Step 10: 데이터 분석 및 통계 시스템 구현

주요 기능:
- Statistics Service 마이크로서비스 구축
- 실시간 메트릭 수집 시스템 (Kafka 연동)
- 시계열 데이터베이스 인터페이스 구현
- 데이터 집계 및 분석 엔진
- 사용자/시스템/이벤트 분석 API
- WebSocket 기반 실시간 대시보드
- 알림 규칙 및 임계값 설정
- CSV 데이터 내보내기

구현된 컴포넌트:
- MetricsCollector: Kafka 이벤트 메트릭 수집
- DataAggregator: 시간별/일별 데이터 집계
- TimeSeriesDB: 시계열 데이터 저장 인터페이스
- WebSocketManager: 실시간 데이터 스트리밍
- Analytics APIs: 다양한 분석 엔드포인트

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

services/statistics/backend/models.py

@@ -0,0 +1,159 @@
+"""
+Data models for Statistics Service
+"""
+from pydantic import BaseModel, Field
+from datetime import datetime
+from typing import Optional, List, Dict, Any, Literal
+from enum import Enum
+
+class MetricType(str, Enum):
+    """Types of metrics"""
+    COUNTER = "counter"
+    GAUGE = "gauge"
+    HISTOGRAM = "histogram"
+    SUMMARY = "summary"
+
+class AggregationType(str, Enum):
+    """Types of aggregation"""
+    AVG = "avg"
+    SUM = "sum"
+    MIN = "min"
+    MAX = "max"
+    COUNT = "count"
+    PERCENTILE = "percentile"
+
+class Granularity(str, Enum):
+    """Time granularity for aggregation"""
+    MINUTE = "minute"
+    HOUR = "hour"
+    DAY = "day"
+    WEEK = "week"
+    MONTH = "month"
+
+class Metric(BaseModel):
+    """Single metric data point"""
+    id: Optional[str] = Field(None, description="Unique metric ID")
+    name: str = Field(..., description="Metric name")
+    type: MetricType = Field(..., description="Metric type")
+    value: float = Field(..., description="Metric value")
+    tags: Dict[str, str] = Field(default_factory=dict, description="Metric tags")
+    timestamp: datetime = Field(default_factory=datetime.now, description="Metric timestamp")
+    service: str = Field(..., description="Source service")
+    
+    class Config:
+        json_encoders = {
+            datetime: lambda v: v.isoformat()
+        }
+
+class AggregatedMetric(BaseModel):
+    """Aggregated metric result"""
+    metric_name: str
+    aggregation_type: AggregationType
+    value: float
+    start_time: datetime
+    end_time: datetime
+    granularity: Optional[Granularity] = None
+    group_by: Optional[str] = None
+    count: int = Field(..., description="Number of data points aggregated")
+    
+    class Config:
+        json_encoders = {
+            datetime: lambda v: v.isoformat()
+        }
+
+class TimeSeriesData(BaseModel):
+    """Time series data response"""
+    metric_name: str
+    start_time: datetime
+    end_time: datetime
+    interval: str
+    data: List[Dict[str, Any]]
+    
+    class Config:
+        json_encoders = {
+            datetime: lambda v: v.isoformat()
+        }
+
+class DashboardConfig(BaseModel):
+    """Dashboard configuration"""
+    id: str
+    name: str
+    description: Optional[str] = None
+    widgets: List[Dict[str, Any]]
+    refresh_interval: int = Field(60, description="Refresh interval in seconds")
+    created_at: datetime = Field(default_factory=datetime.now)
+    updated_at: datetime = Field(default_factory=datetime.now)
+    
+    class Config:
+        json_encoders = {
+            datetime: lambda v: v.isoformat()
+        }
+
+class AlertRule(BaseModel):
+    """Alert rule configuration"""
+    id: Optional[str] = None
+    name: str
+    metric_name: str
+    condition: Literal["gt", "lt", "gte", "lte", "eq", "neq"]
+    threshold: float
+    duration: int = Field(..., description="Duration in seconds")
+    severity: Literal["low", "medium", "high", "critical"]
+    enabled: bool = True
+    notification_channels: List[str] = Field(default_factory=list)
+    created_at: datetime = Field(default_factory=datetime.now)
+    
+    class Config:
+        json_encoders = {
+            datetime: lambda v: v.isoformat()
+        }
+
+class Alert(BaseModel):
+    """Active alert"""
+    id: str
+    rule_id: str
+    rule_name: str
+    metric_name: str
+    current_value: float
+    threshold: float
+    severity: str
+    triggered_at: datetime
+    resolved_at: Optional[datetime] = None
+    status: Literal["active", "resolved", "acknowledged"]
+    
+    class Config:
+        json_encoders = {
+            datetime: lambda v: v.isoformat()
+        }
+
+class UserAnalytics(BaseModel):
+    """User analytics data"""
+    total_users: int
+    active_users: int
+    new_users: int
+    user_growth_rate: float
+    average_session_duration: float
+    top_actions: List[Dict[str, Any]]
+    user_distribution: Dict[str, int]
+    period: str
+    
+class SystemAnalytics(BaseModel):
+    """System performance analytics"""
+    uptime_percentage: float
+    average_response_time: float
+    error_rate: float
+    throughput: float
+    cpu_usage: float
+    memory_usage: float
+    disk_usage: float
+    active_connections: int
+    services_health: Dict[str, str]
+    
+class EventAnalytics(BaseModel):
+    """Event analytics data"""
+    total_events: int
+    events_per_second: float
+    event_types: Dict[str, int]
+    top_events: List[Dict[str, Any]]
+    error_events: int
+    success_rate: float
+    processing_time: Dict[str, float]