IoT Telemetry
Swallow a device-fleet firehose without dropping a datapoint.
Swallow a device-fleet firehose through a reconnect storm while dashboards stay fast and the TSDB survives.
Millions of devices phone home on a schedule — until a firmware bug makes them all reconnect at once. The ingest path must buffer the firehose in a stream before it reaches the time-series store, while fleet-status dashboards read from a cache so they never depend on the hot write path.
Components in play
- Ingest gateway — Terminates millions of device connections and splits telemetry writes from dashboard reads.
- Telemetry stream — Durable log that buffers the ping firehose so a burst never hits the store raw.
- Ingest workers — Drain the stream, batch points, and write them into the TSDB.
- Time-series DB — Stores every datapoint; the one tier that must absorb the full ingest rate.
- Fleet-status cache — Serves 'is my fleet healthy' dashboard reads from memory, off the write path.
Graded on these SLOs
- ≥ 98.00%Telemetry accepted
- ≤ 120 msIngest-to-dashboard p99
- ≥ 18k rpsSustain the firehose
- ≤ 90%Ingest headroom
- ≤ 12Lean fleet
The brief
Design the ingest pipeline behind a fleet of IoT devices: millions of them phone home on a schedule, and a firmware bug or a returning outage can make them all reconnect at once. The ingest path has to absorb that firehose into a time-series store without losing datapoints, while fleet-status dashboards read from a cache that never depends on the write path being healthy.
Functional
- Ingest a steady stream of device telemetry
- Absorb a fleet-wide reconnect storm without dropping data
- Serve 'is my fleet healthy' dashboard reads
Non-functional
- No lost datapoints, even under a 4-5x reconnect spike
- Ingest-to-dashboard latency stays low under load
- Dashboard reads must not depend on the time-series DB being up
Warm-up: design decisions
Optional theory to prime the calls a senior engineer would make before you build.
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