Optimizing Retrofit Calls to Handle Increased Load in a Scalable Android Application
Currently developing an Android app that interfaces with a microservices architecture. This app uses Retrofit for network calls to retrieve data, and it’s crucial for us to ensure that it can handle a growing number of users without performance degradation. After implementing basic caching strategies using OkHttp, I noticed that when the number of concurrent users spikes, the API response times become inconsistent. To mitigate this, I've experimented with different configurations for Retrofit and OkHttp. For instance, I tried increasing the connection pool size: ```kotlin val client = OkHttpClient.Builder() .connectionPool(ConnectionPool(5, 5, TimeUnit.MINUTES)) .build() val retrofit = Retrofit.Builder() .baseUrl("https://api.example.com/") .client(client) .addConverterFactory(GsonConverterFactory.create()) .build() ``` However, even with this change, I still experience timeouts when multiple users hit the same endpoints simultaneously. I’ve also implemented Rate Limiting on the server side, but I’m concerned it might not be sufficient. Considering the asynchronous nature of Retrofit, the use of `enqueue()` calls works fine for loading data, but I wonder if switching to Kotlin Coroutines with `suspend` functions might yield better results. I have looked into using `Dispatchers.IO` for these calls, thinking it could reduce the blocking on the main thread, but I need clarity on best practices for this. Would using coroutines result in better throughput? Are there additional techniques, like bulk requests or more efficient data serialization methods (maybe using Moshi instead of Gson), that could address the performance bottleneck without rewriting large portions of the codebase? Any insights or recommendations on optimizing the Retrofit implementation while maintaining code maintainability would be greatly appreciated. Hoping someone can shed some light on this.