site11/services/statistics/backend/time_series_db.py

"""
Time Series Database Interface (Simplified for InfluxDB)
"""
import logging
from typing import List, Dict, Any, Optional
from datetime import datetime
from models import Metric, AggregatedMetric

logger = logging.getLogger(__name__)

class TimeSeriesDB:
    """Time series database interface"""
    
    def __init__(self, host: str, port: int, database: str):
        self.host = host
        self.port = port
        self.database = database
        self.is_connected = False
        # In production, would use actual InfluxDB client
        self.data_store = []  # Simplified in-memory storage
        
    async def connect(self):
        """Connect to database"""
        # Simplified connection
        self.is_connected = True
        logger.info(f"Connected to time series database at {self.host}:{self.port}")
    
    async def close(self):
        """Close database connection"""
        self.is_connected = False
        logger.info("Disconnected from time series database")
    
    async def write_metrics(self, metrics: List[Metric]):
        """Write metrics to database"""
        for metric in metrics:
            self.data_store.append({
                "name": metric.name,
                "value": metric.value,
                "timestamp": metric.timestamp,
                "tags": metric.tags,
                "service": metric.service
            })
    
    async def query_metrics(
        self,
        metric_type: str,
        start_time: datetime,
        end_time: datetime
    ) -> List[Dict[str, Any]]:
        """Query metrics from database"""
        results = []
        for data in self.data_store:
            if (data["name"].startswith(metric_type) and
                start_time <= data["timestamp"] <= end_time):
                results.append(data)
        return results
    
    async def get_time_series(
        self,
        metric_name: str,
        start_time: datetime,
        end_time: datetime,
        interval: str
    ) -> List[Dict[str, Any]]:
        """Get time series data"""
        return await self.query_metrics(metric_name, start_time, end_time)
    
    async def store_aggregated_metric(self, metric: AggregatedMetric):
        """Store aggregated metric"""
        self.data_store.append({
            "name": f"agg.{metric.metric_name}",
            "value": metric.value,
            "timestamp": metric.end_time,
            "tags": {"aggregation": metric.aggregation_type},
            "service": "statistics"
        })
    
    async def count_metrics(
        self,
        metric_type: str,
        start_time: datetime,
        end_time: datetime
    ) -> int:
        """Count metrics"""
        metrics = await self.query_metrics(metric_type, start_time, end_time)
        return len(metrics)
    
    async def get_average(
        self,
        metric_name: str,
        start_time: datetime,
        end_time: datetime
    ) -> Optional[float]:
        """Get average value"""
        metrics = await self.query_metrics(metric_name, start_time, end_time)
        if not metrics:
            return None
        values = [m["value"] for m in metrics]
        return sum(values) / len(values)
    
    async def count_distinct_tags(
        self,
        metric_type: str,
        tag_name: str,
        start_time: datetime,
        end_time: datetime
    ) -> int:
        """Count distinct tag values"""
        metrics = await self.query_metrics(metric_type, start_time, end_time)
        unique_values = set()
        for metric in metrics:
            if tag_name in metric.get("tags", {}):
                unique_values.add(metric["tags"][tag_name])
        return len(unique_values)
    
    async def get_top_metrics(
        self,
        metric_type: str,
        group_by: str,
        start_time: datetime,
        end_time: datetime,
        limit: int = 10
    ) -> List[Dict[str, Any]]:
        """Get top metrics grouped by tag"""
        metrics = await self.query_metrics(metric_type, start_time, end_time)
        grouped = {}
        for metric in metrics:
            key = metric.get("tags", {}).get(group_by, "unknown")
            grouped[key] = grouped.get(key, 0) + 1
        
        sorted_items = sorted(grouped.items(), key=lambda x: x[1], reverse=True)
        return [{"name": k, "count": v} for k, v in sorted_items[:limit]]
    
    async def count_metrics_with_value(
        self,
        metric_name: str,
        value: float,
        start_time: datetime,
        end_time: datetime
    ) -> int:
        """Count metrics with specific value"""
        metrics = await self.query_metrics(metric_name, start_time, end_time)
        return sum(1 for m in metrics if m["value"] == value)
    
    async def get_metric_distribution(
        self,
        metric_type: str,
        tag_name: str,
        start_time: datetime,
        end_time: datetime
    ) -> Dict[str, int]:
        """Get metric distribution by tag"""
        metrics = await self.query_metrics(metric_type, start_time, end_time)
        distribution = {}
        for metric in metrics:
            key = metric.get("tags", {}).get(tag_name, "unknown")
            distribution[key] = distribution.get(key, 0) + 1
        return distribution
    
    async def delete_old_data(self, cutoff_date: datetime):
        """Delete old data"""
        self.data_store = [
            d for d in self.data_store
            if d["timestamp"] >= cutoff_date
        ]