Melhores práticas de registro estruturado
Implemente o registro estruturado para facilitar a análise, a interpretação e uma depuração mais rápida de problemas em produção.
Melhores práticas de registro estruturado é uma aula grátis de Production Debugging & Incident Response Playbook no CoddyKit. Esta é a aula 1 de 4. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de Production Debugging & Incident Response Playbook, e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de Production Debugging & Incident Response Playbook inclui 4 aulas no total.
Partes desta aula ainda não foram traduzidas e aparecem em inglês.
What are Logs?
Logs are records of events that happen in your application or system. Think of them as a diary for your software!
They're crucial for understanding what your program is doing, especially when things go wrong in a live "production" environment.
The Messy Truth
Often, logs are just plain text strings. This is called unstructured logging. While easy to write, unstructured logs are hard for computers to read and analyze, making debugging a slow, manual process.
Consider this example:
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def process_order(order_id, item_count):
logger.info(f"Processing order {order_id} with {item_count} items.")
if item_count > 10:
logger.warning(f"Large order detected for {order_id}. Items: {item_count}.")
logger.info(f"Order {order_id} processed successfully.")
if __name__ == "__main__":
process_order("ORD-123", 5)
process_order("ORD-456", 12)What is Structured Logging?
Structured logging means your logs are formatted as machine-readable data, not just free-form text. The most common format is JSON.
Instead of a single string, each log entry is an object with key-value pairs. This makes them easy to search, filter, and analyze programmatically.
Why Structured Logging Rocks
Structured logs offer many advantages:
- Faster Debugging: Quickly find relevant events.
- Better Analysis: Easily query and aggregate data.
- Automated Tools: Integrate with monitoring and alerting systems.
- Consistency: Ensures all logs contain expected fields.
JSON is King
While other formats exist, JSON (JavaScript Object Notation) is the most popular choice for structured logging due to its simplicity and widespread support.
A JSON log entry is a self-contained object, making it incredibly versatile for storing varied data. Here's what a structured log might look like:
{
"timestamp": "2023-10-27T10:30:00Z",
"level": "INFO",
"service": "order-processor",
"message": "Order processed successfully",
"order_id": "ORD-123",
"item_count": 5
}Code It Up!
Let's see how to implement structured logging. Many languages have libraries that make this easy. Here's a basic Python example using the standard logging module with a custom JSON formatter.
import logging
import json
class JsonFormatter(logging.Formatter):
def format(self, record):
log_entry = {
"timestamp": self.formatTime(record, self.datefmt),
"level": record.levelname,
"name": record.name,
"message": record.getMessage(),
"file": record.filename,
"line": record.lineno
}
if hasattr(record, 'order_id'):
log_entry['order_id'] = record.order_id
if hasattr(record, 'item_count'):
log_entry['item_count'] = record.item_count
return json.dumps(log_entry)
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
handler.setFormatter(JsonFormatter())
logger.addHandler(handler)
def process_order(order_id, item_count):
extra_data = {'order_id': order_id, 'item_count': item_count}
logger.info("Processing order", extra=extra_data)
if item_count > 10:
logger.warning("Large order detected", extra=extra_data)
logger.info("Order processed successfully", extra=extra_data)
if __name__ == "__main__":
process_order("ORD-123", 5)
process_order("ORD-456", 12)Must-Have Fields
Every structured log entry should include these core fields for effective analysis:
timestamp: When the event happened (ISO 8601 format).level: Severity (INFO, WARN, ERROR, DEBUG).service: Which service or application generated the log.message: A human-readable summary of the event.hostname/pod_name: Where the log originated.
Enrich Your Logs
Beyond essential fields, add contextual data specific to the event. This is key for tracing requests across distributed systems, helping you connect the dots when debugging complex issues.
request_id: To track a single user request.user_id: To identify the user involved.transaction_id: For specific business transactions.
import logging
import json
import uuid
# Reusing the JsonFormatter from previous scene
class JsonFormatter(logging.Formatter):
def format(self, record):
log_entry = {
"timestamp": self.formatTime(record, self.datefmt),
"level": record.levelname,
"message": record.getMessage()
}
for key, value in record.__dict__.items():
if not key.startswith('_') and key not in ['name', 'levelname', 'pathname', 'filename', 'module', 'exc_info', 'exc_text', 'stack_info', 'lineno', 'funcName', 'created', 'msecs', 'relativeCreated', 'thread', 'threadName', 'processName', 'process', 'args', 'msg', 'asctime']:
log_entry[key] = value
return json.dumps(log_entry)
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
handler.setFormatter(JsonFormatter())
logger.addHandler(handler)
def handle_web_request(user_id):
request_id = str(uuid.uuid4())[:8]
extra_data = {'request_id': request_id, 'user_id': user_id, 'service': 'api-gateway'}
logger.info("Received web request", extra=extra_data)
if user_id == "user-vip":
logger.info("VIP user request detected", extra=extra_data)
else:
logger.debug("Standard user request", extra=extra_data)
logger.info("Request processed", extra=extra_data)
if __name__ == "__main__":
handle_web_request("user-123")
handle_web_request("user-vip")Log Levels
Log levels help categorize the severity and importance of a log message. Common levels include:
- DEBUG: Detailed info, only useful when diagnosing problems.
- INFO: Confirmation that things are working as expected.
- WARN: An unexpected event, but the application is still running.
- ERROR: An error that prevents some functionality from working.
- CRITICAL: A severe error, application might be unable to continue.
Quick Check
Structured logging is a powerful technique for improving observability. Let's test your understanding of its key advantages.
Structured Logging Recap
You've learned about the power of structured logging! By formatting your logs as machine-readable data (like JSON), you unlock faster debugging, better analysis, and seamless integration with monitoring tools.
Remember to include essential fields and contextual data to make your logs truly useful for diagnosing issues in production.
Perguntas Frequentes
A aula “Melhores práticas de registro estruturado” é grátis?
Sim — o texto completo de “Melhores práticas de registro estruturado” é grátis para ler aqui na web. Para praticá-la interativamente (um editor de código integrado e um tutor de IA 24/7) e desbloquear o restante do curso de Production Debugging & Incident Response Playbook, atualize para CoddyKit PRO. O curso de Production Debugging & Incident Response Playbook inclui 4 aulas no total.
O que vou aprender em “Melhores práticas de registro estruturado”?
Implemente o registro estruturado para facilitar a análise, a interpretação e uma depuração mais rápida de problemas em produção. Você pratica Production Debugging & Incident Response Playbook com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.
Preciso ter experiência prévia para começar Production Debugging & Incident Response Playbook?
Nenhuma experiência prévia é necessária. Production Debugging & Incident Response Playbook no CoddyKit é estruturado para alunos iniciantes até avançados, então você pode começar aqui ou desde o início e aprender no seu ritmo. Esta é a aula 1 de 4.
Quanto tempo leva a aula “Melhores práticas de registro estruturado”?
A maioria das aulas CoddyKit leva cerca de 5–10 minutos. Cada uma é compacta e interativa, então você faz progresso constante e retoma exatamente de onde parou entre web e app.
Posso escrever e executar código nesta aula de Production Debugging & Incident Response Playbook?
Sim. Cada aula de Production Debugging & Incident Response Playbook inclui um editor de código integrado, então você escreve e executa código real direto no navegador e recebe feedback de IA instantaneamente — nenhuma configuração local necessária.
Todas as aulas deste curso
- Melhores práticas de registro estruturado
- Métricas, painéis e observabilidade
- Projetando estratégias inteligentes de alertas
- Estratégias de agregação e retenção de registros