# 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**:
  1. Deploy v2 alongside v1.
  2. Route a small subset of requests to v2.
  3. Monitor performance and error rates.
  4. 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**.