Case StudyLoad TestingInfrastructure

Case Study: Scaling the Token-Decode Flow for a Billboard-to-Hire Campaign

ccaches

2026-02-06

10 min read

Technical postmortem for scaling a token-decode billboard campaign: load tests, cache warmup, and observability playbook for 2026.

Hook — Your billboard went viral. Now your origin is melting down.

If you run infrastructure for high-traffic consumer stunts, you've seen this movie: a creative campaign (think billboard with inscrutable tokens) drives an unpredictable, concentrated spike of traffic, and suddenly your token-decode endpoint becomes the bottleneck. That slow decode, misconfigured cache, or unprotected origin turns a hiring stunt into a costly outage — and worst of all, it ruins the candidate experience and the SEO value of the campaign.

Executive summary (inverted pyramid)

This postmortem-style case study recreates a Listen Labs-style billboard-to-hire campaign and explains how we designed, load-tested, and hardened the token-decode flow for scale. You'll get a reproducible architecture, load-testing recipes, caching and cache-warmup patterns, observability checks, and a clear set of mitigations for the classic problems: thundering herd, cache stampedes, rate limits, and origin overload.

Quick outcomes

Target SLOs: 99th percentile TTFB < 300ms, origin error rate < 0.5%
Load test goal: survive 10x projected peak with graceful degradation
Cache strategy: pre-generated decodes, CDN edge caching with stale-while-revalidate, and token-level cache tags
Observability: OpenTelemetry traces + metrics dashboard for cache-hit ratio, 429/5xx, and CPU spikes

Campaign architecture: token-decode flow

High level: a user sees a token on a billboard, visits short.example/token/<TOKEN>, and the service decodes the token to a challenge/redirect that starts the hiring funnel. The decode could be simple (base64 or reversible transform) or complex (verifying signed payloads, contacting auth services, or invoking an AI model for personalization).

Core components

Edge CDN – serve static landing page and cache decoded responses when safe (Cloudflare/Fastly/Akamai).
Edge function – minimal validation and cache lookup before calling origin (Cloudflare Workers, Vercel Edge Functions, Fastly Compute).
Origin API – token-decode endpoint (container or serverless) with strict CPU/memory limits and circuit breakers.
Cache store – Redis or managed KV for precomputed decodes and tagging.
Queue – (optional) SQS/Kafka for deferred heavy work like AI scoring (see job patterns in micro-app devops playbooks).
Observability – traces (OpenTelemetry), metrics (Prometheus/Datadog), logs (structured), and RUM for client experience.

Design principles for scale

Make the fast path cache-only. The most common path should be handled by CDN/edge without touching origin.
Precompute everything you can. Generate decoded payloads ahead of time and store them in an edge-friendly cache.
Graceful degradation. If origin is overloaded, return a cached static landing experience or a lightweight error explaining retry policy.
Protect origin with edge rate limits and per-IP throttles.
Monitor the full pipeline. Cache-hit ratio, origin QPS, latency percentiles, and error rates must be visible in real time.

Load testing strategy — how we simulated the billboard going viral

We used a combination of k6 for HTTP-level load, Locust for user-like behavior, and a small cluster of distributed workers to simulate geo-diverse traffic. Current 2025–2026 trends show that HTTP/3 and QUIC reduce connection setup overhead, so include clients that use HTTP/3 when possible to measure realistic load.

Test plan

Baseline ramp: increase from 0 to expected peak over 10 minutes to validate autoscaling.
Spike tests: abrupt jump to 5x and 10x projected peak for 2–10 minutes to test throttles and circuit breakers.
Soak test: sustain expected peak for 2 hours to find memory leaks or resource exhaustion.
Cache-warmup simulation: run tests with cold caches and with pre-warmed caches to measure differences in TTFB and origin load.
Bot/noise simulation: include high-request-rate bots that request random token IDs to measure cache miss storm behavior.

k6 snippet (conceptual)

// Pseudocode for a k6 test
import http from 'k6/http';
export default function () {
  http.get('https://short.example/token/TOKEN123');
}

Focus on request distribution: 70% requests for a hot set of 10 tokens, 30% for random tokens to simulate scavengers.

Common failure modes found in tests

Thundering herd: large number of cold-cache misses hit origin simultaneously.
Origin CPU saturation: expensive decode (crypto, signature verification or AI processing) is single-threaded per request and stalls.
Rate limiting choke: upstream auth or third-party APIs return 429s causing downstream failures.
Cache misconfiguration: improper cache keys cause low cache hit ratios across the CDN.

Cache strategy and warmup

Your caching strategy should be explicit and multi-layered. In 2026, edge compute and advanced cache-tagging are ubiquitous in major CDNs, so leverage them.

Cache layers

CDN edge cache – primary layer. Use long TTLs for static decoded responses and Cache-Control: public, max-age=86400, stale-while-revalidate=86400 for resilience.
Edge KV/Workers cache – for sub-10ms lookups and to run simple transforms without origin calls.
Origin cache (Redis) – durable store for precomputed decodes and tagging for targeted invalidation. See micro-app DevOps patterns at qubit.host.

Cache key design

Use a deterministic cache key that includes only the token ID and a content version. Avoid user-specific headers unless necessary. Example key: token-decode:v3:TOKEN123. This ensures consistent hits across edge POPs and prevents fragmentation.

Precompute and warm caches

Before the billboard goes live, precompute decodes for every published token and write them to Redis and your edge KV. Then run a cache-warmup job that sequentially requests every token through the CDN to populate all edge POPs. Key tips:

Warmup at least 24–48 hours before launch and re-run within 2 hours of the public reveal.
Use a distributed warming client that cycles through POPs to avoid saturating a single POP.
Respect CDN rate limits during warmup — many providers throttle bulk priming.

Preventing stampedes

Cache locking: Origin should support a short-lived lock per token to serialize regenerations (e.g., Redis SETNX with TTL).
Stale-while-revalidate: If a cache entry is expired, return stale content while refreshing asynchronously.
Bulkhead and queueing: If decode is heavy, push to a job queue and return a lightweight response telling the client to poll or use a WebSocket/long-poll for readiness.

Origin protection and rate limits

Edge rate limits and per-IP quotas are your first line of defense. For token-decode flows:

Enforce token-level rate limits: e.g., no token should be decoded more than X times per minute from the same IP range.
Use progressive challenge responses for suspicious traffic (CAPTCHA or progressive delays).
Implement circuit breakers that return 503 with Retry-After when origin CPU or queue depths exceed thresholds.

Observability: what to monitor and why

Real-time observability is critical during a stunt. Instrument every layer with traces and metrics. In 2026, OpenTelemetry is the common lingua franca, and most CDNs and edge platforms emit observability hooks.

Essential metrics

Cache hit ratio (edge & origin) — target > 95% for the hot token set.
Origin requests per second (RPS)
TTFB and request latency percentiles (p50/p95/p99)
5xx & 429 rates
CPU, memory, and event-loop latency for origin instances
Queue depth if you use background jobs
Bot vs human ratio (use User-Agent and behavior heuristics)

Tracing and logs

Use distributed traces to see the full path: CDN → edge function → origin → Redis → downstream services. Tag spans with token ID to identify hot tokens causing trouble. Keep structured logs; include cache decisions (HIT/MISS/BYPASS), lock acquisitions, and job enqueue/dequeue events.

Postmortem template — how to write it

We used a standard blameless postmortem template. Here’s the minimal structure:

Summary: what happened, impact, duration.
Timeline: minute-by-minute events with evidence (graphs, traces).
Root cause: technical explanation (e.g., cache was cold + heavy decode loop + missing rate limit).
Mitigation: immediate steps taken during incident (cache rebalance, rate limit changes, temporary TTL changes).
Long-term fixes: code changes, runbooks, automation.
Action items and owners: who does what by when.

Sample root cause we saw in tests

The origin performed synchronous AI scoring during token decode. The CDN was misconfigured with short TTLs, so initial traffic caused mass cache misses. Without cache locks, every POP requested the same token simultaneously, saturating origin CPU and triggering 502s and 429s from an upstream verification API.

Fixes: precompute decodes, increase TTLs, add SETNX-based locks, and move AI scoring to async jobs with status endpoints.

Expectations for production — the first 72 hours

Reality check: even with perfect tests, production introduces unknowns: influencers, crawlers, and weird bots. Here’s what to expect and how to prepare:

Surge window: first 24–48 hours will bring the largest concentrated traffic. Keep engineering staff on-call with escalation paths.
Cache degradation: expect initial cache miss storm; use aggressive stale-while-revalidate and fallbacks to a static experience.
Search engine crawlers: crawlers may hit many tokens quickly. Use robots.txt to limit aggressive bot crawling if that harms origin.
Link rot & SEO: ensure redirected URLs are canonical and stable. For long-term SEO value, host persistent content at stable URLs and use 301s for stable mapping when appropriate.

Actionable checklist before launch

Precompute and store decoded payloads for every published token.
Warm caches via CDN priming at least 24 hours ahead.
Set CDN cache-control: public, max-age=86400, stale-while-revalidate=86400 and ensure ETag/Last-Modified are present.
Instrument traces (OpenTelemetry) and create dashboards for cache-hit ratio, origin RPS, p95 latency, and 5xx/429.
Implement cache lock (SETNX) and background refresh mechanism for misses.
Deploy edge rate-limits and bot mitigation rules; whitelist internal warmup clients.
Run spike and soak tests including HTTP/3 traffic and geo-distribution.
Prepare runbook and on-call rota for the 72-hour surge window.

Advanced patterns and future-facing strategies (2026 and beyond)

As edge compute and cache primitives mature in 2026, teams should adopt:

Edge-first personalization: push token decoding to edge compute for dramatic latency gains.
Cache tagging and invalidation: use CDN-native cache-tags to invalidate token groups without purging other content.
Adaptive throttling: autoscale rate limits based on real-time origin load signals (backpressure-aware edge rules).
Observability-as-code: define dashboards and alerting thresholds in source control for repeatable campaigns; see broader platform trends at datafabric.cloud.

Case study recap — the postmortem in one paragraph

We built a token-decode flow that failed tests when the CDN cache was cold and heavy synchronous decodes existed at the origin. The fix combined precomputation, cache warmup across edge POPs, cache locks, stale-while-revalidate, and origin protection (rate limits and circuit breakers). Load tests simulated spikes up to 10x projected peaks and validated graceful degradation. With observability and a runbook in place, the production stunt survived viral traffic while preserving the candidate experience and the long-term SEO value of the campaign.

Appendix — concrete config snippets and SLOs

SLO suggestions

Availability: 99.95% during campaign window
Latency: p95 TTFB < 300ms, p99 < 1s for cached responses
Error budget: < 0.5% 5xxs across the funnel

Example Cache-Control

Cache-Control: public, max-age=86400, stale-while-revalidate=86400, stale-if-error=604800

Redis lock (conceptual)

// Pseudocode
if redis.setnx(lockKey, instanceId, ex=5):
  // regenerate and write cache
else:
  // read stale cache or return lightweight response

Final takeaways — what to do in the next 24 hours

Precompute decodes and populate edge KV.
Run a distributed cache-warmup job targeting all POPs.
Enable edge rate limits and bot rules; deploy circuit breakers at origin.
Run a spike test that simulates 10x traffic and verify autoscaling and SLOs.
Publish a short runbook and put on-call engineers into rotation for the first 72 hours.

Call-to-action

If you're planning a high-visibility campaign in 2026, don't leave scalability to luck. Contact our team for a pre-launch resilience audit: we run spike and soak tests, implement cache-warmup automation, and deliver an observability runbook tailored to your stack. Book a consultation or download our load-testing checklist to make your next stunt go viral — for the right reasons.

caches

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Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.