feat(6): phase 6 planning complete - deployment, scale testing, backup/restore
Phase 6 comprehensive plans ready for execution: Plan 1: Docker Containerization & Deployment Automation (6 tasks) - Enhance backend/frontend Dockerfiles with health checks - Create deploy.sh for single-command deployment - Environment automation and validation - Quick start guide and troubleshooting docs Plan 2: Scale Testing & Performance Optimization (6 tasks) - Locust-based load testing framework (5 concurrent users) - Database seeding (10K items with realistic data) - Metrics collection (CPU, memory, response times) - Performance baseline establishment and SLO documentation - Health check monitoring automation - Load test execution guide Plan 3: Backup/Restore & Operational Runbook (7 tasks) - Automated backup script (daily/weekly with retention) - Restore validation and disaster recovery procedures - Cron job configuration for scheduled backups - Comprehensive operational runbook (deployment, scaling, troubleshooting) - Health monitoring checklist (daily/weekly/monthly) - Disaster recovery plan (3+ scenarios, <10min RTO) - Operations documentation index and integration guide Context document summarizes: - Phase goal: Production-ready multi-site deployment - Key decisions: Docker strategy, automation scope, scale limits - Upstream dependencies: Phase 5 complete - Success criteria: Single-command deploy, 10K items + 5 users <2s latency - Backup strategy: Daily incremental, weekly full (30/90 day retention) All plans include: - Detailed task breakdowns (5-7 per plan) - Acceptance criteria and testing procedures - Dependencies and blockers - Effort estimates and risk assessment - Success metrics and monitoring guidance Ready for execution phase (estimated 4-5 weeks total).
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.planning/phases/06-deployment-scale/CONTEXT.md
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.planning/phases/06-deployment-scale/CONTEXT.md
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# Phase 6: Deployment & Scale — Context & Strategic Overview
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**Phase Goal**: Production-ready multi-site deployment with automated setup, scale testing, and operational runbooks.
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**Duration**: 1 month
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**Target Version**: v2.0 stable
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---
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## Phase Overview
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Phase 6 bridges the gap between fully-featured code (Phases 4.1 + 5) and production deployment at scale. After Phase 5 delivers search, exports, and quick quantity adjustment, the system needs:
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1. **Containerization** — Reliable Docker/Compose setup for rapid multi-site rollout
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2. **Automation** — Single-command deployment (environment agnostic)
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3. **Scale validation** — Prove system handles 10K items + 5 concurrent users without degradation
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4. **Performance tuning** — Identify and fix bottlenecks revealed by load testing
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5. **Operational readiness** — Backup/restore, disaster recovery, runbook documentation
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---
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## Key Decisions Made During Planning
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### 1. Docker Strategy
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- **Existing**: docker-compose.yml and Dockerfiles already in place (backend/, proxy/)
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- **Gap**: Automated deployment scripts, environment templates, CI/CD hooks
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- **Plan 1 Focus**: Enhance existing Dockerfiles → production-grade, add health checks, optimize layers
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- **Multi-site**: Single docker-compose.yml template with `.env` overrides per site
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### 2. Deployment Automation
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- **Target**: `./deploy.sh` (single entry point) — no manual steps
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- **Scope**: Config validation, DB initialization, certificate generation, health checks
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- **Fallback**: Documented manual steps for troubleshooting
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- **Testing**: Pre-flight checks (port availability, storage, permissions)
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### 3. Scale Testing Approach
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- **Load Profile**: 10K items + 5 concurrent users (realistic field scenario)
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- **Tools**: Locust (Python) for backend load testing, Playwright for frontend workflow
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- **Metrics**: Response time <2s for search/scan, CPU/memory usage, sync reliability
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- **Plan 2 Focus**: Load testing infrastructure + performance baseline + optimization recommendations
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### 4. Backup/Restore Philosophy
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- **Data**: SQLite DB + config files + certificate state
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- **Versioning**: Backup includes timestamp + version number for easy rollback
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- **Testing**: Automated restore test on each backup cycle
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- **Plan 3 Focus**: Backup automation script, restore validation, documented RTO/RPO
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### 5. Operational Documentation
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- **Audience**: Ops teams deploying to new sites; minimal Docker/Python knowledge required
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- **Format**: Runbook style (step-by-step checklists)
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- **Coverage**: Deployment, scaling, troubleshooting, health monitoring, upgrade path
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---
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## Upstream Dependencies
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### Phase 5 Completion Required
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- ✓ Quick Quantity Adjustment feature (UI + API)
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- ✓ Search & Filtering feature (modal + backend)
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- ✓ Export/Reports feature (CSV/Excel + admin UI)
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- ✓ All tests passing (Vitest + Pytest)
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- ✓ No critical bugs in dev branch
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### Existing Infrastructure
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- ✓ docker-compose.yml (3 services: backend, frontend, proxy)
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- ✓ Backend Dockerfile (Python 3.12 + FastAPI)
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- ✓ Frontend Dockerfile (Node.js + Next.js)
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- ✓ Caddy proxy with HTTPS (self-signed certs)
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- ✓ Environment file system (inventory.env)
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---
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## Technical Approach
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### Plan 1: Docker & Deployment Automation (Week 1-2)
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- Refine Dockerfiles (health checks, logging, layer optimization)
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- Create deployment automation script (`deploy.sh`)
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- Environment template with validation
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- Pre-flight checks + error handling
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- Docker Compose enhancements (healthchecks, volumes, networking)
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### Plan 2: Scale Testing & Performance (Week 2-3)
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- Load testing framework (Locust)
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- Database seeding (10K items with realistic categories)
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- Concurrent user simulation (5 users, multiple workflows)
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- Metrics collection (response time, CPU, memory)
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- Bottleneck identification + optimization PR recommendations
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- Health check automation
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### Plan 3: Backup/Restore & Runbook (Week 3-4)
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- Backup automation script (daily/weekly cycles)
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- Restore validation + testing
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- Runbook documentation (deployment, scaling, troubleshooting)
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- Disaster recovery procedures
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- Health monitoring guidelines
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---
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## Success Criteria
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### Deployment Automation
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- [ ] `./deploy.sh` deploys full stack in <5 minutes
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- [ ] Automatic DB initialization on first run
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- [ ] Health checks confirm all services running
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- [ ] Env validation prevents misconfiguration
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- [ ] Works on clean Ubuntu 22.04+ LTS system
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### Scale Testing
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- [ ] Load test with 10K items + 5 concurrent users completes
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- [ ] Response times stable: search <500ms, scan <1s, sync <2s
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- [ ] CPU usage <70%, memory <2GB during load
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- [ ] Sync reliability 99%+ (no dropped transactions)
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- [ ] Baseline metrics documented for future comparisons
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### Backup/Restore
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- [ ] Backup script creates timestamped archives
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- [ ] Restore fully recovers system state (DB + config)
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- [ ] Zero data loss on restore test
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- [ ] RTO <10 minutes, RPO 1 day (configurable)
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### Documentation
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- [ ] Deployment runbook (step-by-step, no domain knowledge required)
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- [ ] Scaling guide (adding more users, larger DB)
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- [ ] Troubleshooting guide (common issues + solutions)
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- [ ] Health monitoring checklist
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---
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## Testing Strategy
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### Automated Testing
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- Pre-deployment validation (docker build, env checks)
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- Health check validation (all services respond)
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- Scale testing suite (Locust + Playwright)
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- Backup/restore automated tests
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### Manual Testing
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- First-time deployment on fresh VM
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- Multi-site deployment (verify isolation)
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- Failover testing (service restart, data integrity)
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### Success Metrics
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- All automated tests pass
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- Manual deployment completes without human intervention
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- Scale test shows <2s latency at 5 concurrent users
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- Backup/restore cycle succeeds with zero data loss
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---
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## Blockers & Workarounds
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### Known Constraints
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1. **SQLite single-writer limitation** — No true concurrent writes; acceptable for 5 users
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- Workaround: WAL mode enabled, connection pooling limits contention
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2. **Certificate persistence** — Caddy certs need stable volume mount
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- Workaround: Use persistent named volumes for `/data/caddy_*`
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3. **Environment variability** — Different orgs may have different network configs
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- Workaround: Pre-flight checks validate critical assumptions (ports, storage)
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### Potential Issues
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- Docker daemon availability (some restricted environments)
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- HTTPS certificate warnings on first-time access
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- Network isolation (VPN/Tailscale may affect CORS detection)
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---
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## Execution Checklist
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- [ ] Phase 5 complete + all tests passing
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- [ ] Create Phase 6 directory structure
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- [ ] Write 3 PLAN.md files (Deployment, Scale Testing, Backup/Runbook)
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- [ ] Execute Plan 1: Docker + deploy.sh
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- [ ] Execute Plan 2: Load testing + performance baseline
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- [ ] Execute Plan 3: Backup/restore + runbook
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- [ ] Integration testing (full deployment cycle)
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- [ ] Documentation review
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- [ ] Commit all changes with `feat(6): phase 6 planning complete...`
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- [ ] Tag v2.0-rc1 for release candidate validation
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---
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**Last Updated**: 2026-04-22 (Planning Phase)
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@@ -0,0 +1,483 @@
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# Phase 6, Plan 1: Docker Containerization & Deployment Automation
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---
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**plan**: 06-deployment-scale/01-docker-deployment
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**feature**: Docker containerization, automated deployment, environment automation
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**status**: Ready for execution
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**estimated_tasks**: 6
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**total_lines**: ~450 (Dockerfile updates ~200, deploy.sh ~180, compose enhancements ~70)
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---
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## Overview
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This plan hardens the existing Docker setup and creates a single-command deployment script. The system already has Dockerfiles and docker-compose.yml; this plan:
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1. **Enhances Dockerfiles** — Add health checks, optimize layers, improve logging
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2. **Creates deploy.sh** — Automated deployment with validation, initialization, health checks
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3. **Environment automation** — Template generation, pre-flight validation
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4. **Docker Compose improvements** — Health checks, volume management, dependency ordering
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5. **Documentation** — Quick start guide for operators
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**Success**: `./deploy.sh` deploys full stack on fresh Ubuntu 22.04+ in <5 minutes with zero manual steps.
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---
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## Tasks
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### Task 1: Enhance Backend Dockerfile
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**File**: `backend/Dockerfile`
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**Status**: Ready
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**Description**: Add health checks, optimize build layers, improve production readiness
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**Changes**:
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- Add `HEALTHCHECK` instruction (GET /health endpoint)
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- Optimize RUN commands (reduce layers)
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- Add metadata labels (version, maintainer, build-date)
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- Ensure logs go to stdout for Docker log capture
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- Pin Python version to 3.12
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**Acceptance Criteria**:
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- [ ] Dockerfile builds without warnings
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- [ ] Health check responds correctly
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- [ ] Image size <500MB
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- [ ] Container logs visible in `docker logs`
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- [ ] All tests still pass
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**Testing**:
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```bash
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docker build -t inventory-backend:test backend/
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docker run --rm -p 8000:8000 inventory-backend:test
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curl http://localhost:8000/health # Should return 200
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```
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---
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### Task 2: Enhance Frontend Dockerfile
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**File**: `frontend/Dockerfile`
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**Status**: Ready
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**Description**: Add health checks, optimize Next.js production build, logging
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**Changes**:
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- Add `HEALTHCHECK` instruction (curl to /health or equivalent)
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- Multi-stage build (builder + runtime) to reduce image size
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- Ensure Next.js logs to stdout
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- Optimize node_modules caching layer
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- Pin Node.js version to LTS
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**Acceptance Criteria**:
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- [ ] Dockerfile builds without warnings
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- [ ] Health check responds correctly
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- [ ] Image size <300MB
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- [ ] Next.js startup logs appear in `docker logs`
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**Testing**:
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```bash
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docker build -t inventory-frontend:test frontend/
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docker run --rm -p 3000:3000 inventory-frontend:test
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curl http://localhost:3000/_next/health # Or custom endpoint
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```
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---
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### Task 3: Update docker-compose.yml
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**File**: `docker-compose.yml`
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**Status**: Ready
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**Description**: Add health checks, improve service dependencies, enhance resilience
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**Changes**:
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- Add `healthcheck` to all three services (backend, frontend, proxy)
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- Update `depends_on` to use health checks (wait_for: service_healthy)
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- Add resource limits (memory, CPU)
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- Improve volume definitions (use named volumes for persistence)
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- Add restart policies (restart: unless-stopped already present; verify)
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- Document all environment variables inline
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**Acceptance Criteria**:
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- [ ] docker-compose up starts all services in order
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- [ ] Health checks report healthy status
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- [ ] Services restart on failure
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- [ ] Logs from all services visible via `docker-compose logs`
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**Testing**:
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```bash
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docker-compose up -d
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docker-compose ps # Should show all healthy
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docker-compose logs -f
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docker-compose down
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```
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---
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### Task 4: Create deploy.sh (Automated Deployment)
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**File**: `deploy.sh` (new, executable)
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**Status**: Ready
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**Description**: Single-command deployment with initialization, validation, health checks
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**Content** (~180 lines):
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```bash
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#!/bin/bash
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set -euo pipefail
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# Phase 6, Plan 1, Task 4: Automated Deployment Script
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# Usage: ./deploy.sh [production|staging|development] [--rebuild]
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DEPLOYMENT_ENV="${1:-production}"
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REBUILD="${2:---no-rebuild}"
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# Color output
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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NC='\033[0m' # No Color
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log_info() { echo -e "${GREEN}[INFO]${NC} $1"; }
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log_warn() { echo -e "${YELLOW}[WARN]${NC} $1"; }
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log_error() { echo -e "${RED}[ERROR]${NC} $1"; exit 1; }
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# 1. Pre-flight checks
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log_info "Running pre-flight checks..."
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command -v docker &> /dev/null || log_error "Docker not installed"
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command -v docker-compose &> /dev/null || log_error "Docker Compose not installed"
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[[ -f "docker-compose.yml" ]] || log_error "docker-compose.yml not found"
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[[ -f "inventory.env" ]] || log_error "inventory.env not found; copy from template"
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# 2. Validate environment file
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log_info "Validating inventory.env..."
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source inventory.env
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[[ -z "${BACKEND_PORT:-}" ]] && log_warn "BACKEND_PORT not set; using default 8000"
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[[ -z "${FRONTEND_PORT:-}" ]] && log_warn "FRONTEND_PORT not set; using default 3000"
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# 3. Check port availability
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log_info "Checking port availability..."
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for port in ${BACKEND_PORT:-8000} ${FRONTEND_PORT:-3000} ${BACKEND_SSL_PORT:-8918} ${FRONTEND_SSL_PORT:-8919}; do
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netstat -tuln 2>/dev/null | grep -q ":$port " && log_error "Port $port already in use"
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done
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# 4. Build or pull images
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log_info "Building Docker images (${REBUILD})..."
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if [[ "$REBUILD" == "--rebuild" ]]; then
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docker-compose build --no-cache
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else
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docker-compose build
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fi
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# 5. Create data directories
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log_info "Creating data directories..."
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mkdir -p data logs config
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[[ -d "data/caddy_data" ]] || mkdir -p data/caddy_data
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[[ -d "data/caddy_config" ]] || mkdir -p data/caddy_config
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|
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# 6. Initialize database (if not exists)
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if [[ ! -f "data/inventory.db" ]]; then
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log_info "Initializing database..."
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# This will be handled by backend startup; just log the action
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log_info "Database will be initialized on first backend startup"
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fi
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# 7. Start services
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log_info "Starting services..."
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docker-compose up -d
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|
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# 8. Wait for health checks
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log_info "Waiting for services to be healthy..."
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max_attempts=30
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attempt=0
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while [[ $attempt -lt $max_attempts ]]; do
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healthy=$(docker-compose ps | grep -c "healthy" || echo "0")
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if [[ $healthy -eq 3 ]]; then
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log_info "All services healthy!"
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break
|
||||
fi
|
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attempt=$((attempt + 1))
|
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sleep 2
|
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done
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|
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if [[ $attempt -eq $max_attempts ]]; then
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log_warn "Services did not become healthy within 60 seconds"
|
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docker-compose logs
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exit 1
|
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fi
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|
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# 9. Verify connectivity
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log_info "Verifying connectivity..."
|
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if curl -sf "http://localhost:${BACKEND_PORT:-8000}/health" &> /dev/null; then
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log_info "Backend healthy: http://localhost:${BACKEND_PORT:-8000}"
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else
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log_error "Backend health check failed"
|
||||
fi
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# 10. Display summary
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log_info "Deployment successful!"
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echo ""
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echo "Access points:"
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echo " Frontend: http://localhost:${FRONTEND_PORT:-3000}"
|
||||
echo " Backend API: http://localhost:${BACKEND_PORT:-8000}"
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echo " Secure (HTTPS): https://localhost:${FRONTEND_SSL_PORT:-8919}"
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||||
echo ""
|
||||
echo "View logs: docker-compose logs -f"
|
||||
echo "Stop services: docker-compose down"
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||||
```
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||||
|
||||
**Acceptance Criteria**:
|
||||
- [ ] Script exits with error on missing Docker/Compose
|
||||
- [ ] Pre-flight checks validate all prerequisites
|
||||
- [ ] Images build successfully
|
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- [ ] Services start and health checks pass
|
||||
- [ ] Displays access URLs at end
|
||||
- [ ] Exit code 0 on success, non-zero on failure
|
||||
|
||||
**Testing**:
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```bash
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chmod +x deploy.sh
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./deploy.sh production
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# Verify all services running and accessible
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./deploy.sh staging --rebuild
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||||
# Clean up
|
||||
docker-compose down
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||||
```
|
||||
|
||||
---
|
||||
|
||||
### Task 5: Create Environment Template & Validation
|
||||
**Files**:
|
||||
- `inventory.env.template` (new)
|
||||
- `.env.validation.sh` (new, 80 lines)
|
||||
|
||||
**Status**: Ready
|
||||
**Description**: Provide environment template and validation script to prevent misconfiguration
|
||||
|
||||
**inventory.env.template**:
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||||
```env
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# TFM aInventory Deployment Configuration
|
||||
# Copy to inventory.env and customize for your deployment
|
||||
|
||||
# Service Ports
|
||||
BACKEND_PORT=8000
|
||||
FRONTEND_PORT=3000
|
||||
BACKEND_SSL_PORT=8918
|
||||
FRONTEND_SSL_PORT=8919
|
||||
|
||||
# Security
|
||||
JWT_SECRET_KEY=change_me_in_production_use_openssl_rand_hex_32
|
||||
|
||||
# AI Configuration (Optional, uses defaults if not set)
|
||||
AI_PROVIDER=gemini
|
||||
GEMINI_API_KEY=
|
||||
CLAUDE_API_KEY=
|
||||
|
||||
# LDAP Configuration (Optional, uses local auth if not set)
|
||||
LDAP_SERVER=
|
||||
LDAP_PORT=389
|
||||
LDAP_BASE_DN=
|
||||
LDAP_USE_SSL=false
|
||||
|
||||
# Database
|
||||
DATA_DIR=/app/data
|
||||
DB_PATH=/app/data/inventory.db
|
||||
|
||||
# Logging
|
||||
LOGS_DIR=/app/logs
|
||||
LOG_LEVEL=INFO
|
||||
|
||||
# Network (CORS)
|
||||
ALLOWED_ORIGINS=http://localhost:3000,https://localhost:8919
|
||||
EXTRA_ALLOWED_ORIGINS=
|
||||
|
||||
# Deployment metadata
|
||||
DEPLOYMENT_NAME=production
|
||||
DEPLOYMENT_REGION=default
|
||||
```
|
||||
|
||||
**.env.validation.sh**:
|
||||
```bash
|
||||
#!/bin/bash
|
||||
# Validate inventory.env before deployment
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
log_error() { echo "[ERROR] $1" >&2; exit 1; }
|
||||
log_warn() { echo "[WARN] $1" >&2; }
|
||||
|
||||
[[ -f "inventory.env" ]] || log_error "inventory.env not found"
|
||||
source inventory.env
|
||||
|
||||
# Validate required variables
|
||||
[[ -z "${BACKEND_PORT:-}" ]] && log_error "BACKEND_PORT not set"
|
||||
[[ -z "${FRONTEND_PORT:-}" ]] && log_error "FRONTEND_PORT not set"
|
||||
[[ "$BACKEND_PORT" =~ ^[0-9]+$ ]] || log_error "BACKEND_PORT must be numeric"
|
||||
[[ "$FRONTEND_PORT" =~ ^[0-9]+$ ]] || log_error "FRONTEND_PORT must be numeric"
|
||||
|
||||
# Warn on defaults
|
||||
if [[ "${JWT_SECRET_KEY:-}" == "change_me_in_production_use_openssl_rand_hex_32" ]]; then
|
||||
log_warn "JWT_SECRET_KEY is using default value; regenerate for production"
|
||||
fi
|
||||
|
||||
echo "[OK] inventory.env validation passed"
|
||||
```
|
||||
|
||||
**Acceptance Criteria**:
|
||||
- [ ] Template covers all deployment scenarios (dev/staging/prod)
|
||||
- [ ] Validation script checks all critical variables
|
||||
- [ ] Clear comments explaining each setting
|
||||
- [ ] Example values provided (not actual secrets)
|
||||
|
||||
---
|
||||
|
||||
### Task 6: Create Quick Start Guide & Troubleshooting
|
||||
**File**: `docs/DEPLOYMENT_QUICKSTART.md` (new, ~150 lines)
|
||||
**Status**: Ready
|
||||
**Description**: Operator-friendly deployment guide, no domain knowledge required
|
||||
|
||||
**Content**:
|
||||
```markdown
|
||||
# Deployment Quick Start Guide
|
||||
|
||||
## Prerequisites
|
||||
- Ubuntu 22.04 LTS or similar Linux distro
|
||||
- Docker 24.0+
|
||||
- Docker Compose 2.0+
|
||||
- 2GB RAM, 10GB free disk
|
||||
|
||||
## Installation (5 minutes)
|
||||
|
||||
### 1. Prepare Environment
|
||||
\`\`\`bash
|
||||
git clone <repo> tfm-inventory
|
||||
cd tfm-inventory
|
||||
cp inventory.env.template inventory.env
|
||||
# Edit inventory.env with your deployment settings
|
||||
\`\`\`
|
||||
|
||||
### 2. Generate Secure Secret
|
||||
\`\`\`bash
|
||||
openssl rand -hex 32 > /tmp/jwt_secret
|
||||
# Copy output into inventory.env JWT_SECRET_KEY
|
||||
\`\`\`
|
||||
|
||||
### 3. Deploy
|
||||
\`\`\`bash
|
||||
chmod +x deploy.sh
|
||||
./deploy.sh production
|
||||
\`\`\`
|
||||
|
||||
### 4. Verify Access
|
||||
- Frontend: http://localhost:3000
|
||||
- Backend: http://localhost:8000
|
||||
- API Docs: http://localhost:8000/docs
|
||||
|
||||
## Scaling (Adding Users)
|
||||
|
||||
System tested and stable with 5 concurrent users. For more:
|
||||
1. Increase BACKEND_PORT pool (run multiple instances behind load balancer)
|
||||
2. Monitor logs for errors: `docker-compose logs -f backend`
|
||||
3. Check database locks: `docker-compose exec backend python -c "import sqlite3; db=sqlite3.connect('/app/data/inventory.db'); print(db.execute('PRAGMA database_list').fetchall())"`
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
| Issue | Solution |
|
||||
|-------|----------|
|
||||
| Port already in use | Change BACKEND_PORT/FRONTEND_PORT in inventory.env |
|
||||
| Health check failing | Wait 30s, then: `docker-compose logs` to check service logs |
|
||||
| Database locked | Restart backend: `docker-compose restart backend` |
|
||||
| HTTPS certificate warning | First-time is normal; trust the certificate in browser |
|
||||
|
||||
## Backup & Restore
|
||||
\`\`\`bash
|
||||
# Automatic daily backups (see BACKUP_RUNBOOK.md)
|
||||
./backup.sh daily
|
||||
./restore.sh data/backups/inventory-2026-04-22.tar.gz
|
||||
\`\`\`
|
||||
|
||||
## Support
|
||||
- Logs: `docker-compose logs -f`
|
||||
- Health status: `docker-compose ps`
|
||||
- Stop all: `docker-compose down`
|
||||
- Full reset: `docker-compose down -v` (⚠️ deletes data)
|
||||
```
|
||||
|
||||
**Acceptance Criteria**:
|
||||
- [ ] Guide is readable by non-technical ops teams
|
||||
- [ ] All 5 steps complete in <5 minutes
|
||||
- [ ] Troubleshooting covers common issues
|
||||
- [ ] Links to related docs (backup, scaling, health monitoring)
|
||||
|
||||
---
|
||||
|
||||
## Dependencies
|
||||
|
||||
**Upstream**:
|
||||
- Phase 5 complete (all features implemented, tests passing)
|
||||
- Existing Dockerfiles and docker-compose.yml
|
||||
|
||||
**Cross-Plan**:
|
||||
- Plan 2 (Scale Testing) uses `deploy.sh` from this plan
|
||||
- Plan 3 (Backup/Restore) integrates with deployment structure
|
||||
|
||||
**Blocked By**: None
|
||||
|
||||
---
|
||||
|
||||
## Testing Strategy
|
||||
|
||||
### Unit Testing (Standalone)
|
||||
```bash
|
||||
# Each component can be tested independently
|
||||
docker build -t inventory-backend:test backend/
|
||||
docker build -t inventory-frontend:test frontend/
|
||||
docker run --rm inventory-backend:test pytest backend/tests/ # Verify tests still pass
|
||||
```
|
||||
|
||||
### Integration Testing
|
||||
```bash
|
||||
# Deploy stack
|
||||
./deploy.sh production --rebuild
|
||||
# Verify all services healthy
|
||||
docker-compose ps | grep healthy
|
||||
# Run smoke tests
|
||||
curl http://localhost:8000/health
|
||||
curl http://localhost:3000/
|
||||
# Verify data persistence
|
||||
# (detailed in Plan 3)
|
||||
```
|
||||
|
||||
### Deployment Validation
|
||||
```bash
|
||||
# On fresh VM with only Docker installed
|
||||
git clone <repo>
|
||||
cd tfm-inventory
|
||||
./deploy.sh production
|
||||
# Should complete without errors
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Success Metrics
|
||||
|
||||
- [ ] `./deploy.sh` completes in <5 minutes
|
||||
- [ ] Zero manual intervention required
|
||||
- [ ] All services report healthy
|
||||
- [ ] Backend API responds at /health
|
||||
- [ ] Frontend loads in browser
|
||||
- [ ] Logs accessible via docker-compose logs
|
||||
- [ ] Environment validation prevents misconfiguration
|
||||
|
||||
---
|
||||
|
||||
## Notes
|
||||
|
||||
- Existing docker-compose.yml already has 3 services; we enhance, not replace
|
||||
- Health checks will help automation tools (Kubernetes, Docker Swarm) manage restarts
|
||||
- Pre-flight checks prevent common pitfalls (port conflicts, missing files)
|
||||
- Logging to stdout ensures compatibility with container log aggregation
|
||||
|
||||
---
|
||||
|
||||
**Effort Estimate**: 16 hours (2 days)
|
||||
**Dependencies**: None (Phase 5 complete assumed)
|
||||
**Risk**: Low (mostly additive enhancements to existing Dockerfiles)
|
||||
|
||||
---
|
||||
|
||||
Last updated: 2026-04-22 (Planning Phase)
|
||||
800
.planning/phases/06-deployment-scale/PLAN-02-SCALE-TESTING.md
Normal file
800
.planning/phases/06-deployment-scale/PLAN-02-SCALE-TESTING.md
Normal file
@@ -0,0 +1,800 @@
|
||||
# Phase 6, Plan 2: Scale Testing & Performance Optimization
|
||||
|
||||
---
|
||||
|
||||
**plan**: 06-deployment-scale/02-scale-testing
|
||||
**feature**: Load testing infrastructure, performance baseline, bottleneck identification
|
||||
**status**: Ready for execution
|
||||
**estimated_tasks**: 6
|
||||
**total_lines**: ~600 (load testing suite ~250, DB seeding ~100, metrics collection ~150, runbook ~100)
|
||||
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
This plan builds the infrastructure to validate that the system handles production load (10K items + 5 concurrent users) without degradation. It creates:
|
||||
|
||||
1. **Load testing suite** (Locust) — Simulates concurrent users performing realistic workflows
|
||||
2. **Database seeding** — Populates 10K items with realistic categories and attributes
|
||||
3. **Metrics collection** — Monitors CPU, memory, response times during load
|
||||
4. **Baseline establishment** — Documents performance envelope for future comparisons
|
||||
5. **Health automation** — Automated health check monitoring during load tests
|
||||
|
||||
**Success**: Load test runs to completion with <2s latency at 5 concurrent users; baseline metrics published.
|
||||
|
||||
---
|
||||
|
||||
## Tasks
|
||||
|
||||
### Task 1: Create Load Testing Framework (Locust)
|
||||
**File**: `backend/tests/load_test.py` (new, ~250 lines)
|
||||
**Status**: Ready
|
||||
**Description**: Locust-based load testing simulating realistic field workflows
|
||||
|
||||
**Content** (~250 lines):
|
||||
```python
|
||||
"""
|
||||
Phase 6, Plan 2, Task 1: Load Testing Framework
|
||||
Simulates realistic field workflows: scan → check-in/out → search → export
|
||||
"""
|
||||
|
||||
from locust import HttpUser, task, between
|
||||
from locust.contrib.fasthttp import FastHttpUser
|
||||
import random
|
||||
import time
|
||||
|
||||
class InventoryUser(FastHttpUser):
|
||||
"""Simulates a field operator using the inventory system."""
|
||||
|
||||
wait_time = between(2, 5) # 2-5 seconds between actions
|
||||
|
||||
def on_start(self):
|
||||
"""Login before starting tasks."""
|
||||
response = self.client.post("/auth/login", json={
|
||||
"username": "testuser",
|
||||
"password": "testpass"
|
||||
}, catch_response=True)
|
||||
if response.status_code == 200:
|
||||
self.token = response.json().get("access_token")
|
||||
self.headers = {"Authorization": f"Bearer {self.token}"}
|
||||
else:
|
||||
response.failure(f"Login failed: {response.status_code}")
|
||||
|
||||
@task(3)
|
||||
def search_item(self):
|
||||
"""Search for an item (most common operation)."""
|
||||
query = f"item-{random.randint(1, 10000)}"
|
||||
response = self.client.get(
|
||||
f"/search?q={query}",
|
||||
headers=self.headers,
|
||||
name="/search",
|
||||
catch_response=True
|
||||
)
|
||||
if response.status_code == 200:
|
||||
response.success()
|
||||
else:
|
||||
response.failure(f"Search failed: {response.status_code}")
|
||||
|
||||
@task(2)
|
||||
def check_in_item(self):
|
||||
"""Check in an item (adjust quantity +1)."""
|
||||
item_id = random.randint(1, 10000)
|
||||
response = self.client.patch(
|
||||
f"/items/{item_id}",
|
||||
json={"quantity": random.randint(1, 100)},
|
||||
headers=self.headers,
|
||||
name="/items/{itemId} [PATCH]",
|
||||
catch_response=True
|
||||
)
|
||||
if response.status_code in [200, 404]: # 404 expected for some items
|
||||
response.success()
|
||||
else:
|
||||
response.failure(f"Check-in failed: {response.status_code}")
|
||||
|
||||
@task(1)
|
||||
def export_inventory(self):
|
||||
"""Export inventory snapshot (less frequent)."""
|
||||
response = self.client.get(
|
||||
"/admin/exports/inventory",
|
||||
headers=self.headers,
|
||||
name="/admin/exports/inventory",
|
||||
catch_response=True
|
||||
)
|
||||
if response.status_code in [200, 202]:
|
||||
response.success()
|
||||
else:
|
||||
response.failure(f"Export failed: {response.status_code}")
|
||||
|
||||
@task(1)
|
||||
def get_health(self):
|
||||
"""Health check (baseline)."""
|
||||
response = self.client.get(
|
||||
"/health",
|
||||
name="/health",
|
||||
catch_response=True
|
||||
)
|
||||
if response.status_code == 200:
|
||||
response.success()
|
||||
else:
|
||||
response.failure(f"Health check failed: {response.status_code}")
|
||||
|
||||
class AdminUser(FastHttpUser):
|
||||
"""Simulates an admin performing dashboard operations."""
|
||||
|
||||
wait_time = between(5, 10)
|
||||
|
||||
def on_start(self):
|
||||
"""Login as admin."""
|
||||
response = self.client.post("/auth/login", json={
|
||||
"username": "admin",
|
||||
"password": "adminpass"
|
||||
}, catch_response=True)
|
||||
if response.status_code == 200:
|
||||
self.token = response.json().get("access_token")
|
||||
self.headers = {"Authorization": f"Bearer {self.token}"}
|
||||
else:
|
||||
response.failure(f"Admin login failed: {response.status_code}")
|
||||
|
||||
@task(2)
|
||||
def list_items(self):
|
||||
"""List items with pagination."""
|
||||
skip = random.randint(0, 9900)
|
||||
response = self.client.get(
|
||||
f"/items?skip={skip}&limit=50",
|
||||
headers=self.headers,
|
||||
name="/items [paginated]",
|
||||
catch_response=True
|
||||
)
|
||||
if response.status_code == 200:
|
||||
response.success()
|
||||
else:
|
||||
response.failure(f"List items failed: {response.status_code}")
|
||||
|
||||
@task(1)
|
||||
def get_audit_logs(self):
|
||||
"""Retrieve audit logs."""
|
||||
response = self.client.get(
|
||||
"/admin/audit-logs?limit=100",
|
||||
headers=self.headers,
|
||||
name="/admin/audit-logs",
|
||||
catch_response=True
|
||||
)
|
||||
if response.status_code == 200:
|
||||
response.success()
|
||||
else:
|
||||
response.failure(f"Audit logs failed: {response.status_code}")
|
||||
```
|
||||
|
||||
**Acceptance Criteria**:
|
||||
- [ ] File uses Locust FastHttpUser (efficient)
|
||||
- [ ] Simulates 5 realistic workflows (search, check-in, export, health, admin)
|
||||
- [ ] Includes weight distribution (3:2:1 for common:moderate:rare)
|
||||
- [ ] Can spawn multiple user types concurrently
|
||||
- [ ] Task names are descriptive for reporting
|
||||
|
||||
**Testing**:
|
||||
```bash
|
||||
cd backend/tests
|
||||
locust -f load_test.py --host=http://localhost:8000 --users=5 --spawn-rate=1 --run-time=5m
|
||||
# Monitor: Response times, failure rates, requests/sec
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Task 2: Database Seeding Script (10K Items)
|
||||
**File**: `scripts/seed_load_test_db.py` (new, ~100 lines)
|
||||
**Status**: Ready
|
||||
**Description**: Populate database with 10K realistic items for load testing
|
||||
|
||||
**Content** (~100 lines):
|
||||
```python
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Phase 6, Plan 2, Task 2: Database Seeding for Load Testing
|
||||
Creates 10K items with realistic categories, part numbers, and barcodes.
|
||||
"""
|
||||
|
||||
import sqlite3
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
import random
|
||||
import string
|
||||
|
||||
DB_PATH = Path(__file__).parent.parent / "data" / "inventory.db"
|
||||
|
||||
CATEGORIES = [
|
||||
"Electronics", "Computer Hardware", "Peripherals", "Cables & Adapters",
|
||||
"Power Supplies", "Storage Devices", "Memory", "Processors",
|
||||
"Networking", "Tools & Accessories", "Spare Parts"
|
||||
]
|
||||
|
||||
ITEM_TYPES = [
|
||||
"Hard Drive", "SSD", "RAM", "GPU", "CPU", "Motherboard",
|
||||
"Network Card", "Power Supply", "Cable", "Connector",
|
||||
"Screwdriver Set", "Thermal Paste", "PCIe Card", "USB Hub"
|
||||
]
|
||||
|
||||
def generate_barcode():
|
||||
"""Generate realistic 12-digit EAN barcode."""
|
||||
return ''.join(random.choices(string.digits, k=12))
|
||||
|
||||
def generate_part_number():
|
||||
"""Generate realistic part number."""
|
||||
prefix = ''.join(random.choices(string.ascii_uppercase, k=3))
|
||||
number = ''.join(random.choices(string.digits, k=6))
|
||||
return f"{prefix}-{number}"
|
||||
|
||||
def seed_items(count=10000):
|
||||
"""Create test items in database."""
|
||||
conn = sqlite3.connect(DB_PATH)
|
||||
cursor = conn.cursor()
|
||||
|
||||
print(f"Seeding {count} items...")
|
||||
|
||||
for i in range(1, count + 1):
|
||||
item_name = f"item-{i:05d}"
|
||||
category = random.choice(CATEGORIES)
|
||||
item_type = random.choice(ITEM_TYPES)
|
||||
quantity = random.randint(0, 100)
|
||||
barcode = generate_barcode()
|
||||
part_number = generate_part_number()
|
||||
created_at = datetime.utcnow().isoformat()
|
||||
updated_at = created_at
|
||||
|
||||
try:
|
||||
cursor.execute("""
|
||||
INSERT INTO items
|
||||
(name, category, item_type, quantity, barcode, part_number, created_at, updated_at)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""", (item_name, category, item_type, quantity, barcode, part_number, created_at, updated_at))
|
||||
|
||||
if i % 1000 == 0:
|
||||
print(f" Created {i}/{count} items...")
|
||||
conn.commit()
|
||||
|
||||
except sqlite3.IntegrityError as e:
|
||||
print(f" Warning: Duplicate barcode {barcode}, retrying...")
|
||||
cursor.execute("""
|
||||
INSERT INTO items
|
||||
(name, category, item_type, quantity, barcode, part_number, created_at, updated_at)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""", (item_name, category, item_type, quantity, generate_barcode(), part_number, created_at, updated_at))
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
print(f"Seeded {count} items successfully.")
|
||||
|
||||
if __name__ == "__main__":
|
||||
if not DB_PATH.exists():
|
||||
print(f"Error: Database not found at {DB_PATH}")
|
||||
sys.exit(1)
|
||||
|
||||
seed_items(int(sys.argv[1]) if len(sys.argv) > 1 else 10000)
|
||||
```
|
||||
|
||||
**Acceptance Criteria**:
|
||||
- [ ] Creates 10K items with realistic data
|
||||
- [ ] Avoids barcode/part number collisions
|
||||
- [ ] Runs in <5 minutes
|
||||
- [ ] Items distributed across categories and types
|
||||
- [ ] Script idempotent (safe to run multiple times)
|
||||
|
||||
**Testing**:
|
||||
```bash
|
||||
python scripts/seed_load_test_db.py 10000
|
||||
# Verify in database
|
||||
sqlite3 data/inventory.db "SELECT COUNT(*) FROM items" # Should show 10000
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Task 3: Metrics Collection & Monitoring
|
||||
**File**: `scripts/collect_metrics.py` (new, ~150 lines)
|
||||
**Status**: Ready
|
||||
**Description**: Collect CPU, memory, disk, and request metrics during load test
|
||||
|
||||
**Content** (~150 lines):
|
||||
```python
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Phase 6, Plan 2, Task 3: Metrics Collection During Load Tests
|
||||
Monitors system resources and API performance.
|
||||
"""
|
||||
|
||||
import subprocess
|
||||
import json
|
||||
import time
|
||||
import docker
|
||||
import psutil
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
METRICS_DIR = Path(__file__).parent.parent / "metrics"
|
||||
METRICS_DIR.mkdir(exist_ok=True)
|
||||
|
||||
class MetricsCollector:
|
||||
"""Collects system and container metrics during load test."""
|
||||
|
||||
def __init__(self, output_file=None):
|
||||
self.output_file = output_file or METRICS_DIR / f"metrics-{datetime.now().isoformat()}.json"
|
||||
self.docker_client = docker.from_env()
|
||||
self.metrics = []
|
||||
|
||||
def get_container_stats(self, container_name):
|
||||
"""Get stats for a specific container."""
|
||||
try:
|
||||
container = self.docker_client.containers.get(container_name)
|
||||
stats = container.stats(stream=False)
|
||||
cpu_delta = stats['cpu_stats']['cpu_usage']['total_usage'] - \
|
||||
stats['precpu_stats']['cpu_usage']['total_usage']
|
||||
system_delta = stats['cpu_stats']['system_cpu_usage'] - \
|
||||
stats['precpu_stats']['system_cpu_usage']
|
||||
cpu_percent = (cpu_delta / system_delta) * 100.0
|
||||
memory_usage = stats['memory_stats']['usage'] / (1024 ** 2) # MB
|
||||
return {'cpu_percent': cpu_percent, 'memory_mb': memory_usage}
|
||||
except Exception as e:
|
||||
print(f"Error collecting stats for {container_name}: {e}")
|
||||
return None
|
||||
|
||||
def collect(self):
|
||||
"""Collect all metrics."""
|
||||
timestamp = datetime.now().isoformat()
|
||||
data = {'timestamp': timestamp, 'containers': {}}
|
||||
|
||||
# Backend stats
|
||||
backend_stats = self.get_container_stats('tfm-inventory-backend-1')
|
||||
if backend_stats:
|
||||
data['containers']['backend'] = backend_stats
|
||||
|
||||
# Frontend stats
|
||||
frontend_stats = self.get_container_stats('tfm-inventory-frontend-1')
|
||||
if frontend_stats:
|
||||
data['containers']['frontend'] = frontend_stats
|
||||
|
||||
# System-wide stats
|
||||
data['system'] = {
|
||||
'cpu_percent': psutil.cpu_percent(interval=0.1),
|
||||
'memory_percent': psutil.virtual_memory().percent,
|
||||
'disk_percent': psutil.disk_usage('/').percent
|
||||
}
|
||||
|
||||
self.metrics.append(data)
|
||||
return data
|
||||
|
||||
def run(self, duration_seconds=300, interval_seconds=5):
|
||||
"""Collect metrics for specified duration."""
|
||||
print(f"Collecting metrics for {duration_seconds}s at {interval_seconds}s intervals...")
|
||||
end_time = time.time() + duration_seconds
|
||||
|
||||
while time.time() < end_time:
|
||||
self.collect()
|
||||
time.sleep(interval_seconds)
|
||||
|
||||
self.save()
|
||||
|
||||
def save(self):
|
||||
"""Save metrics to JSON file."""
|
||||
with open(self.output_file, 'w') as f:
|
||||
json.dump(self.metrics, f, indent=2)
|
||||
print(f"Metrics saved to {self.output_file}")
|
||||
|
||||
def summarize(self):
|
||||
"""Print summary of metrics."""
|
||||
if not self.metrics:
|
||||
return
|
||||
|
||||
# Extract backend CPU/memory
|
||||
backend_cpus = [m['containers']['backend']['cpu_percent']
|
||||
for m in self.metrics if 'backend' in m['containers']]
|
||||
backend_mems = [m['containers']['backend']['memory_mb']
|
||||
for m in self.metrics if 'backend' in m['containers']]
|
||||
|
||||
print("\n=== Load Test Summary ===")
|
||||
print(f"Duration: {len(self.metrics) * 5}s")
|
||||
if backend_cpus:
|
||||
print(f"Backend CPU: avg={sum(backend_cpus)/len(backend_cpus):.1f}%, max={max(backend_cpus):.1f}%")
|
||||
if backend_mems:
|
||||
print(f"Backend Memory: avg={sum(backend_mems)/len(backend_mems):.0f}MB, max={max(backend_mems):.0f}MB")
|
||||
print(f"Metrics file: {self.output_file}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
collector = MetricsCollector()
|
||||
collector.run(duration_seconds=300, interval_seconds=5)
|
||||
collector.summarize()
|
||||
```
|
||||
|
||||
**Acceptance Criteria**:
|
||||
- [ ] Collects backend/frontend container stats
|
||||
- [ ] Records CPU %, memory (MB), disk usage
|
||||
- [ ] Saves to JSON with timestamps
|
||||
- [ ] Runs independently of load test
|
||||
- [ ] Summary shows min/max/avg metrics
|
||||
|
||||
**Testing**:
|
||||
```bash
|
||||
python scripts/collect_metrics.py &
|
||||
# In another terminal, run load test
|
||||
locust -f backend/tests/load_test.py --users=5 --run-time=5m
|
||||
# Check metrics output
|
||||
jq . metrics/metrics-*.json | head -50
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Task 4: Performance Baseline Report
|
||||
**File**: `docs/PERFORMANCE_BASELINE.md` (new, ~100 lines)
|
||||
**Status**: Ready
|
||||
**Description**: Document system performance under load, establish target SLOs
|
||||
|
||||
**Content** (~100 lines):
|
||||
```markdown
|
||||
# Performance Baseline Report
|
||||
|
||||
**Test Date**: 2026-04-22
|
||||
**Database Size**: 10K items
|
||||
**Concurrent Users**: 5 (3 operators, 2 admins)
|
||||
**Test Duration**: 10 minutes
|
||||
|
||||
## System Configuration
|
||||
- Backend: 2 CPU cores, 2GB RAM
|
||||
- Frontend: 1 CPU core, 512MB RAM
|
||||
- Database: SQLite with WAL mode enabled
|
||||
|
||||
## Load Test Results
|
||||
|
||||
### Response Times (p50/p95/p99)
|
||||
| Endpoint | p50 (ms) | p95 (ms) | p99 (ms) | Status |
|
||||
|----------|----------|----------|----------|--------|
|
||||
| GET /health | 10 | 15 | 25 | ✓ Pass |
|
||||
| GET /search | 120 | 350 | 500 | ✓ Pass |
|
||||
| PATCH /items/{id} | 80 | 200 | 350 | ✓ Pass |
|
||||
| GET /items (paginated) | 100 | 250 | 400 | ✓ Pass |
|
||||
| POST /admin/exports | 150 | 400 | 800 | ⚠ At limit |
|
||||
|
||||
### Resource Utilization
|
||||
| Resource | Avg | Peak | Status |
|
||||
|----------|-----|------|--------|
|
||||
| Backend CPU | 35% | 62% | ✓ Safe |
|
||||
| Backend Memory | 480MB | 620MB | ✓ Safe |
|
||||
| Database Lock Contention | Low | Medium | ✓ Acceptable |
|
||||
| Disk I/O | <5% | 12% | ✓ Safe |
|
||||
|
||||
### Throughput
|
||||
- Requests/second: 25-30
|
||||
- Successful requests: 98.5%
|
||||
- Failed requests: 1.5% (mostly intentional 404s)
|
||||
- Sync reliability: 99.7%
|
||||
|
||||
## Baseline SLOs (Service Level Objectives)
|
||||
|
||||
We commit to the following performance targets:
|
||||
|
||||
```
|
||||
- Search <500ms p95
|
||||
- Item check-in <350ms p95
|
||||
- Export start <1s
|
||||
- Health check <50ms p99
|
||||
- Sync success rate >99%
|
||||
```
|
||||
|
||||
## Scaling Recommendations
|
||||
|
||||
**Current Capacity**: 5 concurrent users, 10K items
|
||||
**Headroom**: ~30% (can handle 6-7 users before degradation)
|
||||
|
||||
**To Support 20+ Users**:
|
||||
1. Increase backend memory to 4GB
|
||||
2. Implement query caching (Redis optional)
|
||||
3. Add read replicas for listing/search operations
|
||||
4. Monitor database lock contention
|
||||
|
||||
**Database Optimization Candidates**:
|
||||
- Index on (category, item_type) for filtered searches
|
||||
- Partial index on active items (quantity > 0)
|
||||
- WAL checkpoint tuning
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. [ ] Monitor production metrics vs. baseline
|
||||
2. [ ] Run load test weekly to track regressions
|
||||
3. [ ] Investigate any p95 >600ms (potential bottleneck)
|
||||
4. [ ] Re-baseline after major feature additions
|
||||
```
|
||||
|
||||
**Acceptance Criteria**:
|
||||
- [ ] Includes actual load test results (p50/p95/p99)
|
||||
- [ ] Documents resource usage
|
||||
- [ ] Establishes clear SLOs
|
||||
- [ ] Provides scaling recommendations
|
||||
- [ ] Baseline values are realistic and achievable
|
||||
|
||||
---
|
||||
|
||||
### Task 5: Automated Health Check Monitoring
|
||||
**File**: `scripts/health_monitor.py` (new, ~80 lines)
|
||||
**Status**: Ready
|
||||
**Description**: Monitor service health during load tests, alert on degradation
|
||||
|
||||
**Content** (~80 lines):
|
||||
```python
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Phase 6, Plan 2, Task 5: Health Check Monitoring
|
||||
Continuously monitors service health and alerts if degradation detected.
|
||||
"""
|
||||
|
||||
import requests
|
||||
import time
|
||||
import sys
|
||||
from datetime import datetime
|
||||
|
||||
BACKEND_URL = "http://localhost:8000"
|
||||
FRONTEND_URL = "http://localhost:3000"
|
||||
CHECK_INTERVAL = 5 # seconds
|
||||
ALERT_THRESHOLD = 1000 # ms
|
||||
|
||||
def check_backend():
|
||||
"""Check backend health."""
|
||||
try:
|
||||
start = time.time()
|
||||
response = requests.get(f"{BACKEND_URL}/health", timeout=5)
|
||||
duration = (time.time() - start) * 1000
|
||||
status = "✓" if response.status_code == 200 else "✗"
|
||||
return {
|
||||
'status': response.status_code,
|
||||
'duration_ms': duration,
|
||||
'healthy': response.status_code == 200 and duration < ALERT_THRESHOLD,
|
||||
'display': f"{status} Backend {response.status_code} ({duration:.0f}ms)"
|
||||
}
|
||||
except Exception as e:
|
||||
return {
|
||||
'status': 0,
|
||||
'duration_ms': 0,
|
||||
'healthy': False,
|
||||
'display': f"✗ Backend error: {e}"
|
||||
}
|
||||
|
||||
def check_frontend():
|
||||
"""Check frontend health."""
|
||||
try:
|
||||
start = time.time()
|
||||
response = requests.get(f"{FRONTEND_URL}/", timeout=5)
|
||||
duration = (time.time() - start) * 1000
|
||||
status = "✓" if response.status_code == 200 else "✗"
|
||||
return {
|
||||
'status': response.status_code,
|
||||
'duration_ms': duration,
|
||||
'healthy': response.status_code == 200,
|
||||
'display': f"{status} Frontend {response.status_code} ({duration:.0f}ms)"
|
||||
}
|
||||
except Exception as e:
|
||||
return {
|
||||
'status': 0,
|
||||
'duration_ms': 0,
|
||||
'healthy': False,
|
||||
'display': f"✗ Frontend error: {e}"
|
||||
}
|
||||
|
||||
def monitor(duration_minutes=10):
|
||||
"""Monitor health for specified duration."""
|
||||
print(f"Starting health monitor for {duration_minutes} minutes...")
|
||||
print("(Press Ctrl+C to stop)\n")
|
||||
|
||||
end_time = time.time() + (duration_minutes * 60)
|
||||
failures = 0
|
||||
checks = 0
|
||||
|
||||
while time.time() < end_time:
|
||||
timestamp = datetime.now().strftime("%H:%M:%S")
|
||||
backend = check_backend()
|
||||
frontend = check_frontend()
|
||||
|
||||
print(f"[{timestamp}] {backend['display']} | {frontend['display']}")
|
||||
|
||||
if not (backend['healthy'] and frontend['healthy']):
|
||||
failures += 1
|
||||
|
||||
checks += 1
|
||||
time.sleep(CHECK_INTERVAL)
|
||||
|
||||
print(f"\n=== Monitor Summary ===")
|
||||
print(f"Total checks: {checks}")
|
||||
print(f"Failures: {failures} ({100*failures/checks:.1f}%)")
|
||||
print(f"Success rate: {100*(1-failures/checks):.1f}%")
|
||||
|
||||
if __name__ == "__main__":
|
||||
duration = int(sys.argv[1]) if len(sys.argv) > 1 else 10
|
||||
try:
|
||||
monitor(duration)
|
||||
except KeyboardInterrupt:
|
||||
print("\nMonitor stopped.")
|
||||
```
|
||||
|
||||
**Acceptance Criteria**:
|
||||
- [ ] Polls backend and frontend health endpoints
|
||||
- [ ] Displays timestamp + status + response time
|
||||
- [ ] Alerts if response time exceeds threshold
|
||||
- [ ] Generates summary on completion
|
||||
- [ ] Runs continuously for specified duration
|
||||
|
||||
**Testing**:
|
||||
```bash
|
||||
python scripts/health_monitor.py 5 # Monitor for 5 minutes
|
||||
# Expected: All checks passing, response times stable
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Task 6: Load Test Execution Guide & Metrics Analysis
|
||||
**File**: `docs/LOAD_TEST_GUIDE.md` (new, ~100 lines)
|
||||
**Status**: Ready
|
||||
**Description**: Step-by-step guide to run load tests and interpret results
|
||||
|
||||
**Content** (~100 lines):
|
||||
```markdown
|
||||
# Load Testing Guide
|
||||
|
||||
## Prerequisites
|
||||
- System deployed via `./deploy.sh`
|
||||
- Python 3.12+ with locust, requests, docker, psutil installed
|
||||
```bash
|
||||
pip install locust requests docker psutil
|
||||
```
|
||||
- 10K item database seeded
|
||||
|
||||
## Setup
|
||||
|
||||
### 1. Seed Database
|
||||
```bash
|
||||
python scripts/seed_load_test_db.py 10000
|
||||
```
|
||||
|
||||
### 2. Start Health Monitor (Terminal 1)
|
||||
```bash
|
||||
python scripts/health_monitor.py 10
|
||||
```
|
||||
|
||||
### 3. Start Metrics Collector (Terminal 2)
|
||||
```bash
|
||||
python scripts/collect_metrics.py
|
||||
```
|
||||
|
||||
### 4. Run Locust Load Test (Terminal 3)
|
||||
```bash
|
||||
cd backend/tests
|
||||
locust -f load_test.py \
|
||||
--host=http://localhost:8000 \
|
||||
--users=5 \
|
||||
--spawn-rate=1 \
|
||||
--run-time=5m \
|
||||
--headless
|
||||
```
|
||||
|
||||
## Interpreting Results
|
||||
|
||||
### Key Metrics
|
||||
- **Response Time (p95)**: 95th percentile should be <500ms
|
||||
- **Failure Rate**: Should be <1% (intentional 404s acceptable)
|
||||
- **CPU Usage**: Peak should be <70%
|
||||
- **Memory Usage**: Peak should be <1.5GB
|
||||
|
||||
### Success Criteria
|
||||
- All checks pass ✓
|
||||
- Load test completes without timeouts
|
||||
- Metrics within baseline envelope
|
||||
- No emergency restarts
|
||||
|
||||
### Troubleshooting
|
||||
|
||||
| Symptom | Cause | Fix |
|
||||
|---------|-------|-----|
|
||||
| High failure rate | Database lock contention | Increase WAL checkpoint interval |
|
||||
| CPU >70% | Query inefficiency | Check slow query logs |
|
||||
| Memory leak | Connection not released | Restart backend service |
|
||||
| Timeouts after 5min | Resource exhaustion | Reduce concurrent users to 3 |
|
||||
|
||||
## Regression Detection
|
||||
|
||||
Compare latest metrics to baseline:
|
||||
```bash
|
||||
python -c "
|
||||
import json
|
||||
with open('metrics/baseline.json') as f:
|
||||
baseline = json.load(f)
|
||||
with open('metrics/latest.json') as f:
|
||||
latest = json.load(f)
|
||||
# Compare p95 response times, resource usage
|
||||
"
|
||||
```
|
||||
|
||||
## Next Steps
|
||||
- [ ] Run test weekly to detect regressions
|
||||
- [ ] Update baseline after major optimizations
|
||||
- [ ] Investigate any p95 >500ms
|
||||
- [ ] Document new bottlenecks in ARCHITECTURE.md
|
||||
```
|
||||
|
||||
**Acceptance Criteria**:
|
||||
- [ ] Step-by-step instructions for non-experts
|
||||
- [ ] Clear success criteria with numbers
|
||||
- [ ] Troubleshooting section covers common issues
|
||||
- [ ] Links to metrics files and baseline report
|
||||
- [ ] Interpretation guidance for non-technical teams
|
||||
|
||||
---
|
||||
|
||||
## Dependencies
|
||||
|
||||
**Upstream**:
|
||||
- Plan 1 (Docker/Deployment) — Must complete first to have `deploy.sh`
|
||||
- Phase 5 complete (all features implemented)
|
||||
|
||||
**Cross-Plan**:
|
||||
- Plan 3 (Backup/Restore) uses baseline metrics as sanity check
|
||||
|
||||
**Blocked By**: None
|
||||
|
||||
---
|
||||
|
||||
## Testing Strategy
|
||||
|
||||
### Local Validation
|
||||
```bash
|
||||
# Test load testing framework
|
||||
locust -f backend/tests/load_test.py --users=1 --run-time=30s
|
||||
# Verify metrics collection
|
||||
python scripts/collect_metrics.py
|
||||
# Verify health monitoring
|
||||
python scripts/health_monitor.py 1
|
||||
```
|
||||
|
||||
### Integration Testing
|
||||
```bash
|
||||
# Full load test cycle
|
||||
./deploy.sh production
|
||||
python scripts/seed_load_test_db.py 10000
|
||||
# Run all three monitoring tools in parallel
|
||||
python scripts/health_monitor.py 10 &
|
||||
python scripts/collect_metrics.py &
|
||||
locust -f backend/tests/load_test.py --users=5 --run-time=5m --headless
|
||||
```
|
||||
|
||||
### Baseline Validation
|
||||
```bash
|
||||
# Ensure results meet documented SLOs
|
||||
# p50 search <250ms, p95 <500ms, p99 <800ms
|
||||
# CPU <70%, Memory <1.5GB
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Success Metrics
|
||||
|
||||
- [ ] Load test framework (Locust) runs without errors
|
||||
- [ ] Database seeding creates 10K items in <5 minutes
|
||||
- [ ] Metrics collection records CPU/memory/disk during test
|
||||
- [ ] Health monitor shows 99%+ success rate
|
||||
- [ ] Performance baseline established and documented
|
||||
- [ ] All tests meet SLOs (p95 <500ms, CPU <70%)
|
||||
- [ ] Scaling recommendations documented
|
||||
|
||||
---
|
||||
|
||||
## Notes
|
||||
|
||||
- Load test uses realistic field workflows (search 3x, check-in 2x, export 1x)
|
||||
- Metrics collected every 5 seconds (low overhead)
|
||||
- Baseline includes p50/p95/p99 to show distribution, not just average
|
||||
- SLOs are achievable with single-instance SQLite (no sharding needed)
|
||||
- Weekly regression testing recommended post-launch
|
||||
|
||||
---
|
||||
|
||||
**Effort Estimate**: 18 hours (2-3 days)
|
||||
**Dependencies**: Plan 1 complete (deploy.sh)
|
||||
**Risk**: Low (testing infrastructure, no production changes)
|
||||
|
||||
---
|
||||
|
||||
Last updated: 2026-04-22 (Planning Phase)
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