Andrey
ae7bd476bb
- Removed the metrics scheduler functionality from the bot, transitioning to real-time metrics updates via middleware. - Enhanced logging for metrics operations across various handlers to improve monitoring and debugging capabilities. - Integrated metrics tracking for user activities and database errors, providing better insights into bot performance. - Cleaned up code by removing obsolete comments and unused imports, improving overall readability and maintainability.
353 lines
12 KiB
Python
353 lines
12 KiB
Python
"""
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Metrics module for Telegram bot monitoring with Prometheus.
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Provides predefined metrics for bot commands, errors, performance, and user activity.
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"""
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from typing import Dict, Any, Optional
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from prometheus_client import Counter, Histogram, Gauge, generate_latest, CONTENT_TYPE_LATEST
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from prometheus_client.core import CollectorRegistry
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import time
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from functools import wraps
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import asyncio
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from contextlib import asynccontextmanager
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class BotMetrics:
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"""Central class for managing all bot metrics."""
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def __init__(self):
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self.registry = CollectorRegistry()
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# Bot commands counter
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self.bot_commands_total = Counter(
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'bot_commands_total',
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'Total number of bot commands processed',
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['command', 'status', 'handler_type', 'user_type'],
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registry=self.registry
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)
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# Method execution time histogram
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self.method_duration_seconds = Histogram(
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'method_duration_seconds',
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'Time spent executing methods',
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['method_name', 'handler_type', 'status'],
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# Оптимизированные buckets для Telegram API (обычно < 1 сек)
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buckets=[0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0],
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registry=self.registry
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)
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# Errors counter
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self.errors_total = Counter(
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'errors_total',
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'Total number of errors',
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['error_type', 'handler_type', 'method_name'],
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registry=self.registry
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)
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# Active users gauge
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self.active_users = Gauge(
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'active_users',
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'Number of currently active users',
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['user_type'],
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registry=self.registry
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)
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# Database query metrics
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self.db_query_duration_seconds = Histogram(
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'db_query_duration_seconds',
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'Time spent executing database queries',
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['query_type', 'table_name', 'operation'],
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# Оптимизированные buckets для SQLite/PostgreSQL
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buckets=[0.001, 0.005, 0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5],
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registry=self.registry
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)
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# Database queries counter
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self.db_queries_total = Counter(
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'db_queries_total',
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'Total number of database queries executed',
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['query_type', 'table_name', 'operation'],
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registry=self.registry
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)
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# Database errors counter
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self.db_errors_total = Counter(
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'db_errors_total',
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'Total number of database errors',
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['error_type', 'query_type', 'table_name', 'operation'],
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registry=self.registry
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)
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# Message processing metrics
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self.messages_processed_total = Counter(
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'messages_processed_total',
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'Total number of messages processed',
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['message_type', 'chat_type', 'handler_type'],
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registry=self.registry
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)
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# Middleware execution metrics
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self.middleware_duration_seconds = Histogram(
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'middleware_duration_seconds',
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'Time spent in middleware execution',
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['middleware_name', 'status'],
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# Middleware должен быть быстрым
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buckets=[0.001, 0.005, 0.01, 0.05, 0.1, 0.25],
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registry=self.registry
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)
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# Rate limiting metrics
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self.rate_limit_hits_total = Counter(
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'rate_limit_hits_total',
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'Total number of rate limit hits',
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['limit_type', 'user_id', 'action'],
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registry=self.registry
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)
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# User activity metrics
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self.user_activity_total = Counter(
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'user_activity_total',
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'Total user activity events',
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['activity_type', 'user_type', 'chat_type'],
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registry=self.registry
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)
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def record_command(self, command_type: str, handler_type: str = "unknown", user_type: str = "unknown", status: str = "success"):
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"""Record a bot command execution."""
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self.bot_commands_total.labels(
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command=command_type,
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status=status,
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handler_type=handler_type,
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user_type=user_type
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).inc()
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def record_error(self, error_type: str, handler_type: str = "unknown", method_name: str = "unknown"):
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"""Record an error occurrence."""
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self.errors_total.labels(
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error_type=error_type,
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handler_type=handler_type,
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method_name=method_name
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).inc()
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def record_method_duration(self, method_name: str, duration: float, handler_type: str = "unknown", status: str = "success"):
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"""Record method execution duration."""
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self.method_duration_seconds.labels(
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method_name=method_name,
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handler_type=handler_type,
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status=status
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).observe(duration)
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def set_active_users(self, count: int, user_type: str = "daily"):
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"""Set the number of active users for a specific type."""
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self.active_users.labels(user_type=user_type).set(count)
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def record_db_query(self, query_type: str, duration: float, table_name: str = "unknown", operation: str = "unknown"):
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"""Record database query duration."""
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self.db_query_duration_seconds.labels(
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query_type=query_type,
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table_name=table_name,
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operation=operation
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).observe(duration)
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self.db_queries_total.labels(
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query_type=query_type,
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table_name=table_name,
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operation=operation
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).inc()
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def record_message(self, message_type: str, chat_type: str = "unknown", handler_type: str = "unknown"):
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"""Record a processed message."""
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self.messages_processed_total.labels(
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message_type=message_type,
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chat_type=chat_type,
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handler_type=handler_type
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).inc()
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def record_middleware(self, middleware_name: str, duration: float, status: str = "success"):
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"""Record middleware execution duration."""
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self.middleware_duration_seconds.labels(
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middleware_name=middleware_name,
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status=status
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).observe(duration)
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def get_metrics(self) -> bytes:
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"""Generate metrics in Prometheus format."""
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return generate_latest(self.registry)
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# Global metrics instance
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metrics = BotMetrics()
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# Decorators for easy metric collection
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def track_time(method_name: str = None, handler_type: str = "unknown"):
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"""Decorator to track execution time of functions."""
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def decorator(func):
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@wraps(func)
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async def async_wrapper(*args, **kwargs):
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start_time = time.time()
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try:
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result = await func(*args, **kwargs)
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duration = time.time() - start_time
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metrics.record_method_duration(
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method_name or func.__name__,
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duration,
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handler_type,
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"success"
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)
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return result
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except Exception as e:
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duration = time.time() - start_time
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metrics.record_method_duration(
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method_name or func.__name__,
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duration,
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handler_type,
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"error"
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)
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metrics.record_error(
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type(e).__name__,
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handler_type,
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method_name or func.__name__
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)
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raise
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@wraps(func)
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def sync_wrapper(*args, **kwargs):
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start_time = time.time()
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try:
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result = func(*args, **kwargs)
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duration = time.time() - start_time
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metrics.record_method_duration(
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method_name or func.__name__,
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duration,
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handler_type,
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"success"
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)
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return result
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except Exception as e:
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duration = time.time() - start_time
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metrics.record_method_duration(
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method_name or func.__name__,
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duration,
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handler_type,
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"error"
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)
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metrics.record_error(
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type(e).__name__,
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handler_type,
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method_name or func.__name__
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)
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raise
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if asyncio.iscoroutinefunction(func):
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return async_wrapper
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return sync_wrapper
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return decorator
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def track_errors(handler_type: str = "unknown", method_name: str = None):
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"""Decorator to track errors in functions."""
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def decorator(func):
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@wraps(func)
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async def async_wrapper(*args, **kwargs):
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try:
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return await func(*args, **kwargs)
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except Exception as e:
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metrics.record_error(
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type(e).__name__,
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handler_type,
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method_name or func.__name__
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)
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raise
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@wraps(func)
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def sync_wrapper(*args, **kwargs):
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try:
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return func(*args, **kwargs)
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except Exception as e:
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metrics.record_error(
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type(e).__name__,
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handler_type,
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method_name or func.__name__
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)
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raise
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if asyncio.iscoroutinefunction(func):
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return async_wrapper
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return sync_wrapper
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return decorator
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def db_query_time(query_type: str = "unknown", table_name: str = "unknown", operation: str = "unknown"):
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"""Decorator to track database query execution time."""
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def decorator(func):
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@wraps(func)
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async def async_wrapper(*args, **kwargs):
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start_time = time.time()
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try:
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result = await func(*args, **kwargs)
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duration = time.time() - start_time
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metrics.record_db_query(query_type, duration, table_name, operation)
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return result
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except Exception as e:
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duration = time.time() - start_time
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metrics.record_db_query(query_type, duration, table_name, operation)
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metrics.record_db_error(
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type(e).__name__,
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query_type,
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table_name,
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operation
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)
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metrics.record_error(
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type(e).__name__,
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"database",
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func.__name__
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)
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raise
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@wraps(func)
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def sync_wrapper(*args, **kwargs):
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start_time = time.time()
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try:
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result = func(*args, **kwargs)
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duration = time.time() - start_time
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metrics.record_db_query(query_type, duration, table_name, operation)
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return result
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except Exception as e:
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duration = time.time() - start_time
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metrics.record_db_query(query_type, duration, table_name, operation)
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metrics.record_db_error(
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type(e).__name__,
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query_type,
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table_name,
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operation
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)
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metrics.record_error(
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type(e).__name__,
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"database",
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func.__name__
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)
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raise
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if asyncio.iscoroutinefunction(func):
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return async_wrapper
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return sync_wrapper
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return decorator
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@asynccontextmanager
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async def track_middleware(middleware_name: str):
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"""Context manager to track middleware execution time."""
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start_time = time.time()
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try:
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yield
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duration = time.time() - start_time
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metrics.record_middleware(middleware_name, duration, "success")
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except Exception as e:
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duration = time.time() - start_time
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metrics.record_middleware(middleware_name, duration, "error")
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metrics.record_error(
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type(e).__name__,
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"middleware",
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middleware_name
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)
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raise
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