Improving Production Deployment: Best Practices for Error Handling & Updating

Learn how to improve your production deployment process using best practices for IT deployment planning, testing, error handling, and risk mitigation techniques.

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Time-consuming testing, unpredictable errors, and accumulating upgrade requests can stall any DevOps team. While some practices and tools help deploy to production more efficiently, others can complicate the production deployment process.

Drawing from our successful DevOps and app deployment planning experience, we've identified the best techniques to cut down time to deploy and simplify error handling. In this article, we’ll focus on applicable methods that helped us, so you can use them to refine your IT deployment plan. These strategies also minimize risk while ensuring consistent quality of software releases.

Why optimize the production deployment process?

An efficient production deployment process simplifies moving code from development to production. The best prod deployment techniques can improve your deployments in many ways.

  • Improved time to deploy. Implementing automated tools into the production environment ensures consistency across deployments. Teams follow the exact steps and best practices to resolve issues, improving the frequency of deployments.
  • Fewer risks of downtime. If your team runs into critical issues, practices like parallel environments allow them to quickly revert to a working deployment version.
  • Flexibility in testing. Feature flags and blue/green strategies allow developers to experiment with specific functions without deploying code. This provides more control over releases and helps test application versions on different subsets of users.
  • Proactive error handling. Continuous monitoring, automated compliance checks, and security scans allow teams to diagnose problems. The result is a code that meets your quality standards, industry regulations, and user expectations.
  • Alignment among teams. Version control and configuration management help track changes and maintain the desired app state. They improve team collaboration and smooth the transition between development, testing, and production environments.
  • Eliminated redundancy in data. Database normalization — standardizing relationships in data tables — eliminates duplicate, inconsistent, and corrupt data that waste your storage space and create risks of data loss.
  • Performance benefits. By properly organizing data and mapping dependencies with normalization techniques, your apps can quickly fetch data from other systems with fewer errors.
  • Better interoperability. Standardized data tables with common formats facilitate data exchange and reduce the risk of integration issues in deployment environments that involve multiple interconnected databases.

A streamlined process improves your time to deploy by automating manual tasks, reducing human errors, and mitigating different risks. It basically removes bottlenecks that distract your teams from properly testing, validating, and optimizing the code.

You have a lot of preparation to do before you can establish an effective deployment environment with efficient practices. Let’s guide you through the process.

Preparing the environment: things to do before deployment

The production environment shapes the success of your deployments. Here are the key areas you should consider to optimize processes and mitigate risks.

1. Optimize the infrastructure for prod deployment

Modern software deployment environments require an agile IT infrastructure and architecture design. Your databases, servers, resources, and security mechanisms should match your business goals, technical requirements, and budgetary contractions. 

Scalability is essential. A flexible deployment environment helps you utilize resources effectively, improve system resilience, and handle a growing IT team.

Key considerations:

  • Loosely coupled architecture means breaking down monolithic applications into small, independent services that can be individually scaled, deployed, and upgraded. Microservices also help teams choose the optimal tools for individual services.
  • Containerization packages an application with all its dependencies into a "container." Using platforms like Kubernetes, you can ensure the app works the same across environments, whether on a developer's laptop or a production server.
  • Interoperability is achieved via well-defined APIs to ensure your services communicate securely with external systems.
  • Network policies for load balancing, backup, disaster recovery, and monitoring tools help maintain a resilient deployment environment. Meanwhile, intrusion prevention systems and alerting tools improve your security posture.
  • Authentication mechanisms help prevent unauthorized entities from accessing your deployment environment.

2. Define the testing frameworks and quality model

Testing and quality assurance are continuous processes that help your software adhere to your technical requirements and behave as expected after release.

Key considerations:

  • Unit testing practices outline how you verify individual components and functions of your apps for debugging and issue handling.
  • Integration testing ensures that different services work together as expected and helps identify compatibility issues.
  • End-to-end testing examines the entire app flow from the user’s perspective to ensure the process matches expected outcomes and user requirements.
  • A/B testing methodology includes releasing two versions of the same feature for different target groups to understand their preferences.
  • Regular code review according to predefined standards enables you to identify more programming mistakes, inefficiencies, and vulnerabilities.

3. Set up version control and configuration management

Your team should be able to track changes, reproduce outcomes, and revert changes to code. This is why your deployment environment should implement version control systems, configuration management tools, and standardization guidelines.

Key considerations:

  • Version control systems let you record changes to your code and manage different product iterations. Based on your project, you may opt for a system with a central repository or a distributed system (like Git).
  • Configuration management tools like Ansible, Puppet, and Chef simplify infrastructure setup to ensure your code runs consistently across platforms.
  • Infrastructure as Code (IAC) platforms like Terraform and Chef allow automation infrastructure provisioning through machine-readable code (rather than managing resources manually).
  • Standardized rules for database objects (such as tables, keys, triggers, and indexes) help your teams navigate and coordinate efforts in the production environment.

4. Document the deploy to prod process

Clear documentation and logging enable your teams to benchmark performance, understand system behavior, and effectively handle post-deployment issues. 

Key considerations:

  • Multiple categories for logs assist system administrators and security analysts in finding root causes for common bottlenecks and bugs.
  • Quality assurance models with relevant metrics ensure the software meets your technical requirements, performance benchmarks, compliance standards, and broader business objectives.
  • Control access to the permission model by restricting how different employees interact with the production pipeline.
  • Post-deployment documentation helps you share knowledge about your product with in-house teams and third-party companies that may maintain it in the future.

An appropriate IT infrastructure, version control, standardization, monitoring, and testing frameworks are core elements of an effective production environment. However, successful deployment also depends on the techniques you use.

Optimizing production deployment: best practices

Specific deployment strategies can significantly reduce your deployment time and software quality. We have combined proven techniques and tools to help you refine your IT deployment plan for greater efficiency and effectiveness.

Parallel testing

Instead of waiting for a test to complete, the DevOps team runs multiple tests concurrently on different machines. Frameworks like Selenium Grid let you execute tests across diverse operating systems. In addition, Docker allows your teams to run the application code and underlying dependencies consistently in different environments.

Blue/Green deployments

Companies can maintain two identical deployment environments (Blue and Green). One is used for live traffic, while the other is kept for testing. Teams test new releases in the test environment before moving them to the live environment. This way, issues in new versions don’t interrupt critical processes, and your teams have more time to fix the problem.

Canary deployments

Teams can roll new software updates incrementally to a small subset of users. You can deploy the app traffic to 10% of customers and monitor for problems. The release is then gradually expanded until the software version is available to the entire user base.

Staged deployments

Staged deployments involve rolling out app versions to different environments, including development, testing, and staging. This helps your teams verify that a new update is compatible with various dependencies and configurations before you deploy to production.


Rollback lets you revert software deployments to a stable build if the release causes problems. Companies can implement rollback functionality differently, depending on their infrastructure. For instance, Git has tools that restore previous code versions, and the Kubernetes container orchestration platform has a native rollback feature.

Feature flags

Implementing feature flags (feature toggles or flippers) lets you turn on or off specific app features for testing, experimentation, or gradual rollout. Like other production deployment best practices, it improves error handling and allows quick rollback. Tools like ConfigCat help you manage feature flags for different languages and platforms.

Continuous integration (CI)

CI tools like Jenkins and Travis CI help integrate code change from multiple contributors into a shared repository. Every integration is followed by automatic tests and validations that catch bugs and ensure the code is deployable.

Continuous deployment (CD)

CD software automatically configures and releases the verified code into the production environment. This practice is often used with CI as part of a shared pipeline that deploys code that passes CI checks. For example, GitLab CI/CD is a popular tool that automates code commits to the prod deployment environment.

Log rotation and retention

Storing logs indefinitely can consume considerable disk space and affect your computing power. Tools like AWS CloudWatch Logs let you create policies for archiving and deleting old log files based on different rules (like their creation time, frequency, and file size).

Post-deployment reviews

Thorough post-deployment reviews let DevOps teams identify the strengths and weaknesses of your production deployment process. They help focus on practices that improve deployment frequency and simplify error handling while getting rid of tools that add complexity to the workflow without providing significant value. Regular assessments also verify the IT system meets your company’s performance and cost requirements.

By implementing these strategies, DevOps experts and system admins can optimize their workflow. Now, let’s focus on specific issues that help your team spend less time troubleshooting.

Error handling: strategies to resolve common mistakes

Effective error handling helps anticipate, diagnose, and handle issues before they affect end users or create critical vulnerabilities. Here's a brief overview of key practices, tools, and techniques for effective error handling:

  • Test doubles. Testing objects, like mock and stub objects, let you simulate error conditions and assess how the system responds to different scenarios.
  • System monitoring. Monitoring tools like Sentry, Datadog, and Grafana let you measure key performance indicators in real time and introduce custom alerts on critical issues.
  • Exception handling. Divide errors into classes with extra information about the errors to help different teams resolve them throughout the deployment.
  • Circuit breakers. Utilizing circuit breakers and exponential backoffs can stop operations with a high chance of failure to avoid wasting CPU cycles and network resources (until the issues are properly fixed).
  • Error recovery mechanisms. Libraries like Polly, Tenacity, and Retry allow your systems to resume normal operations after transient errors (such as service overload, resource exhaustion, and network congestion). 
  • Log reviews. DevOps teams should regularly analyze system logs for errors to capture contextual information about what causes problems.
  • Centralized knowledge base. Keep a centralized document with known error scenarios, troubleshooting steps, and remediation practices.

Investing time in designing error-handling techniques pays back relatively fast. These techniques are essential for DevOps teams as they improve software systems' resilience and deployment time. They let you quickly respond to transient errors and enable your systems to recover gracefully after failures.

Practical case to streamline the upgrade process with Python script

At Alpacked, we use JIRA to track and manage changes during prod deployment. For each upgrade, we create a ticket detailing the changes (these can be bug fixes, enhancements, or upgrades). This workflow allows our team to test only those changes that were planned for the patch release and not waste time retesting the entire app.

This update process can introduce errors when tickets pile up. When dealing with about 20 tickets (with multiple commits per ticket), you can mix up the update order or miss critical commits. 

To deal with these challenges, we developed a Python script. Here’s how this tool enhances our deployment workflow:

  1. Branch comparison. The script compares GitLab's main and release branches to identify the commits and associated upgrade tickets.
  2. Commit table generation. Python generates a table comparing main and release branch commits chronologically, with SHA hashes intact.
  3. Cherry-picking commits. We just run the script and copy the SHA-1 hashes from the table instead of manually checking GitLab for commits.

The copied SHA-1 hashes are used to cherry-pick commits for the release branch. This ensures all changes are applied in the correct order and properly prioritized. Our team can skip most administrative tasks and focus on the upgrade process.

Deploy to production efficiently with Alpacked

Missteps in error handling, data naming conventions, and upgrade tracking can lead to cascading issues that affect deployment frequency, time to deploy, and the quality of a released product. However, the best practices and tools can standardize the production deployment process. 

Do you want your team to be in full control of the production deployment process instead of routinely going through the steps? Contact Alpacked today, and we’ll help you improve your practices or design a deployment plan tailored to your needs.

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