chore(security): enhance environment configuration, CI workflows, and wallet daemon with security improvements
- Restructure .env.example with security-focused documentation, service-specific environment file references, and AWS Secrets Manager integration - Update CLI tests workflow to single Python 3.13 version, add pytest-mock dependency, and consolidate test execution with coverage - Add comprehensive security validation to package publishing workflow with manual approval gates, secret scanning, and release
This commit is contained in:
oib
@@ -1,32 +1,510 @@
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"""
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Marketplace Analytics System Integration Tests
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Marketplace Analytics System Tests
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Comprehensive testing for analytics, insights, reporting, and dashboards
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"""
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import pytest
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import asyncio
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import json
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import statistics
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from datetime import datetime, timedelta
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from uuid import uuid4
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from typing import Dict, Any
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from unittest.mock import Mock, patch
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from typing import Dict, Any, List
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from sqlmodel import Session, select
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from sqlalchemy.exc import SQLAlchemyError
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from apps.coordinator_api.src.app.services.analytics_service import (
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MarketplaceAnalytics, DataCollector, AnalyticsEngine, DashboardManager
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)
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from apps.coordinator_api.src.app.domain.analytics import (
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MarketMetric, MarketInsight, AnalyticsReport, DashboardConfig,
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AnalyticsPeriod, MetricType, InsightType, ReportType
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)
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class TestMarketplaceAnalytics:
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"""Test marketplace analytics functionality"""
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def test_market_metrics_calculation(self):
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"""Test market metrics calculation"""
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# Sample market data
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market_data = [
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{'price': 0.10, 'gpu_type': 'RTX 3080', 'timestamp': '2024-01-01T10:00:00Z'},
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{'price': 0.12, 'gpu_type': 'RTX 3080', 'timestamp': '2024-01-01T11:00:00Z'},
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{'price': 0.11, 'gpu_type': 'RTX 3080', 'timestamp': '2024-01-01T12:00:00Z'},
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{'price': 0.15, 'gpu_type': 'RTX 3090', 'timestamp': '2024-01-01T10:00:00Z'},
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{'price': 0.14, 'gpu_type': 'RTX 3090', 'timestamp': '2024-01-01T11:00:00Z'},
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]
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# Calculate metrics
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rtx3080_prices = [d['price'] for d in market_data if d['gpu_type'] == 'RTX 3080']
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rtx3090_prices = [d['price'] for d in market_data if d['gpu_type'] == 'RTX 3090']
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# Calculate statistics
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metrics = {
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'RTX 3080': {
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'avg_price': statistics.mean(rtx3080_prices),
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'min_price': min(rtx3080_prices),
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'max_price': max(rtx3080_prices),
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'price_volatility': statistics.stdev(rtx3080_prices) if len(rtx3080_prices) > 1 else 0
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},
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'RTX 3090': {
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'avg_price': statistics.mean(rtx3090_prices),
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'min_price': min(rtx3090_prices),
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'max_price': max(rtx3090_prices),
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'price_volatility': statistics.stdev(rtx3090_prices) if len(rtx3090_prices) > 1 else 0
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}
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}
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# Validate metrics
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assert metrics['RTX 3080']['avg_price'] == 0.11
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assert metrics['RTX 3080']['min_price'] == 0.10
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assert metrics['RTX 3080']['max_price'] == 0.12
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assert metrics['RTX 3090']['avg_price'] == 0.145
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assert metrics['RTX 3090']['min_price'] == 0.14
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assert metrics['RTX 3090']['max_price'] == 0.15
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def test_demand_analysis(self):
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"""Test demand analysis functionality"""
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# Sample demand data
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demand_data = [
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{'date': '2024-01-01', 'requests': 120, 'fulfilled': 100},
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{'date': '2024-01-02', 'requests': 150, 'fulfilled': 130},
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{'date': '2024-01-03', 'requests': 180, 'fulfilled': 160},
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{'date': '2024-01-04', 'requests': 140, 'fulfilled': 125},
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]
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# Calculate demand metrics
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total_requests = sum(d['requests'] for d in demand_data)
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total_fulfilled = sum(d['fulfilled'] for d in demand_data)
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fulfillment_rate = (total_fulfilled / total_requests) * 100
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# Calculate trend
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daily_rates = [(d['fulfilled'] / d['requests']) * 100 for d in demand_data]
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trend = 'increasing' if daily_rates[-1] > daily_rates[0] else 'decreasing'
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# Validate analysis
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assert total_requests == 590
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assert total_fulfilled == 515
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assert fulfillment_rate == 87.29 # Approximately
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assert trend == 'increasing'
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assert all(0 <= rate <= 100 for rate in daily_rates)
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def test_provider_performance(self):
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"""Test provider performance analytics"""
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# Sample provider data
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provider_data = [
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{
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'provider_id': 'provider_1',
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'total_jobs': 50,
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'completed_jobs': 45,
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'avg_completion_time': 25.5, # minutes
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'avg_rating': 4.8,
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'gpu_types': ['RTX 3080', 'RTX 3090']
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},
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{
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'provider_id': 'provider_2',
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'total_jobs': 30,
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'completed_jobs': 28,
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'avg_completion_time': 30.2,
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'avg_rating': 4.6,
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'gpu_types': ['RTX 3080']
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},
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{
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'provider_id': 'provider_3',
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'total_jobs': 40,
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'completed_jobs': 35,
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'avg_completion_time': 22.1,
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'avg_rating': 4.9,
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'gpu_types': ['RTX 3090', 'RTX 4090']
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}
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]
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# Calculate performance metrics
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for provider in provider_data:
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success_rate = (provider['completed_jobs'] / provider['total_jobs']) * 100
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provider['success_rate'] = success_rate
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# Sort by performance
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top_providers = sorted(provider_data, key=lambda x: x['success_rate'], reverse=True)
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# Validate calculations
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assert top_providers[0]['provider_id'] == 'provider_1'
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assert top_providers[0]['success_rate'] == 90.0
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assert top_providers[1]['success_rate'] == 93.33 # provider_2
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assert top_providers[2]['success_rate'] == 87.5 # provider_3
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# Validate data integrity
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for provider in provider_data:
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assert 0 <= provider['success_rate'] <= 100
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assert provider['avg_rating'] >= 0 and provider['avg_rating'] <= 5
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assert provider['avg_completion_time'] > 0
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class TestAnalyticsEngine:
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"""Test analytics engine functionality"""
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def test_data_aggregation(self):
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"""Test data aggregation capabilities"""
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# Sample time series data
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time_series_data = [
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{'timestamp': '2024-01-01T00:00:00Z', 'value': 100},
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{'timestamp': '2024-01-01T01:00:00Z', 'value': 110},
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{'timestamp': '2024-01-01T02:00:00Z', 'value': 105},
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{'timestamp': '2024-01-01T03:00:00Z', 'value': 120},
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{'timestamp': '2024-01-01T04:00:00Z', 'value': 115},
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]
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# Aggregate by hour (already hourly data)
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hourly_avg = statistics.mean([d['value'] for d in time_series_data])
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hourly_max = max([d['value'] for d in time_series_data])
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hourly_min = min([d['value'] for d in time_series_data])
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# Create aggregated summary
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aggregated_data = {
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'period': 'hourly',
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'data_points': len(time_series_data),
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'average': hourly_avg,
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'maximum': hourly_max,
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'minimum': hourly_min,
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'trend': 'up' if time_series_data[-1]['value'] > time_series_data[0]['value'] else 'down'
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}
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# Validate aggregation
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assert aggregated_data['period'] == 'hourly'
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assert aggregated_data['data_points'] == 5
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assert aggregated_data['average'] == 110.0
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assert aggregated_data['maximum'] == 120
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assert aggregated_data['minimum'] == 100
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assert aggregated_data['trend'] == 'up'
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def test_anomaly_detection(self):
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"""Test anomaly detection in metrics"""
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# Sample metrics with anomalies
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metrics_data = [
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{'timestamp': '2024-01-01T00:00:00Z', 'response_time': 100},
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{'timestamp': '2024-01-01T01:00:00Z', 'response_time': 105},
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{'timestamp': '2024-01-01T02:00:00Z', 'response_time': 98},
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{'timestamp': '2024-01-01T03:00:00Z', 'response_time': 500}, # Anomaly
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{'timestamp': '2024-01-01T04:00:00Z', 'response_time': 102},
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{'timestamp': '2024-01-01T05:00:00Z', 'response_time': 95},
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]
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# Calculate statistics for anomaly detection
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response_times = [d['response_time'] for d in metrics_data]
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mean_time = statistics.mean(response_times)
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stdev_time = statistics.stdev(response_times) if len(response_times) > 1 else 0
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# Detect anomalies (values > 2 standard deviations from mean)
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threshold = mean_time + (2 * stdev_time)
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anomalies = [
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d for d in metrics_data
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if d['response_time'] > threshold
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]
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# Validate anomaly detection
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assert len(anomalies) == 1
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assert anomalies[0]['response_time'] == 500
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assert anomalies[0]['timestamp'] == '2024-01-01T03:00:00Z'
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def test_forecasting_model(self):
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"""Test simple forecasting model"""
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# Historical data for forecasting
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historical_data = [
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{'period': '2024-01-01', 'demand': 100},
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{'period': '2024-01-02', 'demand': 110},
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{'period': '2024-01-03', 'demand': 105},
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{'period': '2024-01-04', 'demand': 120},
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{'period': '2024-01-05', 'demand': 115},
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]
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# Simple moving average forecast
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demand_values = [d['demand'] for d in historical_data]
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forecast_period = 3
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forecast = statistics.mean(demand_values[-forecast_period:])
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# Calculate forecast accuracy (using last known value as "actual")
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last_actual = demand_values[-1]
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forecast_error = abs(forecast - last_actual)
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forecast_accuracy = max(0, 100 - (forecast_error / last_actual * 100))
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# Validate forecast
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assert forecast > 0
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assert forecast_accuracy >= 0
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assert forecast_accuracy <= 100
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class TestDashboardManager:
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"""Test dashboard management functionality"""
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def test_dashboard_configuration(self):
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"""Test dashboard configuration management"""
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# Sample dashboard configuration
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dashboard_config = {
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'dashboard_id': 'marketplace_overview',
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'title': 'Marketplace Overview',
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'layout': 'grid',
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'widgets': [
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{
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'id': 'market_metrics',
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'type': 'metric_card',
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'title': 'Market Metrics',
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'position': {'x': 0, 'y': 0, 'w': 4, 'h': 2},
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'data_source': 'market_metrics_api'
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},
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{
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'id': 'price_chart',
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'type': 'line_chart',
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'title': 'Price Trends',
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'position': {'x': 4, 'y': 0, 'w': 8, 'h': 4},
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'data_source': 'price_history_api'
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},
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{
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'id': 'provider_ranking',
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'type': 'table',
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'title': 'Top Providers',
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'position': {'x': 0, 'y': 2, 'w': 6, 'h': 3},
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'data_source': 'provider_ranking_api'
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}
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],
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'refresh_interval': 300, # 5 minutes
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'permissions': ['read', 'write']
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}
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# Validate configuration
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assert dashboard_config['dashboard_id'] == 'marketplace_overview'
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assert len(dashboard_config['widgets']) == 3
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assert dashboard_config['refresh_interval'] == 300
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assert 'read' in dashboard_config['permissions']
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# Validate widgets
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for widget in dashboard_config['widgets']:
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assert 'id' in widget
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assert 'type' in widget
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assert 'title' in widget
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assert 'position' in widget
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assert 'data_source' in widget
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def test_widget_data_processing(self):
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"""Test widget data processing"""
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# Sample data for different widget types
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widget_data = {
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'metric_card': {
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'value': 1250,
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'change': 5.2,
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'change_type': 'increase',
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'unit': 'AITBC',
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'timestamp': datetime.utcnow().isoformat()
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},
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'line_chart': {
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'labels': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
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'datasets': [
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{
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'label': 'RTX 3080',
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'data': [0.10, 0.11, 0.12, 0.11, 0.13],
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'borderColor': '#007bff'
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},
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{
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'label': 'RTX 3090',
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'data': [0.15, 0.14, 0.16, 0.15, 0.17],
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'borderColor': '#28a745'
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}
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]
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},
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'table': {
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'columns': ['provider', 'jobs_completed', 'avg_rating', 'success_rate'],
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'rows': [
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['provider_1', 45, 4.8, '90%'],
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['provider_2', 28, 4.6, '93%'],
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['provider_3', 35, 4.9, '88%']
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]
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}
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}
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# Validate metric card data
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metric_data = widget_data['metric_card']
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assert isinstance(metric_data['value'], (int, float))
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assert isinstance(metric_data['change'], (int, float))
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assert metric_data['change_type'] in ['increase', 'decrease']
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assert 'timestamp' in metric_data
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# Validate line chart data
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chart_data = widget_data['line_chart']
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assert 'labels' in chart_data
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assert 'datasets' in chart_data
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assert len(chart_data['datasets']) == 2
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assert len(chart_data['labels']) == len(chart_data['datasets'][0]['data'])
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# Validate table data
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table_data = widget_data['table']
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assert 'columns' in table_data
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assert 'rows' in table_data
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assert len(table_data['columns']) == 4
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assert len(table_data['rows']) == 3
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def test_dashboard_permissions(self):
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"""Test dashboard permission management"""
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# Sample user permissions
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user_permissions = {
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'admin': ['read', 'write', 'delete', 'share'],
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'analyst': ['read', 'write', 'share'],
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'viewer': ['read'],
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'guest': []
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}
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# Sample dashboard access rules
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dashboard_access = {
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'marketplace_overview': ['admin', 'analyst', 'viewer'],
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'system_metrics': ['admin'],
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'public_stats': ['admin', 'analyst', 'viewer', 'guest']
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}
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# Test permission checking
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def check_permission(user_role, dashboard_id, action):
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if action not in user_permissions[user_role]:
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return False
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if user_role not in dashboard_access[dashboard_id]:
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return False
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return True
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# Validate permissions
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assert check_permission('admin', 'marketplace_overview', 'read') is True
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assert check_permission('admin', 'system_metrics', 'write') is True
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assert check_permission('viewer', 'system_metrics', 'read') is False
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assert check_permission('guest', 'public_stats', 'read') is True
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assert check_permission('analyst', 'marketplace_overview', 'delete') is False
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class TestReportingSystem:
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"""Test reporting system functionality"""
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def test_report_generation(self):
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"""Test report generation capabilities"""
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# Sample report data
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report_data = {
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'report_id': 'monthly_marketplace_report',
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'title': 'Monthly Marketplace Performance',
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'period': {
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'start': '2024-01-01',
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'end': '2024-01-31'
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},
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'sections': [
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{
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'title': 'Executive Summary',
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'content': {
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'total_transactions': 1250,
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'total_volume': 156.78,
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'active_providers': 45,
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'satisfaction_rate': 4.7
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}
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},
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{
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'title': 'Price Analysis',
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'content': {
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'avg_gpu_price': 0.12,
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'price_trend': 'stable',
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'volatility_index': 0.05
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}
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}
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],
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'generated_at': datetime.utcnow().isoformat(),
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'format': 'json'
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}
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# Validate report structure
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assert 'report_id' in report_data
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assert 'title' in report_data
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assert 'period' in report_data
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assert 'sections' in report_data
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assert 'generated_at' in report_data
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||||
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# Validate sections
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for section in report_data['sections']:
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assert 'title' in section
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assert 'content' in section
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# Validate data integrity
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summary = report_data['sections'][0]['content']
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assert summary['total_transactions'] > 0
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assert summary['total_volume'] > 0
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assert summary['active_providers'] > 0
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assert 0 <= summary['satisfaction_rate'] <= 5
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def test_report_export(self):
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"""Test report export functionality"""
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# Sample report for export
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report = {
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'title': 'Marketplace Analysis',
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'data': {
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'metrics': {'transactions': 100, 'volume': 50.5},
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'trends': {'price': 'up', 'demand': 'stable'}
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||||
},
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'metadata': {
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'generated_by': 'analytics_system',
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'generated_at': datetime.utcnow().isoformat()
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}
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||||
}
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||||
# Test JSON export
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json_export = json.dumps(report, indent=2)
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assert isinstance(json_export, str)
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assert 'Marketplace Analysis' in json_export
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# Test CSV export (simplified)
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csv_data = "Metric,Value\n"
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csv_data += f"Transactions,{report['data']['metrics']['transactions']}\n"
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csv_data += f"Volume,{report['data']['metrics']['volume']}\n"
|
||||
|
||||
assert 'Transactions,100' in csv_data
|
||||
assert 'Volume,50.5' in csv_data
|
||||
assert csv_data.count('\n') == 3 # Header + 2 data rows
|
||||
|
||||
def test_report_scheduling(self):
|
||||
"""Test report scheduling functionality"""
|
||||
# Sample schedule configuration
|
||||
schedule_config = {
|
||||
'report_id': 'daily_marketplace_summary',
|
||||
'frequency': 'daily',
|
||||
'time': '08:00',
|
||||
'recipients': ['admin@aitbc.com', 'ops@aitbc.com'],
|
||||
'format': 'pdf',
|
||||
'enabled': True,
|
||||
'last_run': '2024-01-01T08:00:00Z',
|
||||
'next_run': '2024-01-02T08:00:00Z'
|
||||
}
|
||||
|
||||
# Validate schedule configuration
|
||||
assert schedule_config['frequency'] in ['daily', 'weekly', 'monthly']
|
||||
assert schedule_config['time'] == '08:00'
|
||||
assert len(schedule_config['recipients']) > 0
|
||||
assert schedule_config['enabled'] is True
|
||||
assert 'next_run' in schedule_config
|
||||
|
||||
# Test next run calculation
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
last_run = datetime.fromisoformat(schedule_config['last_run'].replace('Z', '+00:00'))
|
||||
next_run = datetime.fromisoformat(schedule_config['next_run'].replace('Z', '+00:00'))
|
||||
|
||||
expected_next_run = last_run + timedelta(days=1)
|
||||
assert next_run.date() == expected_next_run.date()
|
||||
assert next_run.hour == 8
|
||||
assert next_run.minute == 0
|
||||
|
||||
|
||||
class TestDataCollector:
|
||||
"""Test data collection functionality"""
|
||||
|
||||
@pytest.fixture
|
||||
def data_collector(self):
|
||||
return DataCollector()
|
||||
def test_data_collection_metrics(self):
|
||||
"""Test data collection metrics gathering"""
|
||||
# Sample data collection metrics
|
||||
collection_metrics = {
|
||||
'total_records_collected': 10000,
|
||||
'collection_duration_seconds': 300,
|
||||
'error_rate': 0.02, # 2%
|
||||
'data_sources': ['marketplace_api', 'blockchain_api', 'user_activity'],
|
||||
'last_collection': datetime.utcnow().isoformat()
|
||||
}
|
||||
|
||||
# Validate metrics
|
||||
assert collection_metrics['total_records_collected'] > 0
|
||||
assert collection_metrics['collection_duration_seconds'] > 0
|
||||
assert 0 <= collection_metrics['error_rate'] <= 1
|
||||
assert len(collection_metrics['data_sources']) > 0
|
||||
assert 'last_collection' in collection_metrics
|
||||
|
||||
# Calculate collection rate
|
||||
collection_rate = collection_metrics['total_records_collected'] / collection_metrics['collection_duration_seconds']
|
||||
assert collection_rate > 10 # Should collect at least 10 records per second
|
||||
|
||||
def test_collect_transaction_volume(self, data_collector):
|
||||
"""Test transaction volume collection"""
|
||||
|
||||
Reference in New Issue
Block a user