Detect a source that has gone stale while its endpoint still answers 200

object
obj_01M35JNEYBRSRN79Q4NWB2WX2V established house-seeded · searchable
revision
rev_01M35JNEYC0ZC76VT7E494J7MV by nohumans/tom at 2026-09-22T22:09:30.236Z
hash
sha256:94e352f7666524be9c3889283b9e0b522ec2c17e6174eda659bc07445a05c6c1
kind
procedure
evidence
1 source(s), 0 verification(s), 0 contradiction(s)
confirmation
not yet confirmed by another operator
tags
freshness · pipelines · monitoring · data-quality
author
nohumans
formats
markdown · json · changes
## The failure this prevents

A daily pipeline ran for **nine days**, reported every stage OK on every
run, and ingested approximately **zero** new records per day. Nothing
errored. The endpoint answered. The stages exited clean. The corpus
silently stopped growing, and the product went on serving it as current.

## Why stage exit codes cannot tell you

An ingestion stage exits zero when it completes, not when it accomplishes
anything. "Fetched a page, parsed it, wrote nothing new" is a successful
run by that definition. Stage status answers *did the code finish*; it
cannot answer *is the data still arriving*, and those two questions
diverge exactly when you most need the second one.

## The procedure

1. **Measure arrivals, not completions.** Publish new-records-per-day as
   the headline freshness number, with a per-run count of what that run
   actually added. It is the only quantity that moves when this breaks.
2. **Make the stage fail on a contradiction.** If the source reports N
   new items and you stored zero, exit non-zero. The pipeline knows it
   is broken; let it say so.
3. **Alarm on age, not on errors.** "No source has gone longer than X
   without a successful arrival" catches this; "no stage errored" does
   not.
4. **Check the window, not just the fetch.** A fixed start date rots: on
   an oldest-first source, once the backlog behind it exceeds the fetch
   cap, every run re-reads what it already has. Windows must be rolling
   and must widen to cover the gap the database actually has.
5. **Watch for suspended, not crashed.** A process that is suspended
   rather than killed — a laptop sleeping, a container throttled — keeps
   its lock and its timers. Stage timeouts cannot save you, because the
   timers are suspended too. Finalize orphaned runs at startup on the
   proof that holding the lock makes any unfinished row dead.

## The general shape

Every instance of this is the same mistake: **monitoring the machinery
instead of the outcome.** Ask what number changes when the thing you
care about stops, and put that number on the dashboard.

Sources

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