41 docs tagged with "architecture"

An exhaustive architectural study guide synthesizing 60 core system design topics across 7 architectural domains with 60 high-impact scenario breakdowns.

A comprehensive guide to advanced, scenario-based, and practical Spring Boot interview questions.

Complete architectural overview of Apache Kafka — origins, the M×N integration problem, the 4 pillars of Kafka's extreme speed (sequential I/O, page cache, zero-copy, batching), message anatomy, and the dumb broker / smart consumer paradigm.

Core principles of distributed systems architecture including CAP theorem, consistency models, availability, partition tolerance, and key trade-offs every engineer must understand.

Deep-dive into the Backend for Frontend pattern — aggregation mechanics, parallel fan-out, GraphQL BFF, OAuth Token Handler, response shaping, caching strategies, observability, and production failure modes for senior Spring Boot engineers.

Full-depth guide to CAP theorem and PACELC — the GitHub 2018 incident, why 'choose 2 of 3' is misleading, PACELC's EL trade-off (latency vs consistency during normal operation), database classification matrix, conflict resolution costs, and Brewer's 12-year correction.

Martin demolishes the false distinction between "design" and "architecture", then presents the singular goal of software architecture: minimizing the human effort required to build and maintain a system.

Components are the units of deployment — JARs, DLLs, shared libraries. Martin traces their history from early relocatable binaries to modern package managers, establishing the foundation for the component cohesion and coupling principles that follow.

Three principles govern which classes belong in which component: REP (Reuse/Release Equivalence), CCP (Common Closure), and CRP (Common Reuse). They form a tension triangle — understanding the trade-offs helps you choose the right grouping strategy for your project's stage.

Three principles govern dependencies between components: ADP (no dependency cycles), SDP (depend in the direction of stability), and SAP (stable components should be abstract). These principles shape large-scale architecture and determine which components can evolve independently.

Architecture is the shape of a system — the decisions that divide a system into components, arrange those components, and specify how they communicate. A good architect maximizes the number of decisions NOT made, keeping options open as long as possible.

A good architecture supports independent developability, deployability, and operability. Martin explains the use-case, operational, and deployment decoupling modes and why the monolith-vs-microservices decision can and should be deferred.

Every software system delivers two values — behavior and structure. Martin argues that structure (architecture) is almost always the more important of the two, yet teams chronically neglect it. Learn to use Eisenhower's Matrix to fight for architectural health.

The three programming paradigms — structured, object-oriented, and functional — each impose discipline by removing capabilities from the programmer. Martin shows how each paradigm maps directly to a fundamental concern of software architecture.

Functional programming's core discipline — immutability — eliminates entire classes of concurrency bugs. Learn how immutability, segregation of mutability, and event sourcing shape modern system design.

A software artifact should be open for extension but closed for modification. OCP is the architectural goal that drives Clean Architecture: protect high-level policy from changes in low-level details by controlling the direction of dependencies.

Boundaries separate software elements and restrict knowledge between them. This section covers drawing lines between components, anatomy of boundaries, policy levels, entities, and use cases — the core of Clean Architecture.

The Main Component is the dirtiest place in the system — where everything is wired together. Services are not architecturally special. Tests are a component too — they must respect boundaries or become a maintenance burden.

Databases, web frameworks, and all third-party frameworks are details — implementation choices that should be deferred and hidden behind boundaries. Learn why treating them as the center of your architecture leads to rigidity and how to protect your business rules from them.

Comprehensive guide on Command Query Responsibility Segregation (CQRS) and Event Sourcing, detailing architecture, implementation patterns, comparisons with alternatives, and deep dives for senior engineers.

A comprehensive architectural overview of Elasticsearch and the ELK Stack — the book index analogy, inverted index mechanics, BM25 relevance scoring, typo tolerance, aggregations, and the dual-storage pattern.

A comprehensive guide covering real technical interview questions and answers from an EPAM Java Developer interview for a candidate with 3 to 7 years of experience.

A complete, end-to-end cloud production architecture guide on AWS — covering Multi-AZ VPCs, ALB, EC2 Auto Scaling, RDS Multi-AZ, S3 Event Pipelines, Lambda, SQS, Secrets Manager, and KMS.

A comprehensive guide to the 7 core coding laws that separate senior software engineers from junior developers — covering guard clauses, domain naming, anti-corruption boundaries, algebraic states, functional cores, structured error contracts, and atomic pull requests.

Scenario-based interview questions for Java lead developers on scaling, optimization, and architecture.

A comprehensive guide to JPA and Hibernate persistence methods — entity lifecycle states, persist vs save vs merge vs update, primary key strategies, performance implications, and production deep dives for senior engineers.

Comprehensive guide comparing Apache Kafka's legacy ZooKeeper architecture with the modern KRaft (Kafka Raft) metadata mode — covering internal mechanics, failure scenarios, Strimzi Kubernetes deployment, migration strategies, and production deep dives for senior engineers.

Kubernetes architecture for beginners — control plane components, worker nodes, the API server, etcd, scheduler, kubelet, kube-proxy, and how they work together to run containerised workloads.

Deep dive into the Microservice Chassis pattern — building a Spring Boot auto-configuration starter that standardizes logging, tracing, error handling, health checks, security headers, and Resilience4j defaults across all services.

Exhaustive analysis of the 5 lock types: version column, SELECT FOR UPDATE, advisory locks, lock columns, and Redis locks; physical lifespans, failure modes, and why optimistic retry storms crush high-contention ticketing systems.

Payment Hub vs Payment Gateway vs Payment Factory — architecture patterns, orchestration, channel abstraction, and Java/Spring engineering design for centralised payment processing.

Deep dive into landmark real-world system designs — Shopify Pod cell-based architecture, Twitter hybrid timeline fan-out, Netflix 7-year microservices evolution, Facebook TAO social graph datastore, Uber DOMA & Schemaless, Slack Flannel edge caching, and LinkedIn Kafka origin.

A senior deep dive into Redis advanced data types — Bitmaps, HyperLogLog, Geospatial, Streams, Sorted Sets, and Bloom Filters — with internals, memory math, production patterns, and when to use each.

Deep dive into Redis Lua script execution: solving data corruption between batch imports and point updates, production-grade distributed lock release, Redis TIME as a monotonic cluster clock, cooperative pod handovers, and the network RTT math.

Redis architecture internals, single-threaded model, I/O multiplexing, and use case decision guide for senior engineers.

A comprehensive guide comparing reverse proxies, load balancers, and API gateways — their differences, internal mechanics, architectural roles, alternatives, and production deep dives for senior engineers.

Advanced Serverless Integration Patterns for DVA-C02 and Senior Engineering roles. Orchestration vs Choreography, Saga Pattern, and Strangler Fig.

Strategic guide to microservice decomposition, applying Domain-Driven Design (DDD), high cohesion, loose coupling, and avoiding the distributed monolith.

A comprehensive knowledge base for system design patterns, architectural principles, scalability strategies, and interview preparation for software engineers.

A comprehensive guide comparing Virtual Machines, Docker, and Kubernetes — starting with ELI5 analogies for beginners and ending with kernel-level deep dives for seniors.

Comprehensive breakdown of failure modes when applications scale from a single server to millions of users — session traps, connection pool exhaustion, replication lag, cache stampede, synchronous HTTP bottlenecks, AI vector scaling, and the architectural judgment rubric based on JavaScript Mastery fundamentals.