Resilience is the ability of something to withstand or recover quickly from difficulties or challenges.
Resiliency is an integral aspect of designing, developing, and operating microservice architectures. Application-to-application communication over the network is inevitable in distributed systems. Anything can go wrong during app-to-app communication, like network glitches or timeouts, application unavailability, data center failovers, etc. Due to these events, applications may overload, stop responding, or even crash the entire system.
In this article, I would like to talk about the common resiliency patterns and how you can apply them to your microservices.
What is resiliency?
In very simple terms, resilience is the ability of something to withstand or recover quickly from difficulties or challenges. In the context of architecture and design, resilience refers to the ability of systems to adapt and recover from failures or disruptions. It’s like having a strong and flexible foundation that can handle unexpected problems and keep going.
Why do you need resilience?
Below are several benefits that contribute to the overall success and stability of enterprise cloud-native applications, such as:
- Enhanced Customer Experience: Resilient applications ensure functioning and serving users without significant disruptions, ensuring a seamless user experience.
- Business Continuity: By handling failures gracefully, resilient applications ensure business continuity and minimize the impact of disruptions on critical operations.
- Stability and Reliability: Resilient systems are better equipped to recover from failures, reducing downtime and enhancing overall system stability.
Resiliency Patterns
Below are the commonly used resiliency patterns in microservices.
Strategies to Design Resilient Systems
Building resilient systems requires careful planning and design at various levels. The strategy must be included in all phases of SDLC. Below are a couple of strategies you can consider:
- Embracing ‘Design for Failure’
- Adopting Containerization and Orchestration
- Adopt Chaos Engineering
- Automated Deployments and Rollbacks
- Learn, experiment, adapt and iterate
Embracing ‘Design for Failure': “Design for failure” is a concept in system design that involves intentionally planning and designing a system with the assumption that components will fail or experience faults. The goal is to create a system that can continue to operate or quickly recover from failures without causing a complete breakdown of functionality. Rather than trying to eliminate the possibility of failure, the focus is on minimizing the impact and downtime associated with failures.
Key principles and practices associated with designing for failure include:
- Adopt resilience patterns: adopt resilience patterns like circuit breaker, retry, bulkhead and timeout patterns.
- Decentralization: Avoid having a single point of failure by decentralizing components. Distribute functionality across multiple servers, data centers, or even geographical locations to reduce the impact of failures.
- Redundancy and Replication: Introduce redundancy at various levels of the system to ensure that if one component fails, another can take over. This can include redundant servers, databases, and network paths.
- Fault Isolation: Design systems in a way that isolates faults to prevent them from cascading through the entire system. Isolating failures helps contain the impact and allows the rest of the system to continue functioning.
- Automated Recovery: Automate recovery processes to reduce the time it takes to restore services after a failure. This includes automated backups, configuration management, and deployment rollback procedures.
- Graceful Shutdown: Design systems to gracefully degrade in case of failures. When certain components fail, the system should still provide basic functionality rather than completely breaking.
- Proactive Monitoring and Alerting: Adopt comprehensive monitoring tools to continuously track the health and performance of the system. Design alerts that notify the respective stakeholders when issues are detected, enabling prompt responses to failures.
Adopting Containerization and Orchestration: Adopt containerization platforms like Docker and orchestration tools like Kubernetes to deploy and manage your applications and microservices. Containers provide isolation and consistency, while orchestration simplifies the deployment, scaling, and monitoring of microservices.
Adopt Chaos Engineering: Chaos engineering is a discipline that involves intentionally introducing controlled and well-monitored instances of failure into a system to identify weaknesses and vulnerabilities. The primary goal of chaos engineering is to proactively discover and address potential issues before they can lead to widespread outages or disruptions.
Chaos Engineering require a its own article to delve into details. Below are the high level steps involved.
Automated Deployments and Rollbacks: Implement robust CI/CD pipelines to automate the deployment and rollback of microservices, along with automated testing. This ensures consistent and reliable deployment processes, reduces manual errors, and enables rapid recovery in case of failure.
Learn, experiment, adapt, and iterate: Adopt a mindset of continuous learning and improvement. Regularly review and update the system architecture and recovery processes based on evolving requirements, technological advancements, and lessons learned from past failures.
That’s all for today!
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