Traffic Shifting
About
Traffic Shifting (also called Traffic Splitting or Progressive Delivery) is a deployment strategy where incoming user requests are gradually routed to a new version of an application or service instead of instantly switching 100% of traffic.
The goal is to minimize risk during releases by progressively exposing the new version to real-world traffic, monitoring its behavior, and deciding whether to proceed, pause, or roll back.
Key Characteristics
Gradual Rollout: New code is exposed to a small fraction of users at first.
Observability Driven: Monitoring and alerting systems are critical for decision-making.
Dynamic Control: Operators can change routing rules in real time.
Compatible with Multiple Deployment Strategies: Often works with Canary Releases, Blue-Green Deployments, or A/B Testing.
Typical Use Cases
Rolling out a new microservice version without risking full outages.
Testing new API changes on a subset of traffic.
Introducing UI changes gradually to gauge user reaction.
Deploying machine learning models in production and comparing predictions.
Ways of Working
Traffic Shifting can be implemented using multiple strategies, each varying in risk, control, and complexity.
1. Canary Releases
Deploy the new version to a small percentage of traffic (e.g., 1–5%) while the majority continues to use the stable version.
Process:
Deploy v2 alongside v1.
Route a small subset of requests to v2.
Monitor performance and error rates.
Gradually increase percentage until 100% traffic is on v2.
When to Use: Feature rollouts, API changes, or sensitive deployments that need close monitoring.
2. Weighted Routing
Assign explicit weights to different versions, controlling traffic distribution with fine granularity (e.g., v1 = 70%, v2 = 30%).
Implementation Tools:
API Gateways (e.g., Kong, Apigee)
Service Mesh (e.g., Istio, Linkerd)
Load Balancers with weight configs (e.g., NGINX, Envoy)
Benefit: Allows precise traffic control for progressive rollouts or A/B testing.
3. Blue-Green Deployment with Gradual Switch
Traditionally, Blue-Green deployments switch all traffic instantly, but in hybrid form, we can shift small percentages at a time.
How It Works:
Keep Blue (current) and Green (new) running in parallel.
Shift traffic in controlled increments instead of all at once.
4. Session-Based or User-Based Routing
Route traffic based on user identity or session state instead of random percentages.
Example:
Only employees or beta testers get the new version.
Only logged-in premium users are routed to v2.
Advantage: Reduces risk by targeting known, controlled audiences.
5. Shadow Traffic / Traffic Mirroring
Duplicate live traffic to a new version without affecting the user’s actual experience.
Purpose:
Test performance in real conditions without impacting production results.
Validate data processing pipelines, ML models, or backend changes.
Benefits of Traffic Shifting
Traffic Shifting offers a controlled, data-driven approach to deploying changes, balancing innovation speed with system stability.
1. Reduced Deployment Risk
Instead of pushing an update to 100% of users instantly, traffic shifting lets we limit blast radius.
If something fails, only a fraction of users are affected, making rollbacks faster and less disruptive.
2. Real-World Performance Validation
Staging environments can’t always simulate real-world production load or diverse user behavior.
Traffic shifting allows testing under actual traffic patterns, revealing performance bottlenecks early.
3. Easier Rollback and Recovery
Because old and new versions run side-by-side, we can instantly revert traffic to the old version without a full redeployment.
This is especially useful in mission-critical applications where downtime is unacceptable.
4. Enables Progressive Delivery
Supports modern CI/CD practices where features are rolled out gradually instead of all at once.
Works well with feature flags, allowing fine-tuned control over who sees new features.
5. Improves Confidence for High-Risk Changes
Teams can deploy large architectural changes, such as database migrations or API redesigns, with less fear.
Monitoring metrics during partial rollouts ensures issues are detected early.
6. Better Experimentation & A/B Testing
Allows controlled routing for comparative experiments.
Example: Route 50% of traffic to an optimized API and compare conversion rates or response times before a full rollout.
7. Seamless User Experience
Gradual rollouts mean no large-scale downtime or sudden behavior changes for all users.
The shift happens invisibly, improving user trust.
Limitations of Traffic Shifting
While Traffic Shifting is a powerful deployment technique, it comes with operational, architectural, and monitoring challenges that teams must be prepared to handle.
1. Increased Infrastructure Complexity
Requires load balancers, routing rules, or service mesh configurations to selectively direct traffic.
Often needs multiple application instances or versions running simultaneously, which increases operational overhead.
2. Higher Operational Costs
Running old and new versions in parallel means double resource consumption during rollout periods.
Can become costly for compute-heavy applications or services with large data sets.
3. Data Inconsistency Risks
If both versions write to the same database but use different schemas or logic, it can cause data corruption or mismatches.
Requires carefully planned database migrations and backward-compatible changes.
4. Monitoring & Observability Requirements
Traffic shifting only works well if metrics, logs, and alerts are robust.
Without real-time monitoring, issues may go unnoticed until more users are affected.
5. Load Balancing & Routing Limitations
Some legacy infrastructure may not support granular traffic control (e.g., routing only certain user segments).
Service mesh solutions like Istio or Linkerd add flexibility but also bring a steeper learning curve.
6. Testing Coverage Gaps
Gradual rollout might delay issue detection if the affected feature is rarely used by early rollout users.
Edge cases may only appear after a wider audience starts using it, creating a false sense of security.
7. Rollback Complexity in Stateful Systems
Easy rollback applies mostly to stateless services.
In stateful systems (e.g., streaming platforms, financial services), data drift between versions can make rollback impractical without data repair.
8. Requires Strong Coordination
Dev, QA, Ops, and SRE teams must communicate closely during rollout.
If monitoring, routing, and testing are not coordinated, the rollout can fail silently.
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