John Lancaster
98 lines
3.5 KiB
Markdown
98 lines
3.5 KiB
Markdown
---
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name: python-logging-dictconfig
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description: 'Set up idiomatic Python logging with logging.config.dictConfig. Use when creating or refactoring logging setup, standardizing handlers/formatters, and enforcing centralized config.'
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argument-hint: 'Target context (single script, package, FastAPI app, or CLI) and desired log destinations'
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---
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# Idiomatic Python Logging with dictConfig
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Use this skill to produce a minimal, centralized logging setup using `logging.config.dictConfig`.
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Load references only when needed:
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- Python logging overview and hierarchy: [./references/python-logging-docs.md](./references/python-logging-docs.md)
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## When to Use
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- A project configures logging ad hoc with `basicConfig` across multiple modules.
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- You need one canonical logging configuration for app startup.
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- You need consistent formatting and levels across console/file handlers.
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- You want library modules to use named loggers without configuring logging themselves.
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## Inputs To Collect
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1. Runtime type: script, library, web app, worker, CLI.
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2. Destinations: stdout only, file only, or both.
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3. Desired default level: `INFO`, `DEBUG`, etc.
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4. Whether third-party loggers should be tuned (for example `uvicorn`, `sqlalchemy`).
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If missing, assume:
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- stdout handler
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- human-readable formatter
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- root level `INFO`
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- `disable_existing_loggers: False`
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## Procedure
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1. Define a single `LOGGING` dictionary in one startup-oriented module (for example `logging_config.py`).
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2. Include `version: 1` and set `disable_existing_loggers: False` unless there is a specific reason to silence existing loggers.
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3. Define formatters first, then handlers, then logger routing (`root` and optional named `loggers`).
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4. Use `logging.config.dictConfig(LOGGING)` exactly once during application startup.
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5. In all modules, get loggers via `logger = logging.getLogger(__name__)` and never call `basicConfig`.
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6. Keep libraries configuration-free: libraries should emit logs, applications decide routing.
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7. Verify behavior with a quick smoke check at multiple levels (`DEBUG`, `INFO`, `WARNING`, `ERROR`).
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## Minimal Baseline Template
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```python
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# logging_config.py
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from logging.config import dictConfig
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LOGGING = {
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"version": 1,
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"disable_existing_loggers": False,
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"formatters": {
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"standard": {
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"format": "%(asctime)s %(levelname)s %(name)s: %(message)s"
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}
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},
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"handlers": {
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"console": {
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"class": "logging.StreamHandler",
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"level": "INFO",
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"formatter": "standard",
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"stream": "ext://sys.stdout",
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}
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},
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"root": {
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"level": "INFO",
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"handlers": ["console"],
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},
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}
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def configure_logging() -> None:
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dictConfig(LOGGING)
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```
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```python
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# app startup
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from .logging_config import configure_logging
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configure_logging()
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```
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```python
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# any module
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import logging
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logger = logging.getLogger(__name__)
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logger.info("module initialized")
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```
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## Completion Checks
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1. `dictConfig` is called once at startup, not per module.
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2. No `basicConfig` calls remain.
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3. Modules use `getLogger(__name__)`.
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4. Logs appear at expected level and destination.
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5. Third-party logger noise is intentionally configured or left at defaults.
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## Branching Guidance
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- If structured logs are required: switch formatter output to JSON while keeping `dictConfig` topology unchanged.
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- If both console and file output are needed: add a file handler and attach it to `root`.
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- If a specific framework logger is too noisy: add a named logger override under `loggers`.
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