The ops dashboard marked subscription-notifier DOWN ~2/3 of the time after the
notifier was gated to a single leader worker. It judged the daemon by whether
its thread was present on whichever worker answered /api/v2/metrics, but only
the leader runs it — so polls landing on the other two workers saw no thread and
reported DOWN for a perfectly healthy notifier. Per-worker thread checks can't
describe a single-leader daemon, and worse, they could no longer distinguish a
real crash from a normal non-leader.
Report it by liveness instead: the notifier stamps a heartbeat (timestamp +
expected interval) into the shared metrics store each loop iteration, including
once at startup so the dashboard shows it up immediately after a restart. Since
the beat lives in the shared SQLite store, any worker's metrics response carries
it, so the dashboard sees the notifier's health whichever worker it polls.
- metrics.py: both stores record/expose heartbeats (SQLite reuses the meta
table, so every worker's beats share one file); snapshot() converts each beat
to a server-computed age so readers needn't reconcile clock skew.
- notify.py: beat at loop start and every wake.
- dashboard.py: judge heartbeat daemons by freshness (up to 2 missed intervals
before DOWN, with beat age shown); neighbor-warmer keeps its per-worker thread
check, which is correct since every worker runs it.
- Tests: heartbeat store/snapshot shape + cross-worker sharing; dashboard
fresh/stale/missing rendering and the unchanged warmer check. 194 passed.
Co-authored-by: root <root@vmi3417050.contaboserver.net>
Make the app installable and deliver existing alert notifications as OS
push, alongside the in-app bell.
Backend:
- PushSubscription model (per-device endpoint + keys, owned by a user) and
register/unregister/test endpoints under /api/v2/push, cookie-auth scoped
to the user like the subscription routes.
- push.py: VAPID key management (env -> data/vapid.json -> generated) and a
pywebpush send helper that reports gone endpoints for pruning. No DB coupling.
- notify.py: after creating an in-app Notification, dispatch Web Push to the
user's devices (guarded — a push failure never affects the in-app write;
endpoints reported gone are pruned).
- Serve the .webmanifest with the correct media type.
Frontend:
- manifest.webmanifest + 192/maskable icons; <link rel="manifest"> on all pages.
- sw.js: push + notificationclick handlers (push-only; no fetch caching, so it
doesn't fight the existing IndexedDB cache). Registered globally in nav.js in
secure contexts.
- push-client.js + a "Notifications on this device" toggle and test-send on the
/alerts page, subscribing through the existing cookie-aware apiFetch.
Push and service workers require a secure context, so this is active over HTTPS
(or http://localhost) and cleanly no-ops on a plain-HTTP LAN origin.
A subscription to a city that had never been viewed had no cached ~45-year
archive, so the evaluator skipped it and it never fired. Now when a subscribed
cell has no cached archive, the pass fetches it once via get_history (which caches
it); every later pass reads it from cache and never re-fetches. A per-pass budget
(THERMOGRAPH_NOTIFY_ARCHIVE_FETCHES, default 4) caps how many missing archives one
pass will fetch so a burst of new subscriptions can't exhaust the archive quota,
and a rate-limited fetch just retries on the next pass. The recent/forecast bundle
keeps refreshing on its own hourly cadence.
Add integration tests covering fetch-when-missing and never-refetch-when-cached.
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
* Add account system foundation: email/password auth with cookie sessions
Introduce the app's first authoritative, user-owned data in a separate
data/accounts.sqlite (SQLAlchemy), kept apart from the disposable derived-cache
DB. Wire fastapi-users for email/password signup, cookie-based login/logout, and
a session-check endpoint, backed by a database session strategy so logins survive
restarts and are revocable.
- db.py: async (aiosqlite) + sync SQLAlchemy engines over accounts.sqlite, WAL +
foreign keys, create_db_and_tables().
- models.py: User, AccessToken, Subscription, Notification tables.
- users.py: pwdlib hashing, HttpOnly cookie transport (path-scoped, SameSite=Lax,
Secure via env), DatabaseStrategy sessions, current-user dependencies.
- schemas.py: user + subscription + notification Pydantic models.
- app.py: mount auth/register/users routers on v2, create tables at startup.
- Pin fastapi-users[sqlalchemy]/aiosqlite; ignore data/accounts.sqlite*.
* Add account header entry and auth modal (frontend)
account.js self-injects a header entry (following the units.js pattern) that
shows a Sign in button when logged out and an account menu when logged in, plus
an auth modal reusing the existing .mp-overlay/.mp-modal chrome for email/password
sign-in and account creation. A shared apiFetch helper sends the same-origin
cookie for authed calls; exported getUser/openAuth/onAuthChange back later phases.
Imported by every page entry module. On narrow screens the entry collapses to an
icon-only button so it doesn't crowd the title.
Enforce an 8-character minimum password in the user manager.
* Add subscription CRUD API and the alerts management page
Backend api_accounts.py adds user-scoped, cookie-authenticated endpoints to
create/list/update/delete subscriptions (and the notification reads used next):
POST snaps lat/lon to a grid cell, resolves a label, and rejects a duplicate
location+kind with 409; PATCH/DELETE are ownership-checked (404 on mismatch).
Mounted on the v2 prefix.
Frontend subscriptions.js + subscriptions.html serve the /alerts page: a sign-in
gate when logged out, an add flow that reuses the shared map picker and an editor
modal (kind, watched metrics, 95-99 percentile, two-sided), and a card list with
inline threshold/active edits and remove. Reachable from the account menu.
* Add background subscription evaluation engine
notify.py runs a daemon thread that periodically evaluates every active
subscription: it groups them by grid cell, reads history from the parquet cache
only (never spends archive quota) plus the hourly recent/forecast bundle, and
grades candidate days with the existing grading.grade_day. A watched metric that
lands at or beyond the threshold percentile fires a 'high' alert; a two-sided
subscription also fires 'low' for the symmetric cold/calm/dry tail (precip stays
one-directional). Observed subscriptions look at the last few recorded days,
forecast subscriptions at the coming week.
Two guards keep it quiet: a UNIQUE(subscription, event_date, metric, direction,
kind) constraint dedups repeat events, and a per-subscription weekly cap
(last_notified_at) limits each alert to one notification per 7 days. The loop
tolerates a bad cell or an upstream rate limit without aborting the pass. Started
and stopped from the app lifespan; gated by THERMOGRAPH_ENABLE_NOTIFIER.
* Add in-app notification center (header bell)
Extend account.js with a notification bell beside the account menu: an unread
badge, a dropdown listing recent notifications (title, body, relative time), a
per-item mark-read on click, and a Mark all read action, all through the
cookie-authed notifications API. Unread state refreshes on open and polls every
two minutes while signed in; polling stops on sign-out. Styled to match the app,
responsive down to mobile.
* Harden accounts: expired-session cleanup, engine tests, ops docs
- notify.py sweeps expired login sessions (access tokens past their lifetime)
once per evaluation pass.
- Add hermetic unit tests for the evaluation engine's trigger detection (high/low
tails, precip one-directional, normal = no trigger) and notification wording.
- Document accounts.sqlite (authoritative, back it up), the single-worker
requirement for the in-process evaluator, and the new env vars in DEPLOY.md.
Emi Griffith