Optimize the authentication service

Oct 13, 2025 by thesweetdesigns.com 6 views

Fetch Auth Session Failed: TooManyRequestsException

Introduction

When dealing with large-scale applications, it's not uncommon to encounter issues related to rate limiting or excessive requests. In this article, we'll delve into a specific problem that arises when sending push notifications to a large number of users, resulting in a TooManyRequestsException when attempting to fetch the authentication session.

Background

Our application utilizes the Amplify framework to manage authentication, API, and storage services. When a user opens the app, we initialize Amplify and fetch the authentication session to determine the user's state. However, when we blast a notification to all users, a significant number of them open the app simultaneously, leading to a high volume of requests to the authentication service.

The Issue

The error message indicates that the Fetch auth session failed due to a TooManyRequestsException, which is a rate limiting exception thrown by the AWS Cognito service. This exception occurs when the service receives too many requests within a short period, exceeding the allowed rate.

Code Analysis

Let's examine the code snippet where we fetch the authentication session:

Disposable sessionDisposable = RxAmplify.Auth.fetchAuthSession()
        .subscribeOn(Schedulers.io())
        .observeOn(AndroidSchedulers.mainThread())
        .subscribe(authSession -> {
            AWSCognitoAuthSession session = (AWSCognitoAuthSession) authSession;
            switch (session.getIdentityIdResult().getType()) {
                case AuthSessionResult.Type.SUCCESS:
                    callback.onSuccess(session.isSignedIn(),
                            session.getIdentityIdResult().getValue() != null ? session.getIdentityIdResult().getValue() : "");
                    break;
                case AuthSessionResult.Type.FAILURE:
                    Throwable error = session.getIdentityIdResult().getError() != null
                            ? session.getIdentityIdResult().getError()
                            : new Exception("Unknown error");
                    callback.onFailed(error);
                    break;
            }
        }, callback::onFailed);

In this code, we're using the RxAmplify.Auth.fetchAuthSession() method to fetch the authentication session. However, when a large number of users open the app simultaneously, this method is called multiple times, leading to a high volume of requests to the authentication service.

Solution

To mitigate this issue, we can implement a few strategies:

Rate limiting: Implement a rate limiting mechanism to control the number of requests made to the authentication service. This can be achieved using a library like Guava or RateLimiter.
Caching: Implement caching to store the authentication session results. This way, when a user opens the app, we can retrieve the cached result instead of making a new request to the authentication service.
Load balancing: Use load balancing techniques to distribute the traffic across multiple instances of the authentication service. This can help reduce the load on individual instances and prevent rate limiting exceptions.
Optimize the authentication service: Optimize the authentication service to handle a high volume of requests. This can involve scaling up the service, implementing caching, or using a more efficient authentication mechanism.

Conclusion

In conclusion, the TooManyRequestsException when fetching the authentication session is a common issue that arises when dealing with large-scale applications. By implementing rate limiting, caching, load balancing, or optimizing the authentication service, we can mitigate this issue and ensure a smooth user experience.

Recommendations

Implement rate limiting using a library like Guava or RateLimiter.
Use caching to store the authentication session results.
Use load balancing techniques to distribute the traffic across multiple instances of the authentication service.
Optimize the authentication service to handle a high volume of requests.

Additional Resources

AWS Amplify documentation: https://aws-amplify.github.io/docs/android/authentication
AWS Cognito documentation: https://docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html
Rate limiting libraries: https://github.com/google/guava/wiki/RateLimiterExplained
Fetch Auth Session Failed: TooManyRequestsException - Q&A

Introduction

In our previous article, we discussed the issue of Fetch auth session failed due to a TooManyRequestsException when attempting to fetch the authentication session. In this article, we'll provide a Q&A section to address some common questions related to this issue.

Q: What is a TooManyRequestsException?

A: A TooManyRequestsException is a rate limiting exception thrown by the AWS Cognito service when it receives too many requests within a short period, exceeding the allowed rate.

Q: Why do I get a TooManyRequestsException when fetching the authentication session?

A: You get a TooManyRequestsException when fetching the authentication session because the AWS Cognito service is receiving too many requests within a short period, exceeding the allowed rate. This can happen when a large number of users open the app simultaneously, leading to a high volume of requests to the authentication service.

Q: How can I prevent a TooManyRequestsException when fetching the authentication session?

A: You can prevent a TooManyRequestsException when fetching the authentication session by implementing rate limiting, caching, load balancing, or optimizing the authentication service.

Q: What is rate limiting, and how can I implement it?

A: Rate limiting is a mechanism that controls the number of requests made to a service within a short period. You can implement rate limiting using a library like Guava or RateLimiter. Here's an example of how to implement rate limiting using Guava:

import com.google.common.util.concurrent.RateLimiter;

public class RateLimiterExample {
    private static final RateLimiter rateLimiter = RateLimiter.create(10); // 10 requests per second

    public static void main(String[] args) {
        // Acquire a permit to make a request
        if (rateLimiter.tryAcquire()) {
            // Make the request
            System.out.println("Making request...");
        } else {
            // Rate limiting exceeded
            System.out.println("Rate limiting exceeded");
        }
    }
}

Q: What is caching, and how can I implement it?

A: Caching is a mechanism that stores the results of a request to avoid making the same request multiple times. You can implement caching using a library like LruCache or Cache. Here's an example of how to implement caching using LruCache:

import android.support.v4.util.LruCache;

public class CacheExample {
    private static final int MAX_SIZE = 100; // Maximum cache size

    private LruCache<String, String> cache = new LruCache<String, String>(MAX_SIZE) {
        @Override
        protected int sizeOf(String key, String value) {
            return value.length();
        }
    };

    public String get(String key) {
        return cache.get(key);
    }

    public void put(String key, String value) {
        cache.put(key, value);
    }
}

Q: What is load balancing, and how can I implement it?

A: Load balancing is a mechanism that distributes traffic across multiple instances of a service to improve performance and availability. You can implement load balancing using a library like OkHttp or LoadBalancer. Here's an example of how to implement load balancing using OkHttp:

import okhttp3.OkHttpClient;
import okhttp3.Request;
import okhttp3.Response;

public class LoadBalancerExample {
    private static final OkHttpClient client = new OkHttpClient();

    public Response get(String url) {
        Request request = new Request.Builder()
                .url(url)
                .build();

        return client.newCall(request).execute();
    }
}

Q: How can I optimize the authentication service?

A: You can optimize the authentication service by scaling up the service, implementing caching, or using a more efficient authentication mechanism. Here are some tips to optimize the authentication service:

Scale up the service to handle a high volume of requests.
Implement caching to store the results of authentication requests.
Use a more efficient authentication mechanism, such as OAuth or OpenID Connect.
Optimize the database schema to improve query performance.
Use a load balancer to distribute traffic across multiple instances of the service.

Conclusion

In conclusion, the TooManyRequestsException when fetching the authentication session is a common issue that arises when dealing with large-scale applications. By implementing rate limiting, caching, load balancing, or optimizing the authentication service, you can mitigate this issue and ensure a smooth user experience.