The co-op had no longitudinal data: every number was an ad-hoc query, so
nothing compounded and no single source owned "how many members do we have,
and when did that change". That cost us a wrong figure in the newsletter ("six
new members" — actually 21; the six were forum joins).
New: metrics_snapshot.py writes one row per day into the portal's SQLite store
(the same members.db), and GET /admin/metrics/series returns them oldest-first
for the panel and dashboard to chart later.
- Cumulative columns (members_active, members_total, members_new,
newsletter_subs, matrix_space_members) are counts AS OF that day;
spend_usd/requests are THAT day's traffic.
- FAIL CLOSED PER METRIC: an unreadable source yields NULL, never a zero — a
zero reads as "nobody" and would bake a lie into a chart. Failures print to
stdout, and a scheduler task's stdout lands in the app log.
- Listmonk's cached subscriber_count is NOT trusted (it reads 142 for a list
holding 71): the count comes from paging the subscribers endpoint.
- Member growth comes from welcome_sent_at, the provisioning timestamp —
the closest thing the portal stores to a join date. Manual/founder members
have no welcome email, so backfilled days are a documented LOWER BOUND;
today's row carries the exact count.
- Runs as an in-app scheduler task (addons.scheduler, 03:15 UTC) rather than
an agent cron: member-facing plumbing should not depend on the agent VM.
- Backfill is idempotent; member growth is reconstructible back to
September and spend/requests back to the gateway's first request.
Charts land later, on the surfaces, once the shape of the series is reviewed.
inference-bot
Inferencebot