Agent Skills: Application Logging

Application Logging

Overview

Implement comprehensive structured logging with proper levels, context, and centralized aggregation for effective debugging and monitoring.

When to Use

• Application debugging
• Audit trail creation
• Performance analysis
• Compliance requirements
• Centralized log aggregation

Instructions

1. Node.js Structured Logging with Winston

// logger.js
const winston = require("winston");

const logFormat = winston.format.combine(
  winston.format.timestamp({ format: "YYYY-MM-DD HH:mm:ss" }),
  winston.format.errors({ stack: true }),
  winston.format.json(),
);

const logger = winston.createLogger({
  level: process.env.LOG_LEVEL || "info",
  format: logFormat,
  defaultMeta: {
    service: "api-service",
    environment: process.env.NODE_ENV || "development",
  },
  transports: [
    new winston.transports.Console({
      format: winston.format.combine(
        winston.format.colorize(),
        winston.format.simple(),
      ),
    }),
    new winston.transports.File({
      filename: "logs/error.log",
      level: "error",
    }),
    new winston.transports.File({
      filename: "logs/combined.log",
    }),
  ],
});

module.exports = logger;

2. Express HTTP Request Logging

// Express middleware
const express = require("express");
const expressWinston = require("express-winston");
const logger = require("./logger");

const app = express();

app.use(
  expressWinston.logger({
    transports: [
      new winston.transports.Console(),
      new winston.transports.File({ filename: "logs/http.log" }),
    ],
    format: winston.format.combine(
      winston.format.timestamp(),
      winston.format.json(),
    ),
    meta: true,
    msg: "HTTP {{req.method}} {{req.url}}",
    expressFormat: true,
  }),
);

app.get("/api/users/:id", (req, res) => {
  const requestId = req.headers["x-request-id"] || Math.random().toString();

  logger.info("User request started", { requestId, userId: req.params.id });

  try {
    const user = { id: req.params.id, name: "John Doe" };
    logger.debug("User data retrieved", { requestId, user });
    res.json(user);
  } catch (error) {
    logger.error("User retrieval failed", {
      requestId,
      error: error.message,
      stack: error.stack,
    });
    res.status(500).json({ error: "Internal server error" });
  }
});

3. Python Structured Logging

# logger_config.py
import logging
import json
from pythonjsonlogger import jsonlogger
import sys

class CustomJsonFormatter(jsonlogger.JsonFormatter):
    def add_fields(self, log_record, record, message_dict):
        super().add_fields(log_record, record, message_dict)
        log_record['timestamp'] = self.formatTime(record)
        log_record['service'] = 'api-service'
        log_record['level'] = record.levelname

def setup_logging():
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)

    console_handler = logging.StreamHandler(sys.stdout)
    formatter = CustomJsonFormatter()
    console_handler.setFormatter(formatter)
    logger.addHandler(console_handler)

    return logger

logger = setup_logging()

4. Flask Integration

# Flask app
from flask import Flask, request, g
import uuid
import time

app = Flask(__name__)

@app.before_request
def before_request():
    g.start_time = time.time()
    g.request_id = request.headers.get('X-Request-ID', str(uuid.uuid4()))

@app.after_request
def after_request(response):
    duration = time.time() - g.start_time
    logger.info('HTTP Request', extra={
        'method': request.method,
        'path': request.path,
        'status_code': response.status_code,
        'duration_ms': duration * 1000,
        'request_id': g.request_id
    })
    return response

@app.route('/api/orders/<order_id>')
def get_order(order_id):
    logger.info('Order request', extra={
        'order_id': order_id,
        'request_id': g.request_id
    })

    try:
        order = db.query(f'SELECT * FROM orders WHERE id = {order_id}')
        logger.debug('Order retrieved', extra={'order_id': order_id})
        return {'order': order}
    except Exception as e:
        logger.error('Order retrieval failed', extra={
            'order_id': order_id,
            'error': str(e),
            'request_id': g.request_id
        }, exc_info=True)
        return {'error': 'Internal server error'}, 500

5. ELK Stack Setup

# docker-compose.yml
version: "3.8"
services:
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:8.0.0
    environment:
      - discovery.type=single-node
      - xpack.security.enabled=false
      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
    ports:
      - "9200:9200"
    volumes:
      - elasticsearch_data:/usr/share/elasticsearch/data

  logstash:
    image: docker.elastic.co/logstash/logstash:8.0.0
    ports:
      - "5000:5000"
    volumes:
      - ./logstash.conf:/usr/share/logstash/pipeline/logstash.conf
    depends_on:
      - elasticsearch

  kibana:
    image: docker.elastic.co/kibana/kibana:8.0.0
    ports:
      - "5601:5601"
    environment:
      ELASTICSEARCH_HOSTS: http://elasticsearch:9200
    depends_on:
      - elasticsearch

volumes:
  elasticsearch_data:

6. Logstash Configuration

# logstash.conf
input {
  tcp {
    port => 5000
    codec => json
  }
}

filter {
  date {
    match => [ "timestamp", "YYYY-MM-dd HH:mm:ss" ]
    target => "@timestamp"
  }

  mutate {
    add_field => { "[@metadata][index_name]" => "logs-%{+YYYY.MM.dd}" }
  }
}

output {
  elasticsearch {
    hosts => ["elasticsearch:9200"]
    index => "%{[@metadata][index_name]}"
  }
}

Best Practices

✅ DO

• Use structured JSON logging
• Include request IDs for tracing
• Log at appropriate levels
• Add context to error logs
• Implement log rotation
• Use timestamps consistently
• Aggregate logs centrally
• Filter sensitive data

❌ DON'T

• Log passwords or secrets
• Log at INFO for every operation
• Use unstructured messages
• Ignore log storage limits
• Skip context information
• Log to stdout in production
• Create unbounded log files

Log Levels

• ERROR: Application error requiring immediate attention
• WARN: Potential issues requiring investigation
• INFO: Significant application events
• DEBUG: Detailed diagnostic information