Backend - A run's outcome write sits outside the job's own try: a database blip after a run that succeeded no longer records it as failed (the row waits for the next start's sweep). - An interrupted run keeps what it spent (the sweep sums its quota rows, as finish does). - The Scheduler's ad-hoc row says running while any backfill runs, not only the newest. - recent_runs reads each job's last runs off its index instead of ranking the whole table. - An interval change is stored with its audit row in one transaction, then rescheduled. - One API instance per database is the stated contract (.env.example, jobruns). - Turning full history on for a channel already complete records no run. - Migration 0109 round-trips interrupted and ad-hoc runs; the run words' DB CHECKs are test-held to the enums; the account filter is one helper for the four activity reads. - An audit row says how many of its request's log lines are still kept. Frontend - An "All" period reaches every row still kept (runs and admin actions outlive 90 days). - Back/Forward is followed in place by the server log: a closing dialog no longer turns Live off; a pivot still opens a fresh log. - Runs moved over from the audit log read their one-liner in the reader's language. - Filter choices or accounts that failed to load say so beside the filters, with a retry. - An old server-log link keeps its account, text and range; a remembered retired page opens its tab. - A request whose lines are gone says so instead of linking to an empty list. - The log lines and the history rows share one row frame, and the server log one list frame with the other tabs; an empty list is announced; each day's quota is readable.
178 lines
7.3 KiB
Python
178 lines
7.3 KiB
Python
"""The admin diagnostics console's read side: query `log_events`, the server's stored log.
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Admin-only and read-only — rows leave the table through the retention job (`log_retention_days`),
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never through here. Reading is not audited: it changes nothing, and the console's live tail polls,
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so a row per read would bury the audit log's real entries.
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What a row holds was redacted when it was written (`logs.record_fields`, and the ingest route for a
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browser's rows), so this serves the columns as they are; a value the redactor took out reads as
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`[redacted]` / `[email]`, which is the point — the console shows that something was there.
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Paging is a keyset over `(ts desc, id desc)` (`_keyset`). The live tail re-reads the first page rather than asking for "ids after N":
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several processes write this table, and a batch's ids are taken when it inserts, not when it
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commits, so a slower batch can land below an id the tail already passed — and would never show.
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"""
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from dataclasses import dataclass
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from datetime import datetime
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from typing import Literal
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from fastapi import APIRouter, Depends, Query
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from sqlalchemy import Text, cast, func, or_, select
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from sqlalchemy.orm import Session
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from app import logsink
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from app.auth import admin_user
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from app.db import get_db
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from app.models import LogEvent, User
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from app.routes import _account as account
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from app.routes._keyset import PAGE_MAX, accounts, older_than, page, utc
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from app.utils import like_escape
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router = APIRouter(prefix="/api/admin/logs", tags=["admin"])
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# A facet list is a chip row, not a report: past this many distinct events the rest are reachable
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# through the free-text search, and the response stays small whatever a self-host logs.
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FACET_EVENTS_MAX = 100
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Level = Literal["TRACE", "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
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# The filters a facet can leave out of its own count (`LogFilters.conditions`).
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Facet = Literal["levels", "sources", "user", "event"]
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# `none` selects the rows that have no event code, as the account filter's `none` (app/routes/_account)
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# selects the rows of no account — a real value to filter on, not the absence of a filter.
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NONE = account.NONE
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@dataclass(frozen=True)
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class LogFilters:
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"""The console's filters, shared by the page and its facet counts."""
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levels: list[str]
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sources: list[str]
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user: str | None
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event: str | None
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request_id: str | None
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job_id: int | None
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run_id: int | None
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q: str | None
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since: datetime | None
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until: datetime | None
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def conditions(self, skip: Facet | None = None) -> list:
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"""The WHERE conditions, ANDed by the caller. `skip` leaves one filter out: a facet counts
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its own values under every OTHER filter, so picking a level does not zero the other
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levels' counts (the way back), while a request pivot does narrow them (honest counts)."""
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out = []
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if self.levels and skip != "levels":
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out.append(LogEvent.level.in_(self.levels))
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if self.sources and skip != "sources":
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out.append(LogEvent.source.in_(self.sources))
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if self.user is not None and skip != "user":
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out.append(account.condition(LogEvent.user_id, self.user))
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if self.event is not None and skip != "event":
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out.append(
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LogEvent.event.is_(None) if self.event == NONE else LogEvent.event == self.event
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)
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if self.request_id is not None:
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out.append(LogEvent.request_id == self.request_id)
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if self.job_id is not None:
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out.append(LogEvent.job_id == self.job_id)
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if self.run_id is not None:
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out.append(LogEvent.run_id == self.run_id)
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if self.since is not None:
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out.append(LogEvent.ts >= utc(self.since))
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if self.until is not None:
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out.append(LogEvent.ts < utc(self.until))
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if self.q and self.q.strip():
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like = f"%{like_escape(self.q.strip())}%"
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out.append(
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or_(
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LogEvent.msg.ilike(like),
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LogEvent.event.ilike(like),
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LogEvent.request_id.ilike(like),
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LogEvent.exc.ilike(like),
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cast(LogEvent.attrs, Text).ilike(like),
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)
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)
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return out
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def log_filters(
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levels: list[Level] = Query(default=[]),
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sources: list[str] = Query(default=[], max_length=8),
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user: str | None = Query(default=None, pattern=account.PATTERN),
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event: str | None = Query(
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default=None, max_length=64, pattern=r"^(none|[a-z][a-z0-9_]*(\.[a-z0-9_]+)*)$"
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),
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request_id: str | None = Query(default=None, pattern=r"^[A-Za-z0-9._-]{8,64}$"),
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job_id: int | None = Query(default=None, ge=1),
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run_id: int | None = Query(default=None, ge=1),
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q: str | None = Query(default=None, max_length=200),
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since: datetime | None = None,
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until: datetime | None = None,
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) -> LogFilters:
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return LogFilters(
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list(levels), list(sources), user, event, request_id, job_id, run_id, q, since, until
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)
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@router.get("")
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def list_logs(
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filters: LogFilters = Depends(log_filters),
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before: str | None = Query(default=None, max_length=200),
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limit: int = Query(default=100, ge=1, le=PAGE_MAX),
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_: User = Depends(admin_user),
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db: Session = Depends(get_db),
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) -> dict:
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"""One page of rows, newest first, matching every filter given (they AND together); `before`
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continues from a previous page's `next_cursor`."""
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query = select(LogEvent).where(*filters.conditions())
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if before is not None:
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query = query.where(older_than(LogEvent.ts, LogEvent.id, before))
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rows, cursor = page(
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list(
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db.execute(query.order_by(LogEvent.ts.desc(), LogEvent.id.desc()).limit(limit + 1))
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.scalars()
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.all()
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),
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limit,
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lambda r: r.ts,
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)
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return {
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"items": [logsink.serialize(r) for r in rows],
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"accounts": accounts(db, {r.user_id for r in rows if r.user_id is not None}),
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"next_cursor": cursor,
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}
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@router.get("/facets")
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def log_facets(
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filters: LogFilters = Depends(log_filters),
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_: User = Depends(admin_user),
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db: Session = Depends(get_db),
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) -> dict:
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"""What the filter chips offer, with counts: the levels, sources and accounts present, and the
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most frequent events — each counted under every filter but its own (`LogFilters.conditions`).
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`total` is the "all levels" count: every filter but the level one."""
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def counts(col, skip: Facet, cap: int | None = None) -> list[dict]:
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n = func.count().label("n")
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stmt = select(col, n).where(*filters.conditions(skip)).group_by(col).order_by(n.desc(), col)
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if cap is not None:
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stmt = stmt.limit(cap)
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return [
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{"value": NONE if value is None else value, "count": count}
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for value, count in db.execute(stmt).all()
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]
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users = counts(LogEvent.user_id, "user")
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total = select(func.count()).select_from(LogEvent).where(*filters.conditions("levels"))
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return {
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"levels": counts(LogEvent.level, "levels"),
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"sources": counts(LogEvent.source, "sources"),
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"events": counts(LogEvent.event, "event", FACET_EVENTS_MAX),
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"users": [{**u, "value": str(u["value"])} for u in users],
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"accounts": accounts(db, {u["value"] for u in users if u["value"] != NONE}),
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"total": db.scalar(total) or 0,
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}
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