lib-x17-log 1.0.5

logging module for x17 ecosystem

xlog

A flexible and structured logging library for Python with support for multiple outputs, formats, and async processing.

Features

• Structured Events - Log with context, tags, metrics, and metadata
• Multiple Formats - JSON, Text, ColorJSON, ColorText
• Multiple Outputs - Console, files, custom sinks
• Async Processing - Non-blocking event handling
• Type Safe - Full protocol support for extensibility

Installation

pip install -e .

Quick Start

from xlog import LogStream, FileGroup

# Simple console logging
stream = LogStream(name="app", level="INFO")
stream.log("Application started")

# Log with context
stream.log(
    "User login",
    level="INFO",
    context={"user_id": "123", "ip": "192.168.1.1"}
)

# Add file output
file_group = FileGroup(path="./logs", name="app", async_=True)
stream.add_group(file_group)
stream.log("This goes to console and file")
file_group.close()

Core Components

Events

Create structured log events:

from xlog import Log, Procs

# Log event
event = Log(
    message="Request processed",
    level="INFO",
    code=200,
    context={"user": "alice"},
    metrics={"duration_ms": "45"}
)

# Subprocess event
import subprocess
result = subprocess.run(["echo", "hello"], capture_output=True)
proc_event = Procs(proc=result)

Streams

Distribute events to multiple destinations:

from xlog import LogStream, LogGroup, FileGroup

# Create stream with multiple outputs
memory_group = LogGroup(name="memory", store=True)
file_group = FileGroup(path="./logs", name="app")

stream = LogStream(
    name="app",
    level="INFO",
    groups=[memory_group, file_group]
)

Formatters

Choose output format:

from xlog import Json, Text, ColorJson, ColorText, LogStream

# JSON format
stream = LogStream(name="app", format=Json(indent=2))

# Colored text for console
stream = LogStream(name="app", format=ColorText())

Groups (Sinks)

Process and store events:

from xlog import LogGroup, FileGroup

# In-memory storage
group = LogGroup(name="memory", store=True, async_=False)

# File storage with async processing
group = FileGroup(
    path="./logs",
    name="app",
    async_=True,
    max_queue=1000
)

Real-World Examples

Web Application Logging

from xlog import LogStream, FileGroup, ColorText

access_log = FileGroup(path="./logs", name="access", async_=True)
error_log = FileGroup(path="./logs", name="error", async_=True)

access_stream = LogStream(
    name="access",
    level="INFO",
    format=ColorText(),
    groups=[access_log]
)

access_stream.log(
    "GET /api/users 200",
    level="INFO",
    code=200,
    context={"user_id": "123", "ip": "192.168.1.1"},
    metrics={"response_time_ms": "45"}
)

Data Pipeline Logging

from xlog import LogStream, FileGroup

pipeline = LogStream(
    name="etl-pipeline",
    groups=[FileGroup(path="./logs", name="pipeline")]
)

pipeline.log("Extract started", context={"source": "database"})
pipeline.log("Transform completed", metrics={"records": "1000"})
pipeline.log("Load finished", context={"target": "warehouse"})

Microservice Tracing

from xlog import LogStream
import uuid

stream = LogStream(name="order-service", level="INFO")

correlation_id = str(uuid.uuid4())

stream.log(
    "Processing order",
    context={
        "correlation_id": correlation_id,
        "order_id": "ORD-123"
    }
)

stream.log(
    "Calling payment-service",
    context={
        "correlation_id": correlation_id,
        "target_service": "payment-service"
    }
)

More Examples

See the examples/ directory for complete working examples:

• example_basic_logging.py - Getting started
• example_file_logging.py - File output
• example_formatters.py - All formatters
• example_web_application.py - HTTP logging
• example_data_pipeline.py - ETL pipeline
• example_microservice.py - Distributed tracing
• example_monitoring.py - System monitoring

Documentation

Each module includes comprehensive docstrings:

from xlog import LogStream
help(LogStream)

License

MIT