Lukas
7ec80fdeec
- Remove structlog dependency from backend/pyproject.toml - Add app.utils.logging_compat shim for keyword-arg logging API - Add app.utils.json_formatter for JSON log output with extra fields - Update all backend modules to use logging_compat.get_logger() - Update docstrings in log_sanitizer.py and json_formatter.py - Update test comment in test_async_utils.py - Record 406 failing tests in Docs/Tasks.md for tracking
142 lines
4.7 KiB
Python
142 lines
4.7 KiB
Python
"""Health-check background task.
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Registers an APScheduler job that probes the fail2ban socket every 30 seconds
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and stores the result on ``app.state.server_status``. The dashboard endpoint
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reads from this cache, keeping HTTP responses fast and the daemon connection
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decoupled from user-facing requests.
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Crash detection (Task 3)
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------------------------
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When a jail activation is performed, the router stores a timestamp on
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``app.state.last_activation`` (a ``dict`` with ``jail_name`` and ``at``
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keys). If the health probe subsequently detects an online→offline transition
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within 60 seconds of that activation, a
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:class:`~app.models.config.PendingRecovery` record is written to
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``app.state.pending_recovery`` so the UI can offer a one-click rollback.
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Correlation IDs are propagated through the task using :mod:`app.utils.correlation`
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so that task logs can be correlated across runs.
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"""
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from __future__ import annotations
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import asyncio
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import datetime
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import uuid
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from contextvars import copy_context
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from typing import TYPE_CHECKING
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from app.utils.logging_compat import get_logger
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from app.models.server import ServerStatus
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from app.services import health_service
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from app.tasks.timeout_utils import run_with_timeout
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from app.utils.correlation import reset_correlation_id, set_correlation_id
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from app.utils.runtime_state import (
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RuntimeState,
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get_effective_settings,
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get_runtime_state,
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process_health_probe_result,
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)
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if TYPE_CHECKING: # pragma: no cover
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from fastapi import FastAPI
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from app.config import Settings
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log = get_logger(__name__)
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#: How often the probe fires (seconds).
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HEALTH_CHECK_INTERVAL: int = 30
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#: Maximum seconds to allow for health probe to complete.
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HEALTH_PROBE_TIMEOUT_SECONDS: int = 10
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async def _run_probe_with_resources(
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settings: Settings,
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runtime_state: RuntimeState,
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correlation_id: str | None = None,
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) -> None:
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"""Probe fail2ban and cache the result on the runtime state.
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Args:
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settings: The resolved application settings used for the probe.
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runtime_state: The mutable runtime state manager.
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correlation_id: Optional correlation ID for log correlation.
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"""
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if correlation_id is None:
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correlation_id = str(uuid.uuid4())
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token = set_correlation_id(correlation_id)
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try:
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# Use copy_context() so ContextVar values (e.g. correlation_id)
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# propagate to any child asyncio tasks spawned inside the coroutine.
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probe_task = asyncio.create_task(
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_do_probe_with_resources(settings, runtime_state),
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context=copy_context(),
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)
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await run_with_timeout("health_check", probe_task, HEALTH_PROBE_TIMEOUT_SECONDS)
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finally:
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# Reset AFTER run_with_timeout completes, so child tasks still
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# have the correlation ID in their context while they log.
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reset_correlation_id(token)
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async def _do_probe_with_resources(settings: Settings, runtime_state: RuntimeState) -> None:
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"""Inner probe logic that runs with correlation context set."""
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socket_path: str = settings.fail2ban_socket
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status: ServerStatus = await health_service.probe(socket_path)
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process_health_probe_result(runtime_state, status)
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async def _run_probe(app: FastAPI) -> None:
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await _run_probe_with_resources(
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get_effective_settings(app),
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get_runtime_state(app),
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)
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async def run_probe(app: FastAPI) -> None:
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"""Run a single health probe outside the scheduled job context."""
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await _run_probe(app)
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def register(app: FastAPI) -> None:
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"""Add the health-check job to the application scheduler.
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Must be called after the scheduler has been started (i.e., inside the
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lifespan handler, after ``scheduler.start()``).
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Args:
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app: The :class:`fastapi.FastAPI` application instance whose
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``app.state.scheduler`` will receive the job.
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"""
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# Initialise the cache with an offline placeholder so the dashboard
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# endpoint is always able to return a valid response even before the
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# first probe fires.
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settings = get_effective_settings(app)
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runtime_state = get_runtime_state(app)
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runtime_state.server_status = ServerStatus(online=False)
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# Initialise activation tracking state.
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runtime_state.last_activation = None
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runtime_state.pending_recovery = None
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app.state.scheduler.add_job(
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_run_probe_with_resources,
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trigger="interval",
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seconds=HEALTH_CHECK_INTERVAL,
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kwargs={"settings": settings, "runtime_state": runtime_state},
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id="health_check",
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replace_existing=True,
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# Fire immediately on startup too, so the UI isn't dark for 30 s.
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next_run_time=datetime.datetime.now(tz=datetime.UTC),
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)
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log.info(
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"health_check_scheduled",
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interval_seconds=HEALTH_CHECK_INTERVAL,
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)
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