Microservices: An Overview
Microservices is a software architecture style where an application is composed of small, independent, and loosely coupled services. Each microservice is responsible for a specific business function and communicates with other services via well-defined APIs (usually HTTP/REST, gRPC, or messaging queues).
Key Characteristics of Microservices
Decentralized & Independent
Each service runs in its own process and can be developed, deployed, and scaled independently.
Teams can use different programming languages, databases, and frameworks best suited for their service.
Single Responsibility
Each microservice focuses on a single business capability (e.g., User Service, Payment Service, Order Service).
Lightweight Communication
Services interact via APIs (REST, GraphQL) or async messaging (Kafka, RabbitMQ).
Decentralized Data Management
Each service has its own database (SQL or NoSQL) to ensure loose coupling.
Automated DevOps & CI/CD
Microservices rely on containers (Docker), orchestration (Kubernetes), and CI/CD pipelines for rapid deployments.
Resilience & Fault Isolation
A failure in one service doesn’t bring down the entire system.
Microservices architecture design patterns
Microservices architecture follows several design patterns to address common challenges like service decomposition, communication, data management, and resilience. Here are some key design patterns used in microservices:
1. Decomposition Patterns
These patterns help break down a monolithic application into microservices.
a. Domain-Driven Design (DDD)
Decompose services based on business domains (Bounded Contexts).
Example: Separate
OrderService,PaymentService,InventoryService.
b. Strangler Fig Pattern
Gradually replace a monolithic system by incrementally migrating features to microservices.
Example: Move one module (e.g., user authentication) at a time.
c. Sidecar Pattern
Attach a helper service (sidecar) to a primary service for cross-cutting concerns (logging, monitoring).
Example: Envoy proxy in service mesh.
2. Communication Patterns
How microservices interact with each other.
a. API Gateway
Single entry point for clients to access multiple microservices (routing, aggregation, authentication).
Example: Netflix Zuul, Kong, AWS API Gateway.
b. Synchronous (Request-Response)
REST/gRPC calls between services.
Example:
OrderServicecallsPaymentServicevia HTTP.
c. Asynchronous (Event-Driven)
Use messaging (pub/sub) for decoupled communication.
Example: Kafka, RabbitMQ for order processing events.
d. Service Mesh
Handles service-to-service communication (load balancing, retries, security) via sidecars.
Example: Istio, Linkerd.
3. Data Management Patterns
How data is stored and shared across services.
a. Database per Service
Each microservice has its own private database (avoids tight coupling).
Example:
UserServiceuses PostgreSQL,OrderServiceuses MongoDB.
b. Saga Pattern
Manages distributed transactions using a sequence of events or compensating transactions.
Example: Choreography (events) vs Orchestration (central coordinator).
c. CQRS (Command Query Responsibility Segregation)
Separate read (query) and write (command) operations for scalability.
Example: Different databases for reads (optimized for queries) and writes.
d. Event Sourcing
Store state changes as a sequence of events (useful for auditing/recovery).
Example: Banking transactions stored as events.
4. Resilience & Fault Tolerance
Handling failures gracefully.
a. Circuit Breaker
Prevents cascading failures by stopping requests to a failing service.
Example: Netflix Hystrix, Resilience4j.
b. Retry Pattern
Automatically retry failed requests with exponential backoff.
Example: Retry failed HTTP calls to
PaymentService.
c. Bulkhead Pattern
Isolate failures by partitioning resources (threads, connections).
Example: Separate thread pools for different services.
d. Rate Limiting
Control the number of requests a service can handle.
Example: API Gateway limiting user requests.
5. Observability & Monitoring
Track system health and performance.
a. Distributed Tracing
Track requests across services (e.g., Jaeger, Zipkin).
b. Log Aggregation
Centralized logging (ELK Stack, Fluentd).
c. Health Check API
Expose endpoints for monitoring service status (Kubernetes liveness probes).
6. Deployment Patterns
How microservices are deployed and scaled.
a. Containerization
Deploy services in containers (Docker) for consistency.
b. Kubernetes (Orchestration)
Automate scaling, load balancing, and failover.
c. Blue-Green Deployment
Deploy a new version alongside the old one, then switch traffic.
d. Canary Release
Gradually roll out updates to a subset of users.
When to Use Which Pattern?
| Problem | Pattern |
|---|---|
| Decomposing a monolith | Strangler Fig, DDD |
| Service communication | API Gateway, Event-Driven |
| Data consistency | Saga, CQRS |
| Fault tolerance | Circuit Breaker, Retry |
| Scalability | Kubernetes, CQRS |
Conclusion
Microservices design patterns help solve challenges like scalability, fault tolerance, and distributed data management. The right pattern depends on your use case (e.g., event-driven for async workflows, Saga for transactions).
Advantages of Microservices
✅ Scalability – Scale only the required services.
✅ Flexibility – Use the best tech stack per service.
✅ Faster Deployments – Independent releases.
✅ Improved Fault Isolation – One service failure ≠ system crash.
✅ Team Autonomy – Small teams own specific services.
Challenges of Microservices
❌ Complexity – More services = harder to manage.
❌ Network Latency – Inter-service calls can slow performance.
❌ Data Consistency – Distributed transactions are tricky (use Saga pattern).
❌ Operational Overhead – Needs DevOps, monitoring (Prometheus, ELK), and service mesh (Istio, Linkerd).
Microservices vs. Monolithic Architecture
| Feature | Monolithic | Microservices |
|---|---|---|
| Codebase | Single, large | Small, independent |
| Scalability | Scale entire app | Scale individual services |
| Deployment | Single deployment | Independent deployments |
| Tech Stack | Uniform | Polyglot (mixed) |
| Database | Shared DB | DB per service |
When to Use Microservices?
✔ Large, complex applications with multiple teams.
✔ Need for independent scaling of components.
✔ Frequent updates without full redeployment.
When to Avoid Microservices?
✖ Small applications (overkill).
✖ Teams lacking DevOps expertise.
Popular Microservices Tools
Containerization: Docker
Orchestration: Kubernetes, Docker Swarm
API Gateway: Kong, Apigee
Service Mesh: Istio, Linkerd
Monitoring: Prometheus, Grafana
Logging: ELK Stack (Elasticsearch, Logstash, Kibana)
Microservices Tools in Azure
Microsoft Azure provides a robust ecosystem for building, deploying, and managing microservices-based applications. Below is a categorized list of key Azure tools and services that support microservices architecture.
1. Compute & Orchestration
Azure Kubernetes Service (AKS)
→ Managed Kubernetes for deploying, scaling, and managing containerized microservices.
→ Supports auto-scaling, service mesh (Istio), and Helm charts.
Azure Container Instances (ACI)
→ Serverless containers for lightweight microservices or sidecar patterns.
Azure Service Fabric
→ Microsoft’s distributed systems platform for stateful & stateless microservices.
→ Supports actors model (Reliable Actors) and automatic failover.
Azure App Service (Containers)
→ PaaS for running containerized microservices with CI/CD integration.
2. API Management & Gateway
Azure API Management (APIM)
→ Publish, secure, and monitor APIs (REST/gRPC) for microservices.
→ Features:
Rate limiting
JWT/OAuth validation
Request/response transformation
Azure Application Gateway
→ Layer-7 load balancer with path-based routing for microservices.
Azure Front Door
→ Global traffic manager with WAF (Web Application Firewall) for microservices.
3. Messaging & Event-Driven Architecture
Azure Service Bus
→ Reliable messaging (queues & topics) for decoupled microservices.
→ Supports pub/sub and competing consumers.
Azure Event Grid
→ Event-driven architecture with serverless event routing.
→ Used for real-time notifications (e.g., order processed, payment completed).
Azure Event Hubs
→ High-throughput event ingestion (e.g., logs, telemetry).
→ Kafka-compatible.
4. Databases & Storage (Decentralized Data)
Azure Cosmos DB
→ Globally distributed NoSQL DB with multi-model support (MongoDB, Cassandra, Gremlin).
→ Low-latency, automatic scaling.
Azure SQL Database
→ Managed relational DB for microservices needing SQL.
Azure Cache for Redis
→ In-memory cache for high-performance microservices.
5. Monitoring & Observability
Azure Monitor
→ Logs, metrics, and alerts for microservices.
→ Integrates with Application Insights (APM).
Application Insights
→ Distributed tracing, performance monitoring, and dependency tracking.
Azure Log Analytics
→ Centralized logging for microservices.
6. DevOps & CI/CD
Azure DevOps / GitHub Actions
→ Automated CI/CD pipelines for microservices.
Azure Arc
→ Manage Kubernetes clusters across clouds (hybrid/multi-cloud).
Azure Container Registry (ACR)
→ Private Docker registry for storing microservices images.
7. Security & Identity
Azure Active Directory (AAD)
→ OAuth2/OIDC for securing microservices APIs.
Azure Key Vault
→ Manage secrets, certificates, and keys securely.
Microsoft Defender for Cloud
→ Threat detection for containers/Kubernetes.
8. Service Mesh (Advanced Networking)
Azure Service Mesh (Istio-based)
→ Traffic management, mTLS, and observability for microservices.
Azure Load Balancer
→ Distributes traffic across microservices instances.
Comparison: Which Azure Tool to Use?
| Use Case | Recommended Azure Service |
|---|---|
| Container Orchestration | AKS, Service Fabric |
| Serverless Containers | Azure Container Instances (ACI) |
| API Management | Azure API Management (APIM) |
| Event-Driven Messaging | Azure Service Bus, Event Grid, Event Hubs |
| Global Database | Azure Cosmos DB |
| Monitoring | Azure Monitor + Application Insights |
| CI/CD Pipelines | Azure DevOps / GitHub Actions |
| Security | Azure AD, Key Vault |
Final Thoughts
Azure provides end-to-end support for microservices, from development (AKS, Service Fabric) to monitoring (Azure Monitor) and security (AAD, Key Vault).
Would you like a real-world example of microservices on Azure (e.g., e-commerce app)? 🚀
Global Traffic Manager with WAF for Microservices: Azure Solution
To ensure high availability, security, and optimal performance for microservices, a global traffic manager with WAF is essential. Below is a structured approach using Azure services:
1. Key Components
A. Global Traffic Distribution
Azure Traffic Manager (DNS-based load balancing)
Distributes traffic across regions based on latency, priority, or geography 1.
Provides automatic failover if a region fails 2.
Azure Front Door (HTTP/HTTPS-based global routing + WAF)
Combines CDN, WAF, and traffic routing for web apps 47.
Supports SSL offloading, URL-based routing, and DDoS protection 7.
B. Web Application Firewall (WAF)
Azure Application Gateway WAF (regional protection)
Filters Layer-7 attacks (SQLi, XSS) and integrates with Traffic Manager 23.
Azure Front Door WAF (global protection)
Blocks OWASP Top 10 threats at the edge 47.
C. Microservices Deployment
Azure Kubernetes Service (AKS) or Azure Service Fabric for containerized microservices.
Private endpoints (via Azure Private Link) to secure internal traffic 2.
2. Recommended Architecture
For a secure, multi-region microservices deployment, use:
Traffic Manager (DNS routing) → Directs users to the nearest Front Door or Application Gateway 12.
Azure Front Door (if HTTP/HTTPS only)
WAF inspects traffic before reaching microservices 7.
Application Gateway WAF (if deeper regional inspection is needed) 3.
AKS/Service Fabric → Hosts microservices with internal load balancers 2.
Azure Firewall → Additional security for East-West traffic (microservices communication) 2.
3. Key Benefits
✅ High Availability – Failover across regions if one fails 1.
✅ Security – WAF blocks attacks (SQLi, XSS, DDoS) 37.
✅ Performance – Low-latency routing via Traffic Manager or Front Door 4.
✅ Scalability – Auto-scaling microservices with AKS 2.
4. Alternative: Google Cloud Approach
If using GCP, consider:
Traffic Director (global load balancing for service mesh) 6.
Cloud Armor (WAF for GKE microservices).
Final Recommendation
For Azure microservices, combine:
Traffic Manager (DNS failover) + Front Door WAF (global security) + AKS (scalable microservices).
If deeper inspection is needed, add Application Gateway WAF per region 23.
Would you like a step-by-step deployment guide or a cost comparison between these options?
OR
Microservices is an architectural style that structures an application as a collection of loosely coupled, independently deployable services. Each microservice is responsible for a specific business functionality and communicates with other services over standard protocols, usually HTTP or messaging queues.
Here are some key features of microservices:
1. Independence:
Each service is self-contained and can be developed, deployed, and scaled independently. This is one of the main advantages, as teams can work on different services without much dependency on each other.
2. Single Responsibility:
Each microservice focuses on a single business function, which makes it easier to understand, maintain, and scale.
3. Decentralized Data Management:
Each service manages its own database or data store. Unlike monolithic applications where there’s typically a central database, microservices avoid data sharing between services.
4. Technology Agnostic:
Different microservices can be built using different programming languages, databases, or frameworks, depending on the needs of each service.
5. Communication via APIs:
Microservices communicate with each other through APIs, typically over HTTP/REST or messaging systems. This decouples the services and allows them to be more flexible.
6. Scalability:
Since each microservice is independent, it can be scaled independently of the others. This makes it easier to scale parts of the system that are under heavy load without scaling the entire application.
7. Resilience:
Since microservices are designed to be independent, the failure of one service doesn’t necessarily impact the others. Failover mechanisms like circuit breakers and retries are often implemented to maintain the system’s resilience.
8. Continuous Deployment:
Microservices facilitate continuous integration and deployment practices. Since services are independent, they can be updated without affecting the entire application.
Benefits:
-
Flexibility in Development and Deployment: Teams can use different technologies or tools that best suit the problem they're solving.
-
Resilience and Fault Isolation: If one service fails, others can continue to operate normally, avoiding system-wide outages.
-
Faster Time to Market: With microservices, teams can develop, test, and deploy services more quickly.
Challenges:
-
Complexity: Managing multiple microservices, especially when the number of services grows, can lead to operational complexity.
-
Distributed System Challenges: Issues like network latency, consistency, and service discovery can arise when managing multiple distributed services.
-
Data Consistency: Maintaining data consistency across services can be complex because each service has its own data store.
Common Use Cases:
-
E-commerce platforms: Different services for payment processing, inventory management, and user management can each operate as independent services.
-
Social media apps: Microservices can be used for features like posts, notifications, messaging, and user profiles.
===================
or
Microservices is an architectural style that structures an application as a collection of loosely coupled, independently deployable services. Each microservice is responsible for a specific business functionality and communicates with other services over standard protocols, usually HTTP or messaging queues.
Here are some key features of microservices:
1. Independence:
Each service is self-contained and can be developed, deployed, and scaled independently. This is one of the main advantages, as teams can work on different services without much dependency on each other.
2. Single Responsibility:
Each microservice focuses on a single business function, which makes it easier to understand, maintain, and scale.
3. Decentralized Data Management:
Each service manages its own database or data store. Unlike monolithic applications where there’s typically a central database, microservices avoid data sharing between services.
4. Technology Agnostic:
Different microservices can be built using different programming languages, databases, or frameworks, depending on the needs of each service.
5. Communication via APIs:
Microservices communicate with each other through APIs, typically over HTTP/REST or messaging systems. This decouples the services and allows them to be more flexible.
6. Scalability:
Since each microservice is independent, it can be scaled independently of the others. This makes it easier to scale parts of the system that are under heavy load without scaling the entire application.
7. Resilience:
Since microservices are designed to be independent, the failure of one service doesn’t necessarily impact the others. Failover mechanisms like circuit breakers and retries are often implemented to maintain the system’s resilience.
8. Continuous Deployment:
Microservices facilitate continuous integration and deployment practices. Since services are independent, they can be updated without affecting the entire application.
Benefits:
-
Flexibility in Development and Deployment: Teams can use different technologies or tools that best suit the problem they're solving.
-
Resilience and Fault Isolation: If one service fails, others can continue to operate normally, avoiding system-wide outages.
-
Faster Time to Market: With microservices, teams can develop, test, and deploy services more quickly.
Challenges:
-
Complexity: Managing multiple microservices, especially when the number of services grows, can lead to operational complexity.
-
Distributed System Challenges: Issues like network latency, consistency, and service discovery can arise when managing multiple distributed services.
-
Data Consistency: Maintaining data consistency across services can be complex because each service has its own data store.
Common Use Cases:
-
E-commerce platforms: Different services for payment processing, inventory management, and user management can each operate as independent services.
-
Social media apps: Microservices can be used for features like posts, notifications, messaging, and user profiles.
Do you have any specific questions on microservices or would you like to dive deeper into one of these aspects?
You said:
ChatGPT said:
In microservices architecture, design patterns play a crucial role in addressing various challenges such as service communication, fault tolerance, data management, and scalability. Here are some of the most common design patterns used in microservices systems:
1. API Gateway Pattern
-
Problem: Clients need to interact with multiple microservices, which can lead to increased complexity.
-
Solution: The API Gateway acts as a single entry point for all client requests. It routes requests to the appropriate microservice, consolidates results, and handles cross-cutting concerns like authentication, logging, and rate limiting.
-
Advantages: Reduces complexity for the client, provides centralized security, and allows for API versioning.
Use Cases: This pattern is commonly used for managing external client requests and simplifying service interactions, especially for mobile or web applications.
2. Database per Service Pattern
-
Problem: In monolithic architectures, a single database is shared by all components, creating tight coupling between services.
-
Solution: Each microservice has its own dedicated database or data store. This ensures that each service is independent in terms of data management, and there is no single point of failure.
-
Advantages: Decouples services and allows each service to choose the best database type (e.g., SQL, NoSQL) for its needs.
Use Cases: Best suited for applications where each service requires its own data model, allowing independent scaling and optimized data storage.
3. Event Sourcing Pattern
-
Problem: Managing and storing the current state of a system can be complex, especially when dealing with distributed systems.
-
Solution: Instead of storing the current state of an entity, you store a sequence of events that lead to the current state. These events are immutable and are stored in an event log.
-
Advantages: Improves traceability, enables eventual consistency, and allows for easy state reconstruction by replaying events.
Use Cases: This pattern is useful in applications where tracking the history of changes or actions is important, such as in financial systems or auditing systems.
4. CQRS (Command Query Responsibility Segregation) Pattern
-
Problem: In systems where reading and writing data are done in the same models, this can lead to complex and inefficient designs.
-
Solution: CQRS splits the data model into two parts: one for handling commands (write operations) and one for handling queries (read operations). This allows for optimizations specific to reading and writing.
-
Advantages: Improves scalability by enabling optimized queries and allowing different models for reading and writing. It can also simplify complex domain logic.
Use Cases: Ideal for systems with high read/write loads, such as social media platforms or e-commerce sites where the read and write operations have different performance characteristics.
5. Service Discovery Pattern
-
Problem: In a dynamic microservices environment, services may be constantly scaling or moving between instances, making it hard to keep track of their locations.
-
Solution: Service discovery enables services to automatically register themselves with a central registry (e.g., Consul, Eureka, or Kubernetes). Other services can query the registry to find the location of the required service instance.
-
Advantages: Enables dynamic scaling and resilience in large-scale microservices applications.
Use Cases: Useful in environments where services are frequently changing, such as in cloud-native applications with auto-scaling and dynamic load balancing.
6. Circuit Breaker Pattern
-
Problem: A failing service can cause cascading failures across the system if not properly handled.
-
Solution: The circuit breaker pattern ensures that when a service fails repeatedly, requests to that service are temporarily halted, allowing it to recover before traffic is redirected to it again. This prevents further strain on a failing service.
-
Advantages: Increases resilience and prevents a single service failure from affecting other services.
Use Cases: Ideal for high-availability systems where services are prone to intermittent failures (e.g., external third-party APIs or databases).
7. Strangler Fig Pattern
-
Problem: Migrating from a monolithic application to a microservices architecture without causing downtime or disruption.
-
Solution: Gradually replace parts of the monolithic system with microservices, with the new services running alongside the old system. Over time, the monolithic system is "strangled" as its functionality is replaced by microservices.
-
Advantages: Allows a safe and gradual transition from monolithic to microservices without large-scale system rewrites or downtime.
Use Cases: Commonly used when refactoring an existing monolithic application into microservices, especially in large, legacy systems.
8. Saga Pattern
-
Problem: Managing distributed transactions across multiple microservices is complex because traditional ACID transactions do not work across distributed systems.
-
Solution: A saga is a sequence of local transactions where each transaction is handled by a separate microservice. If one transaction fails, compensating transactions are triggered to undo the changes made by previous services.
-
Advantages: Provides a way to maintain consistency across services in an eventually consistent manner, without requiring distributed transactions.
Use Cases: Often used in financial transactions, order processing systems, and any use case where multiple microservices need to coordinate a series of actions in a distributed system.
9. Sidecar Pattern
-
Problem: Certain common functionalities (e.g., logging, monitoring, authentication) need to be implemented across multiple microservices, leading to code duplication.
-
Solution: The sidecar pattern involves deploying a helper service alongside the main microservice. The sidecar handles the common functionality, allowing the main service to remain focused on its core business logic.
-
Advantages: Decouples cross-cutting concerns from business logic, and simplifies the microservice code.
Use Cases: Useful in microservices requiring common functionality, such as logging, security, and monitoring, in systems like service meshes (e.g., Istio).
10. Bulkhead Pattern
-
Problem: A failure in one service can overwhelm other services, leading to system-wide failure.
-
Solution: The bulkhead pattern isolates different parts of the system (like services or containers) to prevent a failure in one part from affecting others. This can be achieved by placing limits on the number of requests, memory usage, etc.
-
Advantages: Improves fault isolation and increases system resilience.
Use Cases: Ideal for large-scale systems with varying service reliability or those where one service failure should not bring down the entire application.
11. Choreography vs. Orchestration
-
Problem: Managing how services interact and coordinate with each other can be complex in a distributed system.
-
Solution:
-
Choreography: In this model, each service knows how to react to events from other services. Services independently take action based on events they receive (event-driven architecture).
-
Orchestration: A central controller or orchestrator manages and coordinates interactions between services.
-
-
Advantages: Choreography can be more flexible and scalable, while orchestration provides more control over the workflow.
Use Cases: Choreography is preferred for event-driven architectures, while orchestration is useful when you need tight control over the sequence of service interactions.
OR
If you're working with .NET Core and Azure for microservices, here’s how you can implement the design patterns using Azure-native tools and .NET Core frameworks:
1. Decomposition Patterns
Domain-Driven Design (DDD)
.NET Tool: Use clean architecture with
MediatR(for CQRS) andEntity Framework Core(per service DB).Azure: Deploy each service as an Azure App Service or Azure Kubernetes Service (AKS).
Strangler Fig Pattern
.NET Tool: Use API Gateways (YARP, Ocelot) to route traffic between monolith and new microservices.
Azure: Azure API Management for gradual migration.
Sidecar Pattern
Azure: Use Azure Service Fabric (built-in sidecar support) or AKS with Dapr (Distributed Application Runtime).
2. Communication Patterns
API Gateway
.NET Tool: Ocelot (lightweight API Gateway for .NET Core).
Azure: Azure API Management (enterprise-grade gateway).
Synchronous (HTTP/REST)
.NET Tool:
HttpClientwith Polly (for retries/circuit breaking).Azure: Azure Load Balancer for scaling.
Asynchronous (Event-Driven)
.NET Tool:
Azure.Messaging.ServiceBus(for Azure Service Bus) orMassTransit(abstraction over messaging).Azure: Azure Service Bus or Azure Event Grid.
Service Mesh
Azure: AKS + Dapr (instead of Istio/Linkerd) for .NET-friendly service mesh.
3. Data Management Patterns
Database per Service
.NET Tool: EF Core (SQL Server, Cosmos DB).
Azure:
Azure SQL Database (relational).
Azure Cosmos DB (NoSQL, multi-model).
Saga Pattern
.NET Tool: Use MassTransit (with Azure Service Bus) or NServiceBus.
Azure: Azure Durable Functions (for orchestration sagas).
CQRS & Event Sourcing
.NET Tool:
MediatR (for CQRS).
EventStoreDB (for event sourcing).
Azure: Azure Cosmos DB (change feed for event sourcing).
4. Resilience & Fault Tolerance
Circuit Breaker / Retry
.NET Tool: Polly (integrated with
HttpClient).Azure: Azure API Management policies.
Bulkhead
.NET Tool: Polly’s
BulkheadPolicy.
Rate Limiting
Azure: Azure API Management (throttling).
5. Observability
Distributed Tracing
.NET Tool: OpenTelemetry +
Application Insights.Azure: Azure Application Insights (auto-instrumentation for .NET Core).
Log Aggregation
.NET Tool: Serilog (sinks to Azure).
Azure: Azure Log Analytics + Application Insights.
Health Checks
.NET Tool:
Microsoft.Extensions.Diagnostics.HealthChecks+AspNetCore.HealthChecks.UI.Azure: Azure Monitor (for health probes).
6. Deployment Patterns
Containerization
.NET Tool:
Dockerfile+Microsoft.NET.Build.Containers.Azure: Azure Container Registry (ACR) + AKS.
Blue-Green / Canary
Azure: Azure DevOps (pipelines) + Traffic Manager / Front Door.
Azure + .NET Core Microservices Stack Example
| Pattern | .NET Tool | Azure Service |
|---|---|---|
| API Gateway | Ocelot | Azure API Management |
| Service Communication | HttpClient + Polly | Azure Service Bus / Event Grid |
| Database per Service | EF Core | Azure SQL / Cosmos DB |
| Saga | Durable Functions | Azure Logic Apps (optional) |
| Observability | App Insights + OpenTelemetry | Azure Monitor |
| Deployment | AKS + Docker | Azure Container Registry |
Key Azure Services Summary
Compute: Azure App Service, AKS, Azure Functions.
Messaging: Azure Service Bus, Event Grid.
Data: Azure SQL, Cosmos DB, Blob Storage.
DevOps: Azure DevOps, GitHub Actions.
Monitoring: Application Insights, Log Analytics.
Would you like a sample .NET Core + Azure implementation for a specific pattern (e.g., Saga with Durable Functions)?
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