feat(observability): ingestion + storage metrics (measure before scaling) - #44
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…ing)
Make the time-series load measurable so the eventual scaling decision (partitioning,
a TSDB) is evidence-driven rather than premature — the responsible first step of the
Phase-5 "scale" thread.
- app/core/observability.py: `ghostmon_values_ingested_total` (counter; `rate()` is
ingestion throughput) and `ghostmon_table_rows_estimate{table=…}` (gauge), the
latter sampled every 5 min from `pg_class.reltuples` — instant, no expensive
`count(*)` on a growing history table.
- `count_ingested()` is called on every history write (the values endpoint/agent via
record_value, the SNMP poller, and the probe status/latency/error mirror). A
scheduler job refreshes the size gauges. Exposed on the existing `/metrics`.
- The module lives in `app/core` (neutral) to keep the layering inward.
The destructive scale work (distributed collection, TSDB) stays deferred — now with
data to trigger it. Tests: the counter increments on record_value and the size gauge
reflects the table. Full suite 219 passed; ruff + mypy(strict) green.
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Context
Phase 5 "scale" — done the responsible way. Rather than migrate the store blind,
measure first: expose the metrics that will tell us when to scale.
Changes
ghostmon_values_ingested_total(counter → throughput viarate()) incremented onevery history write (values endpoint/agent, SNMP poller, probe status/latency/error
mirror).
ghostmon_table_rows_estimate{table=metric_values|metric_trends|monitor_results}(gauge) refreshed every 5 min from
pg_class.reltuples— instant, nocount(*).app/core/observability.py(keeps core's dependencies inward). Exposed onthe existing
/metrics.Distributed collection / TSDB stay deferred — now with data to trigger them.
Tests
Counter increments on
record_value; the size gauge reflects the table. Full suite219 passed; ruff + mypy(strict) green.