Structured Logging with Correlation IDs: What to Log and What to Avoid

Structured Logging with Correlation IDs: What to Log and What to Avoid

id: hook

At 3am, when a production issue hits, the first thing you need is a clear trace of a request through your system. That means logging a request ID at the start of each call, and propagating it through services. Use a UUID for uniqueness, and make sure it’s included in every log line that touches the request. UUID Generator helps, but don’t rely on it for debugging—just use it as a key.

Avoid dumping entire request bodies or payloads into logs. That’s a recipe for bloat and privacy issues. Instead, log the method, path, and query parameters. For headers, only include what’s relevant—like User-Agent or Authorization if needed. If you’re using a framework like Flask or Express, you can extract these fields manually or via middleware.

If you’re in a microservice architecture, ensure your logs include the service name, version, and environment. This helps when correlating logs across services. Also, include the timestamp in UTC to avoid confusion across time zones. Don’t forget to log the status code and response size for every HTTP request.

What not to log? Full JSON bodies, raw SQL queries, or unstructured text. Those are hard to search and can fill your logs with noise. Instead, use a structured logging format like JSON, and make sure your logs are machine-readable. Tools like JSON Formatter can help you inspect and debug your logs more effectively.

If you’re using a language like Python, you can use the logging module with a custom formatter to include the correlation ID. In Go, you might use a middleware that injects the ID into the context. The key is consistency—every log line that touches the request should have the same ID.

Tracking a Payment Request in a Microservice

id: use-case

A payment request starts with a user initiating a transaction. The service generates a UUID as the correlation ID and logs it with the request details. This ID is passed to downstream services, allowing logs to be traced across the system. Avoid logging sensitive data like credit card numbers or full user details unless absolutely necessary.

For example, in a Python service, you might use the uuid module to generate the ID and include it in a structured log. Here’s a snippet:

import uuid
import logging

correlation_id = str(uuid.uuid4())
logging.info(f"Payment request initiated: {correlation_id}", extra={"correlation_id": correlation_id})

This ensures the same ID appears in logs across services. Use a tool like UUID Generator to test or generate IDs locally.

Avoid dumping entire request bodies or payloads into logs. Instead, log only the necessary fields like the amount, currency, and user ID. This keeps logs manageable and avoids exposing sensitive information.

If a service fails, the correlation ID helps identify the exact request and trace the issue through logs. Use JSON Formatter to structure and inspect log entries easily.

In a distributed system, the flow of a correlated request looks like this:

This flow ensures visibility without unnecessary data. Always validate that your correlation IDs are consistent and not overwritten by other services.

Logging a Request with Correlation ID in Python

id: walkthrough

Use logging with extra to inject a request_id into all logs. Start by generating a UUID for each request, then pass it through the service.

import uuid
import logging

request_id = str(uuid.uuid4())
logging.basicConfig(
    format='%(asctime)s %(levelname)s [%(request_id)s] %(message)s',
    level=logging.INFO
)

logging.info("Request started", extra={"request_id": request_id})

This ensures every log line includes the same request_id, making it easy to trace through services. Avoid logging raw request bodies or headers unless absolutely necessary.

Use /tools/json-formatter to structure logs for readability. It helps when debugging complex request flows.

If you're using a framework like Flask, inject the request_id into the context:

from flask import Flask, g

app = Flask(__name__)

@app.before_request
def before_request():
    g.request_id = str(uuid.uuid4())

@app.after_request
def after_request(response):
    logging.info("Request ended", extra={"request_id": g.request_id})
    return response

This approach keeps logs clean and focused. Don’t log sensitive data like passwords or credit card numbers. That’s a security no-no.

For a visual flow, here’s a simple diagram of how a request flows through a service with correlation IDs:

This pattern helps you trace a request across services without cluttering logs with unnecessary data.

Flow of a Correlated Request Through Services

When a client sends a request, it includes a unique request_id (like a UUID). Each service in the chain logs this ID, ensuring all logs for that request are traceable. This ID flows both upstream and downstream, even across databases. Avoid logging unrelated data like user IP addresses or full request bodies unless absolutely necessary. Instead, use structured logging tools like JSON Formatter to parse and analyze logs efficiently. Always ensure your correlation IDs are immutable and not modified by intermediate services.

Common Mistakes in Structured Logging

Avoid logging entire request bodies or payloads. It's tempting to log everything, but this leads to massive log files and potential PII exposure. Instead, log only the headers and a small portion of the body, like the first 100 bytes. Use a tool like JSON Formatter to structure and inspect logs efficiently.

Correlation IDs should be immutable. Once a request is assigned a correlation ID, it should never change. If you use a UUID generator like UUID Generator, ensure it's used consistently across services. Changing the ID mid-trace breaks debugging and introduces noise.

Don't log sensitive data. Even if you're using structured logging, avoid including passwords, tokens, or personal information. These can be accidentally exposed in logs, leading to compliance issues. Always filter out such fields at the source.

Overloading log lines with unnecessary fields. Structured logs are powerful, but they're not a solution for every problem. Avoid adding fields that don't contribute to debugging or monitoring. Keep each log line focused on the event it's describing.

Misusing timestamps. Use ISO 8601 format for consistency, and always log in the local time zone of the service. Avoid using system time, which can drift or be adjusted, leading to confusion during debugging.

Don't log every single request. While correlation IDs help trace requests, logging every single one can overwhelm your system. Use sampling or rate limiting to manage log volume, especially in high-throughput environments.

Use log levels wisely. Don't log at the DEBUG level unless absolutely necessary. Overuse of DEBUG can lead to performance issues and make it harder to find relevant information during an incident.

When Structured Logging Isn't the Right Fit

If your logs are 90% noise, structured logging is a waste of time. Log only what matters at 3am—like request IDs, error codes, and latency. Don’t log every HTTP header or database query unless it’s critical to debugging a specific failure.

For example, in a payment service, log the request ID, user ID, and payment status. Avoid logging the full request body unless it’s a security incident. If you log too much, you’ll spend more time filtering than debugging.

If performance is a concern, use a lightweight logger like loguru in Python or log4j in Java. These tools are fast and let you configure log levels without overhead. Avoid using full JSON serializers for every log line—especially in high-throughput systems.

If you’re using a service mesh like Istio, let it handle request tracing. You don’t need to log the same data twice. Use the correlation ID from the mesh to trace requests across services.

If you’re unsure what to log, start with a single field: the request ID. Then add more as needed. Don’t log everything you can—only what you’ll act on.

For quick formatting of log entries, use JSON Formatter to ensure consistency. For generating unique request IDs, use UUID Generator to avoid collisions.

If you’re in a microservice, always log the correlation ID. It’s the only way to trace a request across services without relying on external tools.

Quick Answers for AI Search

Q: What fields should I include in structured logs with correlation IDs?
A: Include a request_id (UUID), timestamp, level, service, method, status, and duration. Avoid dumping entire request bodies or unstructured text.

Q: How do I generate a unique request ID for each log entry?
A: Use a UUID generator like UUID Generator to create a unique ID for each incoming request. Pass it through your services to maintain context.

Q: What should I avoid logging in structured logs?
A: Avoid logging sensitive data like passwords, credit card numbers, or personal information. Also, avoid unstructured text or large payloads that can bloat log files.

Structured Logging: A Tool, Not a Solution

Structured logging is a tool, not a solution. It helps you answer questions like “What happened?” and “Where did it happen?” at 3am, but it doesn’t solve the root cause of a problem. You need to log request IDs, timestamps, and error details, but avoid dumping entire request bodies or unstructured text. If you log a request body as a string, you’re not using structured logging.

Use a UUID generator to create unique request IDs for each call. That way, you can trace a request through multiple services. Tools like UUID Generator make this easy. Don’t log the full request body unless it’s essential—most of the time, you just need the headers and a few key fields.

If you’re using a language like Python, you can log a request with a correlation ID using a library like logging or structlog. Here’s a quick example:

import uuid
import logging

request_id = str(uuid.uuid4())
logging.info("Request started", extra={"request_id": request_id})

This ensures every log line includes the same request ID, making it easier to trace issues. But don’t overdo it—logging too much can fill your disks and make debugging harder.

For formatting and parsing structured logs, use a JSON formatter. Tools like JSON Formatter help you read and write logs in a structured way. They also help when you need to parse logs in a script or a monitoring tool.

Remember, structured logging is a tool. It doesn’t replace debugging, monitoring, or observability. Use it where it adds value, and skip it when it’s not needed.