**A Coding Guide to Understanding How Retries Trigger Failure Cascades in RPC and Event-Driven Architectures**
I’m excited to share this tutorial with you, where we’ll dive into the world of distributed systems and explore the differences between synchronous RPC-based systems and asynchronous event-driven architectures. We’ll simulate various scenarios, such as variable latency, overload conditions, and transient errors, to see how each architecture handles failures.
**Getting Started**
Before we dive in, let’s set up our core utilities and data structures. We’ll establish timing helpers, percentile calculations, and a unified metrics container to track latency, retries, failures, and tail behavior. This will give us a solid foundation to work from.
**Modeling Failure Behaviors**
Next, we’ll introduce failure models that simulate overload-sensitive latency and failures. We’ll also implement circuit breakers, bulkheads, and exponential backoff to manage cascading failures. This is where things get interesting, as we’ll see how these mechanisms can help or hinder our systems’ resilience.
**Synchronous RPC**
Now, let’s implement the synchronous RPC path and its interaction with downstream providers. We’ll observe how timeouts, retries, and in-flight load immediately impact latency and failure propagation. This will highlight how tight coupling in RPC can amplify transient issues under bursty traffic.
**Asynchronous Event-Driven Architecture**
Next, we’ll build the asynchronous event-driven pipeline using a queue and background consumers. We’ll process events independently of request submission, apply retry logic, and route unrecoverable messages to a dead-letter queue. This will demonstrate how decoupling improves resilience, but also introduces new operational challenges.
**Testing the Experiment**
We’ll drive both architectures with bursty workloads and orchestrate the entire experiment. We’ll accumulate metrics, clean up shoppers, and examine outcomes across RPC and event-driven executions. The final step will tie together latency, throughput, and failure behavior into a coherent system-level comparison.
**The Results**
In this tutorial, we’ve seen the trade-offs between RPC and event-driven architectures in distributed systems. We’ve observed that RPC offers lower latency when dependencies are healthy but becomes fragile under saturation, whereas the event-driven strategy decouples producers from consumers, absorbs bursts through buffering, and localizes failures.
**Key Takeaways**
1. **Resilience requires disciplined failure-handling patterns**: Both RPC and event-driven architectures can be resilient if implemented correctly, but it’s essential to handle retries, backpressure, and dead-letter queues carefully.
2. **Decoupling improves resilience**: Event-driven architecture can absorb bursts and localize failures, but it requires careful design and implementation.
3. **Tight coupling amplifies failures**: RPC’s tight coupling can amplify transient issues under bursty traffic, leading to system-wide failures.
**Get the Code**
Want to see the experiment in action? Check out the **FULL CODE** on GitHub.
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