System design terms make more sense when you follow one request: a client calls an application, the application may call another service, and that service reads or writes data. The choices behind each step affect how easily the system can change, how it handles more traffic, and what happens when part of it slows down or fails.
How does a request move through a system?
Imagine a user opening an order page. The client sends a request to the application. The application handles it directly or calls another service through a defined interface. A service may read or write a database, and it may use a cache to avoid repeating some database reads. A load balancer can direct incoming traffic to one of several service instances.
Each boundary introduces a design choice. Keeping work together can make coordination simpler; splitting work can allow teams to deploy and scale parts independently, but calls between parts now depend on a network. Data choices determine what can be queried and how updates become visible. These terms describe those choices, not a checklist that every application must follow.
What is a monolith, and how is it different from SOA or microservices?
A monolith groups application processes into a more tightly coupled unit that runs together as a service. It can be straightforward to build and operate while the application is small. As it grows, however, a spike in demand for one process may require scaling the whole architecture, and tight dependencies can make one failure affect more of the application. AWS explains the monolith and microservices comparison.
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Service-oriented architecture (SOA) organizes reusable software components behind service interfaces. Microservices are generally smaller, simpler, focused services, each responsible for a business capability. A microservice can be run, deployed, and scaled independently, but the complete application still needs its services to coordinate.
| Design | Separation and independent change | Communication and data implications | Operational tradeoff |
|---|---|---|---|
| Monolith | Application processes are more tightly coupled and run together; changing or scaling one part may involve the whole. | More work can happen within the application rather than across service network calls. | Fewer separately operated services, but dependencies can broaden the impact of a failure. |
| SOA | Reusable components interact through service interfaces. | Components communicate through those interfaces; the reviewed AWS guidance does not establish one fixed data-ownership model for SOA. | Services create boundaries, with the specific deployment and operations model depending on the design. |
| Microservices | Smaller services focus on business capabilities and can be deployed and scaled independently. | Interactions cross service interfaces and often networks; data ownership and consistency require deliberate design. | Targeted changes are possible, but operating, debugging, and tracing multiple services adds work. |
The choice depends on the product’s stage and workload. A service boundary can give a team clearer ownership and allow investment in availability for a specific capability. It also adds interactions that can be slow or fail independently. AWS Well-Architected describes the benefits and costs of architecture segmentation.
What is an API or service interface?
An API (application programming interface) or service interface is the defined contract one component uses to communicate with another. The contract says how to request work and what response to expect; the caller need not share the called service’s internal implementation. Clear boundaries make independent development and deployment more practical, while changes to a contract still need to be managed so existing callers continue to work. AWS describes services communicating through well-defined APIs.
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What do horizontal scaling, load balancing, and fault domains mean?
Horizontal scaling
Horizontal scaling means adding capacity by running more service instances or machines. In a microservices design, a team can add instances for a busy service without scaling every other service. That helps only if the bottleneck is there: a database, network call, or other dependency may still limit the workload.
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A load balancer directs incoming traffic among service instances. It is one way to make use of multiple instances; it does not by itself eliminate an overloaded dependency or make a failing service reliable.
Fault domain
A fault domain is a boundary within which a failure can occur. Smaller service boundaries can help contain problems, but isolation is not automatic: if one service depends on another, the second service’s failure or delay can still affect the first. AWS Well-Architected discusses segmentation and its interaction tradeoffs.
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Why do distributed systems need to handle slow or failed dependencies?
A distributed system has components that communicate over a network. Unlike a call handled entirely within one running application, a network call can take time, lose data, or fail. A service that waits indefinitely for a slow dependency can tie up resources; one that assumes every call succeeds can turn a local failure into a wider outage.
Availability is whether a service can be used when needed. Reliability is whether the workload continues or recovers as intended. Those goals require deciding how the system should behave when a dependency is unavailable or slow—for example, whether it can return a partial result, retry an operation safely, or fail promptly. The right behavior depends on the request and its consequences; no single response fits every service. The AWS Well-Architected Framework (2024-06-27 edition) treats network latency and data loss as risks to account for in distributed workloads.
What is database-per-service, and when does eventual consistency matter?
With database-per-service, each microservice owns its data store and the decisions about managing it. This can let services choose storage that suits their data and access patterns. It also means another service should not be assumed to update or query that data as if it shared one application database; sharing information and coordinating changes need explicit design.
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Eventual consistency describes a situation where an update is not visible in every store or service immediately, though the copies may converge later. This can be an acceptable tradeoff—for example, where a short delay before a status appears elsewhere is tolerable. It is a poor fit if users or business rules require every read to reflect the latest update immediately. Cross-service transactions and consistency therefore need to be considered alongside service boundaries. AWS identifies data ownership and consistency as microservices design concerns.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I choose between relational and NoSQL databases?
Relational databases organize data in related structures and support query and transaction patterns suited to that model. NoSQL databases encompass different non-relational data and query models. Neither label determines whether a database is the right choice or will scale better for every workload. Start with the actual data and the operations the application needs.
- Data shape and queries: What information is stored, and how must the application find or combine it?
- Transactions and consistency: Which changes must be coordinated, and how fresh must reads be?
- Availability, latency, and durability: How should the data store behave when components or connections fail, and what response time and persistence does the workload require?
- Scaling: Which parts of the workload are expected to grow, and what capacity pattern do they need?
AWS Well-Architected recommends selecting a database based on workload requirements and access patterns, rather than choosing by category alone.
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When do I need a cache?
A cache retains reusable data in a faster layer so an application can serve some reads without going to the database each time. Placing one between application servers and a database can lower database read load and improve latency, as AWS describes in its microservices implementation whitepaper.
A cache is useful only when the workload can benefit from reusing data and can tolerate the cache’s behavior. Cached values may be stale, so the design needs a way to decide how fresh they must be and when they should be refreshed or invalidated. A cache does not help every read, and it adds another component whose contents and failures the application must handle.
How do the terms fit together in a design decision?
For the order-page example, first decide whether the application needs separate services at all. If it does, define each service’s responsibility and API, then decide which service owns each piece of data. Examine the network calls between services and specify how the page should behave if one is slow or unavailable. Finally, use the database and cache only where their data and access patterns fit the workload.
- Choose boundaries for independently meaningful responsibilities, not simply to maximize the number of services.
- Scale the component whose demand is growing, while checking whether its dependencies are the actual bottleneck.
- Match data stores to query, transaction, consistency, availability, latency, durability, and scaling needs.
- Accept eventual consistency only where delayed visibility fits user expectations and business rules.
These decisions are connected: splitting a monolith may improve independent deployment, but it turns some in-process work into network communication and makes data coordination more visible. The useful design is the one whose tradeoffs match the product’s actual needs.
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