Achieving High Availability in CI/CD With Observability

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, The Modern DevOps Lifecycle: Shifting CI/CD and Application Architectures.

Forbes estimates that cloud budgets will break all previous records as businesses will spend over $1 trillion on cloud computing infrastructure in 2024. Since most application releases depend on cloud infrastructure, having good continuous integration and continuous delivery (CI/CD) pipelines and end-to-end observability becomes essential for ensuring highly available systems. By integrating observability tools in CI/CD pipelines, organizations can increase deployment frequency, minimize risks, and build highly available systems. Complementing these practices is site reliability engineering (SRE), a discipline ensuring system reliability, performance, and scalability.

This article will help you understand the key concepts of observability and how to integrate observability in CI/CD for creating highly available systems.

Observability and High Availability in SRE 

Observability refers to offering real-time insights into application performance, whereas high availability means ensuring systems remain operational by minimizing downtime. Understanding how the system behaves, performs, and responds to various conditions is central to achieving high availability. Observability equips SRE teams with the necessary tools to gain insights into a system's performance. 

Components of Observability 

Observability involves three essential components:

1. Metrics – measurable data on various aspects of system performance and user experience

2. Logs – detailed event information for post-incident reviews

3. Traces – end-to-end visibility in complex architectures to help you understand requests across services 

Together, they comprehensively picture the system's behavior, performance, and interactions. This observability data can then be analyzed by SRE teams to make data-driven decisions and swiftly resolve issues to make their system highly available.

The Role of Observability in High Availability

Businesses have to ensure that their development and SRE teams are skilled at predicting and resolving system failures, unexpected traffic spikes, network issues, and software bugs to provide a smooth experience to their users. Observability is vital in assessing high availability by continuously monitoring specific metrics that are crucial for system health, such as latency, error rates, throughput, saturation, and more, therefore providing a real-time health check. Deviations from normal behavior trigger alerts, allowing SRE teams to proactively address potential issues before they impact availability.

How Observability Helps SRE Teams 

Each observability component contributes unique insights into different facets of system performance. These components empower SRE teams to proactively monitor, diagnose, and optimize system behavior. Some use cases of metrics, logs, and traces for SRE teams are post-incident reviews, identification of system weaknesses, capacity planning, and performance optimization.

Post-Incident Reviews

Observability tools allow SRE teams to look at past data to analyze and understand system behavior during incidents, anomalies, or outages. Detailed logs, metrics, and traces provide a timeline of events that help identify the root causes of issues.

Identification of System Weaknesses 

Observability data aids in pinpointing system weaknesses by providing insights into how the system behaves under various conditions. By analyzing metrics, logs, and traces, SRE teams can identify patterns or anomalies that may indicate vulnerabilities, performance bottlenecks, or areas prone to failures.

Capacity Planning and Performance Optimization 

By collecting and analyzing metrics related to resource utilization, response times, and system throughput, SRE teams can make informed decisions about capacity requirements. This proactive approach ensures that systems are adequately scaled to handle expected workloads and their performance is optimized to meet user demands. In short, resources can be easily scaled down during non-peak hours or scaled up when demands surge.

SRE Best Practices for Reliability

At its core, SRE practices aim to create scalable and highly reliable software systems using two key principles that guide SRE teams: SRE golden signals and service-level objectives (SLOs).  

Understanding SRE Golden Signals 

The SRE golden signals are a set of critical metrics that provide a holistic view of a system's health and performance. The four primary golden signals are:

1. Latency – Time taken for a system to respond to a request. High latency negatively impacts user experience.

2. Traffic – Volume of requests a system is handling. Monitoring helps anticipate and respond to changing demands.

3. Errors – Elevated error rates can indicate software bugs, infrastructure problems, or other issues that may impact reliability.

4. Saturation – Utilization of system resources such as CPU, memory, or disk. It helps identify potential bottlenecks and ensures the system has sufficient resources to handle the load.

Setting Effective SLOs 

SLOs define the target levels of reliability or performance that a service aims to achieve. They are typically expressed as a percentage over a specific time period. SRE teams use SLOs to set clear expectations for a system’s behavior, availability, and reliability. They continuously monitor the SRE golden signals to assess whether the system meets its SLOs. If the system falls below the defined SLOs, it triggers a reassessment of the service's architecture, capacity, or other aspects to improve availability. Businesses can use observability tools to set up alerts based on predetermined thresholds for key metrics.

Defining Mitigation Strategies 

Automating repetitive tasks, such as configuration management, deployments, and scaling, reduces the risk of human error and improves system reliability. Introducing redundancy in critical components ensures that a failure in one area doesn't lead to a system-wide outage. This could involve redundant servers, data centers, or even cloud providers. Additionally, implementing rollback mechanisms for deployments allows SRE teams to quickly revert to a stable state in the event of issues introduced by new releases.

CI/CD Pipelines for Zero Downtime 

Achieving zero downtime through effective CI/CD pipelines enables services to provide users with continuous access to the latest release. Let’s look at some of the key strategies employed to ensure zero downtime.

Strategies for Designing Pipelines to Ensure Zero Downtime 

Some strategies for minimizing disruptions and maximizing user experience include blue-green deployments, canary releases, and feature toggles. Let’s look at them in more detail.

Blue-Green Deployments

Blue-green deployments involve maintaining two identical environments (blue and green), where only one actively serves production traffic at a time. When deploying updates, traffic is seamlessly switched from the current (blue) environment to the new (green) one. This approach ensures minimal downtime as the transition is instantaneous, allowing quick rollback in case issues arise.

Canary Releases

Canary releases involve deploying updates to a small subset of users before rolling them out to everyone. This gradual and controlled approach allows teams to monitor for potential issues in a real-world environment with reduced impact. The deployment is released to a wider audience if the canary group experiences no significant issues.

Feature Toggles

Feature toggles, or feature flags, enable developers to control the visibility of new features in production independently of other features. By toggling features on or off, teams can release code to production but activate or deactivate specific functionalities dynamically without deploying new code. This approach provides flexibility, allowing features to be gradually rolled out or rolled back without redeploying the entire application.

Best Practices in CI/CD for Ensuring High Availability

Successfully implementing CI/CD pipelines for high availability often requires a good deal of consideration and lots of trial and error. While there are many implementations, adhering to best practices can help you avoid common problems and improve your pipeline faster. Some industry best practices you can implement in your CI/CD pipeline to ensure zero downtime are automated testing, artifact versioning, and Infrastructure as Code (IaC).

Automated Testing

You can use comprehensive test suites — including unit tests, integration tests, and end-to-end tests — to identify potential issues early in the development process. Automated testing during integration provides confidence in the reliability of code changes, reducing the likelihood of introducing critical bugs during deployments.

Artifact Versioning

By assigning unique versions to artifacts, such as compiled binaries or deployable packages, teams can systematically track changes over time. This practice enables precise identification of specific code iterations, thus simplifying debugging, troubleshooting, and rollback processes. Versioning artifacts ensures traceability and facilitates rollback to previous versions in the case of issues during deployment.

Infrastructure as Code

Utilize Infrastructure as Code to define and manage infrastructure configurations, using tools such as OpenTofu, Ansible, Pulumi, Terraform, etc. IaC ensures consistency between development, testing, and production environments, reducing the risk of deployment-related issues.

Integrating Observability Into CI/CD Pipelines

Observing key metrics such as build success rates, deployment durations, and resource utilization during CI/CD provides visibility into the health and efficiency of the CI/CD pipeline. Observability can be implemented during continuous integration (CI) and continuous deployment (CD) as well as post-deployment.

Observability in Continuous Integration

Observability tools capture key metrics during the CI process, such as build success rates, test coverage, and code quality. These metrics provide immediate feedback on the health of the codebase. Logging enables the recording of events and activities during the CI process. Logs help developers and CI/CD administrators troubleshoot issues and understand the execution flow. Tracing tools provide insights into the execution path of CI tasks, allowing teams to identify bottlenecks or areas for optimization.

Observability in Continuous Deployment

Observability platforms monitor the CD pipeline in real time, tracking deployment success rates, deployment durations, and resource utilization. Observability tools integrate with deployment tools to capture data before, during, and after deployment. Alerts based on predefined thresholds or anomalies in CD metrics notify teams of potential issues, enabling quick intervention and minimizing the risk of deploying faulty code.

Post-Deployment Observability

Application performance monitoring tools provide insights into the performance of deployed applications, including response times, error rates, and transaction traces. This information is crucial for identifying and resolving issues introduced during and after deployment. Observability platforms with error-tracking capabilities help pinpoint and prioritize software bugs or issues arising from the deployed code. Aggregating logs from post-deployment environments allows for a comprehensive view of system behavior and facilitates troubleshooting and debugging.

Conclusion

The symbiotic relationship between observability and high availability is integral to meeting the demands of agile, user-centric development environments. With real-time monitoring, alerting, and post-deployment insights, observability plays a major role in achieving and maintaining high availability. Cloud providers are now leveraging drag-and-drop interfaces and natural language tools to eliminate the need for advanced technical skills for deployment and management of cloud infrastructure. Hence, it is easier than ever to create highly available systems by combining the powers of CI/CD and observability.

Resources:

• Continuous Integration Patterns and Anti-Patterns by Nicolas Giron and Hicham Bouissoumer, DZone Refcard 

• Continuous Delivery Patterns and Anti-Patterns by Nicolas Giron and Hicham Bouissoumer, DZone Refcard

• "The 10 Biggest Cloud Computing Trends In 2024 Everyone Must Be Ready For Now" by Bernard Marr, Forbes

  This is an excerpt from DZone's 2024 Trend Report,
  The Modern DevOps Lifecycle: Shifting CI/CD and Application Architectures.
  
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