logifyx 1.0.2

A modern, production-ready Python logging framework with zero configuration.

Logifyx

A modern, production-ready Python logging framework with zero configuration.

View on PyPI

Quick Start • Features • Configuration • Handlers • Kafka Streaming • CLI • API Reference

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Table of Contents

• Features
• Installation
• Quick Start
• Preset Modes
• Configuration
• Handlers
  • Console Handler
  • File Handler
  • Remote HTTP Handler
  • Kafka Handler
• Sensitive Data Masking
• Context Injection
• Kafka Streaming
• CLI Tool
• API Reference
• Examples
• Project Structure
• Contributing
• License

---

Features

Feature	Description
Colored Console Output	Beautiful, readable logs with color-coded levels
Rotating File Logs	Auto-rotating log files with size limits and backup
Remote HTTP Streaming	Send logs to any HTTP endpoint in real-time
Kafka Streaming	Stream logs to Apache Kafka with Avro serialization
Sensitive Data Masking	Auto-mask passwords, tokens, and API keys
JSON Mode	Structured JSON logging for log aggregators
YAML + ENV Config	Configure via yaml file, environment, or code
Zero Config Mode	Works out of the box with sensible defaults
Preset Modes	Quick setup with dev, prod, and simple presets
CLI Tool	Inspect configuration from command line
Global Registration	Use setup_logify() for framework-level integration
Context Injection	Add request_id, user_id to logs with ContextLoggerAdapter
Thread-Safe	Queue-based async architecture for non-blocking remote logging
Hot Reload	Reload configuration without restarting your application
Graceful Shutdown	Automatic flush of pending logs on program exit

---

Installation

pip install logifyx

For Kafka streaming support:

pip install logifyx[kafka]

Dependencies:

• colorlog - Colored console output
• python-json-logger - JSON formatting
• pyyaml - YAML configuration
• concurrent-log-handler - Multi-process safe file handling
• requests - HTTP remote logging
• python-dotenv - Environment variable loading
• aiokafka - Async Kafka producer (optional)
• fastavro - Avro serialization (optional)

---

Quick Start

Basic Usage (Zero Config)

from logifyx import Logifyx

log = Logifyx(name="myapp")

log.info("Application started")
log.warning("This is a warning")
log.error("Something went wrong")

With Presets

from logifyx import Logifyx

# Development mode: DEBUG level, colored output
log = Logifyx(name="myapp", mode="dev")

# Production mode: INFO level, JSON output
log = Logifyx(name="myapp", mode="prod")

Full Configuration

from logifyx import Logifyx

log = Logifyx(
    name="auth-service",
    mode="prod",
    file="auth.log",
    log_dir="logs",
    color=True,
    mask=True,  # Auto-mask sensitive data
    remote_url="http://localhost:5000/logs",
    kafka_servers="localhost:9092",
    kafka_topic="app-logs"
)

log.info("Server started on port 8080")
log.warning("password=secret123 token=abc")  # Masked: **** ****
log.error("Authentication failed", exc_info=True)

Global Registration (Recommended for Large Apps)

from logifyx import setup_logify, get_logify_logger

# Call once at app startup
setup_logify()

# Now use get_logify_logger anywhere in your app
log = get_logify_logger("auth", mode="prod", file="auth.log")
api_log = get_logify_logger("api", mode="prod", file="api.log")

Context Injection (Request Tracking)

from logifyx import Logifyx, ContextLoggerAdapter

log = Logifyx(name="auth", mode="prod")

# Wrap with context for request-scoped logging
request_log = ContextLoggerAdapter(
    log,
    {"request_id": "req-abc123", "user_id": 42}
)

request_log.info("User authenticated")
# Output: request_id=req-abc123 user_id=42 | User authenticated

Graceful Shutdown

from logifyx import Logifyx, flush, shutdown

log = Logifyx(name="myapp", remote_url="http://localhost:5000/logs")

log.info("Processing request")

# Option 1: Wait for queued logs without stopping (use in servers)
flush(timeout=5.0)

# Option 2: Full shutdown (called automatically via atexit)
shutdown()

---

🎯 Preset Modes

Logifyx includes preset configurations for common use cases:

Mode	Level	Color	JSON	Use Case
dev	DEBUG	✅	❌	Local development with verbose, colorful output
prod	INFO	❌	✅	Production with structured JSON logs
simple	INFO	❌	❌	Basic plain-text logging

# Development mode
log = Logifyx(name="myapp", mode="dev")   # Colorful debug logs

# Production mode
log = Logifyx(name="myapp", mode="prod")  # JSON production logs

# Simple mode
log = Logifyx(name="myapp", mode="simple")  # Plain text logs

Preset Details

dev Mode:

{
    "level": "DEBUG",
    "color": True,
    "json_mode": False
}

prod Mode:

{
    "level": "INFO",
    "color": False,
    "json_mode": True
}

simple Mode:

{
    "level": "INFO",
    "color": False,
    "json_mode": False
}

---

Configuration

Logifyx supports multiple configuration sources with clear priority:

Python Code Arguments > Environment Variables > logifyx.yaml > Defaults

1. Python Code (Highest Priority)

from logifyx import Logifyx

log = Logifyx(
    name="myapp",
    level="DEBUG",
    color=True,
    file="myapp.log",
    log_dir="logs",
    mask=True
)

2. Environment Variables

Set environment variables with the LOG_ prefix:

# Linux/macOS
export LOG_LEVEL=DEBUG
export LOG_FILE=app.log
export LOG_COLOR=True
export LOG_KAFKA_SERVERS=localhost:9092

# Windows PowerShell
$env:LOG_LEVEL = "DEBUG"
$env:LOG_FILE = "app.log"

Or use a .env file (loaded automatically via python-dotenv):

LOG_LEVEL=DEBUG
LOG_FILE=app.log
LOG_DIR=logs
LOG_COLOR=True
LOG_MASK=True

Sample .env File

Here's a complete .env file with all available options:

# ===========================================
# Logifyx Configuration - Sample .env File
# ===========================================

# ---- Core Settings ----
LOG_LEVEL=INFO                          # DEBUG, INFO, WARNING, ERROR, CRITICAL
LOG_MODE=dev                            # dev, prod, simple

# ---- Output Settings ----
LOG_FILE=app.log                        # Log file name
LOG_DIR=logs                            # Directory for log files
LOG_COLOR=True                          # Enable colored console output
LOG_JSON=False                          # Enable JSON formatted logs
LOG_MASK=True                           # Mask sensitive data (passwords, tokens, etc.)

# ---- File Rotation ----
LOG_MAX_BYTES=10000000                  # Max file size before rotation (10MB)
LOG_BACKUP_COUNT=5                      # Number of backup files to keep

# ---- Remote HTTP Logging ----
LOG_REMOTE=http://localhost:5000/logs   # HTTP endpoint URL
LOG_REMOTE_TIMEOUT=5                    # Request timeout in seconds
LOG_REMOTE_RETRIES=3                    # Max failures before disabling
LOG_REMOTE_HEADERS={"Content-Type": "application/json", "Authorization": "Bearer your-token"}  # Custom HTTP headers (JSON format)

# ---- Kafka Streaming ----
LOG_KAFKA_SERVERS=localhost:9092        # Kafka bootstrap servers (comma-separated)
LOG_KAFKA_TOPIC=app-logs                # Kafka topic name
LOG_SCHEMA_REGISTRY=http://localhost:8081  # Confluent Schema Registry URL
LOG_SCHEMA_COMPATIBILITY=BACKWARD       # BACKWARD, FORWARD, FULL, NONE

Note: Add .env to your .gitignore to avoid committing secrets.

3. YAML Configuration File

Create a logifyx.yaml file in your project root:

# Logging Settings
LOG_LEVEL: DEBUG
LOG_FILE: app.log
LOG_DIR: logs
LOG_COLOR: True
LOG_JSON: False
LOG_MASK: True
LOG_MODE: dev

# File Rotation
LOG_MAX_BYTES: 10000000
LOG_BACKUP_COUNT: 5

# Remote HTTP Streaming
LOG_REMOTE: http://localhost:5000/logs
LOG_REMOTE_TIMEOUT: 5
LOG_REMOTE_RETRIES: 3
LOG_REMOTE_HEADERS:
  Content-Type: application/json
  Authorization: Bearer your-token

# Kafka Streaming
LOG_KAFKA_SERVERS: localhost:9092
LOG_KAFKA_TOPIC: app-logs
LOG_SCHEMA_REGISTRY: http://localhost:8081
LOG_SCHEMA_COMPATIBILITY: BACKWARD

All Configuration Options

Core Settings

Option	Env Variable	YAML Key	Default	Description
name	-	-	"app"	Logger name (identifies the service)
level	LOG_LEVEL	LOG_LEVEL	"INFO"	Minimum log level
mode	LOG_MODE	LOG_MODE	"dev"	Preset mode (dev/prod/simple)

Output Settings

Option	Env Variable	YAML Key	Default	Description
file	LOG_FILE	LOG_FILE	"app.log"	Log file name
log_dir	LOG_DIR	LOG_DIR	"logs"	Directory for log files
color	LOG_COLOR	LOG_COLOR	False	Enable colored console output
json_mode	LOG_JSON	LOG_JSON	False	Enable JSON formatted logs
mask	LOG_MASK	LOG_MASK	True	Mask sensitive data

File Rotation Settings

Option	Env Variable	YAML Key	Default	Description
max_bytes	LOG_MAX_BYTES	LOG_MAX_BYTES	10000000	Max file size before rotation (bytes)
backup_count	LOG_BACKUP_COUNT	LOG_BACKUP_COUNT	5	Number of backup files to keep

Remote HTTP Settings

Option	Env Variable	YAML Key	Default	Description
remote_url	LOG_REMOTE	LOG_REMOTE	None	HTTP endpoint URL
remote_timeout	LOG_REMOTE_TIMEOUT	LOG_REMOTE_TIMEOUT	5	Request timeout (seconds)
max_remote_retries	LOG_REMOTE_RETRIES	LOG_REMOTE_RETRIES	3	Max failures before disabling
remote_headers	-	LOG_REMOTE_HEADERS	{"Content-Type": "application/json"}	Custom HTTP headers

Kafka Settings

Option	Env Variable	YAML Key	Default	Description
kafka_servers	LOG_KAFKA_SERVERS	LOG_KAFKA_SERVERS	None	Kafka bootstrap servers
kafka_topic	LOG_KAFKA_TOPIC	LOG_KAFKA_TOPIC	"logs"	Kafka topic name
schema_registry_url	LOG_SCHEMA_REGISTRY	LOG_SCHEMA_REGISTRY	None	Schema Registry URL
schema_compatibility	LOG_SCHEMA_COMPATIBILITY	LOG_SCHEMA_COMPATIBILITY	"BACKWARD"	Schema compatibility mode

Log Levels

Level	Value	Description
DEBUG	10	Detailed information for debugging
INFO	20	General operational information
WARNING	30	Something unexpected happened
ERROR	40	A serious problem occurred
CRITICAL	50	Program may not be able to continue

---

Handlers

Logifyx writes logs to multiple destinations simultaneously:

Handler	Description	Auto-enabled
Console	Colored stdout output	✅ Always
File	Rotating file with backups	✅ Always
Remote HTTP	POST to HTTP endpoint	When remote_url set
Kafka	Stream to Kafka topic	When kafka_servers set

Console Handler

Always enabled. Writes logs to stdout with optional color coding.

Color Mapping

Level	Color
DEBUG	Cyan
INFO	Green
WARNING	Yellow
ERROR	Red
CRITICAL	Bold Red

log = Logifyx(name="myapp", color=True)

Example Output

2024-02-11 15:30:45 - myapp - INFO - Server started - /app/main.py - main.py - 42
2024-02-11 15:30:46 - myapp - WARNING - High memory usage - /app/main.py - main.py - 56
2024-02-11 15:30:47 - myapp - ERROR - Connection failed - /app/main.py - main.py - 78

File Handler

Always enabled. Writes logs to a rotating file with automatic backup management.

Features

• Rotating files: Automatically rotates when file reaches size limit
• Backup management: Keeps N backup files, deletes oldest
• Concurrent-safe: Uses ConcurrentRotatingFileHandler for multi-process safety
• Auto-creates directory: Creates log directory if it doesn't exist

log = Logifyx(
    name="myapp",
    file="myapp.log",       # Log file name
    log_dir="logs",         # Directory for logs
    max_bytes=10_000_000,   # 10MB max file size
    backup_count=5          # Keep 5 backup files
)

File Structure

logs/
├── myapp.log           # Current log file
├── myapp.log.1         # Previous (most recent backup)
├── myapp.log.2         # Older backup
├── myapp.log.3
├── myapp.log.4
└── myapp.log.5         # Oldest backup

Remote HTTP Handler

Enabled when remote_url is set. Sends log records to an HTTP endpoint via POST requests.

Features

• Queue-based async: Uses QueueHandler + QueueListener for non-blocking sends
• Thread-safe: Internal locking for safe concurrent access
• Auto-retry: Retries on failures
• Circuit breaker: Disables after N consecutive failures (default: 3)
• JSON payload: Structured log data with exception info

Architecture

Logifyx Logger
    ↓
QueueHandler (instant, non-blocking)
    ↓
QueueListener (background thread)
    ↓
RemoteHandler → HTTP POST

log = Logifyx(
    name="myapp",
    remote_url="http://localhost:5000/logs",
    remote_timeout=5,
    max_remote_retries=3,
    remote_headers={"Authorization": "Bearer token"}
)

Payload Format

{
  "level": "INFO",
  "message": "2024-02-11 15:30:45 - auth - INFO - User logged in",
  "service": "auth-service",
  "timestamp": 1707666000.123456,
  "file": "/app/auth/login.py",
  "line": 42,
  "func": "handle_login",
  "exception": null
}

Example Flask Server

from flask import Flask, request

app = Flask(__name__)

@app.route('/logs', methods=['POST'])
def receive_logs():
    log_data = request.json
    print(f"[{log_data['level']}] {log_data['service']}: {log_data['message']}")
    return {"status": "received"}, 200

if __name__ == '__main__':
    app.run(port=5000)

Kafka Handler

Enabled when kafka_servers is set. Streams logs to Apache Kafka with Avro serialization.

See Kafka Streaming section for detailed documentation.

---

Sensitive Data Masking

Logifyx automatically masks sensitive data patterns in all handlers:

log = Logifyx(name="auth", mask=True)

log.info("User login password=secret123 token=abc123")
# Output: User login **** ****

Masked Patterns

Pattern	Example
password=...	password=secret123 → ****
token=...	token=abc123 → ****
secret=...	secret=mykey → ****
api_key=...	api_key=xyz → ****
access_key=...	access_key=123 → ****
access_token=...	access_token=tok → ****

---

Context Injection

Use ContextLoggerAdapter to inject structured context (request_id, user_id, etc.) into logs:

from logifyx import Logifyx, ContextLoggerAdapter

log = Logifyx(name="auth", mode="prod")

# Wrap logger with context for request-scoped logging
request_log = ContextLoggerAdapter(
    log,
    {"request_id": "abc123", "user_id": 42, "session": "sess-xyz"}
)

request_log.info("User authenticated")
# Text output: request_id=abc123 user_id=42 session=sess-xyz | User authenticated

# JSON output (if json_mode=True): includes context in extra fields

Use Cases

• Request tracking: Add request_id for distributed tracing
• User context: Include user_id for audit logs
• Multi-tenant: Add tenant_id for SaaS applications

---

Kafka Streaming

Stream logs to Apache Kafka with Avro serialization and Schema Registry support.

Features

• Async Producer: Non-blocking log delivery using aiokafka
• Avro Serialization: Efficient binary format with schema validation
• Schema Registry: Confluent Schema Registry integration
• Schema Evolution: BACKWARD, FORWARD, FULL compatibility modes
• Circuit Breaker: Automatic disable after repeated failures
• Compression: Gzip compression for efficient network usage

Quick Start

1. Install Dependencies

pip install aiokafka fastavro

2. Start Kafka (Docker)

Create a docker-compose.yml:

version: '3.8'

services:
  kafka:
    image: obsidiandynamics/kafka
    restart: "no"
    ports:
      - "2181:2181"
      - "9092:9092"
      - "29092:29092"
    environment:
      KAFKA_LISTENERS: "INTERNAL://:29092,EXTERNAL://:9092"
      KAFKA_ADVERTISED_LISTENERS: "INTERNAL://kafka:29092,EXTERNAL://localhost:9092"
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: "INTERNAL:PLAINTEXT,EXTERNAL:PLAINTEXT"
      KAFKA_INTER_BROKER_LISTENER_NAME: "INTERNAL"
      KAFKA_ZOOKEEPER_SESSION_TIMEOUT: "6000"
      KAFKA_RESTART_ATTEMPTS: "10"
      KAFKA_RESTART_DELAY: "5"
      ZOOKEEPER_AUTOPURGE_PURGE_INTERVAL: "0"

  schema-registry:
    image: confluentinc/cp-schema-registry:7.5.0
    container_name: schema-registry
    depends_on:
      kafka:
        condition: service_healthy
    ports:
      - "8081:8081"
    environment:
      SCHEMA_REGISTRY_HOST_NAME: schema-registry
      SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS: PLAINTEXT://kafka:29092

docker-compose up -d

3. Use Kafka Logging

from logifyx import Logifyx

log = Logifyx(
    name="myapp",
    kafka_servers="localhost:9092",
    kafka_topic="app-logs",
    schema_registry_url="http://localhost:8081",
    schema_compatibility="BACKWARD"
)

log.info("This message goes to Kafka!")

Avro Schema

Logs are serialized using this Avro schema:

{
  "type": "record",
  "name": "LogRecord",
  "namespace": "com.logifyx.logs",
  "doc": "Log record schema v1",
  "fields": [
    {"name": "level", "type": "string", "doc": "Log level (DEBUG, INFO, WARNING, ERROR, CRITICAL)"},
    {"name": "message", "type": "string", "doc": "Log message"},
    {"name": "service", "type": "string", "doc": "Service/logger name"},
    {"name": "timestamp", "type": "string", "doc": "ISO8601 timestamp"},
    {"name": "file", "type": ["null", "string"], "default": null, "doc": "Source file path"},
    {"name": "line", "type": ["null", "int"], "default": null, "doc": "Line number"},
    {"name": "function", "type": ["null", "string"], "default": null, "doc": "Function name"},
    {"name": "exception", "type": ["null", "string"], "default": null, "doc": "Exception traceback if any"},
    {"name": "extra", "type": ["null", "string"], "default": null, "doc": "Extra JSON data"},
    {"name": "schema_version", "type": "int", "default": 1, "doc": "Schema version for evolution"}
  ]
}

Schema Compatibility Modes

Mode	Description	Use Case
BACKWARD	New schema can read old data	Adding optional fields
BACKWARD_TRANSITIVE	All previous schemas	Strict backward compatibility
FORWARD	Old schema can read new data	Removing optional fields
FORWARD_TRANSITIVE	All future schemas	Strict forward compatibility
FULL	Both backward and forward	Most restrictive
FULL_TRANSITIVE	All versions both ways	Maximum compatibility
NONE	No compatibility checks	Development only

Recommended: BACKWARD Compatibility

• ✅ Adding new optional fields (with defaults)
• ✅ Adding new fields with default values
• ❌ Removing fields
• ❌ Changing field types

Consuming Logs

Python Consumer

import asyncio
import json
from aiokafka import AIOKafkaConsumer

async def consume_logs():
    consumer = AIOKafkaConsumer(
        'app-logs',
        bootstrap_servers='localhost:9092',
        auto_offset_reset='earliest',
        group_id='log-viewer'
    )
    
    await consumer.start()
    print("Listening for logs...")
    
    try:
        async for msg in consumer:
            try:
                log = json.loads(msg.value.decode('utf-8'))
            except:
                # Handle Avro (skip 5-byte header)
                import fastavro, io
                from logifyx.kafka import LOG_SCHEMA_V1
                from fastavro.schema import parse_schema
                
                data = msg.value[5:] if msg.value[0] == 0 else msg.value
                schema = parse_schema(LOG_SCHEMA_V1)
                log = fastavro.schemaless_reader(io.BytesIO(data), schema)
            
            print(f"[{log['level']}] {log['service']}: {log['message']}")
    finally:
        await consumer.stop()

asyncio.run(consume_logs())

Kafka Console Consumer

docker exec -it kafka kafka-console-consumer \
  --bootstrap-server localhost:9092 \
  --topic app-logs \
  --from-beginning

Production Configuration

Multiple Brokers

log = Logifyx(
    name="myapp",
    kafka_servers="kafka1:9092,kafka2:9092,kafka3:9092",
    kafka_topic="app-logs"
)

Troubleshooting

Error	Solution
KafkaConnectionError	Ensure Kafka is running: docker-compose ps
Schema registry connection failed	Wait 30-60s after startup, check curl http://localhost:8081
ImportError: aiokafka	Install: pip install aiokafka fastavro

---

CLI Tool

Inspect your Logifyx configuration from the command line.

Commands

logifyx --config

Display the resolved configuration from all sources:

logifyx --config

Output:

Logifyx Configuration (logifyx.yaml: found):

{
    "level": "DEBUG",
    "color": true,
    "max_bytes": 10000000,
    "backup_count": 5,
    "log_dir": "logs",
    "file": "app.log",
    "mode": "dev",
    "json_mode": false,
    "mask": true,
    "remote_url": null,
    "kafka_servers": "localhost:9092",
    "kafka_topic": "logs",
    "schema_registry_url": null,
    "schema_compatibility": "BACKWARD",
    "remote_timeout": 5,
    "max_remote_retries": 3,
    "remote_headers": {"Content-Type": "application/json"}
}

logifyx --help

logifyx --help

Use Cases

# Verify config before deployment
logifyx --config

# Check specific settings
logifyx --config | grep kafka

# Compare environments
logifyx --config > config-snapshot.json

---

API Reference

Logifyx Class

Main logger class extending logging.Logger.

from logifyx import Logifyx

log = Logifyx(
    name: str = "app",                    # Logger name
    level: int = logging.NOTSET,          # Log level
    mode: str = None,                     # Preset mode (dev/prod/simple)
    json_mode: bool = None,               # JSON output
    remote_url: str = None,               # HTTP endpoint
    log_dir: str = None,                  # Log directory
    mask: bool = True,                    # Mask sensitive data
    color: bool = None,                   # Colored output
    backup_count: int = None,             # Backup files count
    max_bytes: int = None,                # Max file size
    file: str = None,                     # Log filename
    kafka_servers: str = None,            # Kafka bootstrap servers
    kafka_topic: str = None,              # Kafka topic
    schema_registry_url: str = None,      # Schema Registry URL
    schema_compatibility: str = None,     # Schema compatibility
    remote_timeout: int = None,           # HTTP timeout
    max_remote_retries: int = None,       # HTTP max retries
    remote_headers: dict = None           # HTTP headers
)

Methods

Method	Description
configure(**kwargs)	Configure the logger with all options
reload()	Reload logger configuration and handlers
reload_from_file()	Reload configuration from logifyx.yaml

ContextLoggerAdapter Class

Adapter for injecting structured context into logs.

from logifyx import ContextLoggerAdapter

adapter = ContextLoggerAdapter(
    logger: Logifyx,              # Base logger
    extra: dict                  # Context dictionary
)

get_logify_logger() Function

Get or create a configured Logifyx logger instance.

from logifyx import get_logify_logger

log = get_logify_logger(
    name: str,                   # Logger name (singleton per name)
    **kwargs                     # Same options as Logifyx constructor
)

Note: Requires calling setup_logify() first.

setup_logify() Function

Register Logifyx as the global logger class.

from logifyx import setup_logify

setup_logify()  # Call once at app startup

flush() Function

Wait for queued logs to be sent without stopping the listener.

from logifyx import flush

success = flush(timeout: float = 5.0)  # Returns True if drained

shutdown() Function

Explicitly flush and stop all async logging handlers.

from logifyx import shutdown

shutdown()  # Call before application exits

Note: Automatically registered with atexit, but call explicitly for immediate cleanup.

---

Examples

Basic Demo

from logifyx import Logifyx, ContextLoggerAdapter, get_logify_logger, setup_logify, flush

# Direct instantiation
log = Logifyx(
    name="auth",
    mode="dev",
    file="auth.log",
    color=True,
    mask=True
)

log.info("Server started")
log.warning("password=123456 token=abcd123")  # Masked
log.error("Login failed")

# Global registration
setup_logify()
api_log = get_logify_logger("api", mode="dev", file="api.log")
api_log.info("API endpoint hit")

# Context injection
request_log = ContextLoggerAdapter(
    log,
    {"request_id": "req-abc123", "user_id": 42}
)
request_log.info("User authenticated")

# Cleanup
flush(timeout=5.0)

Kafka Demo

from logifyx import Logifyx

log = Logifyx(
    name="kafka-demo",
    kafka_servers="localhost:9092",
    kafka_topic="app-logs",
    schema_registry_url="http://localhost:8081",
    schema_compatibility="BACKWARD",
    color=True
)

log.info("Application started")
log.debug("Debug message")
log.warning("Warning message")

try:
    1 / 0
except Exception:
    log.error("Exception occurred", exc_info=True)

log.info("Application finished")

---

Project Structure

logifyx/
├── __init__.py      # Package exports
├── core.py          # Main Logifyx class
├── config.py        # Configuration loading
├── handler.py       # Handler factory
├── formatter.py     # Log formatters
├── filters.py       # Sensitive data masking
├── presets.py       # Mode presets (dev/prod/simple)
├── remote.py        # HTTP remote handler
├── kafka.py         # Kafka + Avro handler
└── cli.py           # CLI tool

---

Contributing

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

1. Fork the repository
2. Create your feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

---

License

MIT License - see LICENSE for details.

---

Made with ❤️ by Madhur Prakash

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