Handling Failures in Distributed Systems
As a software engineer, one thing remains certain: no distributed system is immune to failure. Servers will crash, networks will throttle, disks will fill up, and unexpected bugs will surface at the worst possible moments. While failures are inevitable, your customers don’t have to experience them. The art of building resilient systems lies in hiding these failures effectively.
Your goal isn’t to eliminate every failure—an impossible task—but to design systems that absorb and recover from them seamlessly. Let’s explore how you can achieve this at scale.
1. Use Circuit Breakers
A circuit breaker acts as a safeguard to stop dependent services from bringing down your entire system.
How it works: If Service A relies on Service B and B becomes unresponsive, A should switch to a fallback mode instead of repeatedly trying and risking cascading failures. This fallback can be as simple as serving cached data or returning a default response.
Example: If a payment gateway is down, allow users to complete transactions with a "process later" flow rather than blocking all payments entirely.
Takeaway: Circuit breakers provide a controlled way to fail fast and recover gracefully.
2. Graceful Timeouts and Retries
Failures often occur when systems get overloaded, and excessive retries can worsen the problem. Graceful timeouts and retries prevent this from spiraling out of control.
Timeouts: Define reasonable timeouts for external service calls to avoid waiting indefinitely.
Retries: Use exponential backoff with jitter when retrying failed requests. This means adding a delay that grows exponentially with each retry while introducing a random factor to prevent synchronized retries from multiple clients.
Example: Instead of retrying a failed API call immediately, wait for 1 second, then 2 seconds, then 4 seconds, and so on, adding some randomness to avoid overloading the system.
Takeaway: Avoid overwhelming systems by retrying intelligently and failing quickly when necessary.
3. Cache Strategically
Caching is a powerful technique to shield users from backend failures by serving stale but usable data when real-time systems are unavailable.
What to Cache: Cache frequently accessed data, such as user profiles, product listings, or configuration settings.
Fallback Strategy: If your database goes down, serve cached data until the primary system is back online.
Example: When fetching product details, use a caching layer like Redis to serve data if the database is unavailable.
Takeaway: Smart caching ensures high availability and reduces latency during failures.
4. Load Shedding
Load shedding ensures that critical services stay functional by gracefully dropping non-essential traffic during high load.
How it works: Prioritize requests that matter most and temporarily block less critical ones.
Example: During a surge, prioritize checkout requests while deferring requests for recommendation services or analytics.
Takeaway: Protect critical workflows by shedding non-essential load during peak traffic.
5. Static Fallbacks
Static fallbacks prepare your system to serve pre-rendered or static responses when dynamic components fail.
How it works: Host a static version of critical pages (e.g., "Order Placed" or "Thank You") on a Content Delivery Network (CDN) to ensure availability even during outages.
Example: If your dynamic backend is unavailable, users can still see a static confirmation page, reducing frustration.
Takeaway: Static fallbacks ensure a seamless user experience even when parts of your system are offline.
6. Queue-Based Workflows
Queues decouple user actions from backend processing, allowing you to handle traffic spikes more effectively.
How it works: Accept user requests, enqueue them, and process them asynchronously in the background.
Example: During a flash sale, add users to a queue instead of processing all requests immediately. Notify them once their turn arrives.
Takeaway: Queue-based workflows smooth out traffic spikes and improve system reliability.
7. Chaos Testing
Chaos testing validates your system’s resilience by simulating failures in a controlled environment.
How it works: Introduce controlled chaos, such as server crashes, network throttling, or even complete zone failures, to test how your system responds.
Example: Netflix’s Chaos Monkey randomly shuts down servers in production to ensure the system can handle unexpected disruptions.
Takeaway: Chaos testing prepares your system to handle real-world failures gracefully.
Key Metrics to Monitor
To effectively hide failures, you need to monitor the right metrics:
Error Rates: Watch for spikes in 4xx or 5xx errors to identify failing components.
Latency: Monitor response times to detect bottlenecks before they escalate.
Retry Rates: High retries indicate struggling downstream services.
Cache Hit Ratio: A low hit ratio might signal poor cache usage during failures.
Final Thoughts
Failures are an inherent part of distributed systems, but they don’t have to ruin the user experience. By employing strategies like circuit breakers, caching, load shedding, and chaos testing, you can build systems that absorb and recover from failures without impacting customers.
Remember, the ultimate goal isn’t to eliminate failures—it’s to make them invisible to the user.