The Backend Blueprint: Essential Design Patterns for Developers
Know when to use each pattern
When designing a backend service it’s important to consider the best way for the client/other service to consume the information. Sometimes simple request and response is sufficient but other times we can run into issues. The design pattern you pick can have sizable implications on your application’s scalability, complexity, and latency.
Request Response
Request-response is one of the most common ways that a backend and client can talk to each other. In this pattern, the client sends a request (like asking for some data or performing an action), and the server responds with the needed information or confirmation. This setup works great for APIs and everyday web interactions where you need a clear, predictable reply. But in scenarios that demand real-time updates or handle multiple users at once, other communication methods may be ideal. Overall, request-response is a reliable and straightforward way to manage data and interactions in most applications.
Pros:
Simplicity:
Easy to implement and understand, especially for basic client-server interactions.
Widely supported in frameworks and tools.
Strong Error Handling:
Built-in error handling. The client can easily detect issues based on HTTP status codes or other protocol-level responses.
Predictable and reliable:
A response is always sent back so the client will know if there are any issues.
Is typically stateless especially when used with RESTful APIs.
Cons:
Latency:
The client must wait for a response from the server which is not ideal in some cases(like notifications). This may lead to slow updates, especially over slow networks.
Harder to handle long-running processes or tasks. If the server takes a long time to process the request, it may cause timeouts on the client side.
Not suitable for highly concurrent or real-time systems because each request waits for a response, tying up resources and limiting scalability.
If one part of the system is slow, it can bottleneck the entire communication chain.
Resource Intensive:
Keeping the connection open while waiting for a response consumes server and client resources, which can impact performance under high load.
Short Polling
Short polling is a technique similar to request response where a client repeatedly sends requests to a server at regular intervals to check for updates or new information. If there’s no new data, the server responds with an empty or unchanged result, and the client waits for a set amount of time before sending another request. While straightforward to implement, short polling can be inefficient as the client may continue to request updates even when none are available. This can place unnecessary load on the server and lead to wasted resources, making it less suitable for real-time applications or high-traffic systems.
Pros:
Simplicity:
Easy to understand and set up, especially in systems that don’t need real-time updates.
Failure Tolerance:
If a polling request fails, the client can simply retry without significantly disrupting the system.
Cons:
Inefficient Use of Resources:
The client keeps sending requests even when there are no updates, wasting bandwidth and computational resources.
Constant polling by multiple clients can strain the server, especially under heavy traffic, as it has to handle frequent requests.
As the number of clients increases, the polling behavior can overload servers, making it harder to scale effectively.
Delayed Updates:
Not ideal for time-sensitive applications, as the client only checks for updates at set intervals, leading to potential delays in receiving new information.
Limited Control Over Timing:
If polling is too frequent, it can waste resources, but if it's too infrequent, the client may experience delayed updates. Balancing this can be tricky.
Long Polling
Long polling is a communication technique where a client sends a request to the server and keeps the connection open until the server has new data to send. Unlike short polling, where the server responds immediately, even if there's no update, long polling holds the request open, responding only when there’s new information. Once the client receives a response, it processes the data and usually sends another long-polling request to continue the cycle. This approach reduces unnecessary server load and provides more timely updates than short polling, making it a better fit for near real-time applications. However, long polling can still cause performance issues under high traffic since maintaining many open connections can strain server resources.
Pros:
Reduced Server Load:
Unlike short polling, long polling only responds when there’s new data, reducing the number of unnecessary requests and overall server load.
Simplicity:
Easier to implement compared to more advanced techniques like WebSockets or Server-Sent Events, while still offering near real-time updates.
Cons:
Scalability Challenges:
The server needs to keep many connections open, which can strain resources like memory and connection pools, especially under heavy traffic.
With many clients polling simultaneously, the server might struggle to handle a large number of open or waiting connections, making scaling more difficult.
Timeout and Latency Issues:
Long polling can face timeout issues if the connection is held open for too long, or if the server is slow to respond, which can result in delayed updates or forced retries.
Not Fully Real-Time:
Although it’s close, long polling is not truly real-time. It has inherent latency between when a response is received and when the client sends the next request.
Push
The push model is a communication pattern where the server actively sends updates or data to the client as soon as they are available, without the client having to request them. Unlike the request-response model, where the client initiates communication, the push model allows the server to "push" information to the client, enabling real-time updates. This model is commonly used in applications that require instant notifications or live data, such as chat applications, live sports updates, and stock market tickers. The push model improves responsiveness and reduces latency but can require more sophisticated server-side infrastructure to manage active connections and deliver updates efficiently.
Pros:
Real-Time Updates:
The push model allows instant delivery of data to clients as soon as it’s available, making it ideal for real-time applications like messaging, live events, or stock tracking.
Reduced Latency:
Since the server proactively sends updates, clients don’t need to repeatedly check for new information, which reduces latency between data generation and client awareness.
Better Resource Utilization:
Instead of clients constantly polling the server, push models reduce unnecessary traffic by sending data only when required, resulting in more efficient use of network and server resources.
Cons:
Complex Server Infrastructure:
Requires more advanced server infrastructure to manage persistent connections (e.g., WebSockets), handle large numbers of concurrent clients, and ensure reliable delivery of messages.
Scalability Challenges:
Scaling a push system for thousands or millions of clients can be difficult, as maintaining open connections with numerous clients consumes memory and processing power.
Network Dependency:
Push models depend heavily on the stability and quality of network connections. Poor connectivity can lead to delayed or lost updates.
Increased Server Load:
While efficient for reducing client requests, managing and maintaining constant or open connections to numerous clients can put a significant load on the server.
Client-Side Complexity:
The client needs to handle incoming updates asynchronously, which may complicate the client’s code, especially in systems with complex logic for data updates or notifications.
Potential Overhead for Low-Frequency Events:
In cases where updates are rare, keeping connections open or having the server push data might be inefficient compared to a pull-based model where the client requests information as needed.
Server-Sent Events
Server-Sent Events (SSE) is a technology that enables a server to push real-time updates to web clients over a single, persistent HTTP connection. Unlike traditional request-response models, where the client must continually request new information, SSE allows the server to send updates automatically whenever new data is available. This is particularly useful for applications that require continuous data streams, such as live news feeds, social media updates, or stock price changes. SSE is built on top of standard HTTP, making it easier to implement. It is widely supported in modern web browsers and utilizes a simple text-based format for sending messages, which can include event IDs and retry information, allowing for automatic reconnections if the connection is lost. While SSE is a powerful tool for enabling real-time communication, it does have some limitations, such as being limited to one-way communication (from server to client).
Pros:
Simplicity:
SSE is straightforward to implement since it uses standard HTTP and does not require complex protocols or libraries, making it easy to set up for developers.
Automatic Reconnection:
SSE supports automatic reconnections, allowing clients to seamlessly reconnect and continue receiving events without needing to manage this process manually.
Efficient Data Transfer:
By maintaining a single persistent connection, SSE reduces the overhead associated with establishing multiple connections, resulting in more efficient use of network resources.
Built-in Support for Event IDs:
SSE can send event IDs with messages, allowing clients to track and resume from the last event received, providing resilience in case of connection interruptions.
Cons:
Limited to One-Way Communication:
SSE only allows data to flow from the server to the client, which may not be suitable for applications requiring two-way communication without additional protocols.
Browser Compatibility:
While SSE is supported by most modern browsers, it does not work on Internet Explorer, which may limit its use in certain environments where legacy browsers are still in use.
Potential for Connection Issues:
Long-lived connections can be affected by network issues, proxies, or firewalls, potentially leading to connection drops or latency in message delivery.
Server Resource Management:
Keeping multiple open connections can put a strain on server resources, especially in high-traffic scenarios, as each connection consumes memory and processing power.
Publish/Subscribe
The publish-subscribe (pub-sub) model is a messaging pattern that facilitates communication between multiple components in a decoupled manner. In this model, publishers send messages (or events) to specific topics without needing to know which subscribers will receive them. Subscribers express interest in certain topics and receive updates whenever new messages are published on those topics. This decoupling allows for greater scalability and flexibility, as publishers and subscribers can evolve independently; new subscribers can be added without altering the publisher's code.
Pros:
Decoupled Components:
Publishers and subscribers operate independently, allowing for easier scalability and modifications without affecting other components.
Scalability:
The model can efficiently handle a large number of subscribers, making it suitable for applications with varying loads and user bases.
New subscribers can be added without changing the publisher's code or logic, allowing for easy integration of new features or services.
Asynchronous Communication:
Promotes non-blocking communication, enabling publishers to send messages without waiting for subscribers to process them, improving overall system responsiveness.
Cons:
Complexity:
Implementing a pub-sub architecture can introduce complexity, particularly in managing subscriptions, message routing, and ensuring reliable message delivery.
Message Loss:
If subscribers are offline or unavailable at the time of message publication, they may miss important updates unless the system implements persistent messaging or storage.
Latency:
As messages are routed through a broker or intermediary, there may be some latency involved in delivering messages from publishers to subscribers, especially in high-traffic scenarios.
Testing and Debugging:
The decoupled nature of the model can make testing and debugging more challenging, as tracking the flow of messages and interactions between components may require more effort.