Dapr

Phase 1 is the safest phase of the migration — by design. No write operation touches the new microservices. Magento remains the source of truth for all data modifications. The only thing Phase 1 does is prove that your microservices can serve reads faster and more reliably than Magento. Answer-first: Phase 1 deploys Go microservices in read-only mode, routes GET requests to them via the API Gateway’s per-domain feature flags (with automatic fallback to Magento if the service is unhealthy), and uses Debezium — running in embedded engine mode without a standalone Kafka cluster — to stream Magento MySQL changes to the microservices via Dapr PubSub on Redis Streams. Writes continue to go to Magento. Data latency target: < 2 seconds. ...

In Phase 1, both systems existed but only one wrote data: Magento. In Phase 2, both systems write data simultaneously. This is the most technically complex phase — and the one where most migrations introduce data corruption if they don’t have an explicit conflict resolution strategy. Answer-first: Phase 2 uses event-driven dual-write — not raw database dual-write. Microservices write to their PostgreSQL first, then publish a domain event to Dapr PubSub. The magento-sync-adapter service subscribes to those events and writes back to Magento. Conflicts (both systems update the same record concurrently) are resolved by a 5-policy matrix that differs by data type: timestamp-based for customer profiles, microservices-wins for order status and stock levels, and summation reconciliation for coupon usage counts. ...

When a customer places an order on the Composable Commerce Platform, seven events need to happen in sequence across four independent services: Order created → Payment authorized → Stock reserved → Fulfillment triggered → Notification sent → Loyalty points awarded → Shipping label generated. Any of these can fail. The network can fail. The database can fail. A third-party payment gateway can time out. Without a reliability mechanism, a 2% failure rate on any step means 2% of all orders are stuck in an inconsistent state, requiring manual intervention. ...

21 services. 24 decisions. 3.5 months of deliberation captured in Architecture Decision Records. An ADR (Architecture Decision Record) is a short document that answers the question: “Why did we choose X when Y and Z were also options?” Without ADRs, architectural knowledge lives in engineers’ heads. When they leave, the knowledge leaves too — and the next team rewrites the same component in the way that was already tried and rejected. ...

Answer-first: In 2026, Platform Engineering for AI is no longer about picking the right LLM framework. The real questions are: Who controls token cost? Who routes traffic intelligently to the right GPU pod? Where does agent state go after a crash? Three CNCF projects — Envoy AI Gateway, the K8s Gateway API Inference Extension, and Dapr Agents — are converging to answer those questions at the infrastructure layer, so application code doesn’t have to. ...

Building a simple API that calls Graphhopper via http.Get is easy. Building a Principal-level API Gateway that survives 10,000 concurrent riders requesting routes without crashing is a masterclass in Distributed Systems. Answer-first: Graphhopper is a heavily CPU-bound downstream service. If your Golang API blindly accepts traffic and forwards it, a slight slowdown in Graphhopper will cause your Goroutines to pile up, exhausting your server’s RAM and triggering a cascading failure. You must implement a “Defense in Depth” strategy using Concurrency Bounding, Circuit Breakers, and Asynchronous Pub/Sub. ...

Go microservices from domain design to Kubernetes deployment — gRPC, Dapr, OpenTelemetry, and GitOps patterns from a real 21-service production migration.

Answer-first: A modern banking microservices architecture replaces legacy monolithic ledgers (like T24 or Flexcube) using Go for high-throughput transaction routing. The system achieves distributed consistency without two-phase commit (2PC) by combining Event Sourcing (immutable ledger streams), Saga Orchestration (using Temporal or Dapr), the Transactional Outbox pattern, and PostgreSQL unique constraints for API idempotency. What You’ll Learn That AI Won’t Tell You How to implement transactional outbox pattern to guarantee eventual consistency. Saga Orchestration patterns that handle transient payment gateway timeouts gracefully. 1. Introduction: Deconstructing the Legacy Core For decades, banks relied on monolithic core systems like Temenos T24 or Oracle FLEXCUBE. While robust, these systems present severe bottlenecks for modern digital banking. They were designed for overnight batch processing, not real-time, API-first global transactions. ...

Answer-first: Dapr Workflows implement the Saga pattern in Go by coordinating distributed transactions through stateful, durable orchestration. If a step fails, the orchestrator executes compensating transactions in reverse order, ensuring eventual consistency without requiring complex manual state management or two-phase commit overhead. What You’ll Learn That AI Won’t Tell You Compensation handlers configuration in Dapr to guarantee atomic rollback. How to handle transient workflows when the orchestrator instance restarts mid-transaction. Most Go developers building microservices know the Choreography Saga pattern: service A emits an event, service B reacts, service C reacts to B, and so on. If step C fails, services emit “compensation” events in reverse order. The pattern works elegantly for simple flows, but breaks down as the number of steps grows: debugging a failed saga requires tracing events across five message broker topics, and implementing compensation logic requires every service to understand the full saga’s state. ...

Today’s Tech Radar tracks three massive architectural shifts occurring simultaneously across the backend, frontend, and infrastructure ecosystems in 2026. On the backend, the Dapr project has stabilized its Agents v1.0 framework for Agentic AI. On the frontend, React Three Fiber (R3F) has successfully bridged the gap to WebGPU via the Three Shading Language (TSL). At the infrastructure layer, the upcoming Argo CD 3.4 release introduces critical “Day 2” operational safety mechanisms for Kubernetes GitOps. ...

The strongest TechTask signal in the last 24 hours is not a single framework release. It is the way several platform updates are converging on the same message: commerce modernization is no longer mainly about decomposing a monolith. It is about operating the decomposed system safely. That matters directly for the engineering profile behind this site: Strangler Fig migration from Magento/PHP into a 21-service Golang ecosystem, Dapr Pub/Sub for distributed workflows, Saga compensation for checkout and payment failure, Transactional Outbox for reliable events, GitOps through Kubernetes and ArgoCD, and performance work that pushed p95 latency from 1.2s to 120ms under high-traffic commerce load. ...

Answer-first: Migrate a complex Magento monolith to microservices without downtime by utilizing a 3-Phase Strangler Fig pattern. Stream legacy updates to Go microservices in real time using Debezium CDC, manage bidirectional synchronizations using Dapr Pub/Sub to maintain eventual consistency, and keep Magento as a 30-day hot standby for instant rollback capability. What You’ll Learn That AI Won’t Tell You Decoupling cart and checkout tables from Magento core databases. Data synchronization pipelines that prevent order loss during checkout transitions. “Let’s rewrite everything to Microservices.” ...

The selected items for pipeline run 6 form a coherent picture of where mature platform engineering is heading. After fetching and reading the full source content directly from the original URLs, the common theme is clear: strong systems are not defined only by what they can do, but by how safely they evolve, how predictably they recover, and how much accidental complexity they remove from the teams building on top of them. ...

Answer-first: We decompose the monolith into 21 microservices using Domain-Driven Design (DDD) to isolate business boundaries. Implementing the Kratos framework in Go enables strong structural subtyping for clean layer segregation, while Dapr Workflows handle distributed transactions asynchronously via the Saga pattern to avoid race conditions. What You’ll Learn That AI Won’t Tell You The exact performance overhead of using Go’s structural subtyping versus manual dependency injection in high-throughput microservices. Why scoping database transactions to a single Aggregate root is critical, and how we resolved out-of-order event delivery using Kafka partition keys. Scaling an e-commerce platform past 10,000+ orders per day containing multiple SKUs across dynamic warehouses is where naive architecture breaks down. Hardware scaling ceases to be a magic bullet when distributed transactions, race conditions, and eventual consistency are involved. ...

Answer-first: Build resilient event-driven microservices by using Dapr’s Pub/Sub APIs to decouple message transport. Ensuring eventual consistency requires implementing the Transactional Outbox pattern on writes, utilizing dead-letter queues (DLQs) for failed runs, and designing idempotent message handlers. What You’ll Learn That AI Won’t Tell You How to configure dead-letter queues in Dapr to handle poison messages. Designing idempotent message handlers that process duplicate events safely. In my previous post, we explored how abandoning monolithic architecture in favor of strict Domain-Driven Design (DDD) bounded contexts allowed an e-commerce platform to scale beyond 10,000+ orders per day. However, splitting one big database into 20+ isolated Postgres databases introduces a terrifying new problem: How do we maintain data consistency across disconnected services? ...

Answer-first: Complete architectural blueprint of a Go 21-service e-commerce platform. Covers domain boundaries, traffic flow, and event-driven patterns. What You’ll Learn That AI Won’t Tell You Practical latency and memory metrics comparing an Envoy-based API Gateway to a custom Go reverse proxy under 100k concurrent connections. How to tune circuit breaker thresholds (go-resiliency/breaker) to prevent premature service isolation during temporary network jitters. When transitioning from a monolithic platform to a distributed microservice setup, the hardest question isn’t “How do we write the code?” — it’s “How do these moving parts talk to each other safely, and why is each boundary drawn exactly where it is?” ...