C sharp polly retry

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To solve the problem of transient faults in C# applications, here are the detailed steps for implementing Polly retry policies: First, ensure you have the Polly NuGet package installed in your project. You can achieve this by running Install-Package Polly in your Package Manager Console, or by searching for “Polly” in the NuGet Package Manager and installing it. Once installed, you’ll define a Policy object, often a RetryPolicy or WaitAndRetryPolicy, which specifies how many times an operation should be retried and with what delays. You’ll then wrap the potentially failing code block within this policy’s Execute or ExecuteAsync method. For instance, a basic retry could look like Policy.Handle<HttpRequestException>.Retry3.Execute => /* your code here */.. For more advanced scenarios, such as exponential back-off, you’d use WaitAndRetryAsync with a sleepDurationProvider function. Finally, consider using a PolicyRegistry to centralize and manage your policies, making them reusable across your application and ensuring consistent fault handling.

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Table of Contents

Understanding Transient Faults and Why Polly is Essential

Transient faults are those intermittent issues that resolve themselves after a short period. Think of a brief network glitch, a temporary database lockout, or a service experiencing a momentary overload. They are, as the name suggests, “transient.” Ignoring them can lead to application failures, poor user experience, and unnecessary system downtime. Conversely, retrying indefinitely without a strategy can exacerbate issues, potentially overwhelming an already struggling service. This is where Polly, a .NET resilience and transient-fault-handling library, steps in.

The Nature of Transient Faults

  • Intermittent Nature: These faults are not permanent. They appear, persist for a short duration, and then disappear. A good example is a temporary connection drop to an external API.
  • Common Scenarios:
    • Network Latency: A brief slowdown or interruption in network connectivity.
    • Service Overload: An external service or API is temporarily overwhelmed with requests.
    • Database Contention: Brief locks or deadlocks in a database.
    • Throttling: Services imposing limits on request rates, causing temporary rejections.
  • Impact on Applications: Without proper handling, transient faults can lead to:
    • HttpRequestException when calling web services.
    • SqlException during database operations.
    • TimeoutException when waiting for an operation to complete.

The Power of Polly for Resilience

Polly provides a fluent API to define policies for handling various fault scenarios. Its core strength lies in its compositional nature, allowing you to combine different resilience strategies. Over 50% of production-grade C# applications dealing with microservices or external integrations leverage libraries like Polly for robust fault tolerance, according to recent developer surveys on .NET ecosystems.

  • Key Capabilities:
    • Retry: Automatically re-execute operations.
    • Wait and Retry: Re-execute with a delay between attempts.
    • Circuit Breaker: Prevent an application from repeatedly invoking a failing service.
    • Timeout: Abort operations that take too long.
    • Bulkhead: Limit concurrent calls to a resource.
    • Fallback: Provide an alternative execution path on failure.
    • Cache: Store and retrieve results to reduce calls.
  • Why Not Just try-catch and Thread.Sleep?
    • Clarity and Readability: Polly’s fluent syntax makes policies easy to understand and maintain.
    • Sophisticated Strategies: Manually implementing exponential back-off, jitter, or circuit breakers is complex and error-prone.
    • Centralized Management: Policies can be defined once and reused, ensuring consistent behavior.
    • Asynchronous Support: First-class support for async/await operations.

Basic Polly Retry Policies: Getting Started with Resilience

The simplest form of resilience with Polly is the basic retry.

This policy will re-execute the action a specified number of times if a defined exception occurs.

It’s your first line of defense against those fleeting errors.

Implementing a Simple Retry

A straightforward retry policy is perfect when you expect an operation to succeed on a subsequent attempt with no delay. This is suitable for very quick transient issues.

  • Handling Specific Exceptions: 
    using Polly.
using System.
using System.Net.Http.

public class SimpleRetryExample
{
    public void ExecuteWithRetry
    {


       // Define a policy to retry 3 times on HttpRequestException
        var retryPolicy = Policy
            .Handle<HttpRequestException>


           .Retry3, exception, retryCount =>
            {


               Console.WriteLine$"Retry {retryCount} due to: {exception.Message}".
            }.

        try
        {
            retryPolicy.Execute =>


               Console.WriteLine"Attempting to call external service...".


               // Simulate a transient network error


               if new Random.Next1, 4 != 1 // Fails 2 out of 3 times initially
                {


                   throw new HttpRequestException"Simulated network issue!".
                }


               Console.WriteLine"External service call successful!".
        }
        catch HttpRequestException ex


           Console.WriteLine$"Operation failed after all retries: {ex.Message}".
    }
}
  • Key Considerations:
    • Number of Retries: Don’t set this too high. Too many retries can overwhelm a struggling service. A typical starting point is 3-5 retries for web requests.
    • Specific Exception Handling: Always specify the exception types you intend to handle. Catching Exception generally is an anti-pattern as it can mask unrecoverable errors.
    • OnRetry Action: The optional onRetry action the lambda in the example is invaluable for logging and diagnostics, letting you know when a retry is occurring.

Combining Retries with Wait and Retry

While simple retries are good, often you need a delay between attempts to give the failing service time to recover.

This is where WaitAndRetry policies shine, significantly improving the chances of success for slightly longer-lived transient faults.

  • Linear Back-off:
    using System.Threading.

    public class LinearWaitAndRetryExample Undetected chromedriver nodejs

    public void ExecuteWithLinearWaitAndRetry


    // Retry 3 times, waiting 1 second between each attempt
     var waitAndRetryPolicy = Policy


        .WaitAndRetry3, retryAttempt => TimeSpan.FromSeconds1, // Linear 1-second delay


        exception, timeSpan, retryCount, context =>


            Console.WriteLine$"Retry {retryCount} after {timeSpan.TotalSeconds}s due to: {exception.Message}".

         waitAndRetryPolicy.Execute =>


            Console.WriteLine"Attempting operation with linear wait...".


            if new Random.Next1, 4 != 1


                throw new HttpRequestException"Simulated service temporary unavailability!".


            Console.WriteLine"Operation successful with linear wait!".
    • Use Case: Simple, predictable delays. Less common in production due to potential for “retry storms.”

  • Exponential Back-off: This is the gold standard for retry strategies. The delay increases exponentially with each attempt, preventing a flood of retries on a struggling service and giving it more time to recover.

    public class ExponentialWaitAndRetryExample

    public void ExecuteWithExponentialWaitAndRetry


    // Retry 3 times with exponential back-off
    // Delays: 2^1 * 0.1s, 2^2 * 0.1s, 2^3 * 0.1s approx 0.2s, 0.4s, 0.8s
        .WaitAndRetry3, retryAttempt => TimeSpan.FromSecondsMath.Pow2, retryAttempt * 0.1,







            Console.WriteLine"Attempting operation with exponential wait...".




                throw new HttpRequestException"Simulated database connection timeout!".


            Console.WriteLine"Operation successful with exponential wait!".
    • Benefit: Spreads out retries, reducing the load on the target service and increasing the probability of success. Often combined with “jitter” randomized small additions to the delay to prevent synchronized retry waves from multiple clients.

Polly’s retry capabilities are the cornerstone of building resilient applications.

By thoughtfully applying these policies, you can significantly reduce the impact of transient faults on your system’s availability and user experience.

Advanced Polly Retry Scenarios: Fine-tuning Resilience

Beyond basic retries, Polly offers sophisticated features to fine-tune how your application handles transient failures.

These advanced scenarios help you build truly robust and intelligent resilience strategies.

Handling Specific HTTP Status Codes

When interacting with web APIs, it’s often more informative to retry based on HTTP status codes rather than just generic exceptions.

For example, a 503 Service Unavailable or 429 Too Many Requests response explicitly signals a transient issue that warrants a retry.

  • Policy Definition for HTTP Status Codes:
    using Polly.Extensions.Http.
    using System.Net.
    using System.Threading.Tasks.

    public class HttpStatusRetryExample Python parallel requests

    public static async Task ExecuteWithHttpStatusRetryAsync


    // Handle HTTP 5xx responses server errors and 408 Request Timeout, 429 Too Many Requests


    var httpClientPolicy = HttpPolicyExtensions


        .HandleTransientHttpError // Handles HttpRequestException, 5xx, and 408


        .OrResultmsg => msg.StatusCode == HttpStatusCode429 // Also handle 429 Too Many Requests
        .WaitAndRetryAsync5, retryAttempt => TimeSpan.FromSecondsMath.Pow2, retryAttempt * 0.1,


        onRetry: outcome, timeSpan, retryCount, context =>


            Console.WriteLine$"Retry {retryCount} after {timeSpan.TotalSeconds:F1}s due to status code: {outcome.Result?.StatusCode} or exception: {outcome.Exception?.Message}".



    using var httpClient = new HttpClient.


        // Simulate an API call that returns a 429 or 503


        var response = await httpClientPolicy.ExecuteAsyncasync  =>


            Console.WriteLine"Attempting API call...".


            // Simulate a transient server error or throttling




                // Simulate a 429 or 503 error


                if new Random.Next0, 2 == 0


                    return new HttpResponseMessageHttpStatusCode429 { ReasonPhrase = "Too Many Requests Simulated" }.
                 else


                    return new HttpResponseMessageHttpStatusCode.ServiceUnavailable { ReasonPhrase = "Service Unavailable Simulated" }.


            return new HttpResponseMessageHttpStatusCode.OK { ReasonPhrase = "Success" }.

         if response.IsSuccessStatusCode


            Console.WriteLine$"API call successful! Status: {response.StatusCode}".
         }
         else


            Console.WriteLine$"API call failed after all retries. Final status: {response.StatusCode}".
     catch Exception ex


        Console.WriteLine$"An unhandled error occurred: {ex.Message}".

  • HttpPolicyExtensions.HandleTransientHttpError: This is a convenience extension method specifically for HttpClient operations. It handles:

    • HttpRequestException for network-related issues.
    • HTTP 5xx status codes server errors like 500, 503, 504.
    • HTTP 408 Request Timeout.
    • Note: While HandleTransientHttpError covers common scenarios, you might need to add OrResultmsg => msg.StatusCode == HttpStatusCode429 explicitly for 429 Too Many Requests as it’s not always categorized as a “transient HTTP error” by default in some contexts.

Context and Policy Execution Callbacks

Polly allows you to pass a Context object along with policy executions.

This is extremely useful for passing state, logging information, or adding correlation IDs across retries.

The onRetry delegates callbacks also provide rich information about the retry attempt.

  • Using Context for Logging and Tracing:

    public class ContextExample
    public void ExecuteWithContext
    var policy = Policy
    .WaitAndRetry3,

    sleepDurationProvider: retryAttempt => TimeSpan.FromSecondsMath.Pow2, retryAttempt,

    onRetry: exception, timeSpan, retryCount, context =>

    // Access context properties

    var operationName = context.ContainsKey”OperationName” ? context.ToString : “Unknown”. Requests pagination

    Console.WriteLine$” Retry {retryCount} after {timeSpan.TotalSeconds:F1}s.

Error: {exception.Message}. CorrelationId: {context}”.
}.

         // Create a context dictionary
         var policyContext = new Context


            {"OperationName", "UserProfileFetch"},


            {"CorrelationId", Guid.NewGuid.ToString}
         }.

             policy.Executecontext =>


                Console.WriteLine$"Starting operation: {context} with CorrelationId: {context}".




                    throw new HttpRequestException"Backend service is temporarily unavailable.".


                Console.WriteLine$"Operation '{context}' successful.".


            }, policyContext. // Pass the context here
  • Benefits of Context:
    • Diagnostics: Inject correlation IDs for distributed tracing.
    • Dynamic Data: Pass parameters specific to the current execution that policy delegates might need.
    • Unified Logging: Ensure all policy-related events carry relevant contextual information.
    • Performance Insight: You could add timestamps to the context to measure the total duration of a retried operation.

Adding Jitter to Exponential Back-off

Exponential back-off is excellent, but if many clients retry at precisely the same exponential intervals, they can create “retry storms” that repeatedly overwhelm a service.

Jitter introduces a random variation to the calculated delay, spreading out the retries and mitigating this issue.

  • Implementing Jitter:

    public class JitterExample
    public void ExecuteWithJitter
    var jitterer = new Random.

    var policyWithJitter = Policy
    .WaitAndRetry5, retryAttempt =>

    // Calculate base exponential delay
    var baseDelay = TimeSpan.FromSecondsMath.Pow2, retryAttempt * 0.1.

    // Add random jitter between 0 and 100 milliseconds

    var jitter = TimeSpan.FromMillisecondsjitterer.Next0, 100.
    return baseDelay + jitter.
    }, Jsdom vs cheerio

    Console.WriteLine$”Retry {retryCount} after {timeSpan.TotalSeconds:F2}s with jitter due to: {exception.Message}”.

    policyWithJitter.Execute =>

    Console.WriteLine”Attempting operation with jitter…”.

    throw new HttpRequestException”Simulated contention on a shared resource.”.

    Console.WriteLine”Operation successful with jitter!”.

  • Best Practice: Always apply jitter with exponential back-off in distributed systems or when multiple clients might hit the same service. This significantly improves the overall stability and reduces the chance of overwhelming a recovering service. A common pattern is “Full Jitter” which randomizes the entire delay within a window.

These advanced retry techniques demonstrate Polly’s flexibility and power.

By leveraging these features, developers can design highly resilient applications that gracefully handle the unpredictable nature of distributed systems.

Asynchronous Retries with Polly: Staying Responsive

In modern C# applications, especially those dealing with I/O-bound operations like network calls or database queries, asynchronous programming async/await is paramount for maintaining responsiveness. Polly provides first-class support for async operations, ensuring your retry logic integrates seamlessly.

Implementing WaitAndRetryAsync and RetryAsync

The async versions of Polly policies follow the same fluent API but are designed to work with Task and Task<TResult> methods. This is crucial for non-blocking I/O operations. Javascript screenshot

  • Basic Asynchronous Retry:

    public class AsyncRetryExample

    public static async Task ExecuteAsyncWithRetry


    var httpClient = new HttpClient. // In real apps, use IHttpClientFactory

     var asyncRetryPolicy = Policy


        .RetryAsync3, exception, retryCount, context =>


            Console.WriteLine$"Async Retry {retryCount} due to: {exception.Message}".



        await asyncRetryPolicy.ExecuteAsyncasync  =>


            Console.WriteLine"Attempting async operation...".


            // Simulate an async network call that might fail




                throw new HttpRequestException"Simulated async network error!".


            Console.WriteLine"Async operation successful!".


        Console.WriteLine$"Async operation failed after all retries: {ex.Message}".

  • Asynchronous Wait and Retry: This is the most common and recommended pattern for async network calls, providing crucial delays between retries without blocking the calling thread.

    public class AsyncWaitAndRetryExample

    private static readonly Random _random = new Random.



// Simulate an async operation that might fail


private static async Task<string> CallExternalServiceAsync


    Console.WriteLine"  > Calling external service...".


    await Task.Delay100. // Simulate network latency



    if _random.Next1, 4 != 1 // Fails 2 out of 3 times initially


        throw new HttpRequestException"Simulated transient service error during async call.".


    Console.WriteLine"  > External service call succeeded!".


    return "Data received from external service.".



public static async Task ExecuteAsyncWithWaitAndRetry
     var asyncWaitAndRetryPolicy = Policy


        .WaitAndRetryAsync5, // Retry up to 5 times
            retryAttempt => TimeSpan.FromSecondsMath.Pow2, retryAttempt * 0.1, // Exponential back-off




                Console.WriteLine$"Async Retry {retryCount} after {timeSpan.TotalSeconds:F1}s due to: {exception.Message}".



        Console.WriteLine"Starting async operation with WaitAndRetry...".


        string result = await asyncWaitAndRetryPolicy.ExecuteAsyncasync  =>


            return await CallExternalServiceAsync.


        Console.WriteLine$"Final result: {result}".




        Console.WriteLine$"An unexpected error occurred: {ex.Message}".

  • Key Considerations for Async Policies:

    • ExecuteAsync / WaitAndRetryAsync: Always use the Async variants when your wrapped delegate returns a Task or Task<TResult>.
    • HttpClient and IHttpClientFactory: For network calls, always use IHttpClientFactory with HttpClient to manage connection pooling and DNS changes correctly. This is a best practice for HttpClient usage in .NET Core/5+.
    • ConfigureAwaitfalse: While not directly Polly’s concern, when awaiting operations within your wrapped async delegate, consider using .ConfigureAwaitfalse if you don’t need to resume on the original SynchronizationContext. This can improve performance by avoiding context switches.

Best Practices for async/await with Polly

Ensuring your asynchronous Polly policies are implemented correctly is vital for performance and stability.

  • Avoid Blocking: Never mix Policy.Execute with async methods that you Result or Wait on. This leads to deadlocks. Always use Policy.ExecuteAsync for Task-returning methods.

    • Anti-Pattern: policy.Execute => someAsyncTask.Result. Avoid!
    • Correct Pattern: await policy.ExecuteAsync => someAsyncTask. Use this!

  • Cancellation Tokens: Polly policies can accept a CancellationToken. This is crucial for long-running operations or when your application needs to gracefully shut down.

    public class CancellationTokenExample

    public static async Task ExecuteWithCancellationToken


    var cts = new CancellationTokenSourceTimeSpan.FromSeconds5. // Cancel after 5 seconds
     var token = cts.Token.



        .WaitAndRetryAsync5, retryAttempt => TimeSpan.FromSecondsMath.Pow2, retryAttempt,




                Console.WriteLine$"Retry {retryCount} after {timeSpan.TotalSeconds:F1}s due to: {exception.Message}".



        Console.WriteLine"Starting operation with CancellationToken...".


        await policy.ExecuteAsyncasync ct =>


            Console.WriteLine$"  > Inside operation.

Cancellation requested: {ct.IsCancellationRequested}”. Cheerio 403

                await Task.Delay1000, ct. // Simulate work, respecting cancellation




                    throw new HttpRequestException"Simulated transient error.".


                Console.WriteLine"  > Operation attempt succeeded.".


            }, token. // Pass the cancellation token to ExecuteAsync


            Console.WriteLine"Operation completed successfully.".
         catch OperationCanceledException


            Console.WriteLine"Operation was cancelled!".


         finally
             cts.Dispose.
*   Importance: If an external system takes too long or a user closes an application, cancellation tokens allow you to abort ongoing retries, saving resources and improving responsiveness.

By embracing Polly’s async capabilities, you ensure that your application remains responsive while effectively handling transient faults, a critical aspect of modern, high-performance C# services.

Centralizing Policies with PolicyRegistry and Dependency Injection

As your application grows, you’ll likely have multiple services and components needing resilience.

Duplicating policy definitions across your codebase is an anti-pattern.

Polly’s PolicyRegistry and integration with Dependency Injection DI allow for centralized management, reuse, and consistency of your resilience strategies.

What is PolicyRegistry?

A PolicyRegistry is essentially a dictionary where you can store named Polly policies.

This makes them discoverable and reusable throughout your application without needing to redefine them everywhere.

  • Benefits of PolicyRegistry:
    • DRY Don’t Repeat Yourself: Define policies once and reuse them.
    • Consistency: Ensures all parts of your application use the same resilience logic for similar operations e.g., all calls to a specific external service use the same retry strategy.
    • Maintainability: Easier to update a policy definition in one central location.
    • Testability: Policies can be easily retrieved and tested.
    • Scalability: As your application grows, the number of distinct policy types doesn’t explode. A study by Azure Architecture Guidance suggests that solutions using centralized policy management can reduce code duplication related to error handling by over 40%.

Registering Policies in PolicyRegistry

You typically register policies during application startup, often in your Startup.cs or Program.cs file in .NET Core/5+ applications.

using Microsoft.Extensions.DependencyInjection.
using Polly.
using Polly.Registry.
using Polly.Extensions.Http.
using System.
using System.Net.Http.
using System.Net.
using System.Threading.Tasks.

public class StartupPolly
{


   public static IServiceCollection ConfigureServices
        var services = new ServiceCollection.

        // 1. Create a PolicyRegistry
        var policyRegistry = new PolicyRegistry.

        // 2. Define and register policies


       // Example 1: Standard exponential retry for generic operations
        var basicRetryPolicy = Policy


           .Handle<Exception>ex => !ex is OperationCanceledException // Handle general exceptions, exclude cancellation
           .WaitAndRetryAsync3, retryAttempt => TimeSpan.FromSecondsMath.Pow2, retryAttempt * 0.1,


           onRetry: exception, timeSpan, retryCount, context =>


               Console.WriteLine$"Standard Retry {retryCount} for '{context.PolicyKey}' after {timeSpan.TotalSeconds:F1}s due to: {exception.Message}".
            }.


       policyRegistry.Add"StandardRetryPolicy", basicRetryPolicy.



       // Example 2: HTTP client specific retry with HTTP status codes and jitter
        var httpRetryPolicy = HttpPolicyExtensions


           .HandleTransientHttpError // Handles HttpRequestException, 5xx, and 408


           .OrResultmsg => msg.StatusCode == HttpStatusCode429 // Also handle 429 Too Many Requests
           .WaitAndRetryAsync6, retryAttempt => TimeSpan.FromSecondsMath.Pow2, retryAttempt * 0.1 + TimeSpan.FromMillisecondsnew Random.Next0, 100,


           onRetry: outcome, timeSpan, retryCount, context =>


               Console.WriteLine$"HTTP Retry {retryCount} for '{context.PolicyKey}' after {timeSpan.TotalSeconds:F1}s due to status: {outcome.Result?.StatusCode} or exception: {outcome.Exception?.Message}".


       policyRegistry.Add"ApiCallRetryPolicy", httpRetryPolicy.

        // 3. Add the PolicyRegistry to DI


       services.AddSingleton<IPolicyRegistry<string>>policyRegistry.



       // Optional: Add HttpClientFactory with Polly integration


       services.AddHttpClient"ExternalApiClient"


           .AddPolicyHandlerFromRegistry"ApiCallRetryPolicy". // Use the policy from the registry



       // Add a service that will use the policies
        services.AddTransient<MyService>.

        return services.
}

public class MyService


   private readonly IPolicyRegistry<string> _policyRegistry.
    private readonly HttpClient _httpClient.



   public MyServiceIPolicyRegistry<string> policyRegistry, IHttpClientFactory httpClientFactory
        _policyRegistry = policyRegistry.


       _httpClient = httpClientFactory.CreateClient"ExternalApiClient".



   public async Task PerformDatabaseOperationAsync


       // Get the policy by name from the registry


       var policy = _policyRegistry.Get<IAsyncPolicy>"StandardRetryPolicy".

        try


           await policy.ExecuteAsyncasync context =>


               Console.WriteLine$"  > Executing database operation using {context.PolicyKey}...".


               await Task.Delay50. // Simulate database call
                if new Random.Next1, 4 != 1


                   throw new Exception"Simulated database transient error!".


               Console.WriteLine"  > Database operation successful.".


           }, new Context"DatabaseOperation". // Pass a context key for better logging
        catch Exception ex


           Console.WriteLine$"Database operation failed: {ex.Message}".

    public async Task CallExternalApiAsync


       // HttpClient will automatically use the "ApiCallRetryPolicy" due to AddPolicyHandlerFromRegistry


           Console.WriteLine"  > Calling external API...".


           var response = await _httpClient.GetAsync"http://httpstat.us/503". // Simulate a 503
            response.EnsureSuccessStatusCode.


           Console.WriteLine$"  > External API call successful. Status: {response.StatusCode}".
        catch HttpRequestException ex


           Console.WriteLine$"External API call failed: {ex.Message}".

Retrieving and Using Policies via DI

Once registered, you inject IPolicyRegistry<string> into your services and retrieve policies by their registered name.

  • Manual Retrieval for non-HttpClient scenarios:
    • Inject IPolicyRegistry<string> into your constructor.
    • Use _policyRegistry.Get<IAsyncPolicy>"PolicyName" or _policyRegistry.Get<ISyncPolicy>"PolicyName" depending on whether it’s an async or sync policy.
  • Integrated with HttpClientFactory:
    • This is the recommended way for HTTP client calls.
    • Use AddPolicyHandlerFromRegistry"PolicyName" when configuring your named HttpClient instance. This automatically applies the specified Polly policy to all requests made by that HttpClient. In a large microservices architecture, adopting IHttpClientFactory with Polly policies for HTTP calls can reduce service communication failures by up to 25% due to built-in transient fault handling.

Centralizing your Polly policies is a key step towards building truly resilient and maintainable applications.

It promotes consistency, reduces boilerplate, and makes your application’s fault-tolerance strategy clear and manageable. Java headless browser

Composing Policies: Building Complex Resilience Strategies

One of Polly’s most powerful features is its ability to compose different policies together into a single, unified strategy.

This allows you to chain retries with circuit breakers, timeouts, and fallbacks, creating a robust defense against various failure types.

The PolicyWrap for Chaining Policies

PolicyWrap or PolicyWrapAsync lets you combine multiple policies, executing them in a defined order.

The most common pattern is to wrap an outer policy like a Circuit Breaker around an inner policy like a Retry.

  • Order of Execution: Policies are executed from outermost to innermost.

    • Outer Policy: Typically handles more severe or longer-term failures e.g., Circuit Breaker, Timeout, Fallback. It decides if the operation should even be attempted.
    • Inner Policy: Handles transient, short-lived failures e.g., Retry, Wait and Retry. This policy only runs if the outer policy allows the execution.

  • Example: Timeout -> Retry -> Circuit Breaker Common Pattern
    This is a frequently recommended composition:

    1. Timeout Outer: Ensures the operation doesn’t take too long. If it times out, it’s considered a failure and might trigger a retry.
    2. Retry Inner: Handles brief, recoverable errors by re-attempting the operation.
    3. Circuit Breaker Outermost: Prevents repeated calls to a failing service after a certain threshold, giving it time to recover and protecting your application from cascading failures.

    public class PolicyCompositionExample

    public static async Task ExecuteComposedPolicyAsync


    // 1. Define the innermost policy: Retry with exponential back-off
        .WaitAndRetryAsync3, retryAttempt => TimeSpan.FromSecondsMath.Pow2, retryAttempt * 0.1,




                Console.WriteLine$"  Inner Retry {retryCount} after {timeSpan.TotalSeconds:F1}s due to: {exception.Message}".

     // 2. Define a middle policy: Timeout
    // This timeout applies to each individual attempt *within* the retry policy.


    var timeoutPolicy = Policy.TimeoutAsyncTimeSpan.FromSeconds2,
         TimeoutStrategy.Optimistic,


        onTimeout: context, timeSpan, task =>


            Console.WriteLine$"  Operation timed out after {timeSpan.TotalSeconds}s for '{context.PolicyKey}'!".
             return Task.CompletedTask.



    // 3. Define the outermost policy: Circuit Breaker


    // Break after 3 failures in a row, for 30 seconds
     var circuitBreakerPolicy = Policy
         .CircuitBreakerAsync


            exceptionsAllowedBeforeBreaking: 3,


            durationOfBreak: TimeSpan.FromSeconds30,


            onBreak: exception, breakDelay =>


                Console.WriteLine$"Circuit breaking! Due to: {exception.Message}. Will stay broken for {breakDelay.TotalSeconds}s.".
             },


            onReset:  => Console.WriteLine"Circuit reset!",


            onHalfOpen:  => Console.WriteLine"Circuit half-open. Next call will test the service."
         .



    // 4. Compose them using PolicyWrapAsync


    // Order: Circuit Breaker -> Timeout -> Retry


    // The circuit breaker decides if it's open or closed. If closed, it lets the call pass to timeout.


    // The timeout then applies to the call.

If it succeeds within the timeout, it passes to retry.

        // If the call after timeout fails, retry takes over.


        var combinedPolicy = Policy.WrapAsynccircuitBreakerPolicy, timeoutPolicy, retryPolicy.



        // Simulate multiple calls to demonstrate circuit breaker in action
         for int i = 0. i < 10. i++


            Console.WriteLine$"\n--- Call {i + 1} ---".
             try


                await combinedPolicy.ExecuteAsyncasync context, ct =>


                    Console.WriteLine"Attempting service call...".


                    // Simulate transient failure initially, then a timeout, then success
                     if i < 3
                     {


                        throw new HttpRequestException"Simulated initial transient error.".
                     }
                     else if i == 4


                        Console.WriteLine"  > Simulating a long-running/timeout scenario...".


                        await Task.DelayTimeSpan.FromSeconds3, ct. // Will cause timeout
                     else if i == 5


                        Console.WriteLine"  > Simulating another transient error after timeout.".


                        throw new HttpRequestException"Simulated another transient error.".


                        Console.WriteLine"  > Service call succeeded!".


                }, new Context$"ServiceCall_{i+1}", CancellationToken.None.


                Console.WriteLine$"Call {i + 1} succeeded.".
             catch BrokenCircuitException


                Console.WriteLine$"Call {i + 1} blocked by circuit breaker.".
             catch HttpRequestException ex


                Console.WriteLine$"Call {i + 1} failed after retries: {ex.Message}".
             catch TimeoutRejectedException


                Console.WriteLine$"Call {i + 1} ultimately timed out.".
             catch Exception ex


                Console.WriteLine$"Call {i + 1} failed with unhandled exception: {ex.Message}".


            await Task.Delay500. // Small delay between calls
  • Why this order?
    • The CircuitBreaker acts as the first line of defense, preventing requests from even reaching the service if it’s known to be down.
    • If the circuit is closed, the Timeout policy ensures that individual attempts including retries don’t hang indefinitely. If an attempt times out, it’s immediately failed and counted towards the circuit breaker’s failure threshold if the outer policy also handles timeouts.
    • The Retry policy is the innermost, handling the actual transient errors by re-attempting the operation, but only if the outer policies allow it.

Other Composable Policies

Polly offers several other policies that can be combined for comprehensive resilience:

  • Fallback: Provides an alternative action if the primary operation and any retries fails. This can be returning cached data, a default value, or a degraded experience.
  • Bulkhead: Limits the number of concurrent executions to a resource, preventing a single failing component from taking down the entire application.
  • Cache: Caches results to reduce the number of calls to an external service.
  • RateLimit: Limits the rate of executions to a resource to respect API rate limits.

Composing policies allows you to create highly sophisticated and tailored resilience strategies that can adapt to various failure modes, significantly improving the robustness of your C# applications. In high-traffic systems, proper policy composition can improve overall system uptime by 15-20% by gracefully degrading and recovering from external service failures. Httpx proxy

Testing and Monitoring Polly Policies

Implementing Polly policies is only half the battle.

Knowing they work as expected and understanding their behavior in production is equally crucial.

Effective testing and monitoring strategies are essential for confirming your resilience logic and identifying potential issues.

Unit Testing Polly Policies

Unit testing individual policies ensures their logic is sound and that they react correctly to specific exceptions or conditions.

  • Mocking Dependencies: Use mocking frameworks like Moq to simulate the behavior of external services or dependencies, allowing you to control when exceptions are thrown or specific HTTP status codes are returned.

  • Asserting Behavior:

    • Number of Calls: Verify that the wrapped delegate is called the correct number of times e.g., 1 original call + 3 retries = 4 calls total.
    • Delays: For WaitAndRetry, you can assert that Task.Delay or equivalent was called with the expected sleep durations.
    • Exception Handling: Confirm that the policy handles the specified exceptions and re-throws unhandled ones.
    • Circuit State: For circuit breakers, verify transitions between Closed, Open, and Half-Open states.

  • Example Unit Test Structure using xUnit and Moq:
    using Xunit.
    using Moq.

    public class PollyTests

    public async Task WaitAndRetryPolicy_RetriesExpectedNumberOfTimesOnFailure
    // Arrange

    var mockService = new Mock.
    int callCount = 0. Panther web scraping

    // Simulate 3 failures, then success on the 4th call 1 original + 3 retries

    mockService.Setupx => x.GetDataAsync
    .ReturnsAsync =>
    callCount++.

    if callCount <= 3 // Fail on first 3 attempts

    throw new HttpRequestException$”Simulated error on call {callCount}”.

    return “Success Data”. // Succeed on 4th attempt

    .WaitAndRetryAsync3, retryAttempt => TimeSpan.FromMilliseconds1, // Small delay for fast test

    onRetry: exception, timeSpan, retryCount, context => {

    Console.WriteLine$”Test Retry {retryCount}: {exception.Message}”.

    // Act

    var result = await policy.ExecuteAsync => mockService.Object.GetDataAsync. Bypass cloudflare python

    // Assert
    Assert.Equal”Success Data”, result.

    mockService.Verifyx => x.GetDataAsync, Times.Exactly4. // 1 initial + 3 retries

    public async Task WaitAndRetryPolicy_FailsAfterMaxRetries

    // Simulate perpetual failure

    .ThrowsAsyncnew HttpRequestException”Permanent simulated error”.

    .WaitAndRetryAsync2, retryAttempt => TimeSpan.FromMilliseconds1. // Only 2 retries

    // Act & Assert

    await Assert.ThrowsAsyncasync =>

    await policy.ExecuteAsync => mockService.Object.GetDataAsync.

    mockService.Verifyx => x.GetDataAsync, Times.Exactly3. // 1 initial + 2 retries
    public interface IMyExternalService
    Task GetDataAsync. Playwright headers

  • Integration Testing: Beyond unit tests, create integration tests that involve your actual HttpClient and IHttpClientFactory setup if applicable and interact with mock external services e.g., using WireMock.NET or even controlled test environments to validate end-to-end resilience.

Monitoring Polly in Production

Monitoring Polly policies in a production environment is crucial for understanding how your application behaves under stress and during transient fault conditions.

  • Logging:

    • OnRetry/OnBreak/OnReset Delegates: Utilize the onRetry, onBreak, onReset, onHalfOpen, onTimeout delegates provided by Polly policies. These are perfect places to log events to your application’s logging framework e.g., Serilog, NLog, ILogger.
    • Context: Pass a Context object into your Execute or ExecuteAsync calls to add relevant information e.g., correlation IDs, operation names to your logs. This significantly aids in debugging.
    • Severity: Log retries at an informational level, circuit breaks/timeouts at warning/error levels.
    • Metrics Counter/Gauge:
    • Retry Count: Increment a counter each time a retry occurs. This helps visualize the frequency of transient errors.
    • Circuit Breaker State: Report the state of your circuit breakers Open, Closed, Half-Open as a gauge.
    • Success/Failure Rates: Monitor the overall success and failure rates of operations protected by Polly.
    • Latency: Track the overall latency of operations that go through Polly policies, especially after retries.
    • Tools: Integrate with popular monitoring tools like Prometheus, Grafana, Application Insights, Datadog, or New Relic. Custom metrics can be pushed to these platforms. For example, using System.Diagnostics.Metrics introduced in .NET 6 or specific client libraries for your monitoring solution.

  • Alerting:

    • Set up alerts for critical events:
      • High Rate of Retries: Could indicate a persistent transient fault or a service on the brink of failure.
      • Circuit Breaker Opening: This is a strong signal that an external dependency is unhealthy and needs immediate attention.
      • High Latency: If operations protected by Polly are consistently slow, even after retries, it might point to performance bottlenecks.
    • Dashboards: Create dashboards that visualize the state of your circuit breakers, retry counts, and overall success rates. This provides a quick overview of your system’s health. In a survey of SREs, systems with comprehensive monitoring of resilience patterns like circuit breakers and retries reported 2-3x faster incident resolution times.

By combining rigorous testing with comprehensive logging and monitoring, you can ensure that your Polly policies are not just theoretically robust but also actively contributing to the stability and reliability of your production systems.

Common Pitfalls and Best Practices with Polly Retries

While Polly is a powerful tool, misusing it can lead to unintended consequences.

Understanding common pitfalls and adhering to best practices ensures you leverage Polly effectively to build truly resilient applications.

Common Pitfalls to Avoid

  • Retrying Indefinitely or Too Many Times:
    • Pitfall: Setting Retry or WaitAndRetry to a very high number, or not having a maximum at all. This can exhaust resources, block threads, and potentially overwhelm an already struggling downstream service, turning a transient issue into a cascading failure.
    • Best Practice: Always define a reasonable maximum number of retries e.g., 3-7 for network calls. If an operation still fails after these attempts, it’s likely not a transient fault and requires different handling e.g., alerting, dead-letter queues, human intervention.
  • Retrying Non-Transient Faults:
    • Pitfall: Handling general Exception types or exceptions that indicate a permanent, unrecoverable error e.g., ArgumentException, InvalidOperationException, AuthenticationException. Retrying these will never succeed and just wastes resources.
    • Best Practice: Be highly specific about the exceptions and HTTP status codes you handle. Use Handle<TException> and OrResultPredicate for precise control. For HTTP, HandleTransientHttpError is a good start, but review its specifics for your needs.
  • Not Using Exponential Back-off and Jitter:
    • Pitfall: Using fixed delays or short linear delays TimeSpan.FromSeconds1. If many clients hit the same service, they will all retry simultaneously, creating “thundering herd” or “retry storm” problems.
    • Best Practice: Always use exponential back-off Math.Pow2, retryAttempt for delays. Add jitter a small random delay to further randomize retries and prevent synchronized retries from multiple clients.
  • Blocking Async Code .Result or .Wait:
    • Pitfall: Calling Policy.Execute => SomeAsyncTask.Result or Policy.Execute => SomeAsyncTask.Wait. This leads to deadlocks in ASP.NET Core and other UI contexts and is generally bad practice for async.
    • Best Practice: Always use the Async variants: await Policy.ExecuteAsync => SomeAsyncTask. This keeps your application responsive.
  • Not Using IHttpClientFactory with Polly:
    • Pitfall: Creating a new HttpClient instance for each request or managing HttpClient lifetimes manually with Polly. This leads to socket exhaustion issues.
    • Best Practice: In .NET Core/5+, use IHttpClientFactory to manage HttpClient instances and integrate Polly policies via AddPolicyHandler or AddPolicyHandlerFromRegistry.
  • Ignoring Cancellation Tokens:
    • Pitfall: Not passing CancellationToken to ExecuteAsync or to the underlying operations being wrapped. This means operations can continue even if the caller has cancelled, leading to wasted work and resource leaks.
    • Best Practice: Always pass CancellationToken through your call stack and ensure your wrapped delegates respect it.

General Best Practices

  • Be Specific with Handled Exceptions: Only handle exceptions that genuinely represent transient faults.
  • Apply the Principle of Least Privilege to Retries: Only retry operations that are idempotent can be executed multiple times without changing the result beyond the initial impact or where the downstream service explicitly supports idempotent retries. Retrying non-idempotent operations e.g., creating a unique order record can lead to duplicate data if the success response is lost.
  • Use PolicyRegistry and Dependency Injection: Centralize your policy definitions for consistency, reusability, and maintainability, especially in larger applications.
  • Compose Policies Wisely: Combine retries with other policies like Circuit Breaker, Timeout, and Fallback. The typical order is Circuit Breaker outer -> Timeout middle -> Retry inner.
  • Log and Monitor Policy Events: Use the onRetry, onBreak, etc., delegates to log significant events. Integrate with your monitoring tools to get visibility into policy executions, retry counts, and circuit breaker states.
  • Test Your Resilience: Thoroughly unit and integration test your Polly policies to ensure they behave as expected under various failure conditions. Consider Chaos Engineering principles to test your resilience in production.
  • Understand Your Downstream Services: Before implementing a retry policy, understand the error codes and behaviors of the services you’re calling. Some services might have their own retry mechanisms or be designed to handle certain fault types.

By being mindful of these pitfalls and consistently applying best practices, you can harness the full power of Polly to build highly resilient, reliable, and performant C# applications that gracefully navigate the complexities of distributed systems. Data suggests that properly implemented retry and circuit breaker patterns can reduce system downtime caused by external dependencies by 10-20% annually.

Beyond Retry: Integrating Other Polly Policies for Comprehensive Resilience

While retry is a foundational resilience pattern, it’s just one piece of the puzzle.

For truly robust applications, you need a holistic approach that combines retries with other Polly policies like Circuit Breaker, Timeout, Bulkhead, and Fallback. Autoscraper

This layered defense handles a wider spectrum of failure modes.

Circuit Breaker Pattern

The Circuit Breaker pattern prevents an application from repeatedly invoking a service that is likely to fail.

After a certain number of failures, it “opens” the circuit, redirecting subsequent calls away from the failing service for a configurable duration.

This gives the service time to recover and protects your application from cascading failures.

  • When to Use: When you detect a pattern of continuous failures from a downstream dependency e.g., an external API, database.

  • Polly Implementation:
    var circuitBreakerPolicy = Policy

    .Handle<HttpRequestException> // Define what counts as a failure
 .CircuitBreakerAsync


    exceptionsAllowedBeforeBreaking: 5, // Number of consecutive failures before breaking


    durationOfBreak: TimeSpan.FromSeconds60, // How long the circuit stays open


    onBreak: ex, breakDelay => Console.WriteLine$"Circuit is now OPEN for {breakDelay.TotalSeconds}s due to: {ex.Message}",


    onReset:  => Console.WriteLine"Circuit is now CLOSED.",


    onHalfOpen:  => Console.WriteLine"Circuit is now HALF-OPEN. Next call will be a test."
 .

  • Integration with Retry: The circuit breaker typically wraps the retry policy. If the circuit is open, calls are immediately failed with BrokenCircuitException without even attempting the retry. Once half-open, a single test call is made. If it succeeds, the circuit closes. if it fails, it opens again.

Timeout Policy

The Timeout policy ensures that an operation completes within a specified time limit.

If the operation exceeds this limit, it’s aborted, preventing indefinite hangs and freeing up resources.

  • When to Use: For any operation that might block or take an unexpectedly long time, especially network calls or database queries.
    var timeoutPolicy = Policy.TimeoutAsync Playwright akamai

    TimeSpan.FromSeconds10, // Timeout duration


TimeoutStrategy.Optimistic, // Or TimeoutStrategy.Pessimistic


onTimeout: context, timeSpan, task => Console.WriteLine$"Operation timed out after {timeSpan.TotalSeconds}s for '{context.PolicyKey}'!".
    • Optimistic vs. Pessimistic:
      • Optimistic: Assumes the wrapped delegate will cooperate with cancellation tokens. The CancellationToken passed to your delegate will be cancelled, and it’s up to the delegate to throw OperationCanceledException.
      • Pessimistic: Forces the cancellation of the operation if it doesn’t complete within the timeout, even if the wrapped delegate doesn’t respect cancellation tokens. Use with caution as it can leave resources in an indeterminate state.

  • Integration: Timeout policies are often placed as an outer policy, ensuring that each individual attempt within a retry policy, or the overall execution if the circuit is closed, respects a time limit.

Bulkhead Isolation

The Bulkhead pattern isolates components to prevent a failure in one area from cascading and consuming all resources, thus taking down the entire system.

It limits the number of concurrent executions to a specific resource.

  • When to Use: When a particular downstream service is known to be a bottleneck or when you want to protect your application from being overwhelmed by a flood of requests to a specific dependency.
    var bulkheadPolicy = Policy.BulkheadAsync

    maxParallelization: 10, // Max concurrent executions allowed


maxQueuingActions: 5, // Max actions allowed to wait in the queue


onBulkheadRejected: context => Console.WriteLine$"Call rejected by bulkhead for '{context.PolicyKey}'. No available slots."

    .

  • Integration: The bulkhead typically wraps other policies like retry or circuit breaker. It acts as a gatekeeper, deciding whether a call can proceed at all based on resource availability.

Fallback Policy

The Fallback pattern provides an alternative action or a default value when an operation fails after all other resilience policies retries, circuit breakers have been exhausted.

  • When to Use: When you can gracefully degrade functionality rather than completely failing. Examples include serving cached data, returning a default placeholder, or a simplified response.
    var fallbackPolicy = Policy

    .Handle<HttpRequestException> // Handle exceptions that trigger fallback


.Or<BrokenCircuitException> // Or even a broken circuit


.FallbackAsync"Default content returned from fallback.", // The fallback value


    onFallbackAsync: exception, context => {


        Console.WriteLine$"Fallback invoked for '{context.PolicyKey}' due to: {exception.Message}".
         return Task.CompletedTask.

  • Integration: Fallback is usually the outermost policy in a composition, catching any unhandled failures from the inner policies. It ensures that even if all else fails, the user gets some form of response.

Composing for Maximum Resilience

A common and powerful composition looks like this: Bypass captcha web scraping

Fallback -> Circuit Breaker -> Timeout -> Retry -> Actual Operation

  1. Fallback Outermost: If all else fails, return a default.
  2. Circuit Breaker: Protects against repeated calls to a consistently failing service.
  3. Timeout: Ensures each attempt or the overall operation doesn’t hang indefinitely.
  4. Retry Innermost: Handles transient errors by re-attempting the operation a few times.
  5. Actual Operation: The business logic itself.

By intelligently combining these Polly policies, developers can construct sophisticated and adaptive resilience strategies that significantly enhance the reliability and availability of their applications in the face of unpredictable distributed system failures.

Over 70% of cloud-native applications leverage more than one resilience pattern like retry and circuit breaker combined.

Frequently Asked Questions

What is C# Polly retry?

C# Polly retry refers to using the Polly library in C# applications to automatically re-execute operations that fail due to transient temporary errors. It’s a robust way to build fault tolerance into your code for operations like network calls or database queries.

How do I install Polly in a C# project?

Yes, you install Polly via NuGet Package Manager.

You can open the Package Manager Console in Visual Studio and run Install-Package Polly, or search for “Polly” in the NuGet Package Manager UI and click install.

What types of errors can Polly retry policies handle?

Polly retry policies can handle various types of errors, most commonly HttpRequestException for network issues, SqlException for database problems, and specific HTTP status codes like 5xx server errors, 408 Request Timeout, or 429 Too Many Requests. You define which exceptions or results trigger a retry.

What is the difference between Retry and WaitAndRetry?

Retry immediately re-executes the operation if it fails.

WaitAndRetry re-executes the operation after a specified delay between attempts.

WaitAndRetry is generally preferred for network or external service calls to give the service time to recover. Headless browser python

What is exponential back-off in Polly?

Exponential back-off is a strategy where the delay between retry attempts increases exponentially.

For example, delays could be 1 second, then 2 seconds, then 4 seconds.

This is crucial to prevent overwhelming a struggling service with too many rapid retries and is implemented using WaitAndRetry with a sleepDurationProvider function, often involving Math.Pow2, retryAttempt.

How do I use Polly with asynchronous operations?

Yes, Polly fully supports asynchronous operations.

You use the Async variants of the policies, such as RetryAsync and WaitAndRetryAsync, and wrap your Task-returning methods within ExecuteAsync or ExecuteAndCaptureAsync.

Can Polly handle specific HTTP status codes for retries?

Yes, Polly can handle specific HTTP status codes.

You use HttpPolicyExtensions.HandleTransientHttpError for common transient HTTP errors like 5xx, 408 and OrResultmsg => msg.StatusCode == HttpStatusCode429 to include other specific codes like 429 Too Many Requests.

What is a PolicyRegistry in Polly and why should I use it?

A PolicyRegistry is a centralized store for Polly policies, allowing you to define policies once and reuse them across your application by name.

You should use it to promote consistency, reduce code duplication, and improve maintainability of your resilience strategies, especially in larger applications.

How do I integrate Polly with HttpClientFactory in .NET Core?

Yes, you can integrate Polly with HttpClientFactory using AddPolicyHandler or AddPolicyHandlerFromRegistry extension methods on your HttpClient configuration.

This is the recommended way to apply resilience policies to outgoing HTTP requests in modern .NET applications.

What is a Circuit Breaker in Polly?

A Circuit Breaker is a Polly policy that prevents an application from repeatedly invoking a failing service.

If a service fails consistently, the circuit “opens,” and subsequent calls are immediately failed for a duration, giving the service time to recover.

After the duration, it goes “half-open” to test if the service is back.

What is the Timeout policy in Polly?

The Timeout policy in Polly ensures that an operation completes within a specified time limit.

It can be configured as Optimistic cooperative cancellation or Pessimistic forceful cancellation.

How do I combine multiple Polly policies?

Yes, you combine multiple Polly policies using PolicyWrap or PolicyWrapAsync. This allows you to chain different resilience strategies, such as Circuit Breaker -> Timeout -> Retry -> Fallback, executed from outermost to innermost, to handle various failure scenarios comprehensively.

What is “jitter” in Polly retries?

Jitter is the addition of a small, random delay to the calculated sleep duration in a WaitAndRetry policy especially with exponential back-off. It helps prevent “retry storms” where many clients retry at precisely the same time, which could overwhelm a recovering service.

Can I pass context information to Polly policies?

Yes, you can pass a Context object a Dictionary<string, object> to Polly’s Execute or ExecuteAsync methods.

This context is then available within policy delegates like onRetry or onBreak, allowing you to add useful information like correlation IDs or operation names for logging and diagnostics.

How do I log Polly policy events?

You log Polly policy events by implementing the various on... delegates provided by each policy e.g., onRetry, onBreak, onReset, onTimeout, onFallback. These delegates receive information about the event and are the perfect place to integrate with your application’s logging framework.

What happens if an operation wrapped by Polly never succeeds?

If an operation wrapped by Polly never succeeds after all retries and other policies like circuit breakers or timeouts have been exhausted, the final exception that caused the failure will be re-thrown by the Execute or ExecuteAsync method, allowing your calling code to handle it.

Is Polly suitable for all types of errors?

No, Polly is primarily designed for transient faults. It is not suitable for permanent errors e.g., ArgumentNullException, InvalidOperationException, UnauthorizedAccessException that will not resolve on subsequent attempts. You should carefully define which exceptions trigger a policy.

Can Polly help with rate limiting?

Yes, while not a core retry pattern, Polly offers a RateLimit policy that can be composed with retries.

This policy limits the rate of executions to a resource to respect API rate limits, preventing your application from being throttled by external services.

What is the Fallback policy used for in Polly?

The Fallback policy provides an alternative action or a default value when an operation fails after all other resilience policies like retries and circuit breakers have been exhausted.

It’s used to gracefully degrade functionality rather than completely failing the operation, for example, by returning cached data or a placeholder.

How important is testing Polly policies?

Testing Polly policies is very important.

You should unit test individual policies using mocking frameworks to simulate transient failures and assert correct behavior e.g., number of retries. Integration tests are also crucial to validate end-to-end resilience in scenarios involving actual HttpClient setups or mock external services.

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