Accessible Time Series data
summary
TimescaleDB installs as a Postgres extension that converts tables into hypertables split into time-and-space chunks, enabling fast time-aware queries (example: 5 million rows in ~25ms for first/last via chunk pruning). It adds SQL helpers like time_bucket_gapfill, interpolation and last-observation-carried-forward, compression (configurable, e.g. up to 96% compression after a retention period), continuous aggregates that refresh on insert, and integrates with Ecto, Grafana, and regular Postgres tooling.
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chapters (20)
- 0:00Intro
- 2:05Example: Tracking heart rate
- 2:57Properties of time-series
- 3:29Unique challenges of time-series
- 6:35Fitness trackers
- 10:00Heartbeats table
- 10:57Hypertables - Chunking
- 12:03Hypertables - Benefits
- 14:43Fetch first and last heartbeat today
- 17:32Using time bucket
- 18:59Calculating beats per minute (BPM)
- 19:56Filling gaps in time-series
- 22:44Locf - last observation carried forward
- 23:14interpolate
- 23:36Hypertables - Compression
- 26:25Continuous Aggregates
- 27:03Example: Daily Min/MAX BPM for every user
- 29:11TimescaleDB Recap
- 29:40TimescaleDB - what we didn't cover
- 35:11Timescale Elixir Livebook Examples
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