ff-logger 0.3.0

Fenixflow structured logging package with scoped, instance-based loggers

ff-logger

A scoped, instance-based logging package for Fenixflow applications. Unlike traditional Python logging which uses a global configuration, ff-logger provides self-contained logger instances that can be passed around as objects, with support for context binding and multiple output formats.

Created by Ben Moag at Fenixflow

Quick Start

Installation

From PyPI (when published)

pip install ff-logger

From GitLab (current)

pip install git+https://gitlab.com/fenixflow/fenix-packages.git#subdirectory=ff-logger

Basic Usage

from ff_logger import ConsoleLogger
import logging

# Create a logger instance with permanent context
logger = ConsoleLogger(
    name="my_app",
    level="INFO",  # Can use strings now! (or logging.INFO)
    context={"service": "api", "environment": "production"}
)

# Log messages with the permanent context
logger.info("Application started")
# Output: [2025-08-20 10:00:00] INFO [my_app] Application started | service="api" environment="production"

# Add runtime context with kwargs
logger.info("Request processed", request_id="req-123", duration=45)
# Output includes both permanent and runtime context

Context Binding

Add permanent context fields to your logger instance:

# Add context that will appear in all subsequent logs
logger.bind(
    request_id="req-456",
    user_id=789,
    ip="192.168.1.1"
)

# All messages now include the bound context
logger.info("Processing payment")
logger.error("Payment failed", error_code="CARD_DECLINED")

# bind() returns self for chaining
logger.bind(session_id="xyz").info("Session started")

Note: As of v0.3.0, bind() modifies the logger instance in place rather than creating a new one. This is cleaner and more intuitive. The method validates that fields are not reserved and values are JSON-serializable.

Logger Types

ConsoleLogger

Outputs colored, human-readable logs to console:

from ff_logger import ConsoleLogger

logger = ConsoleLogger(
    name="app",
    level="INFO",  # String or int (logging.INFO)
    colors=True,  # Enable colored output
    show_hostname=False  # Optional hostname in logs
)

JSONLogger

Outputs structured JSON lines, perfect for log aggregation:

from ff_logger import JSONLogger

logger = JSONLogger(
    name="app",
    level="WARNING",  # String or int levels supported
    show_hostname=True,
    include_timestamp=True
)

logger.info("Event occurred", event_type="user_login", user_id=123)
# Output: {"level":"INFO","logger":"app","message":"Event occurred","timestamp":"2025-08-20T10:00:00Z","event_type":"user_login","user_id":123,...}

FileLogger

Writes to files with rotation support:

from ff_logger import FileLogger

logger = FileLogger(
    name="app",
    filename="/var/log/app.log",
    rotation_type="size",  # "size", "time", or "none"
    max_bytes=10*1024*1024,  # 10MB
    backup_count=5
)

NullLogger

Zero-cost logger for testing or when logging is disabled:

from ff_logger import NullLogger

# Preferred: Use directly as a class (no instantiation needed)
NullLogger.info("This does nothing")  # No-op
NullLogger.debug("Debug message")     # No-op

# As a default parameter (perfect for dependency injection)
def process_data(data, logger=NullLogger):
    logger.info("Processing data: %s", data)
    return data * 2

# Call without providing a logger
result = process_data([1, 2, 3])

# Backward compatibility: Can still instantiate if needed
logger = NullLogger()  # All parameters are optional
logger.info("This also does nothing")

DatabaseLogger

Writes logs to a database table (requires ff-storage):

from ff_logger import DatabaseLogger
from ff_storage.db.postgres import PostgresPool

db = PostgresPool(...)
logger = DatabaseLogger(
    name="app",
    db_connection=db,
    table_name="logs",
    schema="public"
)

Key Features (v0.3.0)

Flexible Log Levels

Accepts both string and integer log levels for better developer experience:

• Strings: "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"
• Case-insensitive: "info" works the same as "INFO"
• Integers: Traditional logging.DEBUG, logging.INFO, etc.
• Numeric values: 10, 20, 30, 40, 50

Instance-Based

Each logger is a self-contained instance with its own configuration:

def process_data(logger):
    """Accept any logger instance."""
    logger.info("Processing started")
    # ... do work ...
    logger.info("Processing complete")

# Use with different loggers
console = ConsoleLogger("console")
json_log = JSONLogger("json")

process_data(console)  # Outputs to console
process_data(json_log)  # Outputs as JSON

Context Preservation

Permanent context fields appear in every log message:

logger = ConsoleLogger(
    name="worker",
    context={
        "worker_id": "w-1",
        "datacenter": "us-east-1"
    }
)

# Every log includes worker_id and datacenter
logger.info("Task started")
logger.error("Task failed")

Zero Dependencies

Built entirely on Python's standard logging module - no external dependencies required for core functionality.

Migration from Traditional Logging

# Traditional Python logging (global)
import logging
logging.info("Message")

# ff-logger (instance-based)
from ff_logger import ConsoleLogger
logger = ConsoleLogger("app")
logger.info("Message")

Advanced Usage

Flexible Log Levels

# All of these work now (v0.3.0+):
logger1 = ConsoleLogger("app", level="DEBUG")     # String
logger2 = ConsoleLogger("app", level="info")      # Case-insensitive
logger3 = ConsoleLogger("app", level=logging.INFO) # Traditional int
logger4 = ConsoleLogger("app", level=20)          # Numeric value
logger5 = ConsoleLogger("app")                    # Default: "DEBUG"

# Supported string levels:
# "DEBUG", "INFO", "WARNING"/"WARN", "ERROR", "CRITICAL"

Exception Logging

try:
    risky_operation()
except Exception:
    logger.exception("Operation failed")
    # Automatically includes full traceback

Reserved Fields

Some field names are reserved by Python's logging module. If you use them, they'll be automatically prefixed with x_:

# "module" is reserved, becomes "x_module"
logger.info("Message", module="auth")

Testing

Use NullLogger in tests for zero overhead:

def test_my_function():
    # Option 1: Pass the class directly
    result = my_function(logger=NullLogger)  # No logging output
    assert result == expected
    
    # Option 2: Functions with NullLogger as default
    def my_function(data, logger=NullLogger):
        logger.info("Processing: %s", data)
        return process(data)
    
    # In tests, just call without logger parameter
    result = my_function(test_data)  # Silent by default

Contributing

Contributions are welcome! Please feel free to submit a Pull Request to the GitLab repository.

License

MIT License - see LICENSE file for details.

Copyright (c) 2024 Ben Moag / Fenixflow