System Design

Networking Essentials for Building Reliable Systems Networks connect services and apps across rooms, clouds, and devices. A reliable system depends on clear, predictable communication. Small delays or failed calls can ripple through, so it helps to plan how services talk to each other. Core concepts Latency, jitter, and throughput describe how fast data moves. Keep requests simple and consider compression when helpful. Timeouts matter. Set sensible client and server timeouts to avoid waiting forever. Retries should be cautious. Use exponential backoff and cap the total time spent retrying. Idempotence means repeated requests have the same effect. This helps when networks slip or retries happen. Rate limits protect services from overload and help lines stay responsive. Reliability patterns ...

Middleware Patterns for Scalable Systems Middleware patterns help teams scale systems by decoupling components, smoothing load, and reducing the impact of failures. The goal is to keep services responsive as traffic grows and problems arise. This guide highlights practical patterns that work with modern stacks: queues, backpressure, idempotency, circuit breakers, event-driven flows, and strong observability. Message queues and brokered patterns A message broker lets producers publish work without waiting for each task to finish. Workers pull work later, which absorbs bursts and improves resilience. Benefits include durable storage, replay capability, and built-in retries. Trade-offs include eventual consistency and the need for careful ordering. Tips: choose delivery semantics, design idempotent consumers, and use dead-letter queues for stubborn failures. Example: when a user signs up, publish a welcome event; downstream services handle emails and analytics. ...

Middleware Architecture: Patterns and Practices Middleware sits between applications and services. It handles requests, events, and data flows, helping teams evolve components independently. A thoughtful mix of patterns improves latency, resilience, and security while reducing tight coupling. Key patterns include: API gateway: external entry point for clients, with routing, authentication, rate limiting, and request aggregation. Service mesh: internal service-to-service communication using mTLS, retries, circuit breakers, and centralized observability. Message broker: asynchronous queues that decouple producers and consumers, providing durable storage and backpressure. Event streaming: topics and streams for real-time data, enabling scalable, event-driven processing with durable subscriptions. Orchestration vs choreography: choosing a control model for workflows—centralized decision making or event-driven coordination. Practical practices to apply: ...

Database Design for High Availability High availability means the database stays up and responsive even when parts of the system fail. For most apps, data access is central, so a well‑designed database layer is essential. The goal is to minimize downtime, keep data intact, and respond quickly to problems. Redundancy and replication are the core ideas. Run multiple data copies on different nodes. Use a primary that handles writes and one or more replicas for reads. In many setups, automatic failover is enabled so a replica becomes primary if the old primary dies. Choose the replication mode carefully: synchronous replication waits for a replica to acknowledge writes, which strengthens durability but adds latency; asynchronous replication reduces latency but risks data loss on failure. ...

Middleware Architecture for Scalable Systems Middleware acts as the connective tissue between services. It handles requests, coordinates tasks, and moves data across a system. A well designed middleware layer helps systems scale, boosts reliability, and reduces coupling between components. By keeping the logic in well defined layers, teams can grow capacity without breaking behavior. Key design ideas are simple but powerful. Keep services stateless whenever possible. Make processing idempotent so retries do not cause duplicates. Build clear contracts with your APIs and messages. Let the middleware handle orchestration, retries, and backpressure, so core services stay focused on their business rules. Finally, measure what matters with good observability to detect problems early. ...

API-First Design Building Flexible Systems API-first design means we start by defining the interfaces that other parts of the system will rely on. By agreeing on contracts early, teams can work in parallel, test interactions sooner, and keep options open for different implementations later. In practice, this approach fits web services, internal microservices, and partner integrations. It helps avoid late changes that break clients and raises the likelihood of reusable, stable components. ...

Real-Time Analytics: Streaming Data Pipelines in Practice Real-time analytics means turning data into insights as soon as it arrives. It helps teams spot problems, respond to customers, and refine operations. A streaming data pipeline typically has three layers: ingestion, processing, and serving. The goal is low latency without sacrificing correctness. Designing a streaming pipeline Ingest and transport Choose a durable transport like Kafka or a similar message bus. Plan for back pressure, replayability, and idempotent reads. Consider schema management so downstream systems stay aligned. ...

Microservices Architecture Design and Tradeoffs Microservices break a software system into small, independent services. Each service owns a specific capability and can be built, tested, and deployed separately. This approach can speed up delivery and help teams work in parallel. It also adds complexity: more moving parts, distributed decisions, and new failure modes. The challenge is to gain speed without losing reliability. Start with clear domain boundaries. Use domain-driven design to group related ideas and avoid many tiny services. A practical rule is to align services with business capabilities and with who owns the data. If two parts of the business share data, decide who writes and who reads, and how to keep data in sync. ...

Web Servers Architecture Performance and Security Web servers are a core part of any online service. A solid design helps pages load quickly and pages stay safe from common threats. The goal is to minimize latency while keeping data protected, even as traffic grows. A clear view of layers and responsibilities makes it easier to tune performance without sacrificing security. Architecture overview Front door: a load balancer or content delivery network (CDN) sits at the edge to absorb traffic and route requests. Application tier: one or more app servers run the business logic, often behind a reverse proxy such as Nginx or HAProxy. Data tier: databases and caches follow the principle of separation to avoid bottlenecks. This structure lets you scale parts independently. For example, add more app servers during a surge, or expand the cache layer to reduce database load. Keeping TLS termination at the edge speeds up request handling but requires disciplined certificate management and secure headers everywhere. ...

Hardware-Software Co-Design for Performance Hardware-software co-design means building software and hardware in tandem to meet clear goals. It helps teams reach peak performance and better energy use. Start from the workload itself and the targets, not from a single component. By aligning on metrics early, you can spot bottlenecks and choose the right design split. Principles Start with workload and performance targets Gather data across layers: compiler, OS, and hardware counters Model trade-offs between speed, power, and silicon area Use clear abstractions to keep interfaces stable while exploring options Create fast feedback loops that show the impact of changes Optimize data movement and the memory hierarchy Real-world systems benefit when firmware, drivers, and the OS scheduler are part of the discussion. Data movement often dominates latency; moving computation closer to data can unlock big gains without sprawling hardware. ...