Learn→A/B Testing & Experimentation→Ramp-up Strategies & Canary Analysis→4 of 6

A/B Testing & Experimentation • Ramp-up Strategies & Canary AnalysisMedium⏱️ ~3 min

Trade Offs: Canary vs Blue Green vs Shadow Deployment

CANARY DEPLOYMENT
Canary gradually increases traffic from 1% to 100% over 24-48 hours, monitoring system and product metrics at each step. Pros: Catches problems early with minimal blast radius, validates product impact with real users. Cons: Slow (days not minutes), requires 5-10% extra capacity during parallel operation, complex metric infrastructure.
BLUE-GREEN DEPLOYMENT
Blue-green runs two identical environments. Deploy to green (inactive), validate with synthetic tests, then atomically switch the load balancer. Pros: Fast cutover (seconds), instant rollback, simple mental model. Cons: Requires 2x capacity, validates only system health (synthetic tests cannot measure user behavior), all users hit the new version simultaneously.
SHADOW DEPLOYMENT
Shadow duplicates production traffic to the new version without affecting user responses. Pros: Zero user impact, validates latency and resource usage under real load, useful for cache warming. Cons: Cannot measure user behavior (users do not see shadow responses), doubles request volume (cost), only validates system metrics.
⚠️ Key Trade-off: Shadow validates system health; canary validates user impact. Use shadow first to warm caches and validate latency, then canary to measure CTR.
WHEN TO USE EACH
Use canary: ML models, ranking changes, anything where user behavior matters. Use blue-green: Schema migrations, infrastructure changes, emergency rollbacks. Use shadow: New services before canary, cache warming, validating feature pipelines under load.

💡 Key Takeaways

✓Canary: 24-48 hours, 5-10% extra capacity, validates both system and product metrics with real users

✓Blue-green: seconds to cutover, 2x capacity, validates only system health (synthetic tests), atomic rollback

✓Shadow: zero user impact, doubles request volume, validates system metrics only (cannot measure user behavior)

✓Use shadow first to warm caches and validate latency, then canary to measure product impact

📌 Interview Tips

1When asked about deployment strategies, compare all three: canary for ML, blue-green for infra changes, shadow for warmup

2Explain why ML needs canary: models can pass offline tests but fail online due to training-serving skew

3Mention the shadow-then-canary pattern: shadow at 5% for 1 hour to warm caches, then canary at 1% to measure CTR

← Back to Ramp-up Strategies & Canary Analysis Overview