LogCrest

LogCrest is a high-performance, asynchronous logging framework for Python designed for production-grade tracing and structured logging with minimal overhead. It replaces standard boilerplate with a robust decorator-driven system that ensures execution timing, parent-child correlation (Trace IDs), and machine-readable output.

Core Features

• Asynchronous Processing: Logs are processed in a background thread using a non-blocking queue to ensure zero impact on application latency.
• Correlation Tracing: Automatically propagates Trace IDs across nested function calls, allowing for end-to-end request tracking.
• Dual-Stream Formatting: Outputs colorized, human-readable terminal logs for development and structured JSON for production log aggregators.
• Zero-Config Readiness: Works out of the box with sensible defaults, while remaining fully customizable via JSON.
• SOLID Architecture: Built on a modular foundation, making it easy to extend with custom handlers or formatters.

Installation

pip install logcrest

Practical Examples

1. Basic Usage

Decorate any function to automatically log its entry, exit, arguments, and execution time.

from logcrest import log_decorator, log

@log_decorator
def calculate_metrics(data_points):
    log.info(f"Processing {len(data_points)} points")
    return sum(data_points) / len(data_points)

calculate_metrics([10, 20, 30])

2. Request Tracing (Nested Calls)

LogCrest maintains context across function boundaries. A single Trace ID will be shared across all nested decorated calls originating from a root function.

@log_decorator
def validate_user(user_id):
    return user_id > 0

@log_decorator
def process_request(user_id):
    if validate_user(user_id):
        log.info("Request approved")

# Both functions will share the same Trace ID in the logs
process_request(42)

3. Automated Error Handling

LogCrest catches exceptions, logs the full traceback and execution time before the failure, and re-raises the exception for your application to handle.

@log_decorator
def database_operation():
    raise ConnectionError("Lost connection to host")

try:
    database_operation()
except ConnectionError:
    pass # Error is already logged with full context

4. Custom Log Levels

Control the severity of your logs directly through the decorator.

from logcrest import DEBUG, WARNING

@log_decorator(DEBUG)
def low_priority_task():
    log.debug("Internal step completed")

@log_decorator(WARNING)
def sensitive_operation():
    log.warning("System resources running low")

Advanced Configuration

LogCrest looks for a log_config.json in your project root. If not found, it uses internal defaults.

{
  "base_log_dir": "logs",
  "max_log_size": 5242880,
  "backup_count": 3,
  "use_json": true,
  "log_name": "app_system"
}

Development and Testing

LogCrest is built for stability. To run the test suite:

pip install "logcrest[dev]"
pytest

LogCrest ensures your logs are as reliable as your code.