domain-iot:time-series-data

Time-Series Data for IoT

When to use

• Selecting a time-series database for IoT telemetry ingestion
• Designing InfluxDB tag/field schemas or TimescaleDB hypertables for device data
• Implementing downsampling pipelines to reduce storage costs over time
• Configuring hot/warm/cold retention tiers with automatic expiry and archival
• Choosing between stream, micro-batch, and batch processing for telemetry pipelines
• Building Grafana dashboards for fleet monitoring with query optimization

Core principles

1. Tags are your indexes — cardinality kills them — device IDs as tags are fine; UUIDs as tags are a cardinality bomb; model high-cardinality values as fields
2. Downsample early, archive always — raw data at 10s resolution for 7 days, aggregated at 1min for 90 days, rollups at 1h forever; storage is cheap, queries on raw aren't
3. Hot/warm/cold is not optional at IoT scale — SSD for the last 48 hours, object storage for anything older than 90 days; InfluxDB and TimescaleDB both automate this
4. Stream for alerts, batch for reports — Kafka Streams or Flink for sub-second alerting; Spark or dbt for anything a human looks at once a day
5. Grafana variables prevent dashboards from melting — never load all devices at once; use template variables and top-N queries from the start

Reference Files

• references/database-selection-and-modeling.md — comparison table (InfluxDB, TimescaleDB, Prometheus, QuestDB, ClickHouse), selection criteria, InfluxDB tag/field model, TimescaleDB hypertable setup
• references/downsampling-and-retention.md — InfluxDB Flux tasks, TimescaleDB continuous aggregates, downsampling tier table, hot/warm/cold architecture, compression configuration
• references/processing-and-visualization.md — stream vs micro-batch vs batch comparison, Grafana panel types for IoT, query optimization techniques