Best of Microservices — 2025

  1. 1
    Article
    Avatar of systemdesignnewsSystem Design Newsletter·33w

    7 Best Practices for API Design 🔥

    Seven essential practices for designing robust APIs: REST fundamentals for organizing data resources, proper error handling with clear status codes, API versioning for backward compatibility, rate limiting to prevent abuse, pagination techniques (offset vs cursor) for large datasets, idempotency to avoid duplicate processing, and filtering/sorting for efficient data retrieval. Each practice includes implementation details and trade-offs to consider.

  2. 2
    Article
    Avatar of systemdesigncodexSystem Design Codex·1y

    15 Must-Know Elements of System Design

    A well-designed system leverages various architectural elements to manage distributed systems, enhance scalability, service management, networking efficiency, and data storage. Key components include distributed message queues, caching, task schedulers, content delivery networks, consistent hashing, service discovery, DNS, load balancers, API gateways, databases, object storage, sharding, replication, and monitoring tools. These elements help improve performance, manage traffic, and ensure system resilience and fault tolerance.

  3. 3
    Article
    Avatar of techworld-with-milanTech World With Milan·1y

    How does Netflix manage to show you a movie without interruptions?

    Netflix delivers buffer-free streaming through a sophisticated distributed systems architecture. The platform uses Amazon Web Services for managing control-plane operations and its custom Content Delivery Network, Open Connect, to handle data-plane operations. Key components include hundreds of microservices, a two-tier CDN deployment, adaptive bitrate streaming, and advanced resilience engineering practices. This setup allows for smooth content delivery and high availability, even under heavy load.

  4. 4
    Article
    Avatar of bytebytegoByteByteGo·46w

    Shopify Tech Stack

    Shopify processes massive scale with 173 billion requests on Black Friday using a tech stack built on Ruby on Rails, React, MySQL, and Kafka. The platform uses a modular monolith architecture with strict component boundaries, database sharding through isolated pods, and extensive tooling investments including YJIT compiler and Sorbet type checker. The infrastructure handles 284 million requests per minute at peak, 66 million Kafka messages per second, and processes 216 million ML embeddings daily for semantic search, all while maintaining developer productivity through comprehensive CI/CD pipelines and observability tools.

  5. 5
    Article
    Avatar of systemdesigncodexSystem Design Codex·1y

    8 Must-Know Distributed System Design Patterns

    Distributed systems are crucial for scalability, fault tolerance, and high availability but pose challenges such as state management, failure handling, and communication. Key design patterns like Ambassador Pattern, Circuit Breaker Pattern, CQRS, Sharding, Sidecar Pattern, Pub/Sub Pattern, Leader Election, and Event Sourcing help address these challenges by offloading tasks, preventing cascading failures, separating read/writes, partitioning data, decoupling concerns, enabling async communication, managing shared resources, and capturing state changes as events.

  6. 6
    Article
    Avatar of javarevisitedJavarevisited·25w

    6 Must-Read Books for Backend Developers in 2026

    A curated list of six essential books for backend developers covering software architecture, design patterns, distributed systems, microservices, and data engineering. The recommendations include classics like "Designing Data-Intensive Applications" by Martin Kleppmann, "The Pragmatic Programmer," and "Building Microservices" by Sam Newman, focusing on fundamental principles that remain relevant despite changing frameworks and tools. Each book addresses critical aspects of backend development from API design and scalability to data pipelines and architectural trade-offs.

  7. 7
    Article
    Avatar of javarevisitedJavarevisited·50w

    Microservices Architecture

    Microservices architecture is advocated as an alternative to the monolithic style, offering scalability, flexibility, and independent deployment. It involves breaking down an application into multiple independent services, minimizing tight coupling and easing fault isolation. Considerations for adopting microservices include scalability needs, team expertise, and project complexity, while weighing their advantages and potential high setup costs and management complexity.

  8. 8
    Article
    Avatar of threedotslabsThree Dots Labs·47w

    Synchronous vs Asynchronous Architecture

    Explores the trade-offs between synchronous and asynchronous architecture patterns for backend systems. Synchronous approaches offer simplicity and predictability but can create bottlenecks and tight coupling. Asynchronous patterns using message queues and events provide better scalability and resilience but introduce complexity in debugging, testing, and error handling. The discussion covers common anti-patterns like distributed monoliths, naive background processing approaches, and improper message ordering. Emphasizes that good design matters more than technology choice, and recommends starting with synchronous architecture by default, then adopting async patterns where they provide clear benefits like handling external API failures or traffic spikes.

  9. 9
    Article
    Avatar of itnextITNEXT·1y

    Programming and architectural paradigms

    Different programming paradigms, such as object-oriented, functional, and procedural programming, each have their unique strengths and are suited to particular tasks. These paradigms are also reflected in various distributed system architectures like SOA, microservices, and data mesh. The choice of paradigm and technology depends on specific project requirements and the context in which they're applied.

  10. 10
    Video
    Avatar of letsgetrustyLet's Get Rusty·25w

    Rust intern saved TikTok $300K

    A TikTok intern rewrote CPU-intensive payment service endpoints from Go to Rust, reducing average latency by 30%, P99 latency by 76%, and cutting compute costs by 50%—saving $300K annually. The migration used a gradual rollout strategy with separate Rust clusters under the same service name, avoiding upstream code changes. Key challenges included adapting Go's zero values to Rust's Option enum and optimizing memory allocations. The main lesson: Rust delivers performance gains at the cost of developer productivity, making it ideal for high-usage, stable components where economies of scale justify the rewrite effort.

  11. 11
    Article
    Avatar of bytebytegoByteByteGo·20w

    How Reddit Migrated Comments Functionality from Python to Go

    Reddit migrated their comments functionality from a legacy Python monolith to a Go microservice, handling their largest dataset and highest write throughput. The migration used "tap compare" for read operations and "sister datastores" for writes, allowing validation with real traffic while maintaining zero risk. Key challenges included cross-language serialization issues, database access pattern differences, and race conditions in verification. The migration succeeded with zero user disruption and delivered an unexpected bonus: p99 latency was cut in half, dropping from occasional 15-second spikes to consistently under 100 milliseconds.

  12. 12
    Article
    Avatar of containersolutionContainer Solutions·1y

    Why I'm No Longer Talking to Architects About Microservices

    Microservices discussions are often unproductive due to unclear definitions, abstract conversations disconnected from business goals, and lack of necessary organizational changes. Instead of focusing on microservices as an end, it's crucial to address concrete challenges like faster feature deployment, reducing system coupling, and scaling efficiently. Effective microservices implementation requires cross-functional, autonomous teams and a mature DevOps culture. Organizational change is harder than software change and essential for microservices' success.

  13. 13
    Article
    Avatar of devtoDEV·1y

    System Design for DevOps Engineers

    This guide dives into system design, covering topics like communication protocols, CI/CD, architecture patterns, databases, caches, microservices, payment systems, and developer productivity tools with a focus on DevOps and security. It compares REST and GraphQL, explains how gRPC works, discusses webhooks, outlines common strategies to improve API performance, and reviews the evolution of HTTP protocols from 1.0 to HTTP/3.0. The post emphasizes the importance of efficient and safe API design and explores various caching strategies and best practices for microservice architecture.

  14. 14
    Article
    Avatar of systemdesigncodexSystem Design Codex·41w

    Must-Know Event-Driven Architectural Patterns

    Seven essential event-driven architectural patterns are explored: Competing Consumer for scaling workloads, Asynchronous Task Execution for decoupled processing, Consume and Project for read-optimized views, Saga for distributed transactions, Event Aggregation for combining events, Event Sourcing for complete audit trails, and Transactional Outbox for atomic database and event operations. Each pattern addresses specific challenges in building resilient, scalable event-driven systems with practical examples and implementation considerations.

  15. 15
    Article
    Avatar of microservicesioMicroservices.io·48w

    Authentication and authorization in a microservice architecture: Part 2 - Authentication

    Implementing authentication in a microservice architecture can be complex and error-prone. The API Gateway serves as a central point for handling authentication, delegating the process to an identity and access management service for enhanced security. OAuth 2.0 and OpenID Connect protocols are commonly used, with tokens like JWTs facilitating secure communication between client requests and backend services.

  16. 16
    Article
    Avatar of milanjovanovicMilan Jovanović·1y

    Understanding Microservices: Core Concepts and Benefits

    Microservices are independently deployable services centered around business domains, offering flexibility, adaptability, and targeted scaling. They enable parallel development, technology diversity, and organizational alignment but introduce challenges like distributed system complexity, operational overhead, and data consistency issues. Effective microservices adoption often starts small and evolves over time, focusing on the most beneficial parts of the existing architecture.

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    Video
    Avatar of bytebytegoByteByteGo·44w

    7 System Design Concepts Explained in 10 Minutes

    Seven fundamental concepts power reliable distributed systems: CAP theorem forces choosing between consistency and availability during network partitions, eventual consistency enables high performance through delayed convergence, load balancers distribute traffic using Layer 4 or Layer 7 strategies, consistent hashing minimizes data movement when scaling nodes, circuit breakers prevent cascade failures by blocking requests to failing services, rate limiting protects against overload using token bucket or sliding window algorithms, and monitoring provides visibility through metrics, logs, traces, and events to maintain system health.

  18. 18
    Article
    Avatar of halodocHalodoc·1y

    Log Standardization

    Debugging in microservices is challenging without structured logging. This post explores logging strategies for HTTP requests, Kafka events, and concurrency in Java, Golang, and Python. It addresses context loss in Java’s multi-threading by introducing MDCAwareCompletableFuture and MDCAwareExecutorService, ensuring traceability in concurrent systems. A structured logging approach with Request ID, Transaction ID, and Parent Transaction ID is recommended for better traceability and debugging efficiency.

  19. 19
    Video
    Avatar of techworldwithnanaTechWorld with Nana·33w

    Learn Docker in 2025 - Complete Roadmap Beginner to Pro

    A comprehensive Docker learning roadmap that takes beginners through containerization fundamentals, from understanding the 'it works on my machine' problem to advanced production practices. Covers essential concepts including Docker images, containers, Dockerfile creation, Docker Compose for multi-container applications, networking, volumes for data persistence, and production best practices like security scanning and multi-stage builds. Also introduces container orchestration with Kubernetes as the natural next step for scaling containerized applications.

  20. 20
    Video
    Avatar of codinggopherThe Coding Gopher·1y

    99% of Developers Don't Get RPCs

    RPC, or Remote Procedure Call, is a critical communication protocol in distributed systems, allowing for code execution on remote systems as if they were local. This method abstracts networking complexities, making it ideal for microservices and internal systems that require efficiency and strict contracts. Unlike REST, which uses HTTP verbs and is better for external APIs, RPC offers granular function-level control, better performance with binary formats like Protobuf, and advanced capabilities like streaming and retries. gRPC enhances these benefits with efficient communication and built-in logging and metrics, making it a superior choice for modern backend architectures.

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    Video
    Avatar of codeheadCodeHead·50w

    Wait, Netflix is... Java?!

    Netflix leverages Java for its backend, despite its reputation as outdated or verbose. Java's performance, scalability, and mature ecosystem make it a reliable choice for Netflix's microservices. With tools like Spring Boot, Netflix efficiently manages its vast streaming services. The use of Java demonstrates the company's preference for dependable technology over trends.

  22. 22
    Article
    Avatar of nordicapisNordic APIs·17w

    A Software Architect’s Guide to API-First Strategy

    API-first strategy treats APIs as foundational contracts defined before code implementation, enabling parallel development, improved governance, and better security. The approach requires design-first methodology using OpenAPI specifications, RESTful principles, strategic versioning, and robust authentication/authorization. Key infrastructure includes API gateways for traffic management, IAM systems for access control, observability tools for monitoring, and emerging AI gateways for LLM interactions. Security must be integrated throughout the lifecycle with proper authentication (OAuth 2.0, OIDC), fine-grained authorization (RBAC, ABAC), input validation, rate limiting, and TLS encryption. Industries like fintech, retail, and healthcare demonstrate success through improved interoperability, faster innovation, and new revenue streams. The strategy is essential for AI-readiness, as autonomous agents require well-documented, discoverable APIs with clear semantic contracts.

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    Video
    Avatar of codeheadCodeHead·26w

    99% Of Devs Don't Understand Spring Boot

    Spring Boot simplifies Java development by automating configuration and removing XML complexity, making it production-ready out of the box. Major companies like Netflix, Amazon, and Spotify rely on it to power massive-scale systems with billions of API calls daily. Modern versions support Kotlin, reactive programming through Spring WebFlux, and cloud-native deployments, making it more relevant than its outdated reputation suggests. The framework's strength lies in its reliability, comprehensive ecosystem, and ability to handle enterprise-scale workloads efficiently.

  24. 24
    Article
    Avatar of threedotslabsThree Dots Labs·46w

    Event Driven Architecture: The Hard Parts

    Event-driven architecture offers powerful benefits like scaling and decoupling but comes with significant challenges. Key issues include debugging async systems without proper observability, handling eventual consistency, preventing message loss through the outbox pattern, and designing events that avoid tight coupling. The architecture requires idempotent handlers to manage duplicate message delivery, proper dead letter queue handling, and careful consideration of message ordering. While EDA can solve real problems, it adds complexity that isn't always justified - sometimes synchronous systems or monoliths are better choices.

  25. 25
    Article
    Avatar of bytebytegoByteByteGo·41w

    How Tinder’s API Gateway Handles A Billion Swipes Per Day

    Tinder built TAG (Tinder API Gateway), a custom JVM-based framework on Spring Cloud Gateway, to handle over a billion swipes daily. The solution replaced fragmented third-party gateways with a unified framework that allows each team to deploy independent gateway instances. TAG features configuration-driven routing, global filters for geolocation and session management, custom middleware support, and integration with Envoy service mesh. The system processes requests through a defined pipeline including reverse geo IP lookup, request scanning, session validation, and configurable pre/post filters, enabling faster development cycles while maintaining security and performance at scale.

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