Microservices Migration

Domain-driven analysis of the existing monolith to identify bounded contexts, the natural service boundaries within the codebase that correspond to distinct business domains. Codebase analysis to identify coupling: which modules share data models, which modules call each other directly, and which modules could be extracted with minimal changes to the rest of the codebase. Target architecture design specifying each service's responsibility, API contract, and data ownership. Extraction priority sequencing based on independence, business impact, and team ownership. The architecture definition that guides the migration and prevents extracting services in the wrong order or at the wrong granularity.

In practice, Domain-Driven Design event storming workshops with your engineering team map the core domain events and aggregate boundaries before any code is examined. The output is a context map: bounded contexts with explicit upstream/downstream relationships, shared kernels documented as technical debt to resolve during migration, and anti-corruption layers identified where an extracted service must translate between the new domain model and the legacy data structures still used by unextracted modules. For large monoliths, we apply static analysis tooling (Structure101, CodeScene, or custom AST analysis) to measure actual coupling density between modules and surface the clusters that behave as natural extraction units. This prevents the distributed monolith anti-pattern, where services are separated by network boundaries but remain tightly coupled through shared database tables or synchronous call chains that create latency cascades. The strangler-fig API facade is designed at this stage, not retrofitted after services are built.

Containerisation of extracted services and the remaining monolith into Docker containers for consistent deployment across development, staging, and production environments. Dockerfile authoring with multi-stage builds to minimise image size and surface area. Docker Compose configuration for local development so engineers can run the full service mesh on their laptops. Container image build pipelines integrated into CI/CD. Image security scanning for known vulnerabilities in base images and dependencies. Container registry setup with AWS ECR, Azure Container Registry, or Google Artifact Registry. The containerisation that makes every service deployable consistently regardless of the environment it runs in.

Multi-stage Dockerfiles are the standard here: a build stage compiles and tests the application, a runtime stage copies only the compiled artifact into a minimal base image (distroless or Alpine), and the resulting image is 80 to 95 percent smaller than a naive single-stage build. Smaller images mean faster pull times during Kubernetes pod scheduling and a reduced CVE surface. Trivy or Grype scans run as a required CI gate, builds fail if a critical or high-severity CVE is present in the final image layer. For services that require secrets at runtime, we configure integration with AWS Secrets Manager, Azure Key Vault, or HashiCorp Vault rather than environment variables baked into images. Helm charts are authored for each service at the containerisation stage rather than after Kubernetes cluster setup, so the chart templates and values files are ready when cluster deployment begins. Each service's Helm chart covers deployment, service, horizontal pod autoscaler, pod disruption budget, and ingress resources, parameterized by environment so the same chart deploys to dev, staging, and production with environment-specific overrides in the values layer.

Kubernetes cluster setup and service deployment on AWS EKS, Azure AKS, or GCP GKE. Namespace design for environment and team isolation. Deployment manifests with resource requests and limits for each service. Horizontal Pod Autoscaler configuration for services with variable load. Rolling update strategy for zero-downtime deployments. PodDisruptionBudgets to maintain availability during node drains and cluster upgrades. Persistent volume configuration for stateful workloads. RBAC configuration for least-privilege cluster access. The Kubernetes environment that runs the service mesh reliably rather than adding operational overhead to the engineering team.

API gateway deployment to handle routing, authentication, rate limiting, and request transformation at the edge of the microservices architecture. Kong, AWS API Gateway, or Azure API Management configured to route traffic to the correct service and version. Service mesh deployment with Istio or Linkerd for east-west service-to-service traffic management, mutual TLS, traffic shaping, and circuit breaking between services. Service discovery configuration so services find each other without hardcoded addresses. The gateway and mesh that make the microservices architecture manageable rather than a network configuration problem for individual service teams.

Migration orchestration following the strangler fig pattern, extracting services one domain at a time while the monolith handles everything else. API gateway routing configuration that sends specific paths to new services and falls back to the monolith for everything not yet migrated. Shared database decomposition strategy: each extracted service gets its own database, with CDC replication or event-based sync maintaining consistency during the transition period. Feature flag integration for canary routing, sending a percentage of traffic to the new service before full cutover. Monolith decommission plan by domain, with each extraction reducing the monolith's responsibility until it handles nothing and can be retired.

Inter-service communication design, synchronous (REST or gRPC) for request-response interactions where the caller needs an immediate response, and asynchronous (event-driven via Kafka, SQS, or RabbitMQ) for workflows where the service publishes an event and does not need to wait for downstream processing. Event schema design and evolution strategy so services can consume events from different versions of producers. Dead letter queue configuration for failed message handling. Distributed transaction patterns for operations that span multiple services, saga pattern or outbox pattern depending on consistency requirements. Observability across service boundaries: distributed tracing with Jaeger or AWS X-Ray, structured logging with correlation IDs, and service-level metrics in Prometheus and Grafana.