ArchGen Examples

Design a high-level system architecture for a ride-sharing application like Uber. The diagram must visually distinguish the Rider App and Driver App ecosystems. Central to the design is the Dispatch Service (DISCO) communicating with a Geospatial Service powered by Redis Geohash/Google S2 for real-time location tracking. Include a dedicated WebSocket Gateway for handling continuous GPS streams from drivers, separate from the standard API Gateway for transactional requests. Show the data flow for 'Ride Matching' involving a Supply Service and Demand Service, backed by a distributed database partitioned by region.

System architecture for a scalable photo-sharing platform like Instagram. The diagram should clearly separate the Media Storage flow (uploading images to AWS S3 served via CloudFront CDN) from the Metadata flow (storing user/post info in a sharded PostgreSQL/Cassandra cluster). Highlight the 'Feed Generation Service' using a Redis Cluster for caching user timelines. Include a 'Fan-out on Write' mechanism for delivering posts to followers' feeds asynchronously via a Message Queue (RabbitMQ/Kafka).

Kubernetes cluster architecture diagram illustrating the distinction between the Control Plane and Worker Nodes. The Control Plane must include the API Server, etcd key-value store, Kube-Scheduler, and Controller Manager. Depict multiple Worker Nodes, each containing a Kubelet, Kube-proxy, and Container Runtime hosting multiple Pods. Show the networking layer with an Ingress Controller managing external access and a CNI plugin handling pod-to-pod communication.

Microservices architecture diagram based on the Spring PetClinic reference implementation. The design should feature an API Gateway acting as the entry point, connected to a Service Discovery Server (Eureka) and a Centralized Config Server backed by Git. Break down the backend into distinct domain services: Customers Service, Vets Service, and Visits Service, each owning its separate database schema. Include the Tracing infrastructure (Zipkin/Sleuth) and Circuit Breaker Dashboard (Hystrix/Resilience4j) for observability.

Healthcare interoperability architecture based on HL7 FHIR standards. Visualize the relationship diagram between core resources: Patient, Encounter, Practitioner, and Observation. Show an external 'Patient Portal App' connecting to the FHIR Server via a SMART on FHIR authorization layer (OAuth2). The FHIR Server should interface with a backend Electronic Health Record (EHR) system to map legacy HL7 v2 messages to FHIR resources.

End-to-end DevOps CI/CD pipeline flow. Start with Code Commit triggering a Jenkins/GitHub Actions pipeline. Steps must include: Static Code Analysis (SonarQube), Unit Testing, Docker Image Build, and Pushing to a Container Registry (ECR). The Continuous Deployment phase should utilize a GitOps approach (e.g., ArgoCD) to synchronize the new image definition to a Kubernetes Cluster, separated into Staging and Production environments with manual approval gates.

Multi-Agent LLM system architecture involving a collaborative swarm of agents. Center the design around an Orchestrator Agent that decomposes user tasks. Connect it to specialized worker agents: a Researcher Agent (with web search tools), a Coder Agent (with Python interpreter), and a Critic Agent (for review). All agents should read/write to a Shared Memory module and a Vector Database for long-term knowledge retention.

System architecture diagram for a modern web application. Users access a React/Next.js UI through a CDN with TLS termination and WAF protection. Authentication uses OAuth2/OIDC with MFA and JWT access tokens. Requests flow through an API gateway to core services (User, Content, Search) running in containers with autoscaling. Data storage includes PostgreSQL for transactions, Redis for session and cache, Elasticsearch for search, and S3 for media uploads. Use Kafka for async jobs and event-driven updates. Add CI/CD with GitHub Actions and ArgoCD, plus monitoring with OpenTelemetry, Datadog, and centralized logging. Highlight RBAC, rate limiting, and audit trails.

System architecture diagram for a library management system. Patrons use a web portal to search the catalog, reserve books, and manage loans; librarians use an admin console for acquisitions, cataloging, and circulation. Include self-checkout kiosks with barcode/RFID scanners. Core services should cover catalog, circulation, membership/fines, and notification workflows. Store transactional data in PostgreSQL, index titles in Elasticsearch, and cache sessions in Redis. Use a message bus for loan events and overdue reminders. Authentication should use OAuth2 with RBAC for staff roles and audit logging for changes.

System architecture diagram for a hospital management system. Patients access a portal for appointments and results; clinicians use a workstation for charting and orders. Core services should include EHR, scheduling, pharmacy, and billing/claims. Integrate with imaging (PACS) and external labs using HL7 v2 and FHIR APIs. Store clinical data in a secure database, index notes for search, and use a message bus for order events. Add MFA, audit trails, and HIPAA-aligned access controls.

System architecture diagram for an event management system. Attendees register via a web/mobile app, organizers manage events in a dashboard, and on-site staff use a mobile check-in app with QR scanning. Core services should include registration, ticketing, session scheduling, and payments. Data storage includes PostgreSQL for transactions, Redis for cache, and Elasticsearch for attendee search. Use a message bus for email/SMS notifications and webhook integrations. Add OAuth2 authentication, rate limiting, and audit logging.

System architecture diagram for a secure online voting system. Voters authenticate with government-issued identity verification and MFA, then receive a ballot. Core services should include ballot generation, vote casting, tallying, and audit verification. Encrypt votes end-to-end, store encrypted ballots in a write-once ledger, and keep a separate audit log. Use HSM/KMS for key management and enforce strict RBAC for election officials. Provide monitoring, rate limiting, and anomaly detection.

System architecture diagram for a mobile application platform supporting iOS and Android clients. Users authenticate with OAuth2/OIDC, and the app communicates with a backend via an API gateway. Core services include user profiles, content delivery, notifications, and offline sync. Data storage uses PostgreSQL for transactions, Redis for cache, and S3 for media. Add push notifications via APNs/FCM, analytics pipelines, and CI/CD for mobile releases. Include monitoring and crash reporting.

Create a system block diagram of a SaaS web application. Include client UI, auth/identity, edge gateway, API gateway, core services (user/billing/content), data storage, cache, async messaging/queues, and platform observability/security. Keep it concise with 4–5 layers, 3–5 modules per layer, and 2–3 items, responsibilities, and technologies per module.

AWS architecture diagram for a scalable web platform. Users access the app via Route 53 and CloudFront with WAF and TLS (ACM). Requests are routed through API Gateway and an ALB to containerized services on ECS Fargate, with background jobs on Lambda and Step Functions. Data storage includes RDS PostgreSQL, DynamoDB for high-throughput items, ElastiCache Redis for cache, and S3 for media. Use EventBridge, SQS, and SNS for async workflows. Include CI/CD with CodePipeline and monitoring via CloudWatch and X-Ray, plus IAM and KMS for security.

Cloud architecture diagram for a vendor-neutral cloud-native platform. Route traffic through a global load balancer into a VPC, then to a Kubernetes cluster with service mesh for secure service-to-service calls. Core services run in containers with autoscaling. Data storage uses managed SQL, object storage for blobs, and Redis/OpenSearch for cache and search. Add CI/CD pipelines, observability with Prometheus/Grafana, and policy enforcement with OPA. Include IAM, secrets vault, and audit logging.

Micro service architecture diagram for a modular platform. Clients access services through an API gateway with OAuth2 authentication and rate limiting. Core microservices include User, Order, Inventory, and Billing, each owning its own database. Use Kafka for event-driven communication and Redis for caching. Add service discovery and centralized configuration, plus tracing and logging for observability. Highlight circuit breakers and retries between services.

Generate a detailed RAG (Retrieval-Augmented Generation) pipeline architecture. The flow should start with an Ingestion Pipeline where unstructured documents (PDFs/Wikis) are processed via a Text Chunker and passed to an Embedding Model to generate vectors, stored in a Vector Database (e.g., Pinecone). The Retrieval Pipeline should show a User Query being vectorized, matching against the Vector DB for top-k relevant chunks, and then being fused into a Context Window sent to an LLM (e.g., GPT-4) for final answer synthesis. Include an Orchestration layer (e.g., LangChain) managing this workflow.

Secure Payment Gateway architecture showing the end-to-end transaction flow. The diagram should start with the Client Checkout UI using a Tokenization SDK to encrypt card data. Follow the request to the API Gateway, passing through a Fraud Detection Engine and Risk Service. The Core Payment Processor then routes the transaction to external Card Networks (Visa/Mastercard) and Acquiring Banks. Include a side-car process for the Settlement and Reconciliation System interacting with the General Ledger.

Business Process Flow for E-commerce Order Fulfillment. Use swimlanes to distinguish between the Customer, Order Management System (OMS), Warehouse, and Shipping Carrier. The flow starts with Order Placement, followed by an Inventory Check and Payment Authorization. If approved, the OMS routes the order to the Warehouse for Picking, Packing, and Label Generation. Finally, the package is handed over to the Carrier, triggering a tracking update notification to the customer.

System architecture diagram for an online food ordering system. Customers browse menus in a mobile app, restaurants manage orders in a dashboard, and couriers use a driver app for delivery. Core services should include menu/catalog, order processing, payment, and dispatch. Use PostgreSQL for transactions, Redis for cache, and Kafka for order events. Integrate with a maps service for ETA and tracking. Add fraud checks, rate limiting, and observability.

Create a system block diagram of an AI data processing pipeline. Include data sources (databases, APIs, event streams), batch and streaming ingestion, a data lake/object store, ETL/data cleaning, feature engineering, feature store, model training, model registry, batch inference, real-time inference, and monitoring with feedback/retraining. Keep it high-level with 4–5 layers, 3–5 modules per layer, and specify 2–3 items, responsibilities, and technologies per module.

Data architecture diagram on Azure. Ingest data from Azure SQL, Dynamics 365, and application logs in Blob Storage; stream IoT telemetry via IoT Hub and Event Hubs. Use Azure Data Factory for batch ingestion, Databricks for lakehouse processing, and Stream Analytics for real-time enrichment. Store raw and curated data in ADLS Gen2 with Delta Lake tables and publish curated marts to Synapse Analytics. Expose analytics through Power BI and secure APIs with Azure API Management. Add governance with Microsoft Purview, Key Vault for secrets, and RBAC with Azure AD; include data quality checks and monitoring.

Hadoop architecture diagram for a data platform. Batch data arrives via Sqoop from relational databases and Flume/Kafka for log streams. Store data in HDFS with NameNode metadata and replicated DataNodes. Processing runs on YARN with Spark and MapReduce; SQL analytics use Hive and Presto. Coordinate services with ZooKeeper and secure the cluster with Kerberos and Ranger. Include monitoring and capacity management.

DBMS architecture diagram for a relational database system. Clients connect via JDBC/ODBC and submit SQL. The query processor handles parsing, rewriting, cost-based optimization, and execution planning. The storage engine manages buffer cache, indexes, and file layout, while the transaction manager enforces ACID with MVCC and write-ahead logging. Include system catalog metadata, backup/restore, and monitoring. Show how queries flow through parser, optimizer, and execution engine to storage.