9 docs tagged with "Stream Data"

Aggregation queries compute summary values (averages, totals, extremes) across time-series data points instead of returning individual rows. Use these when you need calculated metrics rather than raw data — for example, the average voltage over the last hour or the total energy produced per machine.

A downsampling query reduces the density of time-series data by dividing a time range into equal-sized buckets and applying an aggregation function to each bucket. This is essential for visualizing large datasets on dashboards and charts — instead of transferring thousands of raw data points, you get a manageable number of summarized values.

This page is the architectural foundation for stream data: the mental model and vocabulary you need before reading either the full Stream Data Archives reference or the Rollups & recompute deep-dive. It explains how the pieces fit together — the two-store split, the three archive types, how rows are stored, how an archive moves through its lifecycle, and what happens on the write and read paths — and then hands off to the reference and deep-dive pages for the exhaustive detail.

Persisted queries are saved query definitions stored as runtime entities. Instead of specifying all query parameters every time, you create a query definition once and execute it by its rtId. This is useful for dashboard widgets, recurring reports, and any scenario where the same query needs to be executed repeatedly.

This page is the engineer-facing companion to Stream Data Archives. It explains how a RollupArchive keeps its derived aggregations correct as the underlying data changes — the detection criterion, the dirty-windows ledger, the dependency graph, the periodic orchestrator, and the optimistic atomic swap that keeps readers on a consistent snapshot throughout.

A simple query retrieves raw time-series rows from CrateDB. You choose which columns to return and can apply filters, sorting, and time range restrictions. This is the most fundamental stream data query type — use it when you need individual data points rather than aggregated summaries.

A Stream Data Archive is the unit of configuration and storage for time-series data in OctoMesh. Each archive is a versioned, typed, per-tenant CrateDB table that captures a curated set of attribute paths from a Construction Kit type. Archives have a strict lifecycle (Created → Activated → Disabled / Failed), an immutable schema once activated, and a three-tier activation gate (instance → tenant → archive) that determines whether the data plane is open.

The Stream Data Archives section of the Refinery Studio lets you create and manage the time-series archives that capture data into CrateDB. Each archive defines a CK type, a curated list of attribute paths, and a status — and the studio walks you through every state transition (Activate, Disable, Enable, Retry, Delete) with confirmation dialogs.

Stream data is always stored in UTC. Many questions, however, are asked in civil (wall-clock) time for a specific place: "what were the values **yesterday**? this week? last month?" — meaning the local day / week / month boundaries of a chosen time zone, not a UTC day that is shifted against the local one.