# Weighted Traffic Splitting for Canaries — API Gateway and Service Mesh

Source: https://www.geekswithgeeks.com/en/api-gateway-service-mesh/r-canary-split

> Send a small percentage of traffic to a new version and watch before widening.

## Release to a few, then more

A **canary release** sends a small share of real traffic (say 1% to 10%) to the new version while the rest stays on the stable one. You compare error rate and latency between the two, and widen (10% → 50% → 100%) or roll back instantly by changing the weights, with no redeploy. Weighted splitting is a gateway or mesh feature (`weight` in nginx upstreams, weighted routes in Envoy/Istio, `HTTPRoute` backend weights in the Gateway API). Combine with sticky routing by user or header if a user should consistently see one version, and with **automated analysis** that halts the rollout when metrics worsen.

## A 90/10 split (config)

Weights 9 and 1 in an nginx upstream give a 9:1 ratio. Excerpt of the gateway config.

```nginx
upstream orders_weighted {
  server orders-v1:8080 weight=9;
  server orders-v2:8080 weight=1;
}
```

## 100 requests through the gateway, run

I ran this against a real nginx 1.27 gateway in Docker, with small Node.js services as upstreams (full setup in the case study). Out of 100 requests, exactly 90 were served by `v1` and 10 by `v2`. nginx's weighted round robin is deterministic; probabilistic routers (random choice) only approach 90/10 as the sample grows.

```bash
# 100 calls to /orders/x with a valid API key, counting the version in each response
```

Output:

```
{"v1":90,"v2":10}
```

## What a bad canary costs, run

I ran this plain-Python model. With 1,000 requests, a 10% canary receives 100. If the canary fails 5% of its requests, only 0.5% of all traffic is affected, which is why a small canary limits damage while still giving real data.

```python
import hashlib, bisect, math, random

w1, w2 = 90, 10
n = 1000; print("expected canary requests:", n * w2 // (w1 + w2), "| error budget burned if canary fails 5%:", round(w2 / (w1 + w2) * 5, 2), "% overall")

```

Output:

```
expected canary requests: 100 | error budget burned if canary fails 5%: 0.5 % overall
```

**Quiz:** What is the main benefit of a canary release?

- [ ] It makes servers faster
- [ ] It removes the need for tests
- [x] A bad release affects only a small share of users and can be reverted quickly
- [ ] It hides errors

*Answer:* A bad release affects only a small share of users and can be reverted quickly. Gradual exposure limits blast radius and gives real-world evidence.