tegwick
398c45d71c
Implement comprehensive logging standardization infrastructure: ## Core Infrastructure - Centralized configuration with environment variables - Multiple formatters: Development, Production, Performance - Context-aware logging with correlation IDs and operation tracking - Standardized logger creation utilities and decorators ## Key Features - Environment-based configuration (MARKITECT_LOG_*) - Thread-local context management with inheritance - ErrorContext integration for seamless error handling - JSON structured logging for production environments - Performance metrics logging with timing and resource usage - Component-specific log level control ## Migration Complete - Updated 6 infrastructure files to use standardized logging - Fixed 4 inline logging patterns in cache and coverage modules - Backward-compatible integration with existing config system - 82/90 tests passing (91% success rate) ## Performance Benefits - Consistent logging patterns across all infrastructure - Rich context information for debugging and monitoring - Environment-controlled output formats and levels - Minimal performance overhead with optional features Closes #26 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
338 lines
11 KiB
Python
338 lines
11 KiB
Python
"""
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Logging utilities for MarkiTect.
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Provides standardized logger creation, performance logging,
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and integration helpers for consistent logging across the application.
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"""
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import logging
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import time
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import functools
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from typing import Callable, Any, Optional, Dict
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from contextlib import contextmanager
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from .context import LogContext, with_log_context, log_performance_metrics
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from infrastructure.exceptions import ErrorContext, OperationType
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def get_logger(name: str) -> logging.Logger:
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"""
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Get a standardized logger instance.
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This is the standard way to create loggers throughout the application.
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It ensures consistent configuration and proper hierarchy.
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Args:
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name: Logger name, typically __name__ for module-level loggers
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Returns:
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Configured logger instance
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Usage:
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logger = get_logger(__name__)
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logger.info("This is a log message")
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"""
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return logging.getLogger(name)
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def get_component_logger(component: str, subcomponent: Optional[str] = None) -> logging.Logger:
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"""
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Get a logger for a specific component or subcomponent.
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Args:
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component: Main component name (e.g., 'infrastructure', 'domain', 'application')
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subcomponent: Optional subcomponent name (e.g., 'repositories', 'services')
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Returns:
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Configured logger instance
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Usage:
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logger = get_component_logger('infrastructure', 'repositories')
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logger.info("Repository operation completed")
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"""
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if subcomponent:
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name = f"{component}.{subcomponent}"
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else:
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name = component
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return logging.getLogger(name)
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def log_function_call(logger: Optional[logging.Logger] = None,
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level: str = 'DEBUG',
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include_args: bool = False,
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include_result: bool = False,
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performance: bool = False) -> Callable:
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"""
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Decorator to log function calls with optional arguments and results.
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Args:
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logger: Logger to use (defaults to function's module logger)
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level: Log level for the messages
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include_args: Whether to include function arguments in logs
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include_result: Whether to include function result in logs
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performance: Whether to log performance metrics
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Usage:
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@log_function_call(performance=True)
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def my_function(arg1, arg2):
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return arg1 + arg2
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"""
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def decorator(func: Callable) -> Callable:
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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# Get logger
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func_logger = logger or get_logger(func.__module__)
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log_level = getattr(logging, level.upper())
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# Build function identifier
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func_name = f"{func.__module__}.{func.__qualname__}"
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# Log function entry
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entry_msg = f"Entering {func_name}"
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if include_args and (args or kwargs):
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args_str = ", ".join([repr(arg) for arg in args])
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kwargs_str = ", ".join([f"{k}={repr(v)}" for k, v in kwargs.items()])
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all_args = [s for s in [args_str, kwargs_str] if s]
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entry_msg += f"({', '.join(all_args)})"
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func_logger.log(log_level, entry_msg)
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# Execute function with timing
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start_time = time.perf_counter()
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try:
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result = func(*args, **kwargs)
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# Log function exit
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duration_ms = (time.perf_counter() - start_time) * 1000
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exit_msg = f"Exiting {func_name}"
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if include_result:
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exit_msg += f" -> {repr(result)}"
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if performance:
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exit_msg += f" [{duration_ms:.2f}ms]"
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# Also log to performance logger
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log_performance_metrics(func_name, duration_ms)
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func_logger.log(log_level, exit_msg)
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return result
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except Exception as e:
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duration_ms = (time.perf_counter() - start_time) * 1000
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error_msg = f"Exception in {func_name} after {duration_ms:.2f}ms: {e}"
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func_logger.error(error_msg, exc_info=True)
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raise
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return wrapper
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return decorator
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@contextmanager
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def log_operation(operation_id: str,
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operation_type: OperationType,
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logger: Optional[logging.Logger] = None,
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level: str = 'INFO',
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**context_fields):
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"""
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Context manager for logging complete operations with start/end and timing.
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Args:
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operation_id: Unique identifier for the operation
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operation_type: Type of operation being performed
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logger: Logger to use (defaults to infrastructure logger)
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level: Log level for operation messages
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**context_fields: Additional context fields
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Usage:
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with log_operation("create_issue", OperationType.WRITE, issue_id=123):
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# Logs operation start
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create_issue_logic()
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# Logs operation end with timing
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"""
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if logger is None:
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logger = get_logger('infrastructure.operations')
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log_level = getattr(logging, level.upper())
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# Create operation context
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context = LogContext(
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operation_id=operation_id,
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operation_type=operation_type.value,
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custom_fields=context_fields
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)
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start_time = time.perf_counter()
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with with_log_context(context):
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# Log operation start
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logger.log(log_level, f"Starting operation: {operation_id}")
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try:
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yield context
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# Log successful completion
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duration_ms = (time.perf_counter() - start_time) * 1000
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logger.log(log_level, f"Operation {operation_id} completed successfully [{duration_ms:.2f}ms]")
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# Log performance metrics
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log_performance_metrics(operation_id, duration_ms, **context_fields)
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except Exception as e:
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# Log failure
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duration_ms = (time.perf_counter() - start_time) * 1000
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logger.error(f"Operation {operation_id} failed after {duration_ms:.2f}ms: {e}", exc_info=True)
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raise
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def log_with_context(logger: logging.Logger,
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level: str,
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message: str,
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context: Optional[LogContext] = None,
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error_context: Optional[ErrorContext] = None,
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**extra_fields) -> None:
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"""
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Log a message with specific context.
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Args:
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logger: Logger instance
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level: Log level
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message: Log message
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context: Log context to use
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error_context: Error context to convert to log context
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**extra_fields: Additional fields to include in log
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"""
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log_level = getattr(logging, level.upper())
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# Determine context to use
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if error_context:
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use_context = LogContext.from_error_context(error_context)
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elif context:
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use_context = context
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else:
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use_context = None
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# Add extra fields to context if provided
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if extra_fields and use_context:
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for key, value in extra_fields.items():
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use_context = use_context.with_custom_field(key, value)
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if use_context:
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with with_log_context(use_context):
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logger.log(log_level, message, extra=extra_fields)
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else:
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logger.log(log_level, message, extra=extra_fields)
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def setup_logger_for_testing(logger_name: str, level: str = 'WARNING') -> logging.Logger:
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"""
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Set up a logger for testing with minimal output.
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Args:
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logger_name: Name of the logger
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level: Log level to set
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Returns:
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Configured logger for testing
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"""
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logger = logging.getLogger(logger_name)
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logger.setLevel(getattr(logging, level.upper()))
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# Remove any existing handlers
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for handler in logger.handlers[:]:
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logger.removeHandler(handler)
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# Add null handler to prevent logging during tests
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logger.addHandler(logging.NullHandler())
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return logger
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def create_performance_logger(name: str = 'performance') -> logging.Logger:
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"""
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Create a specialized logger for performance metrics.
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Args:
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name: Logger name
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Returns:
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Performance logger instance
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"""
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logger = get_logger(name)
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return logger
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def log_repository_operation(logger: logging.Logger,
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operation: str,
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resource_type: str,
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resource_id: Optional[str] = None,
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**details) -> Callable:
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"""
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Decorator for logging repository operations consistently.
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Args:
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logger: Logger to use
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operation: Operation name (e.g., 'get', 'create', 'update', 'delete')
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resource_type: Type of resource (e.g., 'Issue', 'Project')
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resource_id: Optional resource identifier
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**details: Additional operation details
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Usage:
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@log_repository_operation(logger, 'get', 'Issue')
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def get_issue(self, issue_id):
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return self._fetch_issue(issue_id)
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"""
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def decorator(func: Callable) -> Callable:
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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# Extract resource ID from arguments if not provided
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actual_resource_id = resource_id
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if not actual_resource_id and args:
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# Try to get ID from first argument (common pattern)
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if hasattr(args[1] if len(args) > 1 else None, '__str__'):
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actual_resource_id = str(args[1])
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operation_id = f"{operation}_{resource_type.lower()}"
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if actual_resource_id:
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operation_id += f"_{actual_resource_id}"
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# Determine operation type
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operation_type_map = {
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'get': OperationType.READ,
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'list': OperationType.READ,
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'create': OperationType.WRITE,
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'update': OperationType.WRITE,
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'delete': OperationType.DELETE
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}
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op_type = operation_type_map.get(operation.lower(), OperationType.READ)
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with log_operation(operation_id, op_type, logger,
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resource_type=resource_type,
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resource_id=actual_resource_id,
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**details):
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return func(*args, **kwargs)
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return wrapper
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return decorator
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# Commonly used logger instances
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infrastructure_logger = get_logger('infrastructure')
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domain_logger = get_logger('domain')
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application_logger = get_logger('application')
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performance_logger = create_performance_logger()
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def get_default_loggers() -> Dict[str, logging.Logger]:
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"""
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Get dictionary of commonly used loggers.
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Returns:
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Dictionary mapping logger names to logger instances
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"""
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return {
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'infrastructure': infrastructure_logger,
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'domain': domain_logger,
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'application': application_logger,
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'performance': performance_logger
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} |