Tagged: cloud

Building Resilient .NET Applications using Polly

In distributed systems, outages and transient errors are inevitable. Ensuring that your application stays responsive when a dependent service goes down is critical. This article explores service resilience using Polly, a .NET library that helps handle faults gracefully. It covers basic resilience strategies and explains how to keep your service running when a dependency is unavailable.

What Is Service Resilience

Service resilience is the ability of an application to continue operating despite failures such as network issues, temporary service unavailability, or unexpected exceptions. A resilient service degrades gracefully rather than crashing outright, ensuring users receive the best possible experience even during failures.

Key aspects of resilience include:

  • Retrying Failed Operations automatically attempts an operation again when a transient error occurs.
  • Breaking the Circuit prevents a system from continuously attempting operations that are likely to fail.
  • Falling Back provides an alternative response or functionality when a dependent service is unavailable.

Introducing Polly: The .NET Resilience Library

Polly is an open-source library for .NET that simplifies resilience strategies. Polly allows defining policies to handle transient faults, combining strategies into policy wraps, and integrating them into applications via dependency injection.

Polly provides several resilience strategies:

  • Retry automatically reattempts operations when failures occur.
  • Circuit Breaker stops attempts temporarily if failures exceed a threshold.
  • Fallback provides a default value or action when all retries fail.
  • Timeout cancels operations that take too long.

These strategies can be combined to build a robust resilience pipeline.

Key Polly Strategies for Service Resilience

Retry Policy

The retry policy is useful when failures are transient. Polly can automatically re-execute failed operations after a configurable delay. Example:

var retryPolicy = Policy
    .Handle<HttpRequestException>()
    .OrResult<HttpResponseMessage>(r => !r.IsSuccessStatusCode)
    .WaitAndRetryAsync(3, retryAttempt => TimeSpan.FromSeconds(Math.Pow(2, retryAttempt)),
        onRetry: (outcome, timespan, retryCount, context) =>
        {
            Console.WriteLine($"Retry {retryCount}: waiting {timespan} before next attempt.");
        });

Circuit Breaker

A circuit breaker prevents an application from continuously retrying an operation that is likely to fail, protecting it from cascading failures. Example:

var circuitBreakerPolicy = Policy
    .Handle<HttpRequestException>()
    .OrResult<HttpResponseMessage>(r => !r.IsSuccessStatusCode)
    .CircuitBreakerAsync(
        handledEventsAllowedBeforeBreaking: 3,
        durationOfBreak: TimeSpan.FromSeconds(30),
        onBreak: (outcome, breakDelay) =>
        {
            Console.WriteLine("Circuit breaker opened.");
        },
        onReset: () =>
        {
            Console.WriteLine("Circuit breaker reset.");
        });

Fallback Strategy: Keeping Your Service Running

When a dependent service is down, a fallback policy provides a default or cached response instead of propagating an error. Example:

var fallbackPolicy = Policy<HttpResponseMessage>
    .Handle<HttpRequestException>()
    .OrResult(r => !r.IsSuccessStatusCode)
    .FallbackAsync(
         fallbackAction: cancellationToken => Task.FromResult(
             new HttpResponseMessage(HttpStatusCode.OK)
             {
                 Content = new StringContent("Service temporarily unavailable. Please try again later.")
             }),
         onFallbackAsync: (outcome, context) =>
         {
             Console.WriteLine("Fallback executed: dependent service is down.");
             return Task.CompletedTask;
         });

Timeout Policy

A timeout policy ensures that long-running requests do not block system resources indefinitely. Example:

var timeoutPolicy = Policy.TimeoutAsync<HttpResponseMessage>(TimeSpan.FromSeconds(10));

Implementing Basic Service Resilience with Polly

Example Use Case: Online Payment Processing System

Imagine an e-commerce platform, ShopEase, which processes customer payments through an external payment gateway. To ensure a seamless shopping experience, ShopEase implements the following resilience strategies:

  • Retry Policy: If the payment gateway experiences transient network issues, ShopEase retries the request automatically before failing.
  • Circuit Breaker: If the payment gateway goes down for an extended period, the circuit breaker prevents continuous failed attempts.
  • Fallback Policy: If the gateway is unavailable, ShopEase allows customers to save their cart and receive a notification when payment is available.
  • Timeout Policy: If the payment gateway takes too long to respond, ShopEase cancels the request and notifies the customer.

By integrating these resilience patterns, ShopEase ensures a robust payment processing system that enhances customer trust and maintains operational efficiency, even when external services face issues.

Conclusion

Building resilient services means designing systems that remain robust under pressure. Polly enables implementing retries, circuit breakers, timeouts, and fallback strategies to keep services running even when dependencies fail. This improves the user experience and enhances overall application reliability.

I advocate for 12-Factor Apps (https://12factor.net/) and while resilience is not directly a part of the 12-Factor methodology, many of its principles support resilience indirectly. For truly resilient applications, a combination of strategies like Polly for .NET, Kubernetes auto-recovery, and chaos engineering should be incorporated. Encouraging 12-Factor principles, auto-recovery, auto-scaling, and other methods ensures services remain resilient and performant.

By applying these techniques, developers can create resilient architectures that gracefully handle failure scenarios while maintaining consistent functionality for users. Implement Polly and supporting resilience strategies to ensure applications stay operational despite unexpected failures.

A Future Vision of Software Development

From Coders to System Operators

As artificial intelligence (AI) continues reshaping industries, the role of software development is undergoing a profound transformation. Writing code is becoming less about crafting individual lines of code and more about designing systems of services that deliver business value. Development is shifting from writing code to creative problem-solving and systematic orchestration of interconnected services.

The End of Coding as We Know It

Code generation has become increasingly automated. Modern AI tools can write boilerplate code, generate tests, and even create entire applications from high-level specifications. As this trend accelerates, human developers will move beyond writing routine code to defining the architecture and interactions of complex systems and services.

Rather than focusing on syntax or implementation details, the next generation of developers will manage systems holistically, designing services, orchestrating workflows, and ensuring that all components deliver measurable and scalable user, client, and business value.

The Rise of the System Operator

In this emerging paradigm, the role of the System Operator comes into focus. A System Operator oversees a network of AI-driven assistants and specialized agents, ensuring the system delivers maximum value through continuous refinement and coordination.

Key Responsibilities of the System Operator:

  1. Define Value Streams: Identify business goals, define value metrics, and ensure the system workflow aligns with strategic objectives.
  2. Design System Architectures: Structure interconnected services that collaborate to provide seamless functionality.
  3. Manage AI Agents: Lead AI-powered assistants specializing in tasks like strategy, planning, research, design, development, marketing, hosting, and client support.
  4. Optimize System Operations: Continuously monitor and adjust services for efficiency, reliability, and scalability.
  5. Deliver Business Outcomes: Ensure that every aspect of the system contributes directly to business success.

AI-Augmented Teams: A New Kind of Collaboration

Traditional product development teams will evolve into AI-Augmented Teams, where every team member works alongside AI-driven agents. These agents will handle specialized tasks such as market analysis, system design, and performance optimization. The System Operator will orchestrate the work of these agents to create a seamless, value-driven product development process.

Core Roles in an AI-Augmented Team:

  • Strategist: Guides the product’s vision and sets business goals.
  • Planner: Manages delivery timelines, budgets, and project milestones.
  • Researcher & Analyst: Conducts in-depth user, customer, market, technical, and competitive analyses.
  • Architect & Designer: Defines system architecture and creates intuitive user interfaces.
  • Developer & DevOps Tech: Implements features and ensures smooth deployment pipelines.
  • Marketer & Client Success Tech: Drives user adoption, engagement, and retention.
  • Billing & Hosting Tech: Manages infrastructure, costs, and financial tracking.

System Operator: A New Job Description

A System Operator is like an Uber driver for business systems. Product development becomes a part of the gig economy.

Operators need expertise in one or more of the system roles with agents augmenting their experience gaps in other roles. System Operators can be independent contractors or salaried employees.

Title: System Operator – AI-Augmented Development Team

Objective: To manage and orchestrate AI-powered agents, ensuring the seamless delivery of software systems and services that maximize business value.

Responsibilities:

  • Collaborate with other system operators and AI-driven assistants to systematically deliver and maintain system services.
  • Define work item scope, schedule, budget, and value-driven metrics.
  • Oversee service performance, ensuring adaptability, scalability, and reliability.
  • Lead AI assistants in tasks such as data analysis, technical research, and design creation.
  • Ensure alignment with client and agency objectives through continuous feedback and system improvements.

Skills and Qualifications:

  • Expertise in system architecture and service-oriented strategy, planning, and design.
  • Strong understanding of AI tools, agents, and automation frameworks.
  • Ability to manage cross-functional teams, both human and AI-powered.
  • Analytical mindset with a focus on continuous system optimization.

Conclusion: Embracing the Future of Development

The role of developers is rapidly evolving into something much broader, more strategic, and less focused on boilerplate coding. System Operators will lead the charge, leveraging AI-powered agents to transform ideas into scalable, value-driven solutions. As we move toward this new reality, development teams must embrace the change, shifting from code writers to orchestrators of complex service ecosystems that redefine what it means to build software in the AI era.