---
title: "Detect and backfill gaps in the sensor stream"
type: source
description: "Dropped readings left silent gaps that looked like flat readings downstream."
tags: [session]
date: 2026-07-09
source_file: raw/sessions/sensor-mesh/2026-07-09T22-35-sensor-mesh-backfill-gap-detection.md
sessionId: 140c2865-fd31-a60f-b696-b773e16a98d9
slug: backfill-gap-detection
project: sensor-mesh
started: 2026-07-09T22:35:00+00:00
ended: 2026-07-09T23:28:00+00:00
cwd: /home/USER/code/sensor-mesh
gitBranch: feat/backfill
permissionMode: default
model: gpt-5-codex
user_messages: 2
tool_calls: 8
tools_used: [Read, Edit, Bash]
tool_counts: {"Read": 7, "Edit": 3, "Bash": 6}
token_totals: {"input": 8350, "output": 1625}
turn_count: 4
duration_seconds: 3180
is_subagent: false
entrypoint: cli
promptSource: typed
is_headless: false
agent: codex-cli
---

# Session: 8e3d2dcf — 2026-07-09

Dropped readings left silent gaps that looked like flat readings downstream.

## Conversation

### Turn 1 — User

A sensor dropped out for an hour and the dashboard drew a flat line through it.

### Turn 2 — Assistant

There was no distinction between a missing reading and a repeated one. Gaps are now detected against the expected interval and recorded explicitly, so downstream can draw a break instead of interpolating.

Backfill re-requests a bounded window when a device reconnects, and refuses beyond that rather than pulling unbounded history.

### Turn 3 — User

How does it know the expected interval?

### Turn 4 — Assistant

Declared per device rather than inferred. Inference was the original approach and it adapted to the outage, which is exactly when you need it not to.

## Subjects

- [[Time Series]]
- [[Observability]]
- [[Python]]