Tessera DB Tutorial: Bucket and Aggregate Time-Series Data with Zero Copies

Point the time-series view at a set of records and pick which clock to read — valid time (when the reading was true) or transaction time (when it was recorded) — depending on what "when" means for your question.

Group readings into fixed windows — hourly, for instance — and choose how to combine the values in each window: average, sum, min, max, count, first, or last. The result is one value per bucket, ready to plot.

Downsampling is just bucketing with a wider window. Roll per-minute data up to daily averages, daily highs, and daily lows to get an at-a-glance view of a long series.

Derive a rate per interval, smooth a noisy series with a moving average over a chosen number of buckets, or compute a rolling window aggregate such as a one-hour rolling max.

When a sensor goes offline, its buckets are empty. Choose how to fill the gaps — leave them null, carry the previous value forward, interpolate linearly, or substitute a constant — so downstream charts and calculations stay well-behaved.

For dashboards that read the same rollups repeatedly, cache them as a continuous aggregate. It computes once and, on refresh, only recomputes the buckets past its high-water mark — expensive aggregations become cheap to serve without recomputing history.