A/B Testing & Experimentation • Ramp-up Strategies & Canary AnalysisHard⏱️ ~3 min
Implementation: Traffic Routing, Metric Collection, and Decision Engine
Traffic shaping uses a request router that evaluates a hash function on a stable user identifier. Compute hash of user ID modulo 10,000 to get bucket 0 to 9,999. Translate percentage to bucket range: 1 percent equals buckets 0 to 99, 5 percent equals 0 to 499, 25 percent equals 0 to 2,499. A user hashing to bucket 1,234 enters canary when threshold reaches 1,234 (top 88 percent) and stays assigned. For stratified sampling, allocate separate bucket spaces per stratum or compute per segment hashes. If 60 percent of users are mobile and 40 percent desktop, allocate buckets 0 to 5,999 for mobile and 6,000 to 9,999 for desktop, then apply percentage thresholds within each range.
Metric collection tags every request with version, cohort, and capability group. Emit latency histograms (P50, P95, P99), error rates by type (timeout, 5xx, 4xx, client error), resource metrics (CPU, GPU utilization, memory per replica), feature null rate, out of range rate, and business outcomes (CTR, conversion, session length). Aggregate with low latency streaming at 1 to 5 minute granularity, using 5 to 15 minute trailing windows for comparisons. Retain full histograms to analyze tails, not just means. Use exemplars to link anomalous requests to traces.
The decision engine operates two layers. Layer one guardrails use hard thresholds for automatic rollback: error rate delta greater than 0.1 percent absolute for 5 consecutive minutes, P99 latency greater than 300 milliseconds for 10 minutes, feature null rate greater than 0.5 percent. Layer two evaluates product metrics using sequential testing. For CTR, apply CUPED variance reduction to leverage pre period data, then run Mann Whitney U test comparing distributions with false discovery rate correction for multiple metrics. Define minimum detectable effect: 0.3 percent CTR change with 80 percent power over 2 hour window at 5 percent traffic.
Operational playbook defines the ramp schedule: 0.5 percent for 30 minutes, 1 percent for 30 minutes, 5 percent for 2 hours, 10 percent for 6 hours, 25 percent for 12 hours, then 50 percent. Between steps, verify downstream dependency saturation, check error budget burn rate, and run synthetic load tests. Rollback is a fast reweight to 0 percent canary traffic with connection draining over 2 minutes. Google SRE style practices gate rollouts if error budget consumption rate would breach monthly targets. Align ramps with change windows: avoid starting canaries Friday evening or during product launches when baseline metrics are unstable.
💡 Key Takeaways
•Traffic routing: hash(user_id) mod 10,000 maps to buckets 0 to 9,999, 5% equals buckets 0 to 499, user stays assigned for entire canary duration
•Metric tagging: Every request tagged with version, cohort, capability, aggregated at 1 to 5 minute granularity with 5 to 15 minute trailing comparison windows
•Two layer decisions: Layer one auto rollback on error rate delta greater than 0.1% for 5 min or P99 greater than 300ms for 10 min, layer two statistical tests on CTR with CUPED
•Ramp schedule with gates: 0.5% → 1% → 5% → 10% → 25% → 50%, verify downstream QPS budgets and error budget burn rate between steps
•Pre warming before traffic: Replay last hour at 10x speed to populate caches, use 10 minute grace period ignoring P99 spikes during cold start
📌 Examples
Stratified routing: Mobile users (60%) allocated buckets 0 to 5,999, desktop (40%) buckets 6,000 to 9,999, then 5% threshold applied within each segment
CUPED for CTR: Baseline cohort pre period CTR 3.2%, canary 3.1%. Post period canary CTR 3.4%, adjust by pre period delta (add 0.1%) to reduce variance by 30%
Rollback trigger: Canary error rate 0.15% vs baseline 0.08%, delta 0.07% stays under 0.1% threshold, but P99 latency 310ms for 12 minutes exceeds 300ms for 10 min, triggers automatic rollback