Why Is Cloud Native So Hard?

"We moved to microservices, and the system got slower, outages became more frequent, and deployments became even scarier." This is a confession you hear all too easily from organizations that have been through an MSA transformation. Microservices architecture makes an attractive promise of independent deployment, elastic scaling, and fault isolation, but far fewer organizations than you might expect actually see that promise come true.

Why? To get straight to the point, our experience is that the difficulty of microservices comes from 'design', not 'technology'. Adopting Kubernetes and splitting a service into several pieces is no longer hard. What is truly hard are three design questions: "Where do we draw the boundaries?", "Who owns the data?", and "How do we keep things consistent without transactions?" In this article, we identify the three traps uEngine has seen again and again over years of MSA consulting and training, and point to the design principles that answer them, along with in-depth articles on each. We also map out the hard problems you will meet at every stage of the project lifecycle, from design through implementation, operations, and deployment, even after adopting those answers.

The reasons a monolith hits its limits are clear. Because of tight coupling between internal modules, a small change ripples through the whole system, load on a few features forces the entire system to scale up, and a failure in one place brings the whole service down. So we split the system.

And that is where the first misconception begins: "if you split it, it becomes microservices." A badly split system keeps the monolith's coupling intact and merely adds network overhead, becoming a Distributed Monolith. What used to be in-process function calls are now remote calls over the network, but every service is still holding on to every other. The system now has the drawbacks of a monolith (tight coupling) and the drawbacks of a distributed system (latency, partial failures, operational complexity) at the same time.

What separates good decomposition from bad is two long-standing concepts in software engineering: cohesion and coupling. If the elements within a service are grouped around one clear purpose, cohesion is high; if services depend little on one another's data and functionality, coupling is low. High cohesion, low coupling: every principle of microservices design ultimately converges on this one sentence.

The most common mistake in an MSA transformation is splitting services along database (table) lines. There is a customer table, so a 'customer service'; an order table, so an 'order service'; delivery data, so a 'delivery service'... It looks natural at first glance, but split this way, every single business flow has to gather data scattered across multiple services through synchronous request/response calls, one at a time.

This is the classic anti-pattern known as the Chatty Microservice. To use a restaurant analogy, it is like a kitchen that stores bread, vegetables, and meat in separate warehouses by type and makes the rounds of the warehouses to fetch ingredients every time an order comes in. In terms of the factory and warehouse analogy, it is an assembly plant that constantly walks into the parts factory's raw-material warehouse. Inter-service traffic explodes, a slow response from any one service slows the entire flow, and a failure in one place cascades like dominoes.

The solution is to draw the boundaries around behavior (the steps of a business process, the value stream) rather than data. For each step (concern) in the flow 'place order → cook → deliver', bundle the data and functionality that behavior needs into a bounded context. Each service then holds what it needs for its own job (high cohesion), and only passes the outcome of its behavior, a domain event, to other services asynchronously via Pub/Sub (low coupling). Only then does the ideal of microservices appear: one service fails, and the others keep running.

• 🔍 Go deeper: The Core of Microservices Design: Understanding Cohesion and Coupling
• 🏭 Go deeper: Microservices Design Principles: The Factory and Warehouse Analogy
• 📐 Fundamentals: Cohesion and Coupling in Software Design

Once the boundaries are drawn, the next question awaits: "So whose data is this?" Every monolithic system has a giant table where columns from every domain, sales, marketing, reservations, and more, are tangled together. This is the so-called God Table. Because every domain depends on this one table, a change in one domain spreads to all the others, and the central database becomes a bottleneck. If you leave the God Table in place and split only the application, you end up with the shared database anti-pattern, where every service clings to a single DB and the point of MSA is lost.

Three principles of data separation

• 📍 Data Locality: each bounded context owns locally only the data it frequently reads and writes within its own problem space. Entities from other domains are referenced only by an ID Value Object, never pulled in wholesale.
• 📋 Common Data Duplication: data that rarely changes, such as names and gender, is replicated to each service via events. The key is letting go of the monolith-era normalization instinct that "duplication is always evil." Replication is the price of autonomy, and it is far cheaper than the cost of synchronization.
• 📞 Remote Invocation as a Last Resort: only data that changes too often to replicate is fetched at call time, and caching and the flyweight pattern reduce call cost and vulnerability to failures.

The priority among these three principles matters. Localize first, then replicate, and use remote calls last. Many projects apply this order in reverse (solving everything with remote calls) and slide right back into the chatty microservice of trap ①.

• 🗃️ Go deeper: Effective Data Management in Microservices: Data Locality, Common Data Duplication, and Remote Invocation

Something that was taken for granted in a monolith disappears in MSA: the ability to wrap everything in a single transaction. The days of committing the order save and the stock deduction in one transaction are over. The moment data is distributed across services, the safety net of "all succeed or all roll back" is no longer free. This is the most fundamental fear many architects feel when facing MSA.

The answer lies in redefining the scope of consistency. In Domain-Driven Design (DDD), an Aggregate is "a cluster of objects that must be consistent together, immediately," and it defines the smallest boundary that requires strong consistency. Inside an aggregate, updates are atomic so invariants hold; between aggregates and between services, you manage with eventual consistency — "not necessarily right now, but consistent in the end." The order service saves the order and publishes an event; the inventory service subscribes to it and updates stock at its own pace.

It isn't free, of course. The moment you adopt this style, a set of homework comes with it.

• 🔁 Idempotency: Handlers must be designed so that receiving the same event twice causes no side effects.
• ↩️ Compensating transactions and Sagas: When an intermediate step fails, the scenario for rolling back the already-completed earlier steps must be designed explicitly with the Saga pattern.
• ⏱️ Consistency window: "How long can inconsistency be tolerated?" must be agreed with the business and monitored.
• 📏 Aggregate size: Too large, and the lock contention of the monolith era comes back; too small, and coordination costs explode. Drawing the boundary is itself a design skill.

• ⚖️ Go deeper: Balancing Data Consistency and Performance — Aggregates and Eventual Consistency
• 🧱 Go deeper: Aggregate Best Practices
• 🔀 Go deeper: A Comprehensive Guide to Implementing CQRS with Spring Boot

To sum up, microservices are hard because three traps — wrong boundaries, ownerless data, and vanished transactions — interlock with one another. And each trap has a proven remedy.

As you may have noticed, the three remedies join into a single sentence: "Draw boundaries around business behavior, localize data inside those boundaries, and connect the boundaries loosely with events." The real reason microservice design is hard is that this sentence is not a technology problem but a question of how deeply you understand the domain. If domain modeling is new to you, we recommend starting with Understanding the Domain Model Through the Three Little Pigs and Why Business Logic Should Be Managed in Domain Classes, Not SQL.

If you've read this far and concluded, "So I just draw boundaries around behavior and connect them with events," you're only half right. Event-driven microservice architecture (EDMSA) is an outstanding answer for scalability, decoupling, and real-time processing, but the moment you accept this remedy the difficulty doesn't disappear — it changes shape and scatters across every stage of the project lifecycle. As "the complexity of distributed environments" combines with "asynchronous messaging," each stage — design, implementation, operations, deployment — has its own hard problems waiting. Map them out in advance and they become a checklist instead of something to fear.

"Asynchronous event-driven architecture lowers coupling, but in exchange sharply raises the difficulty of understanding and controlling the state of the system as a whole." — That's why, before adopting EDMSA, at least four things should be reviewed in advance.

• 📤 ① Whether the Transactional Outbox pattern and CDC are applied as a standard
• 🔭 ② An OpenTelemetry-based end-to-end distributed tracing system
• 📜 ③ Event schema governance through a Schema Registry
• 🧰 ④ Consumer idempotency handling and DLQ automation logic packaged as a shared library

This list isn't meant to scare you. The point is that every item is a conquerable challenge for which proven standard patterns and tools already exist. CQRS, which tames query complexity by separating the read model, is another weapon in that arsenal. And how these challenges get handled one by one in a real large-scale project is shown by the Gov24 case that follows — Saga, idempotency, retries, and DLQs all appear exactly as described.

The case that shows these principles aren't just textbook theory is the cloud-native transformation of the Gov24 portal. In this project, for which uEngine Solutions performed the design and analysis, the existing Gov24 was a classic tightly coupled monolith, and in particular it carried a single point of failure (SPOF): every civil-service application had to pass through the 'Service Guide' module. If that one module stopped, every online civil service in Korea stopped with it.

The core of the transformation design was exactly the principles we've seen. The civil-service domain was divided into three types according to the nature of the business behavior, and a different pattern was applied to each.

• ⚡ Simple applications and instant issuance: Since response time is everything, synchronous REST calls were kept, but the tight coupling of directly updating other services' databases was replaced with clear API interfaces — not turning everything into events is a design decision too.
• 📨 Deadline-based, non-integrated services: Immediate acknowledgment of receipt, then background processing via event-store-based Pub/Sub. The Saga pattern secures process consistency, while idempotency, retries, and DLQs secure reliable message handling — the exact point where the remedy for trap ③ is applied.
• 🏛️ Major services (building registers, vehicle registration, etc.): Services with heavy traffic and high public impact were isolated and deployed in independent namespaces with dedicated guide channels, structurally eliminating the SPOF problem.

The target figures: automatic scaling of system capacity up to 7.6x, an 81.6% reduction in service downtime, and 36.7% faster processing. It's a case that shows what outcome targets a design can take on once it has confronted "why is this hard" head-on. The detailed AS-IS/TO-BE architecture analysis is available in the full Gov24 transformation story.

Microservice architecture is hard. But the nature of that difficulty is clear: boundary setting, data ownership, and consistency strategy — three design judgments, all of them mountains that can be climbed with learning and experience. And uEngine has tightly linked the entire journey up that mountain — diagnosis → design → implementation → modernization → capability building — into products and services. Follow along and see how each concept covered in this article is executed as a specific feature of a specific product.

If you're not sure where to start, come in through either of these two doors.

• 🧭 CNA/MSA Consulting: Drawing on experience from large-scale transformation projects including Gov24, we support the entire journey, from EventStorming-based boundary design to implementation and operations on the eGovFrame standard framework. → CNA/MSA Consulting · MSA Case Studies · Pilot Implementation Consulting
• 🎓 MSA School: A dedicated microservices training site where you internalize, through hands-on practice, the cohesion and coupling, god-table decomposition, and aggregates and eventual consistency covered in this article. Discover the curriculum more than 8,000 graduates have completed. → Go to MSA School

Read next — MSA Design Series

• The Heart of Microservice Design: Understanding Cohesion and Coupling
• Microservice Design Principles — Understood Through the Factory and Warehouse Analogy
• Effective Data Management in Microservices: Data Locality, Shared Data Replication, and Remote Calls
• Balancing Data Consistency and Performance: Aggregates and Eventual Consistency
• Aggregate Best Practices
• Cohesion and Coupling in Software Design
• A Comprehensive Guide to Implementing CQRS with Spring Boot
• Understanding the Domain Model Easily Through the Three Little Pigs
• The Difference Between Systems That Manage Business Logic in SQL and Those That Manage It in Domain Classes, Part 1 · Part 2
• Gov24 Cloud-Native Transformation: Innovating Public Services with Microservice Architecture