Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsSystem design terms make more sense when you follow one request: a client calls an application, the application may call another service, data is read or written in a database, and a cache may speed up repeat reads. The choices behind that flow affect how the system changes, scales, and behaves when something is slow or unavailable.
How does a request move through a system?
- Client: A browser or mobile app sends a request to the application.
- Service boundary: The application handles the request itself or passes part of the work to another service through an interface such as an API.
- Network: A call between services crosses a network. It can be delayed or fail, so the caller needs a plan for slow or unavailable dependencies.
- Data store: A service reads or writes its data in a database chosen for the workload.
- Optional cache: A cache may return reusable data without asking the database on every read. It also raises questions about how fresh cached data must be.
This flow is a useful map for the terms below: architecture describes how work is divided, scaling describes how capacity is added, and reliability describes what happens when part of the flow has trouble.
What is a monolith?
A monolith groups application processes into a more tightly coupled unit that runs as one service. That can make it straightforward to build and operate while the application is small. But when one part needs more capacity, the whole architecture may need to scale; tight dependencies can also make a failure in one process affect more of the application. AWS’s microservices overview describes these tradeoffs.
What are SOA and microservices?
Service-oriented architecture (SOA)
SOA organizes software components for reuse through service interfaces. A service interface defines how other components can ask for work without relying on the service’s internal implementation.
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Microservices
A microservice is a focused, independently run service associated with a business capability. It communicates with other parts of the application through a well-defined API. An application built this way still has to coordinate its services; independence does not mean the overall product becomes a collection of unrelated programs.
A useful distinction is scale and simplicity: AWS describes microservices as smaller and simpler components than those typically associated with SOA. Both approaches use service interfaces, and neither term by itself tells you whether a system is well designed. AWS Well-Architected guidance on workload segmentation discusses the benefits and costs of splitting a workload.
What is an API or service interface?
An API is a defined contract for communication between software components. It says what a caller can request and what it can expect in return. A clear contract lets one service interact with another without sharing its internal code or implementation details. In a microservices design, these contracts are how independent components cooperate. AWS’s microservices overview describes APIs as the means of communication between services.
How is a monolith different from SOA and microservices?
| Design | Separation and independent change | Communication and operations | Data considerations |
|---|---|---|---|
| Monolith | Processes are more tightly coupled and run together; scaling or deploying a part independently is limited. | Fewer interactions need to cross a service network boundary, but a failure or capacity spike in one area can affect the combined unit. | Data may be managed within the combined application; the design does not require each capability to own a separate store. |
| SOA | Components are organized for reuse through service interfaces. | Service interactions introduce coordination across boundaries; the operational effect depends on how services are divided and run. | Data ownership and consistency depend on the particular design. |
| Microservices | Focused services can be deployed and scaled independently when designed to support that independence. | More network interactions can mean added latency, distributed debugging and tracing work, and operational overhead. | Database-per-service can give each service control over its data, while making shared-data consistency and cross-service transactions harder. |
These are tendencies, not guarantees. Smaller boundaries can isolate some failures and let teams target changes or capacity, but dependencies can still transmit the effects of an outage. The right choice depends on the product’s stage, workload, and ability to operate the resulting system. AWS Well-Architected’s segmentation guidance covers these tradeoffs.
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What does horizontal scaling mean?
Horizontal scaling means adding capacity by running more instances across machines, rather than relying only on a larger individual machine. In a microservices system, a service that receives more demand can be scaled separately from services that do not. A load balancer directs incoming traffic among service instances; this term describes a traffic-distribution role, not a promise that every bottleneck is solved.
Scaling one service does not automatically increase capacity in the database, another service, or a network dependency it relies on. The useful question is where demand or a bottleneck actually sits.
What makes a system distributed, and why does the network matter?
A distributed system has components that communicate over a network. Unlike a call inside one process, a network call can take longer than expected, lose data, or fail. A service therefore needs to account for both errors and slow responses from dependencies; otherwise, one troubled component can cause requests elsewhere to stall or fail too.
Availability is whether a service can be used when needed. Reliability is whether the workload continues to work or recovers as intended. Neither is guaranteed merely by splitting an application into services. The AWS Well-Architected Framework, 2024-06-27 edition, treats network latency and data-loss risks as considerations for distributed workloads.
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What is a fault domain?
A fault domain is a boundary within which a failure can occur. A service boundary can help contain a problem to one part of an application, but that containment is not automatic: other services may depend on the affected service, and their behavior during the failure matters.
What does eventual consistency mean?
When related data is held in different services or stores, an update may not become visible everywhere immediately. That behavior is called eventual consistency: different views can temporarily disagree while updates propagate. It can be acceptable when a brief delay is harmless, but not when users or business rules require every read to reflect a change at once.
Deciding whether that tradeoff fits requires identifying which data is authoritative, which actions need an immediate consistent view, and what users should see while an update is still propagating. AWS identifies cross-store consistency as a design concern in microservices. AWS’s microservices overview discusses the data-management implications.
What is database-per-service?
With database-per-service, each microservice owns and manages its data store. Ownership reduces the need for services to depend directly on another service’s internal database and gives each service room to choose persistence suited to its needs.
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The tradeoff appears when a request needs data owned by multiple services. Keeping changes coordinated can be harder than updating related data inside one application and one transaction. Teams need to design how services share information and what consistency users can expect, rather than assuming that separate databases behave like one shared database. AWS’s microservices overview describes database-per-service and its consistency considerations.
How should you choose a database?
Relational and NoSQL databases offer different data and query models; neither is the universal choice for every workload. Start with the information the application stores and the operations it must perform, then compare the requirements that matter:
- Data shape and queries: What relationships, filters, joins, and access patterns does the application need?
- Transactions and consistency: Which updates must succeed together, and how current must a read be?
- Availability and latency: How should the store behave when parts of the system are unavailable, and how quickly must reads and writes complete?
- Durability and scale: What data must be retained, and how are expected workload and growth handled?
Choose against the actual workload instead of assuming “SQL” or “NoSQL” is automatically faster or more scalable. AWS Well-Architected guidance on selecting a database frames the decision around data characteristics, access patterns, and system requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When do you need a cache?
A cache is a faster layer that keeps reusable data so an application can serve some reads without consulting the database each time. Placed between application servers and a database, it can reduce database read load and improve latency. Those are potential benefits, not a guarantee: the result depends on whether the workload repeatedly requests data that can be reused. AWS’s microservices whitepaper section on caching describes this pattern.
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Cached data can become stale after the underlying record changes. Before adding a cache, decide how long an old value is acceptable, when cached entries should be refreshed or invalidated, and what should happen when a value is missing. If the application cannot safely tolerate a stale response, a cache may add complexity without being appropriate for that read.
How should a fresher developer choose an architecture?
Begin with the smallest design that meets the product’s real needs, then make boundaries where they solve a concrete problem. A monolith can keep early development and operations together; service separation can become useful when independent ownership, deployment, or scaling is valuable enough to justify network and operational costs.
- Identify which parts of the workload actually need different deployment or capacity.
- Define service contracts and data ownership before splitting components.
- For each network dependency, decide how the caller behaves when it is slow or unavailable.
- Choose databases and caches from observed access and consistency needs, not from labels.
- Account for the additional work of tracing requests across services and diagnosing failures.
System design is the practice of making these tradeoffs explicit. The terminology is useful because it gives developers a shared way to discuss boundaries, dependencies, data, and failure—not because one architecture is best for every application.
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