such as the database. The ability to load balance requests is crucial and this may include invoking failover mechanisms. Sometimes, it may be necessary to replicate data if it’s shared between regions, which can be done using tools like AWS Aurora Global Database or Google Cloud Spanner.

Solving configuration management for multi-tenant deployments

Traditionally, multi-tenancy was handled within an application by keying data from a customer-specific tenant identity. This is costly to maintain and scale, and it can be difficult to perform data operations without impacting other customers. Increasingly, multi-tenancy is being handled at the infrastructure level, creating isolated resources dedicated to the customer and allowing a hard wall around their resources and data.

To make tenanted infrastructure possible, you need to handle configuration settings as part of the deployment process so that each instance can connect to the dedicated services created for the customer, such as the database. Tenanted infrastructure is often managed using templated environments or within Kubernetes clusters.

Best practices for effective application deployment

Collect deployment metrics

Deployment metrics help you improve your deployment process and also contribute to understanding your software delivery performance. Metrics such as deployment frequency, lead time for changes, deployment duration, failed deployment rate, and recovery times provide valuable insights into your deployment pipeline.

These metrics can be collected with tools like Prometheus, Grafana, and ELK stack for visualization and so they can be correlated with other events and metrics. This helps identify bottlenecks and find areas for improvement.

Apply resource limits

Applying resource limits during deployment helps ensure applications do not consume excessive resources, affecting other services. By limiting CPU, memory, and storage usage, teams can prevent resource contention and maintain system stability. Resource limits can be configured through deployment tools and orchestration platforms such as Kubernetes, ensuring optimal resource allocation.

Moreover, resource limits aid in capacity planning and cost management. They help predict resource needs accurately and avoid over-provisioning, leading to more efficient use of infrastructure. By managing resources effectively, organizations can enhance application performance, reduce operational costs, and ensure a fair allocation of resources across different services and tenants.

Implement a secrets strategy

Implementing a secrets strategy is crucial for managing sensitive information such as API keys, passwords, and certificates during deployment. Solutions like HashiCorp Vault, AWS Secrets Manager, and Kubernetes Secrets provide secure storage and access mechanisms for secrets. By centralizing and automating secrets management, organizations can ensure that sensitive data is protected and access is tightly controlled.

A well-defined secrets strategy also reduces the risk of accidental exposure and security breaches. It ensures that secrets are encrypted at rest and in transit, adhering to best practices and compliance requirements.

Keep separate isolated environments for production and non-production

Maintaining separate and isolated environments for production and non-production activities is a fundamental practice in application deployment. By segregating these environments, organizations can minimize the risk of unintended consequences impacting live users. Non-production environments, such as development, testing, and staging, provide safe spaces to develop and validate code without affecting the production environment.

Isolating these environments ensures that configuration errors, incomplete features, or performance bottlenecks are identified and resolved before production. This practice also supports compliance and security by preventing unauthorized access to production data and systems from less secure non-production environments.

Automate database updates

Automating database updates is critical for ensuring consistency and reducing downtime during deployment. Automated tools like Liquibase, Flyway, and Alembic manage database schema changes, data migrations, and rollback procedures. Automation ensures that updates are applied consistently across different environments, reducing the risk of human error.

Furthermore, automated database updates enable faster, more reliable deployments. They facilitate Continuous Integration and Continuous Delivery by integrating database changes into CI/CD pipelines. This approach streamlines the deployment process, minimizes manual intervention, and ensures that database updates are synchronized with application changes, maintaining data integrity and system stability.

Application deployment with Octopus

Octopus handles complex deployments at scale. You can capture your deployment process, apply different configurations, and automate the steps to deploy a new software version or upgrade a database.

With Octopus, you can manage all your deployments whether it’s cloud-native microservices on Kubernetes or older monoliths running on virtual servers. This means you can see the state of all your deployments in one place and use the same tools to deploy all your applications and services.

Octopus has a tenanted deployment feature that simplifies deployment to multiple physical locations, regions, or customers.

Why not request a demo or start a free trial to find out more.

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