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This directory contains Locust load tests for validating Atom API performance under concurrent user load.
## Overview
Load testing simulates multiple concurrent users interacting with the API to identify performance bottlenecks, establish capacity limits, and validate that Phase 208 performance benchmarks hold under load.
**Key Differences from Phase 208 Benchmarks:**
- **Phase 208**: Single-user benchmarks establishing targets (<1ms cache, <100ms API)
- **Phase 209**: Multi-user load testing validating targets under concurrent load (100-1000 users)
## Prerequisites
1. **Locust installed** (already in requirements-testing.txt):
```bash
pip install locust>=2.15.0
```
2. **Application running**:
```bash
cd backend
python -m uvicorn main:app --host 0.0.0.0 --port 8000
```
3. **Test database** (optional but recommended):
```bash
export DATABASE_URL=sqlite:///./atom_load_test.db
```
## Running Locust
### Interactive Mode (Web UI)
Start Locust with web UI for real-time monitoring:
```bash
cd backend
locust -f tests/load/locustfile.py
```
Then open http://localhost:8089 in your browser.
**Web UI Features:**
- Real-time request rate (RPS) monitoring
- Response time percentiles (P50, P95, P99)
- Failure rate tracking
- User count adjustment during test
- Stop/start test control
**Default Settings:**
- Host: http://localhost:8000
- Web UI Port: 8089
- Default Users: Start with 100 users
- Spawn Rate: 10 users/second
### Headless Mode (CI/CD)
Run load tests without web UI for automation:
```bash
cd backend
locust -f tests/load/locustfile.py --headless \
-u 100 \ # 100 concurrent users
-r 10 \ # Spawn 10 users/second
-t 5m \ # Run for 5 minutes
--html tests/load/reports/load-test-report.html \
--json tests/load/reports/load-test-results.json
```
**Recommended Load Test Profiles:**
| Profile | Users | Duration | Purpose |
|---------|-------|----------|---------|
| Smoke Test | 50 | 2m | Quick validation, CI/CD |
| Standard Load | 100 | 5m | Normal traffic simulation |
| Peak Load | 500 | 10m | Peak traffic validation |
| Stress Test | 1000 | 15m | Identify breaking point |
### Custom Port (Avoid Conflicts)
If port 8089 is in use:
```bash
locust -f tests/load/locustfile.py --web-port=8090
```
## User Scenarios
### 1. AtomAPIUser (Base User)
**Purpose**: Basic API interactions with health checks
**Tasks:**
- Health check (weight: 1) - GET /health/live
**Wait Time**: 1-3 seconds between tasks
**When to Use**: Baseline health check performance
### 2. AgentAPIUser
**Purpose**: Simulate agent management operations
**Tasks:**
- List agents (weight: 5) - GET /api/v1/agents
- Get agent (weight: 3) - GET /api/v1/agents/{id}
- Create agent (weight: 1) - POST /api/v1/agents
**Wait Time**: 1-3 seconds between tasks
**Phase 208 Targets:**
- List agents: <50ms
- Get agent: <50ms
- Create agent: <100ms
**When to Use**: Testing agent CRUD performance
### 3. WorkflowExecutionUser
**Purpose**: Simulate workflow execution operations
**Tasks:**
- Execute workflow (weight: 2) - POST /api/v1/workflows/{id}/execute
- List workflows (weight: 1) - GET /api/v1/workflows
**Wait Time**: 2-5 seconds (longer for workflow execution)
**Phase 208 Targets:**
- Execute workflow: <100ms
- List workflows: <50ms
**When to Use**: Testing workflow performance under load
### 4. GovernanceCheckUser
**Purpose**: Simulate governance permission checks
**Tasks:**
- Check permission (weight: 4) - POST /api/agent-governance/check-permission
- Get cache stats (weight: 2) - GET /api/agent-governance/cache-stats
**Wait Time**: 1-3 seconds between tasks
**Phase 208 Targets:**
- Cached permission check: <1ms
- Cache stats: <50ms
**When to Use**: Testing governance cache performance
### 5. EpisodeAPIUser
**Purpose**: Simulate episodic memory retrieval
**Tasks:**
- List episodes (weight: 3) - GET /api/v1/episodes
- Get episode (weight: 2) - GET /api/v1/episodes/{id}
**Wait Time**: 1-3 seconds between tasks
**Phase 208 Targets:**
- List episodes: <50ms
- Get episode: <50ms
**When to Use**: Testing episode retrieval performance
## Critical Endpoints
| Endpoint | Method | Purpose | Weight | Phase 208 Target |
|----------|--------|---------|--------|------------------|
| /health/live | GET | Health check | 1 | <10ms |
| /api/v1/agents | GET | List agents | 5 | <50ms |
| /api/v1/agents/{id} | GET | Get agent | 3 | <50ms |
| /api/v1/agents | POST | Create agent | 1 | <100ms |
| /api/v1/workflows/{id}/execute | POST | Execute workflow | 2 | <100ms |
| /api/v1/workflows | GET | List workflows | 1 | <50ms |
| /api/agent-governance/check-permission | POST | Check permission | 4 | <1ms (cached) |
| /api/agent-governance/cache-stats | GET | Cache stats | 2 | <50ms |
| /api/v1/episodes | GET | List episodes | 3 | <50ms |
| /api/v1/episodes/{id} | GET | Get episode | 2 | <50ms |
## Interpreting Results
### Key Metrics
**Requests Per Second (RPS):**
- Measure of throughput
- Higher is better (indicates capacity)
- Target: Maintain RPS as user count increases
**Response Time Percentiles:**
- **P50 (Median)**: 50% of requests complete faster than this
- **P95**: 95% of requests complete faster than this (SLA target)
- **P99**: 99% of requests complete faster than this (tail latency)
**Failure Rate:**
- Percentage of failed requests (non-2xx status codes)
- Target: <1% failure rate
- Common failures: 500 (server error), 503 (service unavailable)
### Example Output
```
Name # reqs # fails | Avg Min Max Med | req/s failures/s
---------------------------------------------------------------------------------------------------------------------------------------
GET /health/live 150 0(0.00%) | 8 5 15 8 | 10.00 0.00
GET /api/v1/agents 75 0(0.00%) | 45 20 80 42 | 5.00 0.00
POST /api/v1/agents 5 0(0.00%) | 95 70 120 90 | 0.33 0.00
---------------------------------------------------------------------------------------------------------------------------------------
Aggregate 230 0(0.00%) | 32 5 120 20 | 15.33 0.00
Response time percentiles (approximate)
50% 20ms
66% 25ms
75% 30ms
80% 35ms
90% 50ms
95% 70ms
98% 90ms
99% 110ms
100% 120ms (longest request)
```
### Performance Indicators
**Good Performance:**
- P95 response time < Phase 208 targets (e.g., <50ms for agents)
- Failure rate <1%
- RPS scales linearly with user count
- No response time spikes as user count increases
**Performance Issues:**
- P95 response time increases with user count (bottleneck)
- Failure rate >5% (capacity limit)
- RPS plateaus or decreases (system overload)
- Response time spikes during test (resource exhaustion)
## Troubleshooting
### Server Not Running
**Symptom:** Connection refused errors
**Solution:**
```bash
# Check if server is running
curl http://localhost:8000/health/live
# Start server if not running
cd backend
python -m uvicorn main:app --host 0.0.0.0 --port 8000
```
### Authentication Failures
**Symptom:** 401 Unauthorized errors
**Solution:**
- Load tests use hardcoded credentials (`load_test@example.com`)
- Ensure test user exists in database
- Check authentication endpoint is working:
```bash
curl -X POST http://localhost:8000/api/v1/auth/login \
-H "Content-Type: application/json" \
-d '{"email": "load_test@example.com", "password": "test_password_123"}'
```
### Port Conflicts
**Symptom:** "Address already in use" error
**Solution:**
```bash
# Use different port for Locust web UI
locust -f tests/load/locustfile.py --web-port=8090
```
### Database Connection Pool Exhaustion
**Symptom:** "Connection pool exhausted" errors, timeouts
**Solution:**
```bash
# Increase connection pool size
export SQLALCHEMY_POOL_SIZE=20
export SQLALCHEMY_MAX_OVERFLOW=40
export SQLALCHEMY_POOL_TIMEOUT=30
# Restart server with new settings
python -m uvicorn main:app --host 0.0.0.0 --port 8000
```
### High Failure Rate
**Symptom:** >5% failure rate with 500 errors
**Possible Causes:**
1. **Database overload**: Reduce concurrent users, increase pool size
2. **Memory pressure**: Check server memory usage, reduce user count
3. **Rate limiting**: Check if API has rate limits configured
4. **External service failures**: Check LLM provider, external APIs
**Debugging:**
```bash
# Check server logs
tail -f logs/atom.log | grep ERROR
# Monitor server resources
htop # or top on macOS
# Check database connections
# For PostgreSQL:
psql -c "SELECT count(*) FROM pg_stat_activity;"
```
### Slow Response Times
**Symptom:** P95 > 2x Phase 208 targets
**Possible Causes:**
1. **Cache misses**: Cold cache, low hit rate
2. **Database queries**: Missing indexes, N+1 queries
3. **Network latency**: Local vs remote database
4. **Resource contention**: CPU, memory, disk I/O
**Debugging:**
```bash
# Check cache hit rate
curl http://localhost:8000/api/agent-governance/cache-stats
# Profile database queries
# Enable query logging in SQLAlchemy
export SQLALCHEMY_ECHO=true
# Run with profiling
python -m cProfile -o profile.stats -m uvicorn main:app
```
## CI Integration
### GitHub Actions Workflow
```yaml
name: Load Tests
on:
schedule:
# Run daily at 2 AM UTC
- cron: '0 2 * * *'
pull_request:
paths:
- 'backend/core/**'
- 'backend/api/**'
jobs:
load-test:
name: Run Load Tests
runs-on: ubuntu-large
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install dependencies
run: |
cd backend
pip install -r requirements.txt
pip install -r requirements-testing.txt
- name: Start application
run: |
cd backend
python -m uvicorn main:app --host 0.0.0.0 --port 8000 &
sleep 10
- name: Run load tests
run: |
cd backend
locust -f tests/load/locustfile.py --headless \
-u 100 \
-r 10 \
-t 5m \
--html tests/load/reports/load-test-report.html \
--json tests/load/reports/load-test-results.json
- name: Upload load test report
uses: actions/upload-artifact@v4
with:
name: load-test-report
path: backend/tests/load/reports/load-test-report.html
```
### Quick Smoke Test for CI
For faster CI feedback, run a 2-minute smoke test:
```bash
locust -f tests/load/locustfile.py --headless \
-u 50 \ # Fewer users for speed
-r 5 \ # Slower spawn rate
-t 2m \ # Shorter duration
--exit-code-on-error # Fail CI if load test fails
```
## Best Practices
1. **Warm up the cache**: Run for 1-2 minutes before collecting metrics
2. **Start small**: Begin with 50 users, scale up gradually
3. **Monitor resources**: Watch CPU, memory, database connections during test
4. **Use realistic data**: Test with production-like data volumes
5. **Run multiple times**: Performance can vary due to system load
6. **Compare to baseline**: Track performance over time to detect regressions
7. **Test in staging**: Never run load tests in production without limits
## Next Steps
After running load tests:
1. Review HTML report for detailed metrics
2. Compare P95 times to Phase 208 targets
3. Identify bottlenecks (database, cache, network)
4. Optimize slow endpoints
5. Re-run load tests to validate improvements
6. Update baseline metrics if performance improved
## References
- **Phase 208 Benchmarks**: `.planning/phases/208-integration-performance-testing/208-07-PERFORMANCE-METRICS.md`
- **Locust Documentation**: https://docs.locust.io/
- **Load Testing Research**: `.planning/phases/209-load-stress-testing/209-RESEARCH.md`
- **Monitoring Setup**: `backend/docs/MONITORING_SETUP.md`
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