The Critical Role of Production Logging#

Logging in a production system serves multiple vital functions beyond basic error tracking:

• Debugging and Troubleshooting: Pinpointing the root cause of errors or unexpected behavior in a live system.
• Performance Monitoring: Identifying bottlenecks or performance degradation by logging timings of key operations.
• Security Auditing: Tracking user actions, authentication attempts, and system events for security analysis and compliance.
• System Health Monitoring: Gauging application stability, resource usage, and identifying potential issues before they cause outages.
• Understanding Application Flow: Tracing the path of a request or process through different parts of the application or across microservices.
• Business Intelligence: Analyzing user interactions or transaction details captured in logs (within privacy guidelines).

Logs generated in production must be consistent, informative, and easy to process by automated systems.

Core Concepts of Python’s logging Module#

Understanding the fundamental components of Python’s logging module is essential for structuring logs effectively:

• Loggers: The entry point for logging. Applications typically define loggers for different modules or functionalities (e.g., logging.getLogger('myapp.web'), logging.getLogger('myapp.db')). Loggers are organized hierarchically. Messages are passed from child loggers up to parent loggers.
• Levels: Define the severity of a log message. Python’s standard levels (in increasing order of severity) are:
  • DEBUG: Detailed information, typically only of interest when diagnosing problems.
  • INFO: Confirmation that things are working as expected.
  • WARNING: An indication that something unexpected happened, or might happen in the near future (e.g., ‘disk space low’). The software is still working as expected.
  • ERROR: Due to a more serious problem, the software has not been able to perform some function.
  • CRITICAL: A serious error, indicating that the program itself may be unable to continue running.
• Handlers: Determine where log records are sent. Examples include StreamHandler (to console/stderr), FileHandler (to a file), SocketHandler (to a network socket), and specialized handlers for external services. A logger can have multiple handlers.
• Formatters: Specify the layout and content of the log message. This is where structuring happens. Formatters convert a LogRecord object into a string or bytes suitable for output.

Why Structured Logging is Crucial for Production#

Traditional, unstructured log messages are human-readable but difficult for machines to parse reliably. An unstructured log entry might look like:

ERROR:myapp.web:User 123 failed login from IP 192.168.1.100 - Invalid password

While understandable to a developer, extracting specific fields like user_id, ip_address, or the specific error_type (Invalid password) requires complex regular expressions or custom parsing scripts that are fragile and prone to breaking with minor format changes.

Structured logging, in contrast, outputs logs in a consistent, machine-readable format, typically JSON (JavaScript Object Notation). Each log entry is a self-contained object with key-value pairs. A structured version of the above log entry might look like:

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{
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  "timestamp": "2023-10-27T10:00:00.123Z",
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  "level": "ERROR",
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  "logger": "myapp.web",
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  "message": "User login failed",
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  "user_id": 123,
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  "ip_address": "192.168.1.100",
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  "error_type": "Invalid password"
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}

Benefits of structured logging:

• Machine Readability: Easily parsed by log management systems (e.g., Elasticsearch, Splunk, Datadog, Loki).
• Searchability and Filtering: Querying logs becomes trivial based on any key-value pair (e.g., find all logs with user_id 123, or all ERROR logs from a specific ip_address).
• Analysis and Aggregation: Tools can easily group, count, and aggregate logs based on structured fields for generating metrics, dashboards, and reports.
• Contextualization: Adding request IDs, trace IDs, user IDs, or other context-specific data fields directly into the log entry makes tracing issues across distributed systems much simpler.
• Consistency: Enforces a standard format across the application, or even across multiple services.

Implementing Structured Logging in Python#

Implementing structured logging involves configuring a formatter that outputs log records as structured data, typically JSON.

1. Using a Custom Formatter: A simple approach is to create a custom logging.Formatter subclass that converts the LogRecord object to a JSON string. This requires manual handling of fields.
2. Using Libraries: Several libraries simplify structured logging:
   • python-json-logger: This library provides a JsonFormatter class that can be easily plugged into the standard logging module configuration. It automatically includes standard LogRecord attributes (like levelname, name, message, asctime) and allows inclusion of extra fields.
   • structlog: This library offers a different philosophy, intercepting logging calls earlier and providing a pipeline for processing and formatting event dictionaries before they hit the standard logging module or are output directly. It’s highly flexible but has a steeper learning curve and replaces the standard logging API calls (logger.info(...)) with its own (logger.info(...)).

For most standard use cases integrating with the logging module, python-json-logger is a straightforward choice. structlog is powerful for complex pipelines or when moving away from the standard API is acceptable.

Best Practices for Structuring Log Data#

Implementing structured logging is the first step; adopting best practices for what to log and how to structure it ensures logs are truly effective in production.

• Adopt a Consistent JSON Format: Standardize the names of common fields (timestamp, level, message, logger). Use snake_case or camelCase consistently for custom fields.
• Include Essential Fields: Every log entry should include at least:
  • timestamp: High precision, often in ISO 8601 format with timezone (UTC recommended).
  • level: The logging severity (DEBUG, INFO, etc.).
  • message: A concise, human-readable summary of the event. This should ideally be static text for recurring events, with variable details placed in separate fields.
  • logger: The name of the logger that generated the message (identifies the code location).
  • pathname, lineno: File path and line number (useful for debugging errors).
  • process, thread: Process and thread IDs (helpful in multi-process/multi-threaded applications).
• Add Contextual Fields: Include data relevant to the specific event:
  • request_id: A unique ID tracing a user request across different functions or services.
  • user_id: Identifier for the user performing an action.
  • order_id, transaction_id: Relevant business identifiers.
  • customer_uuid, account_id: Tenant or customer identifiers.
  • trace_id, span_id: For distributed tracing systems.
  • Function arguments or return values (carefully, avoiding sensitive data).
• Avoid Logging Sensitive Data: Never log passwords, API keys, personal identifiable information (PII), or sensitive business data directly in logs. Implement filtering or masking if necessary.
• Use Appropriate Logging Levels: Log messages at the correct severity level. DEBUG and INFO for normal operations and flow, WARNING for potential issues, ERROR for functional failures, CRITICAL for system-level problems. Configure handlers to process different levels appropriately (e.g., send ERROR and CRITICAL to an alerting system).
• Handle Exceptions Correctly: Log exceptions using logger.exception() (within an except block) or by setting exc_info=True in the logging call (logger.error("An error occurred", exc_info=True)). This ensures the traceback is included in the log record, which JsonFormatter can often include as a structured field.
• Consider Performance: High-volume logging can impact application performance. Using a logging.handlers.QueueHandler in combination with a QueueListener can make logging asynchronous, offloading the formatting and output work to a separate thread.
• Integrate with Centralized Logging Systems: Production logs are most useful when aggregated in a central system (e.g., ELK stack, Splunk, Datadog, Sumo Logic, AWS CloudWatch Logs, Google Cloud Logging). Configure handlers or agents to send structured logs to these systems for indexing, searching, analysis, and visualization.

Configuring Logging for Production#

Python’s logging module offers several ways to configure loggers, handlers, and formatters:

• logging.basicConfig(): Simple setup, mainly for scripts or development. Not recommended for complex production applications.
• logging.fileConfig(): Reads configuration from a .ini style file. Can be useful but less flexible than dictionary configuration.
• logging.dictConfig(): Reads configuration from a dictionary (or a YAML/JSON structure loaded into a dictionary). This is the recommended approach for production applications due to its flexibility, ability to define complex configurations programmatically or load them from configuration files, and ease of integrating with configuration management systems.

A dictConfig structure defines loggers, handlers, and formatters by name and links them together.

Example dictConfig structure (simplified):

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import logging.config
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import json
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import sys
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import datetime
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# Assuming python-json-logger is installed
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from pythonjsonlogger import jsonlogger
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# Define a custom JSON formatter to include extra attributes
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class CustomJsonFormatter(jsonlogger.JsonFormatter):
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    def add_fields(self, log_record, record, message_dict):
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        super(CustomJsonFormatter, self).add_fields(log_record, record, message_dict)
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        # Ensure timestamp is in ISO 8601 format and use UTC
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        if not log_record.get('timestamp'):
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            now = datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%S.%fZ')
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            log_record['timestamp'] = now
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        # Rename default fields for consistency if desired
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        log_record['level'] = log_record.pop('levelname', 'UNKNOWN')
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        log_record['logger'] = log_record.pop('name', 'root')
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        # Add other fields if needed
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        # log_record['service'] = 'my-awesome-service'
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LOGGING_CONFIG = {
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    'version': 1,
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    'disable_existing_loggers': False, # Keep existing loggers, e.g. from libraries
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    'formatters': {
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        'json': {
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            '()': CustomJsonFormatter, # Use our custom formatter
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            'format': '%(timestamp)s %(level)s %(logger)s %(message)s %(pathname)s %(lineno)d %(process)d %(thread)d'
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            # The 'format' string here mainly defines the *order* and *inclusion*
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            # of standard LogRecord attributes handled by JsonFormatter.
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            # Custom attributes from 'extra' are added automatically.
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        },
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        'simple': {
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            'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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        },
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    },
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    'handlers': {
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        'console': {
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            'class': 'logging.StreamHandler',
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            'formatter': 'json', # Use the json formatter
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            'stream': sys.stdout,
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            'level': 'INFO', # Set default level for this handler
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        },
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        # Add other handlers here, e.g., for files, Sentry, etc.
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        # 'file': { ... }
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    },
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    'loggers': {
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        '': { # Root logger
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            'handlers': ['console'],
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            'level': 'INFO', # Default level for the application
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            'propagate': False,
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        },
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        'myapp': { # Specific logger for our application code
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             'handlers': ['console'], # Inherits or specifies handlers
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             'level': 'DEBUG', # Can be more verbose for specific app parts
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             'propagate': False, # Prevent messages from going to parent loggers
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        },
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        # Configure loggers for specific libraries if they are too noisy
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        # 'asyncio': {
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        #     'handlers': ['console'],
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        #     'level': 'WARNING',
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        #     'propagate': False,
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        # },
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    },
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    'root': { # Root logger configuration (alternative syntax)
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        'handlers': ['console'],
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        'level': 'INFO',
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    },
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}
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# Load the configuration
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logging.config.dictConfig(LOGGING_CONFIG)
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# Get a logger instance
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logger = logging.getLogger('myapp.web')
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# Example log messages
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logger.info("Application started")
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logger.warning("Configuration file not found", extra={'config_path': '/app/config.yaml'})
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try:
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    1 / 0
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except ZeroDivisionError:
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    logger.error("Division by zero error occurred", exc_info=True, extra={'operation': 'calculate_ratio', 'input_a': 1, 'input_b': 0})
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logger.debug("This message is only visible if level is DEBUG")

This dictConfig sets up a CustomJsonFormatter using python-json-logger, applies it to a StreamHandler sending output to standard output, and configures the root and a specific myapp logger with appropriate levels. The extra dictionary in logging calls is automatically included as top-level keys in the JSON output by python-json-logger.

Real-World Application Example: Web Request Logging#

Consider a web application built with a framework like Flask or Django. A common requirement is to log details about each incoming request, including a unique request ID, the user involved, and potentially the response status.

Integrating structured logging:

1. Configuration: Use logging.dictConfig as shown above, configuring a JSON formatter.
2. Request ID: Implement a mechanism to generate a unique ID for each incoming request (e.g., a UUID). Store this ID in a request-local context (like threading.local or contextvars in Python 3.7+).
3. Contextual Filter: Create a logging.Filter that accesses the request-local context and adds the request ID and other relevant context (user ID, IP address) to the LogRecord’s extra dictionary before it’s formatted. Alternatively, explicitly pass the context in the extra dict with each log call. Using a filter is cleaner as it automatically applies to all logs within the request context.

Simplified Filter Example:

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import logging
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# In a real web app, this would be request-local context
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# from some_context_library import request_context
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import threading
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# Simple request context simulation for demonstration
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request_context = threading.local()
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request_context.request_id = None
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request_context.user_id = None
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class RequestContextFilter(logging.Filter):
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    def filter(self, record):
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        # Access context and add to the log record
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        record.request_id = getattr(request_context, 'request_id', None)
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        record.user_id = getattr(request_context, 'user_id', None)
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        # Ensure these attributes are added to the output by the formatter
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        # JsonFormatter from python-json-logger handles this if format string includes them
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        # or if they are passed via the 'extra' dict.
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        # For our CustomJsonFormatter, we need to ensure they are picked up.
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        # The easiest way with python-json-logger is often passing via 'extra'.
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        # Let's modify the example to pass via extra for clarity with python-json-logger.
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        return True
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# With the extra dict approach, the filter isn't strictly necessary for adding context,
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# but managing context (like request_id) still requires request-local storage.
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# The application code then explicitly adds the context via extra:
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# logger.info("Processing request", extra={'request_id': request_context.request_id, 'user_id': request_context.user_id})
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# Let's update the example to demonstrate adding context via 'extra'
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# using the previously defined CustomJsonFormatter
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# --- (Rest of the LOGGING_CONFIG and CustomJsonFormatter code from above) ---
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# Simulate setting context in a web request handler
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def handle_request(request_id, user_id):
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    # In a real app, context would be set at the start of the request handler
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    request_context.request_id = request_id
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    request_context.user_id = user_id
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    logger = logging.getLogger('myapp.web')
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    logger.info("Received request", extra={'request_id': request_context.request_id, 'user_id': request_context.user_id})
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    try:
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        # Simulate some operation
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        result = 10 / (user_id % 2) # Potential ZeroDivisionError
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        logger.info("Processed request successfully", extra={'request_id': request_context.request_id, 'user_id': request_context.user_id, 'result': result})
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    except ZeroDivisionError:
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         logger.error("Error processing request", exc_info=True, extra={'request_id': request_context.request_id, 'user_id': request_context.user_id, 'error_step': 'calculation'})
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    finally:
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        # Clean up context at the end of the request
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        request_context.request_id = None
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        request_context.user_id = None
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# Simulate two different requests
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handle_request("req-abc", 123)
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handle_request("req-xyz", 456) # This one might cause an error if 456 is even
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# Example Output (formatted by CustomJsonFormatter):
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# {"timestamp": "...", "level": "INFO", "logger": "myapp.web", "message": "Received request", "request_id": "req-abc", "user_id": 123, "pathname": "...", "lineno": ..., "process": ..., "thread": ...}
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# {"timestamp": "...", "level": "INFO", "logger": "myapp.web", "message": "Processed request successfully", "request_id": "req-abc", "user_id": 123, "result": 10, "pathname": "...", "lineno": ..., "process": ..., "thread": ...}
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# {"timestamp": "...", "level": "INFO", "logger": "myapp.web", "message": "Received request", "request_id": "req-xyz", "user_id": 456, "pathname": "...", "lineno": ..., "process": ..., "thread": ...}
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# {"timestamp": "...", "level": "ERROR", "logger": "myapp.web", "message": "Error processing request", "request_id": "req-xyz", "user_id": 456, "error_step": "calculation", "exc_info": "...", "pathname": "...", "lineno": ..., "process": ..., "thread": ...}

This example demonstrates how relevant context (request_id, user_id) is included directly in each log entry, making it simple to trace all logs related to a specific request in a logging analysis system. The exc_info field contains the formatted traceback when an exception occurs.

Key Takeaways for Production Logging#

• Adopt structured logging, preferably using JSON format, for all production applications to enable machine readability and efficient analysis.
• Utilize Python’s logging.dictConfig for flexible and maintainable logging configuration in production environments.
• Choose a suitable structured logging formatter or library (python-json-logger is a common choice for integrating with the standard module).
• Always include essential fields in log entries: timestamp (UTC), level, message, logger name, and source code location.
• Enrich logs with relevant contextual fields like request IDs, user IDs, or business entity identifiers to facilitate tracing and debugging.
• Implement robust exception logging using logger.exception() or exc_info=True.
• Configure log levels appropriately for different handlers (e.g., verbose debug logs to a file, errors to console/monitoring).
• Avoid logging sensitive information directly in log messages.
• Consider using a QueueHandler for asynchronous logging in high-throughput applications to minimize performance impact.
• Integrate structured logs with a centralized logging system for aggregation, searching, visualization, and alerting.