hynek/structlog

Features

• Structured Log Capture - Treats log events as dictionaries and captures parameters as key-value pairs for precise querying and serialization.
• Dictionary-Based Event Representation - Treats log entries as mutable dictionaries instead of strings to enable precise, machine-readable processing throughout the pipeline.
• Standard Library Adapters - Provides compatibility layers that wrap standard library logging interfaces to inject structured capabilities into existing code.
• Persistent Log Context - Provides mechanisms to attach predefined key-value pairs to loggers that are automatically included in all subsequent log entries.
• Multi-Format Renderers - Converts structured log data into various formats including JSON and colorized text for both machine and human consumption.
• Pipeline Architectures - Utilizes a pipeline architecture that passes log dictionaries through a sequence of independent functions to modify events.
• Asynchronous Logging - Provides a logging system with awaitable methods to prevent blocking the event loop in high-volume asynchronous environments.
• Contextual Logging - Provides utilities for binding key-value metadata to loggers to maintain traceability across execution boundaries.
• Context Attachments - Attaches key-value pairs to a logger so all subsequent entries automatically include the same metadata.
• Log Processing Pipelines - Passes log dictionaries through a processor pipeline to automatically enrich events with timestamps and call stacks.
• Structured Logging - Creates machine-readable log entries as key-value pairs to enable precise filtering and automated data analysis.
• Structured Logging Frameworks - Implements a logging framework that treats log entries as dictionaries for machine-readable output and precise analysis.
• Log Formatters - Converts structured application events into JSON and logfmt for ingestion by centralized monitoring systems.
• Console Log Formatting - Formats Python log output as colorized, human-readable text to simplify troubleshooting during active development.
• Log Target Wrappers - Acts as a wrapper for standard library loggers to add structured capabilities while maintaining compatibility.
• Console Log Formatters - Transforms structured log data into colorized, human-friendly console output for local development debugging.
• Asynchronous Metadata Propagation - Passes metadata to background worker threads to maintain traceability across asynchronous execution boundaries.
• Deferred Log Rendering - Implements a strategy that keeps logs in a structured format until final rendering to optimize processing and flexibility.
• Thread-Local Context Management - Maintains global state across applications using thread-local storage to ensure log consistency without explicit parameter passing.
• Severity Level Filtering - Controls which messages are emitted by filtering them based on predefined severity levels.
• Centralized Logging Systems - Routes structured log data to external services and system journals for unified monitoring in distributed environments.
• Global Log Contexts - Maintains application-wide shared data using local storage variables to ensure consistent logs across different components.
• Log Event Filtering - Provides logic to determine whether a log event should be processed or discarded based on content criteria.
• Log Frequency Throttling - Implements mechanisms to limit the rate of log emission to prevent system flooding during high-volume events.
• Pluggable Logging Backends - Supports directing processed log data to interchangeable output destinations by decoupling event generation from delivery.
• Log Message Assertions - Includes utilities to assert that specific log keys and metadata were emitted during automated test execution.
• Logging - Structured logging framework.
• Developer Tools - Structured logging for Python.
• Development Utilities - Structured logging for Python applications.
• Logging and Output - Structured logging for production-ready applications.

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