What Exactly Should You Log and Compare When a New Traffic Routing Rule Makes Only One Region Unstable?
1. Introduction: When Only One Region Breaks, the Rule Is Telling You Something
You deploy a new traffic routing rule.
Globally, things look fine.
But one region starts to misbehave:
- latency spikes
- success rate drops
- retries increase
- users complain only from that region
Everywhere else stays stable.
This is frustrating because the rule is “global,” yet the failure is local.
Here is the simple truth.
When only one region becomes unstable, the problem is almost never random. It is usually caused by how the rule interacts with local conditions: capacity, routing paths, retries, or shared resources.
This article answers one clear question:
When a new routing rule breaks only one region, what exactly should you log and compare to find the real cause quickly?
2. First Principle: Compare Regions, Not Just Errors
The biggest mistake teams make is staring only at the broken region.
Instead, you must compare:
- the unstable region
- a stable region running the same rule
Your goal is not to find “what is wrong,” but “what is different.”
Every section below follows that idea.
3. Routing Decision Inputs: What Did the Rule Actually See?
3.1 Log the inputs to the routing rule, not just the result
You should log, per request:
- region
- rule version or hash
- inputs used by the rule (latency score, health score, weight, priority)
- chosen route or pool
Common failure:
The rule behaves correctly, but one region feeds it very different inputs.
Example:
- region A reports higher latency due to distance
- rule shifts too much traffic away
- remaining routes overload and collapse
Without input logs, this looks like “random instability.”
3.2 Compare stable vs unstable region inputs side by side
Ask:
- are health scores lower in the failing region?
- are weights normalized differently?
- are some routes missing entirely in that region?
Differences here often explain everything.
4. Effective Route Mapping: What Route Did Traffic Really Take?
4.1 Log the full route path
Do not stop at “which pool was selected.”
Log:
- entry region
- intermediate hops (if any)
- final exit or endpoint
- fallback or failover route used
In many systems, one region silently takes an extra hop:
- cross-region fallback
- cross-zone NAT
- legacy proxy chain
That extra hop alone can cause instability.
4.2 Check for asymmetric routing
Compare:
- outbound route
- return path (if applicable)
Asymmetry often appears only in certain regions and only after routing changes.
5. Retry Behavior: The Silent Amplifier
5.1 Log attempts per request, not just failures
For each region, track:
- average attempts per successful request
- retry reasons
- retry delay distribution
A common pattern:
- new rule slightly increases failure rate
- retries increase traffic volume
- retries overload exits
- instability accelerates
One region may hit this feedback loop first due to lower margin.
5.2 Compare retry amplification across regions
If region X averages 1.8 attempts per success and region Y averages 1.1, the routing rule is indirectly creating load imbalance.
6. Exit and Capacity Metrics: Who Is Being Overused?
6.1 Log per-exit load and saturation
You should be able to answer:
- which exits are hottest in the failing region?
- are those exits unique to that region?
- did load shift suddenly after the rule deploy?
Many routing rules unintentionally concentrate traffic on “best-looking” exits that only exist in one region.
6.2 Compare headroom, not just utilization
An exit at 70% utilization may be fine in one region and fragile in another due to:
- lower upstream capacity
- higher latency variance
- stricter rate limits
7. Time-Based Signals: When Did Drift Begin?
7.1 Align timelines across regions
Overlay:
- rule deployment time
- latency change
- retry increase
- exit saturation
Look for:
- gradual degradation vs sudden step change
- delayed effects (10–30 minutes later)
Delayed failures usually indicate feedback loops, not immediate bugs.
8. Configuration and Version Drift: Are Regions Truly Identical?
Check and compare:
- rule version or config hash
- service version
- feature flags
- default values
It is extremely common that:
- one region missed a rollout
- one region has stale config
- defaults differ due to missing fields
Routing rules are very sensitive to small config drift.
9. Proxy and Routing Systems: A Special Warning
In proxy-based routing systems, regional instability often comes from:
- pool size differences
- exit quality variance
- different ban pressure by geography
A rule that “optimizes globally” can silently starve one region.
Systems using structured proxy pools, like YiLu Proxy, reduce this risk by making regional pools explicit and isolated. When identity traffic, activity traffic, and bulk traffic are clearly separated per region, routing mistakes stay contained and easier to diagnose.
The key benefit is not fewer failures—it is faster understanding when something goes wrong.
10. A Simple Checklist You Can Actually Use
When one region breaks after a routing rule change, compare:
(1) Rule inputs per region
(2) Effective routes taken
(3) Retry rates and attempts per success
(4) Exit-level load and headroom
(5) Timeline alignment
(6) Config and version hashes
If you log these consistently, the cause usually becomes obvious within minutes instead of days.
When a new routing rule destabilizes only one region, the system is not being mysterious. It is exposing a difference you are not observing yet.
The fastest way to debug is not guessing or rolling back blindly.
It is structured comparison.
Log what the rule saw.
Log what route was taken.
Log how retries and load changed.
Do that, and regional instability stops feeling random—and starts feeling diagnosable.