Tips for Migrating from Monolith to Microservices

Monolithic application architecture has served development teams well for many years. However, as application complexity grows, monoliths become harder to develop, test, deploy and scale compared to microservices. Migrating from monolith to microservices brings multiple benefits like scalability, modularity, independent deployability etc. But it also introduces challenges related to rearchitecting the system, database integration, managing communication between services etc.

This complete guide will walk you through the end-to-end process of migrating from a monolithic application to microservices architecture. We’ll cover key concepts, common approaches, steps involved and best practices to make the migration seamless. By the end of this guide, you’ll have a solid understanding of what it takes to successfully migrate from monoliths to microservices.

Understanding Monolithic vs Microservices Architecture

Before diving into the migration process, let’s briefly understand the differences between monolithic and microservices architectures at a high level:

Monolithic Architecture

  • Single large application written as a cohesive unit.
  • All components (front-end, back-end, database) compiled and deployed together.
  • Tightly coupled code makes it difficult to change and update individual components.
  • Scaling the entire application at once to meet load.

Microservices Architecture

  • Application broken into small, independent services.
  • Each service runs its own processes and communicates through APIs.
  • Services are modular, scale independently and can be updated independently.
  • Increased fault isolation, easier deployment, better organization of teams.

The key aspects of microservices like scalability, modularity and independent deployability make it a more future-proof solution compared to monoliths. However, migrating from one to other requires significant efforts.

Common Approaches for Migration

Migrating from monolith to microservices involves multiple approaches that organizations commonly undertake. Let’s look at some of the most common ones:

Big Bang Approach

  • Complete rewrite and replacement of the monolith in one go to microservices.
  • Risky as it requires redesigning and recreating the entire application from scratch.
  • Testing and deployments are complex.

Phased Migration

  • Break monolith into logical modules/services incrementally using Domain Driven Design.
  • Migrate modules one by one to their own services over multiple iterations.
  • Less risky than Big Bang with continuous deployments.

Strangler Application

  • Build a new microservices application using the same functionality along with the monolith.
  • Gradually move functionality from monolith to services over time.
  • Existing app remains available while new services are built incrementally.

Server-per-Service

  • Split monolith into services based on interfaces or db queries.
  • Deploy each service on its own server or container.
  • Maintain code semantics while incrementally shifting to independent deployment.

No single approach fits all cases. Evaluate your system, timelines, risk appetite to decide the best migration strategy. Phased migration and strangler patterns are generally safer options for most organizations.

Common Steps in Migration Process

Regardless of the approach, there are some standard steps involved in migrating from monolith to microservices:

1. Analyze Existing Monolithic Application

Understand application architecture, dependencies, interfaces, traffic patterns, database schemas etc. to identify candidate microservices.

2. Define Target Microservices

Break monolith modules into independent services based on domain boundaries, team structure etc. Define service contracts.

3. Isolate Database Changes

Loosen database schema coupling. Introduce service databases and APIs for data access during migration.

4. Develop New Services Incrementally

Use domain-driven design principles. Build, test and deploy services independently with proper versioning.

5. Manage Communication Between Services

Implement message queues, REST APIs or event-driven architecture for inter-service communication.

6. Implement Security & Monitoring

Add authentication, authorization, TLS encryption, circuit breakers etc. Monitor services individually.

7. Migrate & Retire Modules

Move modules from monolith to services gradually. Decommission modules from monolith over time.

8. Refactor as Needed

Refactor code, databases, deployment pipelines during the migration based on learnings.

This covers the core steps. However, there are additional aspects to consider which we’ll explore later in the article.

Analyze Existing Monolithic Application

The first step is analysing the existing monolithic application. This provides valuable inputs for defining the target microservices architecture. Some areas to analyse include:

Application Modules

Identify logical modules/components and boundaries based on functionality (user profiles, payments etc.), teams, libraries used etc.

Code Architecture & Design

Understand architecture (MVC, layered), coding practices, structural dependencies between modules.

Systems & Interfaces

Identify external systems integrated, interfaces, APIs, data formats used for communication.

Data Model & Schema

Map database schema, relationships, table structures to understand impedance mismatch for migration.

Traffic & Usage Patterns

Analyze volume/load patterns, routes/endpoints used to understand scale requirements of services.

Deployment & Infrastructure

Note deployment pipeline, artifact management, runtime environments like servers, containers etc.

Non-Functional Requirements

Learn SLAs, performance, security, fault-tolerance needs that services must satisfy.

Perform interactive sessions with team, study codebase and documentation for this critical step. It lays the foundation for defining target microservices.

Define Target Microservices Architecture

Leverage insights from monolith analysis to define the target microservices architecture. Some aspects to cover include:

Domain-Based Granular Services

Identify eventual autonomous services based on domain boundaries rather than technical layers.

Service Interface Definition

Document service contracts with request-response formats, data models, error handling etc.

Service Versioning Strategy

Have a concrete plan for deprecating old service versions and rolling out new ones.

Data Model Design

Define data models and relationships suitable for distributed design with services owning data.

Inter-Service Communication

Decide synchronous (REST) vs asynchronous (queue) communication between services.

Non-Functional Requirements

Define SLAs, scalability, security and other NFRs individual services must meet.

Service Teams Alignment

Map domain services to existing/new teams considering skills and ownership.

Deployment Architecture

Outline target deployment approach for each service – monolith, containers etc.

Clearly articulating the microservices design upfront is important for successful migration execution.

Isolate Database Changes

Loosening tight coupling between monolith database schema and code is a crucial precursor to migrating databases. Some strategies include:

Introduce Service Databases

  • Create individual databases owned by each future service.
  • Services expose database access via APIs or query service.

Denormalize for Performance

  • Duplicate relational tables/columns across service schemas
  • Improves joins, sacrifices some data consistency.

Adopt Schema-on-Read Approach

  • Services have autonomy over internal schemas.
  • Common schema maintained only for joins across services.

Domain Modeling with Polyglot Persistence

  • Use multiple database types – relational, document, graph based on data.
  • Each service uses optimal database for its domain.

Implement Data Migration Strategies

  • Plan approach to migrate data from monolith to service databases.
  • Batch/real-time migration, transformation during queries etc.

Proper database normalization strategy is essential foundation before developing independent services.

Develop Services Incrementally

Adopting an iterative approach is key to developing microservices successfully. Some best practices:

Versioning

  • Introduce versioning from start using Semantic Versioning.
  • Version APIs independently of implementations.

Atomic Deployments

  • Each service commit/release should deploy independently.
  • Avoid complex monolithic deployments.

Testing as you build

  • Thorough unit, integration, contract, and acceptance tests.
  • Continuous verification at each step is must.

Develop Only What’s Needed

  • Focus first on critical paths, happy scenarios.
  • Gradually handle edge cases incrementally.

Implement Timeouts, Fallbacks

  • Configure timeouts for external service calls.
  • Fallback handlers for intermittent failures.

Use API Mocking/ stubbing

  • Simulate external service responses during dev.
  • Verify app functionality independent of other services.

Automate Deployments

  • Use pipelines for builds, deployments, environment provisioning, rollbacks etc.
  • This allows migrating services safely without impacting live applications.

Manage Inter-Service Communication

Ensuring effective communication between decoupled services is crucial for their collaboration. Common approaches include:

RESTful APIs

  • Synchronous request-response services using HTTP/HTTPS.
  • Versioned contracts, JSON payload, common data formats.

Message Queues

  • Asynchronous communication between producer-consumer services.
  • Loose coupling improves scalability, performance, fault tolerance.
  • Popular options include RabbitMQ, Apache Kafka, Azure Service Bus.

Event-Driven Architecture

  • Services publish domain events upon state changes.
  • Other concerned services subscribe to relevant events.
  • Examples: event brokers like Kafka, event stores using CQRS.

API Gateways

  • Central entry point routing requests to appropriate services.
  • Handles authentication, monitoring, load balancing etc.
  • Examples: Zuul, AWS API Gateway.

Data Syncing and Replication

  • Services share/replicate data with each other using techniques like:
    • Database replication using Slaves.
    • CQRS with separate read models.
    • Eventual consistency via event replication.

Distributed Tracing

  • Tools like Jaeger, Zipkin help trace requests across services.
  • Helps pinpoint performance bottlenecks in microservices.

Proper choice between synchronous, asynchronous and hybrid patterns coupled with monitoring ensures high performance of inter-service communication.

Develop Consumer-Driven Contract Tests

With services communicating asynchronously, explicit contracts become important to prevent breaking changes. Implement consumer-driven contract testing to validate:

  • Service contracts are versioned and compatible during migrations.
  • Responses match schemas, file formats or data models.
  • Error payloads follow standard formats.

Tools like Pact, Spring Cloud Contract enable contract testing by simulating provider responses.

Implement Circuit Breakers

Services will error and timeout occasionally. Implement intelligent failover mechanisms like Hystrix or Resilience4j circuit breakers to:

  • Gracefully handle failures of downstream services.
  • Prevent cascading failures and degraded performance.
  • Fallback to cached responses during outages.

Apply Security Best Practices

Enforce authentication, authorization, and security at the service layer itself rather than centrally:

  • Use TLS for encrypted service communication.
  • Implement token-based authentication for internal APIs.
  • Ensure strict access controls on services.
  • Monitor for anomalies and security misconfigurations.

Monitor Services Individually

Gathering insights into independently running services requires tweaking monitoring strategies:

  • Instrument services to collect metrics like request counts, latency.
  • Monitor error rates, timeouts, memory usage of each service.
  • Set up log aggregation from all services.
  • Display services’ health and performance centrally.
  • Correlate logs for distributed tracing during issues.

Tools like Prometheus, Grafana, ELK Stack, Jaeger help achieve this.

Conduct Dark Launches

To reduce risk during migration, follow a “dark launch” approach:

  • Develop and deployment new services without replacing old ones.
  • Route a small percentage of live traffic to new services.
  • Gradually increase traffic after validating independently.
  • Cutover to new services once validated under full load.

This prevents impacting the live system during the migration journey.

Migrate Modules over Time

With all foundations in place, actual migration involves:

  • Moving one module from monolith to corresponding service.
  • Redirecting traffic, data migrations, deprecating monolith code.
  • Releasing service independently of monolith.
  • Validating functionality and SLAs are maintained.
  • Repeat for remaining modules incrementally.

Prioritize based on business priority, technical complexity, and risk levels.

Decommission Monolith

Finally, decommission the monolith once all functionality has successfully moved to microservices:

  • Redirect remaining traffic from monolith to services.
  • Gracefully shutdown monolith instances.
  • Remove monolith deployment configurations.
  • Delete monolith code/database from source control.
  • Archive monolith release for future reference.

This marks the completion of migration. Retain monolith for support as needed.

Monitor, Review and Refine

Post migration, the journey of improvement continues:

  • Monitor services continuously for issues or regressions.
  • Gather feedback and review what went well, what didn’t.
  • Refine domain boundaries, communication protocols etc.
  • Continuously address tech debt and optimize infrastructure.
  • Retrain and upskill team on new architecture patterns.

Microservices require ongoing discipline to reap benefits fully.

Common Challenges

While Migrating from Monolith to Microservices, some common challenges you may encounter include:

  • Difficulty finding apt domain boundaries and service granularity.
  • Loosening tightly coupled databases and schema changes.
  • Managing synchronous/asynchronous communication between services.
  • Scaling services independently under varying usage patterns.
  • Implementing authentication, authorization across services.
  • Managing data transformations and migrations.
  • Continuous refactoring required as understanding improves.
  • Educating existing teams on microservices principles.

Proper planning, documentation and leadership helps tackle such issues effectively.

Final Thoughts on Migrating from Monolith to Microservices

To conclude, migrating from monolith to microservices, particularly within the context of Microservices Architecture in .NET Core, is a gradual process that requires significant efforts but reaps rich dividends in the long run through scalability, modularity, and adaptability. By analyzing existing architecture deeply, defining target services iteratively, and following an incremental approach – one module at a time – the risks can be contained effectively during the journey.

Adopting best practices around independent deployments, inter-service communication, monitoring, and security further smoothen the process within the specific framework of Microservices Architecture in .NET Core. With discipline, even complex legacy systems can be split into collaborating microservices reliably.

Other Migration Methods

  1. Microsoft Azure Migration Guide

 

Amelie Lamb

Amelie Lamb

Amelie Lamb is an experienced technical content writer at SoftwareStack.co who specializes in distilling complex software topics into clear, concise explanations. She has a talent for taking dense technical jargon and making it engaging and understandable for readers through her informative, lively writing style.

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