update report

7project/report.md

@@ -22,12 +22,12 @@ filtering and visualization. New transactions are automatically fetched in the b
 ## Architecture Overview
 
 Our system is a full‑stack web application composed of a React frontend, a FastAPI backend,
-a MariaDB database with Maxscale, and asynchronous background workers powered by Celery with RabbitMQ.
-Redis is available for caching/kv and may be used by Celery as a result backend. The backend
-exposes REST endpoints for authentication (email/password and OAuth), users, categories,
-transactions, exchange rates and bank APIs. A thin controller layer (FastAPI routers) lives under app/api.
-Infrastructure for Kubernetes is provided via OpenTofu (Terraform‑compatible) modules and
-the application is packaged via a Helm chart.
+a asynchronousMariaDB database with Maxscale, and background workers powered by Celery with RabbitMQ.
+The backend exposes REST endpoints for authentication (email/password and OAuth), users, categories,
+transactions, exchange rates and bank APIs. Infrastructure for Kubernetes is managed via Terraform/OpenTofu and
+the application is packaged via a Helm chart. This all is deployed on private TalosOS cluster running on Proxmox VE with
+CI/CD and with public access over Cloudflare tunnels. Static files for frontend are served via Cloudflare pages.
+Other services deployed in the cluster includes Longhorn for persistent storage, Prometheus with Grafana for monitoring.
 
 ### High-Level Architecture
 
@@ -50,11 +50,9 @@ The workflow works in the following way:
 
 - Client connects to the frontend. After login, frontend automatically fetches the stored transactions from
   the database via the backend API and currency rates from UniRate API.
-- When the client opts for fetching new transactions via the Bank API, the backend delegates the task
-  to a background worker service via the Message queue.
+- When the client opts for fetching new transactions via the Bank API, cron will trigger periodic fetching
+  using background worker.
 - After successful load, these transactions are stored to the database and displayed to the client
-- There is also a Task planner, that executes periodic tasks, like fetching new transactions automatically from the Bank
-  APIs
 
 ### Features
 
@@ -62,6 +60,9 @@ The workflow works in the following way:
 - For every pull request the full APP is deployed on a separate URL and the tests are run by github CI/CD
 - On every push to main, the production app is automatically updated
 - UI is responsive for mobile devices
+- Slow operations (emails, transactions fetching) are handled 
+in the background by Celery workers.
+- App is monitored using prometheus metrics endpoint and metrics are shown in Grafana dashboard.
 
 ### Components
 
@@ -69,13 +70,10 @@ The workflow works in the following way:
   login/registration, shows latest transactions, filtering, and allows adding transactions.
 - Backend API (backend/app): FastAPI app with routers under app/api for auth, users, categories, transactions, exchange
   rates and bankAPI. Uses FastAPI Users for auth (JWT + OAuth), SQLAlchemy ORM, and Pydantic v2 schemas.
-- Worker service (backend/app/workers): Celery worker handling asynchronous tasks (e.g., sending verification emails,
-  future background processing).
-- Database (PostgreSQL): Persists users, categories, transactions; schema managed by Alembic migrations.
-- Message Queue (RabbitMQ): Transports background jobs from the API to the worker.
-- Cache/Result Store (Redis): Available for caching or Celery result backend.
-- Infrastructure as Code (tofu/): OpenTofu modules provisioning cluster services (RabbitMQ, Redis, Argo CD,
-  cert-manager, Cloudflare tunnel, etc.).
+- Worker service (backend/app/workers): Celery worker handling background tasks (emails, transactions fetching).
+- Database (MariaDB with Maxscale): Persists users, categories, transactions; schema managed by Alembic migrations.
+- Message Queue (RabbitMQ): Queues background tasks for Celery workers.
+- Infrastructure as Code (tofu/): OpenTofu modules provisioning cluster services (RabbitMQ, Redis, Cloudflare tunnel, etc.).
 - Deployment Chart (charts/myapp-chart/): Helm chart to deploy the application to Kubernetes.
 
 ### Technologies Used
@@ -624,24 +622,27 @@ curl -H "Authorization: Bearer $TOKEN" http://127.0.0.1:8000/authenticated-route
 [Reflect on the key technical and collaboration skills learned during this project]
 
 ### Challenges Faced
-#### Slow cluster performance
-This was caused by single SATA SSD disk running all VMs. This was solved by adding second NVMe disk just for Talos VMs.
 
+#### Slow cluster performance
+
+This was caused by single SATA SSD disk running all VMs. This was solved by adding second NVMe disk just for Talos VMs.
 
 [Describe the main challenges and how you overcame them]
 
 ### If We Did This Again
+
 #### Different framework
+
 FastAPI lacks usable build in support for database migrations and implementing Alembic was a bit tricky.
 Tricky was also integrating FastAPI auth system with React frontend, since there is no official project template.
 Using .NET (which we considered initially) would probably solve these issues.
 
-
 [What would you do differently? What worked well that you'd keep?]
 
 ### Individual Growth
 
 #### [Lukas]
+
 This course finally forced me to learn kubernetes (been on by TODO list for at least 3 years).
 I had some prior experience with terraform/opentofu from work but this improved by understanding of it.
 
@@ -661,4 +662,4 @@ used not only by myself.
 ---
 
 **Report Completion Date**: [Date]
-**Last Updated**: 15.10.2025
+**Last Updated**: 13.11.2025