Latest System Design Articles

The CAP theorem describes a fundamental constraint in distributed data systems : during a network partition, a system cannot guarantee both strong consistency and full availability for every request.

Distributed systems often store the same data on multiple nodes to improve availability, fault tolerance, read scalability, and geographic performance . Replication introduces a difficult question: when one copy changes, how quickly must every other copy reflect that update?

A distributed system is a group of independent computers that communicate over a network and cooperate to provide one service. Applications use distributed architectures to handle more traffic, improve availability, process data closer to users, and continue operating when individual components fail

API performance is not limited to producing the lowest possible response time. A production API must remain responsive under normal traffic, sudden spikes, slow downstream services, partial infrastructure failures, and unexpected client behavior.

APIs rarely remain unchanged. New product requirements introduce additional fields, updated validation rules, improved resource models, and different response structures. However, mobile applications, third-party integrations, internal services, and customer systems may continue depending on an olde

API pagination , filtering , and sorting control how clients retrieve large collections without loading every record at once. These features directly affect database performance, response latency, consistency, cache behavior, and the long-term stability of an API contract.

API rate limiting controls how many requests a client may send during a defined period. Throttling controls how the system responds when traffic exceeds its allowed rate or the infrastructure approaches capacity.

API authentication verifies who or what is making a request. API authorization determines what that authenticated identity is allowed to do. They are related security layers, but they solve different problems and should be designed independently.

REST remains the most common architectural style for public APIs , mobile backends, SaaS platforms, and microservices. While building a REST API is relatively simple, designing one that remains intuitive, backward compatible, scalable, and maintainable for years requires much more than choosing HTTP

APIs are the contracts that allow backend systems, frontend applications, mobile apps, third-party integrations, and internal services to communicate. A good API hides implementation details while exposing a stable way to request data, trigger actions, and exchange information between systems.

Amazon ECS production best practices are less about memorizing service options and more about designing reliable container workloads. A production ECS service needs safe deployments, private networking, least-privilege IAM, useful observability, correct scaling signals, and predictable failure handl

Amazon ECS deployment strategies and auto scaling determine how containerized services change over time. Deployments control how new versions replace old versions, while auto scaling controls how many tasks run as traffic, resource usage, or queue backlog changes.

Amazon ECS is often used for two common production workload types: HTTP APIs and background workers . Both run as containers, but they have different traffic patterns, scaling signals, failure modes, and operational requirements.

Amazon ECS networking is the architecture layer that controls how containers receive traffic, communicate with dependencies, stay isolated, and remain reachable during deployments. For production ECS services, networking is usually where reliability and security problems appear first.

AWS Fargate and the EC2 launch type are two ways to run Amazon ECS tasks. Both use the same ECS concepts: task definitions, tasks, services, deployments, IAM roles, networking, and load balancers. The main difference is who owns the compute layer.

Amazon ECS task definitions , tasks , services , and deployments are the core runtime model behind ECS. A task definition describes how a container should run, a task is a running copy of that definition, a service keeps tasks alive, and a deployment replaces one version with another.

Amazon Elastic Container Service , usually called Amazon ECS , is a managed container orchestration service for running containerized applications on AWS . It helps turn Docker images into production workloads by handling task scheduling, service stability, deployments, networking integration, scali

AWS Lambda execution lifecycle explains what happens from the moment a Lambda function is invoked until the function finishes and the execution environment is either reused or removed. Understanding this lifecycle helps explain cold starts , warm starts , global variable reuse , SDK client reuse , d

AWS Lambda cold starts are one of the most discussed topics in serverless architecture. A cold start happens when AWS Lambda needs to create a new execution environment before running your function. This extra initialization time can increase latency, especially for user-facing APIs.

AWS Lambda concurrency is one of the most important concepts to understand before running serverless applications in production. Lambda can scale quickly, but that does not mean every downstream system can handle unlimited parallel requests.