logging-standards

Logging Standards Skill

Purpose

Standards for consistent, useful logging across all application layers.

Auto-Invoke Triggers

• Adding log statements
• Configuring logging infrastructure
• Debugging issues
• Setting up monitoring

---

Log Levels

Level	Use Case	Example
TRACE	Detailed debugging	Variable values, loop iterations
DEBUG	Diagnostic info	Method entry/exit, state changes
INFO	Normal operations	Request completed, job started
WARN	Potential issues	Retry attempt, deprecated usage
ERROR	Failures	Exception caught, operation failed
FATAL	System failure	Cannot start, out of memory

Level Selection Rules

• Production default: INFO
• Development default: DEBUG
• Never log TRACE in production
• ERROR should always have context

---

Log Message Format

Structure

[timestamp] [level] [correlation-id] [logger] - message {structured-data}

Example

2024-01-15T10:30:45.123Z INFO abc-123-def UserService - User created {"userId": 42, "email": "user@example.com"}

Message Guidelines

• Start with action verb: "Creating user", "Processing order"
• Be specific: "User 123 deleted" not "Deleted"
• Include relevant IDs
• Keep messages concise

---

Structured Logging

Required Fields

Field	Description	Example
timestamp	ISO 8601 format	2024-01-15T10:30:45.123Z
level	Log level	INFO, ERROR
message	Human-readable	"User created"
correlationId	Request trace ID	abc-123-def
logger	Source class/module	UserService

Contextual Fields

Field	When to Include
userId	Authenticated request
requestPath	HTTP request
requestMethod	HTTP request
duration	Operation timing
errorCode	Error situations
stackTrace	ERROR level only

---

What to Log

Always Log

• Application startup/shutdown
• Authentication events (login, logout, failures)
• Authorization failures
• External service calls (start, end, errors)
• Database migrations
• Configuration changes
• Business-critical operations

Per Request

• Request start (DEBUG)
• Request completion with duration (INFO)
• Validation errors (WARN)
• Exceptions (ERROR)

Never Log

• Passwords or tokens
• Credit card numbers
• Personal data (PII) without masking
• Session IDs in plain text
• API keys or secrets
• Full request/response bodies with sensitive data

---

PII Masking

Fields to Mask

Field	Masking	Example
Email	Partial	j***@example.com
Phone	Partial	+1***456
Credit Card	Partial	--****-1234
SSN	Full	***
Password	Never log	-
Token	Never log	-

Masking Rules

• Mask at log time, not display time
• Keep enough for debugging (last 4 digits)
• Never log masked values to DEBUG level

---

Error Logging

Required Information

• Error type/code
• Error message
• Stack trace (first occurrence)
• Request context (path, method, user)
• Correlation ID
• Timestamp

Error Logging Rules

• Log full stack trace on first occurrence
• Group repeated errors (rate limit logging)
• Include recovery actions taken
• Don't log expected errors as ERROR

---

Performance Logging

Metrics to Log

Metric	Level	When
Request duration	INFO	Every request
Slow query	WARN	> 1 second
External call	DEBUG	Every call
Cache hit/miss	DEBUG	When relevant

Slow Operation Thresholds

Operation	Warn Threshold
HTTP request	> 1s
Database query	> 500ms
External API	> 2s
Cache operation	> 100ms

---

Log Aggregation

Requirements

• Centralized log storage
• Full-text search
• Correlation ID filtering
• Time-range queries
• Alerting on patterns

Recommended Tools

Tool	Use Case
ELK Stack	Self-hosted, full-featured
Datadog	Cloud, APM integration
Splunk	Enterprise, compliance
Loki	Kubernetes-native

---

Log Retention

Environment	Retention
Development	7 days
Staging	30 days
Production	90 days
Audit logs	1 year+

Archival Rules

• Compress logs older than 7 days
• Archive to cold storage after retention
• Delete non-essential logs aggressively
• Keep audit logs per compliance

---

Best Practices

DO

• Use structured logging (JSON)
• Include correlation IDs
• Log at appropriate levels
• Mask sensitive data
• Use async logging in production
• Set up alerts for ERROR logs

DON'T

• Log passwords or secrets
• Use string concatenation for messages
• Log inside tight loops
• Ignore log level guidelines
• Skip correlation IDs
• Log full stack traces repeatedly

---

Logging Checklist

• Structured logging configured
• Log levels appropriate per environment
• Correlation IDs propagated
• PII masking implemented
• Error logs include context
• Slow operation logging enabled
• Log aggregation configured
• Alerts set up for errors
• Retention policy implemented

---

Example: structlog + OTLP sink (illustrative)

Illustrative — this is one team's logging stack shown as a concrete example. Substitute your own logging library, processor pipeline, and log sink. The universal guidance above (levels, structured fields, correlation IDs, PII masking, event naming) is the reusable part; the specifics below are an example.

structlog Configuration (<your-logging-module>/config.py)

This example uses structlog with stdlib integration, routing all logs through Python's logging module with per-handler formatters.

Processor chain (shared across all handlers):

shared_processors = [
    structlog.contextvars.merge_contextvars,
    _add_log_context,          # Injects task_id, session_id, project_id, user_id
    _inject_context_to_record, # Copies context to LogRecord for OTel attributes
    structlog.stdlib.add_log_level,
    structlog.stdlib.add_logger_name,
    structlog.processors.TimeStamper(fmt="iso"),
    structlog.processors.StackInfoRenderer(),
    structlog.processors.UnicodeDecoder(),
]

Two output pipelines:

1. Console - ConsoleRenderer(colors=True) for dev, JSONRenderer for prod
2. OTLP sink (e.g. Seq, Grafana Loki, any OTLP collector) - JSONRenderer always (no ANSI codes), via OTLPLogExporter

Context Variables

Four contextvars.ContextVar fields are automatically injected into every log event:

from core.logging import set_log_context, clear_log_context
set_log_context(task_id="abc", session_id="sess", project_id="proj", user_id="123")
# All subsequent logs in this async context include these fields

Logger Creation Pattern

from core import get_logger          # Re-exported from core/__init__.py
logger = get_logger(__name__)        # Returns structlog.stdlib.BoundLogger

Log Event Naming Convention

Events use dot-separated domain.action format:

logger.info("app.startup_complete", max_concurrent_tasks=3)
logger.info("redis.connected")
logger.warning("db.schema_mismatch", migration_commands=[...])
logger.info("contributor.added", project_id=pid, user_id=uid, role="owner")
logger.error("redis.queue_push_error", queue=name, error=str(e))
logger.debug("redis.subscribed", channel=ch, task_id=tid)

OTLP Sink / OpenTelemetry Integration

Configured at app startup (this example targets a Seq OTLP endpoint):

setup_logging(
    log_level=LOG_LEVEL,      # From core.config (env: LOG_LEVEL)
    log_format=LOG_FORMAT,    # "console" or "json" (env: LOG_FORMAT)
    log_file=LOG_FILE,        # Optional rotating file (10MB, 5 backups)
    seq_enabled=SEQ_ENABLED,  # env: SEQ_ENABLED
    seq_endpoint=SEQ_ENDPOINT,# Default: https://localhost:5341/ingest/otlp/v1/logs
    seq_api_key=SEQ_API_KEY,  # env: SEQ_API_KEY (X-Seq-ApiKey header)
    service_name=OTEL_SERVICE_NAME,  # Resource: service.name, namespace: "scaffolding"
)

Shutdown flushes pending logs: shutdown_logging() called in app lifespan teardown.

Key Files

File	Purpose
<your-logging-module>/__init__.py	Public API: get_logger, set_log_context, TaskLogger
<your-logging-module>/config.py	structlog setup, OTel/OTLP integration, context vars
<your-logging-module>/task_logger.py	Task-specific step logging
<your-middleware-module>/	CorrelationIdMiddleware, LoggingMiddleware, UserContextMiddleware