Massive Traffic

Designing .NET Systems for Massive Traffic and Millions of Users

If they're hinting "massive traffic, millions of users" in a C# interview, they're really testing whether you think in throughput, latency, backpressure, and failure modes — not just "I'll add more servers".

This guide covers how to actually design and build systems that handle millions of requests in .NET, with detailed explanations, code examples, and the reasoning behind each decision.

Table of Contents

1. Mental Model & Architecture Overview
2. Async & Non-Blocking I/O Patterns - Deep dive into async/await, thread pool management
3. Backpressure & Rate Limiting - Protecting your system under load
4. Caching Strategies - Redis, in-memory, CDN patterns
5. Database Optimization & Scaling - Indexes, partitioning, read replicas
6. Message Queues & Async Processing - Decoupling heavy work
7. Resilience Patterns - Circuit breakers, retries, timeouts
8. Observability & Monitoring - Metrics, tracing, structured logging
9. Complete Example Application - Real-world implementation

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The mental model

You handle massive request volume by combining:

• Stateless APIs + horizontal scaling
• Fast paths (cache) and slow paths (DB / downstream)
• Async I/O end-to-end (don’t block threads)
• Backpressure (bounded queues, rate limits)
• Resilience (timeouts, retries carefully, circuit breakers)
• Data design (indexes, read/write separation, partitioning)
• Observability (metrics + tracing, not just logs)

What you’d implement in C# (practical checklist)

1) Make your API async and non-blocking

• Use async/await for anything I/O (DB, HTTP, Redis, MQ).
• Avoid .Result / .Wait() (threadpool starvation under load).
• Use HttpClientFactory (prevents socket exhaustion).

2) Protect the system with limits (backpressure)

When traffic spikes, the worst thing is letting everything pile up until the system dies.

Implement:

• Rate limiting (per user/IP/token)
• Concurrency limits for expensive endpoints
• Bounded queues for background work (reject/429 when full)

In ASP.NET Core you can use built-in rate limiting (good interview point).

3) Cache aggressively (but correctly)

For millions of users, your DB cannot be the “hot path”.

• In-memory cache for per-instance hot items.
• Distributed cache (Redis) for shared hot items.
• Use cache-aside: read cache → if miss, load from DB → set cache.
• Add TTL + jitter to avoid stampedes.

Also mention:

• ETags / 304 for GETs
• CDN for static and cacheable content

4) Make DB the last resort and design it for scale

• Proper indexes (composite indexes aligned with query patterns)
• Avoid N+1 queries
• Use pagination (keyset pagination, not Skip/Take on huge tables)
• Consider read replicas for heavy read workloads
• Partition/shard by a key if you outgrow a single node
• Keep transactions small and short

5) Decouple heavy work (queues, eventual consistency)

If a request triggers something expensive (emails, reports, settlement, heavy compute):

• Return fast (202 Accepted)
• Push message to RabbitMQ/ZeroMQ/Kafka (they mentioned RabbitMQ/ZeroMQ)
• Process in background workers
• Use idempotency keys so retries don’t double-apply actions

6) Resilience patterns (you must say these out loud)

• Timeouts everywhere (DB + HTTP)
• Retries with exponential backoff only for transient failures
• Circuit breaker to stop hammering a failing dependency
• Bulkheads (separate pools/limits per downstream)
• Graceful degradation (serve stale cache if DB is sick)

7) Observability to keep it alive in production

• Metrics: RPS, p95/p99 latency, error rate, saturation, queue depth
• Tracing: OpenTelemetry
• Logs: structured logs with correlation IDs

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A concrete “interview-ready” coding example (C#): rate limit + bounded concurrency + cache-aside

A) Cache-aside with stampede protection (per-key lock)

using Microsoft.Extensions.Caching.Memory;
using System.Collections.Concurrent;

public class CachedProfileService
{
    private readonly IMemoryCache _cache;
    private static readonly ConcurrentDictionary<string, SemaphoreSlim> _locks = new();

    public CachedProfileService(IMemoryCache cache) => _cache = cache;

    public async Task<UserProfile> GetProfileAsync(
        string userId,
        Func<Task<UserProfile>> loadFromDb,
        CancellationToken ct)
    {
        var cacheKey = $"profile:{userId}";
        if (_cache.TryGetValue(cacheKey, out UserProfile cached))
            return cached;

        var sem = _locks.GetOrAdd(cacheKey, _ => new SemaphoreSlim(1, 1));
        await sem.WaitAsync(ct);
        try
        {
            // double-check after acquiring lock
            if (_cache.TryGetValue(cacheKey, out cached))
                return cached;

            var profile = await loadFromDb();
            _cache.Set(cacheKey, profile, TimeSpan.FromMinutes(5));
            return profile;
        }
        finally
        {
            sem.Release();
            // optional cleanup: don’t let dictionary grow forever
            if (sem.CurrentCount == 1) _locks.TryRemove(cacheKey, out _);
        }
    }
}

public record UserProfile(string Id, string Name);

What this shows:

• async I/O
• caching
• stampede prevention (critical at scale)

B) Concurrency limit around an expensive call (backpressure)

public class ExpensiveGateway
{
    private readonly SemaphoreSlim _limit = new(initialCount: 200); // tune based on load tests

    public async Task<string> CallAsync(Func<CancellationToken, Task<string>> operation, CancellationToken ct)
    {
        // if we can’t get a slot quickly, fail fast
        if (!await _limit.WaitAsync(TimeSpan.FromMilliseconds(50), ct))
            throw new TooManyRequestsException();

        try
        {
            return await operation(ct);
        }
        finally
        {
            _limit.Release();
        }
    }
}

public class TooManyRequestsException : Exception { }

This is the heart of “handling massive requests”: don’t let expensive work explode your resources.

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What interviewers love to hear (say this)

If they ask “how would you do it?” you can answer in 30–60 seconds like:

“I’d keep the API stateless and async, scale horizontally behind a load balancer, put Redis in front of the database for hot reads, use rate limiting and bounded concurrency to apply backpressure, and move expensive tasks to a queue processed by background workers with idempotency. I’d add timeouts, circuit breakers, and good observability so the system degrades gracefully under spikes.”

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If you tell me what kind of “system” they’ll likely ask you to code (e.g., login/token service, feed, trading/order submission, notifications), I’ll give you a more tailored architecture + a coding exercise that matches it.