Key System Design Patterns to Know Before a Big Tech Interview

Event-Driven Architecture Pattern

Event-driven architecture is a design approach where components communicate by producing and responding to events.

Instead of direct calls between services or modules, one component emits an event when something notable happens, and other components listening for that event react accordingly.

This creates a highly decoupled, asynchronous system – producers of events don’t need to know who, if anyone, will act on those events, and consumers of events don’t need to know who produced them.

The pattern is essential in building scalable, real-time systems and is commonly used in modern applications (especially with microservices, serverless, and UI applications).

• How It Works:

  • Event Producer: An event producer detects or initiates a change and publishes an event notification. An “event” is usually a small message (often just a name and some data, e.g., “OrderCreated” with order details) indicating that something occurred. Producers don’t wait for a response; they just fire off events.

  • Event Broker/Bus: Often there is an intermediary (like a message broker or event bus) that routes events from producers to consumers. Examples include message queues (RabbitMQ), streaming platforms (Apache Kafka), or even in-app event buses. The broker can buffer events, handle subscriptions, and deliver events reliably to consumers.

  • Event Consumer: Event consumers subscribe to certain event types and, when an event is received, they perform some action in response. For instance, a consumer might listen for “OrderCreated” events and send a confirmation email, or update inventory. Consumers typically handle events asynchronously – the processing happens in the background, decoupled from the original action that triggered the event.

  Workflow: Suppose a user places an order on an e-commerce site (event producer). Instead of the order service directly calling email, inventory, and analytics services, it simply emits an “OrderPlaced” event.

  The email service (consumer) sees this event and sends a confirmation email; the inventory service (consumer) decrements stock; the analytics service logs the sale. Each service reacts independently, and the order service doesn’t need to be aware of these downstream actions.

  This flexibility is the hallmark of event-driven design.

• Benefits:

  • Loose Coupling: Components are highly decoupled – producers and consumers do not know about each other. You can add, remove, or update consumers without changing the producer, as long as the event contract is maintained. This makes the system more adaptable and extensible. For example, you can introduce a new service that also listens to “OrderPlaced” (say, a fraud detection service) without modifying the order placement code at all.

  • Scalability & Resilience: Because components communicate asynchronously, event-driven systems can handle high load by processing events concurrently. Spikes can be smoothed out by queueing. If a consumer goes down, events can be stored (in a queue) and processed when it comes back, which improves resilience. The decoupling also localizes failures – if one consumer fails, it doesn’t directly break the producer or other flows.

  • Real-Time Processing: Event-driven architecture excels at real-time or near-real-time processing. The system can react to events as they happen, which is great for use cases like live notifications, updating dashboards with streaming data, IoT sensor updates, or financial tickers. For example, in an IoT setup, sensors emit events (data readings) and various consumers immediately process this data for monitoring or alerting, enabling rapid response.

  • Flexibility in Workflow: It’s easier to build complex workflows where multiple things happen in response to one action. Since events can fan-out to many consumers, one trigger can launch numerous parallel processes. This is useful in business processes – e.g., a single user action triggers a chain of events across microservices (audit logs, notifications, calculations, etc.).

  • Better User Experience: In UI applications (like web apps), using an event-driven approach (often via async events or websockets) can avoid blocking the user. For instance, a user action can immediately update the UI (optimistically) and trigger background processing via events, making the app feel faster and more responsive.

• Trade-Offs & Challenges:

  • Complex Debugging: Because the flow of logic is not linear or synchronous, debugging is harder. It’s not always obvious which component will respond to an event, or in what order. The asynchronous nature means if something goes wrong, you may have to dig through logs of multiple services to trace what happened. Setting breakpoints in a distributed, event-driven system (to step through events) is not straightforward. Tools like distributed tracing and good logging are essential, but it’s inherently more complex than debugging a direct function call sequence.

  • Event Management & Design: Deciding the granularity of events and designing event schemas is non-trivial. If events are too fine-grained, the system may be flooded with a huge number of events (some of which might be low value), leading to overhead. If too coarse, consumers may have to do extra work filtering or parsing events. Evolving event formats (adding new fields, etc.) needs coordination to not break consumers.

  • Order & Consistency: Events by nature are often processed asynchronously and possibly in parallel, which can lead to eventual consistency rather than immediate consistency. Consumers might process events out of the original order or at different speeds. This is acceptable in many cases, but in others you have to design carefully. For example, if an inventory service processes “OrderPlaced” before it sees an earlier “InventoryRestocked” event, it might temporarily think stock is lower than it is. Dealing with out-of-order events or duplicates (the same event delivered twice) adds complexity to consumer logic.

  • Testing Complexity: Integration testing of an event-driven system requires simulating events and ensuring all consumers react properly, which can be complex. Also, if your system logic spans multiple events (like an event triggers another event and so on), ensuring the whole chain works requires careful testing of the workflow, possibly in a staging environment with all pieces running.

  • Infrastructure Overhead: Running an event infrastructure (message brokers, event buses) is an added piece of the system. Operating tools like Kafka or RabbitMQ at scale can be complex. They require resources and know-how to ensure reliability (like setting up clusters, handling backpressure, etc.).

  • Latency Considerations: While event-driven systems decouple components, if not designed well, they can introduce latency. For example, if you chain many events (A triggers B, B triggers C), the end-to-end process might actually be slower than a direct synchronous call that does all the work at once. It’s important to ensure critical paths don’t become too event-chain heavy if low latency is a requirement.

• Real-World Examples:

  • User Interfaces: The most immediate example is in frontend development – e.g., JavaScript in browsers is inherently event-driven (user clicks, keystrokes trigger events that handlers respond to). While not “system architecture” in the large-scale sense, it illustrates the decoupling: any number of functions can listen for a click event on a button.

  • Microservices Communication: Many microservices architectures use events to decouple services. For instance, Uber processes a stream of events for things like ride requests, driver location updates, and payments. Instead of services calling each other directly for every action, they often publish events to a stream (Uber uses systems like Kafka) and multiple services consume relevant events (for example, a “RideRequested” event might be consumed by a dispatch service to find a driver, a logging service to record the request, and a notification service to alert nearby drivers). This allows Uber to handle massive scale and real-time updates (like live ride tracking) efficiently.

  • IoT Systems: Consider a smart home system – sensors (temperature, motion, etc.) publish events whenever readings change. An event-driven design can route these events: a temperature change event could go to a climate control system to adjust heating, and also to a logging system to record historical data. If the internet connection drops, sensors can keep emitting events to a local hub which queues them. The decoupled nature ensures each component (thermostat, fan, data logger) just reacts to events and doesn’t directly depend on querying sensors constantly.

  • Financial Services: Stock trading platforms or payment processing systems often use event-driven models. For example, a stock price update might be an event that several algorithms consume to make trading decisions. Or when a payment transaction is processed, an event “PaymentCompleted” might be published; risk analysis services, ledger services, and notification services each listen to that to do their part. This way, adding a new service (say, a service to send a text message receipt) doesn’t require altering the payment processing code – just attach it as a new event consumer.

  • Streaming & Analytics: Modern big data pipelines are frequently event-driven. Systems like Kafka are used to stream events (log entries, user activities, telemetry data) which multiple consumers like analytics engines, monitoring dashboards, or machine learning systems consume in near-real-time. This is the basis of architectures like “lambda architecture” or “Kafka + stream processing” for real-time analytics.

• When to Use Event-Driven Architecture: Use this pattern when you have a system that needs to be highly decoupled, scalable, or reactive in real-time. It’s ideal for applications that handle a lot of asynchronous data or actions – such as microservices at scale, IoT networks, real-time analytics, notifications, and complex workflows that benefit from being broken into steps. If your application has multiple things that need to happen as a result of one action, or if decoupling can increase reliability (so parts can fail without taking the whole system down), event-driven design is very powerful. However, if your interactions are simple and synchronous (like a basic request-response CRUD app), introducing events might add unnecessary complexity.

• Best Practices:

  • Define Clear Event Contracts: Treat events as a public API of your services. Define a clear schema for each event type (what is the event name, what data is included). Use versioning or schema registries if events evolve. This helps all teams understand the data they’ll get. For example, an “OrderPlaced v1” event might include orderId, customerId, totalAmount – if you later add fields, consider a version bump or ensure backward compatibility.

  • Idempotent Consumers: Design event consumers to be idempotent (safe to process the same event more than once). This is crucial because in distributed systems, duplicates can happen (e.g., a publisher might retry an event if it doesn’t get an ack). If your consumer simply logs something or sends an email identified by an event ID, make sure it checks if it already processed that ID. Idempotency ensures that duplicate events don’t cause duplicate side effects (like charging a customer twice or sending two welcome emails).

  • Use a Reliable Broker: Use battle-tested messaging systems that guarantee delivery (at-least-once or exactly-once semantics as needed) and can scale. Systems like Apache Kafka, RabbitMQ, AWS SNS/SQS, or Azure Event Hub can handle high throughput and offer features like persistence, replay, and partitioning. This reduces the chance of lost events and helps handle spikes by buffering.

  • Monitor and Handle Dead Letters: In event-driven systems, you should have a mechanism for events that can’t be processed (e.g., consumer keeps failing). Many brokers support a dead-letter queue – configure this and monitor it. If events land there, it indicates something’s wrong with a consumer or event format. Having alerting on backlogs or dead-letter queues will help catch issues early.

  • Event Sourcing (where appropriate): In some systems, the sequence of events is the source of truth (a pattern called event sourcing). If you go this route, ensure you persist events reliably and can replay them to rebuild state. This can provide great auditability and resiliency (since you can reprocess events if needed). However, it’s a complex approach – weigh the need. If using event sourcing, keep your events immutable and stored long-term.

  • Limit Event Storms: Be mindful of scenarios where one event triggers another event in a feedback loop or a cascade that could overwhelm the system. Put safeguards (like rate limiting or aggregation) to prevent infinite loops or flooding. For instance, if you have an “UpdateCalculated” event that triggers on any change, and those updates themselves trigger more events, be careful to avoid recursion or excessive chatter.

• Common Mistakes to Avoid:

  • Using Events Everywhere Unnecessarily: Not every interaction needs to be asynchronous. A mistake is to turn simple request-response flows into events for no gain. For example, if a user requests their profile data from a service, that can just be a direct API call – making the service publish an event “ProfileRequested” and having a consumer respond is over-engineering. Use events where decoupling or async processing is genuinely beneficial.

  • Neglecting Consistency Requirements: If certain operations truly need to be atomic and strongly consistent (like two pieces of data must change together or not at all), an event-driven async approach might violate that unless supplemented by other techniques. Don’t use eventual consistency via events in places where it’s inappropriate (e.g., moving money between bank accounts might require a transaction rather than two independent event handlers to credit and debit). Always evaluate if eventual consistency is acceptable for a given use case.

  • Poor Logging/Tracing: As mentioned, debugging is hard if you can’t trace events. A mistake is not implementing correlation IDs – a unique ID that travels with an event (often originating from the initial request) so you can trace a transaction through multiple services. Without this, tracking what happened when an event goes through 5 different consumers is extremely painful. Always include some traceability info in events or context.

  • Tight Coupling via Event Schema: If consumers implicitly expect very specific data in events, you can end up with hidden coupling. For instance, if all consumers expect that an “OrderPlaced” event has a field totalPrice and you remove it, things break. This is somewhat inevitable, but mitigate it by treating events like a public API – maintain backward compatibility or clearly communicate changes. Don’t make breaking changes to event structures without a migration strategy.

  • Handling Logic in the Broker: Pushing too much smarts into the messaging layer (like using complex routing logic, or writing business rules in the broker if it allows) can be a mistake. The event transport should ideally remain simple (just routing messages). Keep business logic in the services. If you find yourself encoding a lot of logic in how events are routed or transformed in transit, consider if those should be separate services instead.

  • Overlooking Consumer Performance: It’s easy to focus on the producer and broker, but if a key consumer is slow or down, events pile up. If you don’t scale consumers or if one consumer can’t keep up with the event rate, it can become a bottleneck. Always monitor consumer lag (how far behind in the event stream it is). If one type of event processing is too slow, consider scaling out that consumer horizontally or breaking the work into smaller chunks/events.

Layered Architecture Pattern

The Layered Architecture (also known as n-tier architecture) is one of the most traditional and widely used architectural patterns. In a layered architecture, the system is organized into a set of layers (stacked vertically), each layer with a specific role or responsibility in the application. Typically, each layer only interacts with the layer directly beneath it, and provides services to the layer above it. This arrangement leads to a separation of concerns, where each layer can evolve or be maintained somewhat independently, as long as the interface between layers is respected.

• How It Works: The most common implementation of layered architecture has three layers:

  • Presentation Layer (UI): This top layer handles user interaction and presentation logic. It’s what the user sees and interacts with – for example, in a web application, this would be your HTML/CSS, Angular/React frontend, or the templating in a server-rendered app. It communicates user actions (like form inputs) to the layer below and displays results or errors.

  • Business Logic Layer (Service/Application Layer): This middle layer contains the core functionality and business rules of the application. It processes data, applies rules, and coordinates between the UI and data layers. For instance, in an online store, this layer would handle an “Order” operation – checking inventory, calculating totals, etc. It’s the “brain” of the application.

  • Data Access Layer (Database Layer): The bottom layer manages data persistence and retrieval. It communicates with databases or external data sources. It knows how to store and fetch information (SQL queries, ORM calls, file system access) but doesn’t contain business logic about that data. It provides an interface for the business layer to query or save data.

  Larger or more complex systems might further subdivide these (e.g., splitting business logic into a Domain layer and an Application layer, or adding a separate Integration layer for calls to external services, etc.).

  But the principle remains: each layer has a distinct responsibility and interacts in a one-direction flow (UI -> Business -> Data and back up).

  The UI layer never directly hits the database – it always goes through the business layer, for example. This clear layering makes the structure easy to understand: a change in UI doesn’t ripple directly into data code, etc.

• Benefits:

  • Clear Separation of Concerns: Because responsibilities are separated, each layer can be focused on separately. UI designers can work on front-end without needing to know database queries, and database engineers can optimize queries without affecting how the UI is structured. This isolation makes understanding and modifying the system easier.

  • Ease of Maintenance: Changes in one layer (say, swapping the database or altering the UI framework) have minimal impact on other layers, as long as the interfaces between layers remain consistent. For example, you could replace an in-memory data store with a SQL database by changing the Data Access layer only. Testing and debugging are easier too – if an output is wrong, you can often pinpoint if it’s a UI issue, business logic issue, or data issue based on where the anomaly appears.

  • Team Specialization & Parallelism: Teams can be organized around layers. You might have a front-end team, a back-end service team, and a database/DevOps team. Each team can work somewhat in parallel once the layer interfaces (like API contracts between UI and services, or data schema between service and DB) are agreed. This specialization can increase efficiency since each team focuses on what they’re best at.

  • Reusability: Layers can sometimes be reused by different higher-level layers. For instance, a well-designed business logic layer could serve multiple presentation layers (maybe a web UI and a mobile app use the same service layer). Similarly, if you create a new application, you might reuse the data access layer if it’s connecting to the same databases.

  • Standardization: Using a layered pattern often aligns well with common frameworks and standards. For example, J2EE (Java EE) applications traditionally use a layered approach (JSP/Servlet for presentation, EJB/business components for logic, and JDBC for data). This means there are established best practices and design patterns at each layer (like MVC fits in the presentation layer, DAO patterns in the data layer, etc.).

• Trade-Offs & Drawbacks:

  • Performance Overhead: The primary cost of layering is that a request has to pass through multiple layers. Each layer adds some overhead (function calls, data transformations). In a naive layered implementation, a simple operation might go through many indirections, which can slightly reduce performance. For most applications this overhead is negligible, but in high-performance systems, those extra milliseconds might matter. Sometimes developers will bypass layers for performance (e.g., have the UI query something from the database directly for a read-only operation) – but that breaks the pattern and can introduce tight coupling.

  • Potential for Rigidness: If not designed carefully, layered systems can become rigid. Because each layer depends on the one below, a change in a lower layer’s interface can impact all above layers. If layers are too tightly coupled (for instance, if the UI layer is too aware of database specifics, even though it calls the business layer), then the benefits of separation fade. It requires discipline to truly keep the knowledge confined to each layer.

  • Scaling Challenges: Traditional layered architecture often implies a monolithic deployment (all layers in one application process, scaling by cloning the whole app). Horizontal scaling of individual layers can be tricky if the layers are not separated at deploy time. For example, if your business logic is CPU-heavy, you might want to scale out that layer independently – but if it’s just a library within a monolith, you can only scale by running multiple copies of the whole app. However, one can physically separate layers (like run the UI on a web server, business logic on an app server, DB on a DB server) which allows independent scaling – but that starts to become more like a microservices or distributed system approach.

  • Layer Skipping Temptation: Sometimes developers are tempted to let one layer skip over and talk to another non-adjacent layer (for convenience or speed). For example, writing UI code that calls the database directly for a quick fix, bypassing business logic. This breaks the architecture and can lead to inconsistency (business rules bypassed) or security issues (UI directly fetching sensitive data). It’s a drawback that the architecture relies on adherence to the discipline; if not followed, it degenerates into spaghetti code.

  • Tight Coupling of Layers: If the layers are not well abstracted, changes can ripple. For example, say your business layer was not abstracted well and directly constructs SQL queries (instead of calling a data layer method). Now the business layer is tightly coupled to the database schema – if it changes, both layers break. Proper use of interfaces and abstraction is needed to avoid this, but when not done, you get the downsides of both layering and none of the upsides.

• Real-World Examples:

  • Web Applications: A typical enterprise web application follows a layered approach. For instance, a banking web app might have: a presentation layer (HTML/JS or JSP pages) for the customer website, a business layer (Java or C# classes that implement banking operations like transfer funds, calculate interest), and a data layer (SQL queries or ORM models connecting to an Oracle database). All interactions follow that path, and the structure is clear. Many internal business apps, content management systems, and point-of-sale systems use this pattern.

  • Mobile and Desktop Apps: A desktop app might have a UI layer (forms, dialogs), a domain logic layer (the core functionality), and a data layer (local database or file storage access). On Android or iOS, developers often structure apps with something akin to layers (even if not explicitly labeled as such) – UI Activities/Controllers, a business logic or domain layer (sometimes using patterns like Clean Architecture or MVVM which introduce use-cases or ViewModels as an intermediate layer), and a repository/data layer for persistence.

  • Legacy Systems: Many legacy enterprise systems (from the 90s and 2000s) were built as layered architectures, often physically separated: e.g., an n-tier architecture where you have a client application (UI layer) communicating to an application server (business layer) which in turn communicates to a database server (data layer). This physical separation was common with technologies like CORBA or COM+ or J2EE application servers.

  • E-Commerce Platforms: Large suites like Magento (PHP) or older versions of Shopify, etc., have layered designs – templates for presentation, services for logic, and mappers/SQL for data. This makes it easier to customize one aspect (like change the UI or swap out the database) without rewriting the entire system.

  • API + Service + DB Separation: Even in modern microservices, each service often internally uses layering. For example, within a single microservice, you might still separate the request handling (presentation of API), the core logic, and the data access, following layered principles. It’s not only for monoliths; it’s a general way to organize code.

• When to Use Layered Architecture: Layered architecture is a good default for many applications, especially when requirements are fairly standard (input-process-output style systems) and you want to enforce a clean structure. It’s well-suited for enterprise applications, CRUD apps, and applications where you have distinct front-end and back-end teams. If you value simplicity and clear organization over the absolute maximum performance, layered is a safe choice. It’s also a fit when you expect to maintain and extend the app over time – the clear separation can help manage complexity as new features are added. However, if the application requires extreme performance optimization or has very complex inter-module communication that doesn’t fit a linear layer structure, you might consider other patterns (or carefully tailor the layered approach). Also, extremely large systems might start with layering and later break out into microservices or other architectures as scaling demands.

• Best Practices:

  • Define Layer Boundaries and Contracts: Clearly define what each layer is responsible for and the interface it exposes to the layer above. For instance, the business layer might expose services like placeOrder(orderData) to the presentation layer, and internally it will call data layer methods like InventoryDAO.updateStock() etc. Document these APIs/interfaces. This clarity prevents leakage of logic between layers.

  • Keep Layers Independent: A layer should ideally not skip over or depend on details two layers away. Enforce that the UI never talks to the database directly, etc. One way to ensure this is to physically separate layers (different modules or packages, where the UI module can only call the service module’s public APIs, etc.). Another way is using dependency inversion – the higher layer defines an interface that the lower layer implements, so the higher layer doesn’t need to know about lower layer specifics (commonly used in clean architecture).

  • Limit the Number of Layers: Use the simplest number of layers that makes sense. Three is common, sometimes four (if splitting business logic into domain vs application logic, or adding an integration layer for third-party communications). Too many layers can cause needless indirection and confusion. For example, having both a “service layer” and a “business layer” that do similar things can be merged if they’re not providing clear separation of roles. Each layer should have a reason to exist.

  • Encapsulate Data Access: The data layer should abstract the data source details. Use repository or DAO patterns so that the business layer doesn’t construct SQL queries or manage connections – that’s all handled in the data layer. This way, if you switch from SQL to NoSQL or change schema, you update the data layer and the business logic remains unchanged. Similarly, the business layer should abstract complex operations so the UI layer doesn’t replicate that logic.

  • Error Handling and Validation: Decide where certain checks occur. Common practice: validate inputs as early as possible (presentation layer can do basic format checks, business layer does deeper business rule validation). Ensure that exceptions are either handled or translated appropriately at layer boundaries (for instance, a SQL exception in data layer might become a user-friendly error message in the UI layer after being caught and processed through the business layer). Not handling errors at the right layer can break the abstraction (like SQL exception leaking to UI is not ideal).

  • Caching at Appropriate Layers: If performance is an issue, you can introduce caching – but do it thoughtfully within a layer’s context. For example, cache frequently used data in the data access layer (so multiple calls from business layer don’t always hit the database). Or cache some session data in the business layer if it’s expensive to reconstruct often. Caching can improve performance without breaking the layer separation if done correctly (e.g., the business layer doesn’t need to know if data came from cache or DB – the data layer handles that).

• Common Mistakes to Avoid:

  • Skipping Layers (Leaky Abstractions): One of the worst violations is when a layer bypasses the one below to talk to a lower layer directly, or when higher layers become aware of lower-layer implementation. For instance, if UI code executes an SQL query (bypassing business logic), it not only breaks the pattern but can cause inconsistencies (business rules or security checks might be bypassed). Avoid the temptation of such shortcuts; maintain the disciplined approach to preserve the benefits of layering.

  • Business Logic in the Wrong Place: Sometimes significant logic ends up in the UI (because it was easiest to put it where a button was clicked, for example) or in the database (like through triggers or stored procedures) instead of the business layer. This scattering of logic defeats the purpose of a dedicated business layer. Strive to centralize business decisions in the middle layer. If you find UI making decisions (“if user is VIP then do X”) that should be in business rules, refactor that into the business layer so all front-ends benefit and abide by it.

  • Too Much or Too Little Layering: Over-engineering the layers (like adding an unnecessary extra abstraction for every single call) can lead to “architecture astronauts” syndrome – where you have tons of boilerplate passing through layers doing almost nothing. For example, having a service class that merely calls the DAO without adding any logic – here the service layer might be superfluous. On the flip side, under-engineering (merging all logic in one layer) negates the benefits. It’s a mistake to either split hairs too finely or lump everything together. Aim for a balance where each layer has a clear purpose.

  • Tight Coupling Between Layers: If layers are not properly abstracted, changes ripple. For example, if the UI layer is tightly coupled to a specific data format from the business layer (instead of a generic interface or DTO), any change in the business logic output format might break the UI. Using clearly defined data transfer objects or interfaces can decouple layers. Another example: if your business layer code is littered with SQL queries, it’s tightly coupled to the database schema – a big change in DB would break business layer. Avoid such coupling by sticking to the separation (e.g., let the data layer handle SQL).

  • Neglecting Layer Communication Costs: While layering, be mindful of how data flows. A common mistake is not considering the cost of multiple round trips between layers. For instance, the UI calls a business method in a loop, which calls the data layer each time – causing dozens of database calls. This is a performance anti-pattern that can be solved by adjusting the interface (e.g., one call that fetches all needed data, or the business layer providing a batch method). Always consider optimizing interactions across layers to minimize chattiness (the N+1 query problem is a typical example in layered apps).

  • Ignoring Alternative Patterns as Size Grows: As an application grows much larger, a single layered architecture might become unwieldy (e.g., very large monolithic codebase). A mistake is clinging to the exact layered structure when perhaps parts of the system should evolve (maybe splitting into microservices, or introducing modularization within layers). While layered architecture can scale, beyond a point you should evaluate if a more modular or distributed approach is needed. In summary, don’t treat layered pattern as the only option – be ready to iterate the architecture if required (for example, separate bounded contexts in an application, each with its own layered structure).

Conclusion:
These system design patterns – Microservices, MVC, P2P, Event-Driven, and Layered – each offer a unique approach to structuring software systems.

Understanding how they work and their strengths and weaknesses is crucial for any software architect or developer.

There is no one-size-fits-all solution; the right pattern depends on the specific use case, team expertise, and project requirements.

Often, real-world architectures combine multiple patterns (for example, a microservices system might internally use MVC for each service, or a layered application might incorporate event-driven communications between tiers).

By applying the best practices and learning from common mistakes outlined above, you can leverage these patterns to build software that is robust, scalable, and easier to maintain – the ultimate goal of good software architecture.