logging-metrics 0.2.2

Advanced logging utilities for robust, standardized logs in Python projects, APIs, data engineering, and more.

logging-metrics

Utilities Library for Logging Configuration and Management

A library for configuring and managing logs in Python, focused on simplicity, performance, and observability — with support for PySpark integration.

---

📑 Table of Contents

• ✨ Features
• 📦 Installation
• 📋 Functions and Classes Overview
• 🚀 Quick Start
• 📖 Main Features
• 🏆 Best Practices
• ❌ Avoid
• 🔧 Advanced Configuration
• 🧪 Complete Example
• 🧪 Tests
• ⚙️ CI/CD
• 🔧 Requirements
• 📝 Changelog
• 🤝 Contributing
• 📄 License

---

✨ Features

• 🎨 Colored logs for the terminal with different levels
• 📁 Automatic file rotation by time or size
• ⚡ PySpark DataFrame integration
• 📊 JSON format for observability systems
• ⏱️ Timing with LogTimer
• 📈 Metrics monitoring with LogMetrics
• 🔧 Hierarchical logger configuration
• 🚀 Optimized performance for critical applications

---

📦 Installation

From PyPI:

pip install logging-metrics

For development:

git clone https://github.com/ThaissaTeodoro/logging-metrics.git
cd logging-metrics
pip install -e ".[dev]"

---

📋 Functions and Classes Overview

Name	Type	Description
configure_basic_logging	Function	Configures root logger for colored console logging.
setup_file_logging	Function	Configures a logger with file output (rotation), optional console, JSON formatting.
LogTimer	Class	Context manager and decorator to log execution time of code blocks or functions.
log_spark_dataframe_info	Function	Logs schema, sample, stats of a PySpark DataFrame (row count, sample, stats, etc).
LogMetrics	Class	Utility for collecting, incrementing, timing, and logging custom processing metrics.
get_logger	Function	Returns a logger with custom handlers and caplog-friendly mode for pytest.

---

🚀 Quick Start

import logging
from logging_metrics import setup_file_logging, LogTimer

logger = setup_file_logging(
    logger_name="my_app",
    log_dir="./logs",
    console_level=logging.INFO,
    level=logging.DEBUG
)

logger.info("Application started!")

with LogTimer(logger, "Critical operation"):
    # your code here
    pass

---

📖 Main Features

1. Logging configuration:

import logging
from logging-metrics import configure_basic_logging
logger = configure_basic_logging()
logger.debug("Debug message")     # Gray
logger.info("Info")               # Green  
logger.warning("Warning")         # Yellow
logger.error("Error")             # Red
logger.critical("Critical")       # Bold red

2. Automatic Log Rotation:

from logging-metrics import setup_file_logging, LogTimer
# Size-based rotation
logger = setup_file_logging(
    logger_name="app",
    log_dir="./logs",
    max_bytes=10*1024*1024,  # 10MB
    rotation='size'
)

# Time-based rotation
logger = setup_file_logging(
    logger_name="app", 
    log_dir="./logs",
    rotation='time'    
)

3. Spark/Databricks Integration:

from pyspark.sql import SparkSession
from logging_metrics import configure_basic_logging, log_spark_dataframe_info

spark = SparkSession.builder.getOrCreate()
df = spark.createDataFrame([(1, "Ana"), (2, "Bruno")], ["id", "nome"])

logger = configure_basic_logging()
print("Logger:", logger)

log_spark_dataframe_info(
    df = df,logger = logger, name ="spark_app")

logger.info("Spark processing started")

4. ⏱ Timing with LogTimer:

from logging_metrics import LogTimer, configure_basic_logging

logger = configure_basic_logging()
# As a context manager
with LogTimer(logger, "DB query"):
    logger.info("Test")

# As a decorator
@LogTimer.as_decorator(logger, "Data processing")
def process_data(data):
  return data.transform()

5. 📈 Metrics Monitoring:

from logging_metrics import LogMetrics, configure_basic_logging
import time

logger = configure_basic_logging()

metrics = LogMetrics(logger)

items = [10, 5, 80, 60, 'test1', 'test2']

# Start timer for total operation
metrics.start('total_processing')


for item in items:
    # Increments the processed records counter
    metrics.increment('records_processed')

    # If it is an error (simulation)
    if isinstance(item, str):
        metrics.increment('errors')

    # Simulates item processing
    time.sleep(0.1)

    # Custom value example
    metrics.set('last_item', item)


# Finalize and log all metrics
elapsed = metrics.stop('total_processing')

# Logs all collected metrics
metrics.log_all()

# Output:
# --- Processing Metrics ---
# Counters:
#   - records_processed: 6
#   - errors_found: 2
#  Values:
#   - last_item: test2
#  Completed timers:
#   - total_processing: 0.60 seconds

6. Hierarchical Configuration:

from logging_metrics import setup_file_logging
  import logging
  
  # Main logger
  main_logger = setup_file_logging("my_app", log_dir="./logs")
  
  # Sub-loggers organized hierarchically
  db_logger = logging.getLogger("my_app.database")
  api_logger = logging.getLogger("my_app.api")
  auth_logger = logging.getLogger("my_app.auth")
  
  # Module-specific configuration
  db_logger.setLevel(logging.DEBUG)      # More verbose for DB
  api_logger.setLevel(logging.INFO)      # Normal for API
  auth_logger.setLevel(logging.WARNING)  # Only warnings/errors for auth
  
  db_logger.debug("querying the database")
  db_logger.info("consultation successfully completed")
  db_logger.error("Error connecting to database!")
  
  auth_logger.debug("doing authentication")
  auth_logger.info("authentication successfully completed")
  api_logger.debug("querying the api")
  api_logger.info("consultation successfully completed")
  api_logger.error("Error querying the api")
  auth_logger.error("Auth error!")

7. 📊 JSON Format for Observability:

from logging_metrics import setup_file_logging

# JSON logs for integration with ELK, Grafana, etc.
logger = setup_file_logging(
    logger_name="microservice",
    log_dir="./logs",
    json_format = True
)

logger.info("User logged in", extra={"user_id": 12345, "action": "login"})

# Example JSON output:
# {
#   "timestamp": "2024-08-05T10:30:00.123Z",
#   "level": "INFO", 
#   "name": "microservice",
#   "message": "User logged in",
#   "module": "user-api",
#   "function": "<module>",
#   "line": 160,
#   "taskName": null,
#   "user_id": 12345,
#   "action": "login"
# }

---

🏆 Best Practices

1. Configure logging once at the start:

# In main.py or __init__.py
logger = setup_file_logging("my_app", log_dir="./logs")

2. Use logger hierarchy:

# Organize by modules/features
db_logger = logging.getLogger("app.database")
api_logger = logging.getLogger("app.api")

3. Different levels for console and file:

logger = setup_file_logging(
    console_level=logging.WARNING,  # Less verbose in console
    level=logging.DEBUG             # More detailed in the file
)

4. Use LogTimer for critical operations:

with LogTimer(logger, "Complex query"):
    result = run_heavy_query()

5. Monitor metrics in long processes:

metrics = LogMetrics(logger)
for batch in batches:
    with metrics.timer('batch_processing'):
        process_batch(batch)

---

❌ Avoid

• Configuring loggers multiple times
• Using print() instead of logger
• Excessive logging in critical loops
• Exposing sensitive information in logs
• Ignoring log file rotation

---

🔧 Advanced Configuration

Example of full configuration:

from logging_metrics import setup_file_logging, LogMetrics
import logging

# Main configuration with all options
logger = setup_file_logging(
    logger_name="my_app",
    log_folder: str = "unknown/"
    log_dir="./logs",
    level=logging.DEBUG,
    console_level=logging.INFO,
    rotation='time',
    log_format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    date_format="%Y-%m-%d %H:%M:%S",
    max_bytes=50*1024*1024,  # 50MB
    backup_count=10,
    add_console= True
)

# Sub-module configuration
modules = ['database', 'api', 'auth', 'cache']
for module in modules:
    module_logger = logging.getLogger(f"my_app.{module}")
    module_logger.setLevel(logging.INFO)

---

🧪 Complete Example

import logging
from logging_metrics import setup_file_logging, LogTimer, LogMetrics

def main():
    # Initial configuration
    logger = setup_file_logging(
        logger_name="data_processor",
        log_dir="./logs",
        console_level=logging.INFO,
        level=logging.DEBUG
    )
    
    # Sub-loggers
    db_logger = logging.getLogger("data_processor.database")
    api_logger = logging.getLogger("data_processor.api")
    
    # Metrics
    metrics = LogMetrics(logger)
    
    logger.info("Application started")
    
    try:
        # Main processing with timing
        with LogTimer(logger, "Full processing"):
            metrics.start('total_processing')
            
            # Simulate processing
            for i in range(1000):
                metrics.increment('records_processed')
                
                if i % 100 == 0:
                    logger.info(f"Processed {i} records")
                
                # Simulate occasional error
                if i % 250 == 0:
                    metrics.increment('errors_recovered')
                    logger.warning(f"Recovered error at record {i}")
            
            metrics.stop('total_processing')
            metrics.log_all()
            
        logger.info("Processing successfully completed")
        
    except Exception as e:
        logger.error(f"Error during processing: {e}", exc_info=True)
        raise

if __name__ == "__main__":
    main()

---

🧪 Tests

The library has a complete test suite to ensure quality and reliability.

# Install development dependencies
pip install -e ".[dev]"

# Run all tests
make test

# Tests with coverage
make test-cov

Test structure:

test/
├── conftest.py
├── pytest.ini
├── test-requirements.txt
├── test_logging_metrics.py

---

⚙️ CI/CD

This project uses GitHub Actions for continuous integration and delivery.

CI Workflow (ci.yml):

• Runs on push and PR to main/master.
• Steps:
  1. Install dependencies and package in editable mode.
  2. Lint code with ruff and black.
  3. Run tests with pytest and measure coverage.
  4. Fail build if coverage < 85%.
  5. Upload HTML coverage report and send to Codecov.

CD Workflow (Publish to PyPI.yml):

• Triggered on push tags v*.*.*.
• Steps:
  1. Build wheel and sdist.
  2. Check version tag matches pyproject.toml.

Run CI locally:

make test-ci     # Full pipeline
make test-local  # Install + tests with coverage

How to publish a new version

1. Update the version in pyproject.toml (version field).
2. Update the CHANGELOG with the release notes.
3. Create and push the tag:

 git add .
 git commit -m "release: v0.1.0"
 git tag -a v0.1.0 -m "release: v0.1.0"
 git push origin v0.1.0

This will automatically trigger the publish.yml workflow, which builds the package and uploads it to PyPI.

🔧 Requirements

• Python >= 3.9
• Dependencies: pytz, pyspark

---

📝 Changelog

v0.2.2 (Current)

• Initial stable version
• LogTimer and LogMetrics
• Spark integration
• Colored logs
• JSON log support
• Fixed file rotation bug on Windows
• Expanded documentation with more examples

---

🤝 Contributing

1. Fork the project
2. Create your feature branch
3. Commit your changes
4. Push to your branch
5. Open a Pull Request

---

📄 License

MIT License. See LICENSE for details.