Live visualizations for streaming queries: sliding-window aggregates and animated updates

Context

#59 covers the safe baseline for ClickHouse SELECT … STREAM: bounded memory,
throttled rendering, live lifecycle, Stop behavior, and table tailing.

This issue covers the next layer: making streaming results visually useful.

The goal is not just an appending table. The goal is live values that visibly move:
bars grow and reorder, pie slices resize, stat cells tick, and changed values are easy
to notice.

Problem

The ClickHouse stream is append-only raw rows. For live charts, the browser cannot
assume the server sends updated aggregate rows.

The browser needs a client-side rollup over a bounded window, then it needs to update
existing visualizations in place instead of rebuilding them for every chunk.

Proposed work

• Add a pure client-side aggregation window in src/core/.
  • count-based window, for example last N rows;
  • optional time-based window, for example last 60 seconds when a timestamp column exists;
  • group-by accumulators for count/sum/avg/min/max where feasible;
  • deterministic eviction so memory stays bounded.
• Reuse existing chart selection/pivot logic where possible, especially src/core/chart-data.js.
• Update Chart.js instances in place with chart.update() instead of destroying/recreating the canvas per chunk.
• Batch chart updates on the same render cadence as the streaming table.
• Add live stat/table-cell affordances.
  • changed numeric cells briefly highlight or count up;
  • support prefers-reduced-motion;
  • show the active window, for example last 10k rows or last 60s.
• Keep the raw tail table and aggregate chart views as two views over the same feed state.

Suggested architecture

Acceptance criteria

• A streaming query can feed a chart without unbounded memory growth.
• Chart updates are batched and smooth under sustained row arrival.
• Existing chart instances are updated in place where possible.
• The UI makes the aggregation window explicit.
• Aggregation, eviction, null handling, and update batching decisions have unit coverage.
• Motion can be reduced through standard reduced-motion preferences.

Open questions

1. Should the default chart window be last-N-rows, time-based, or user-selectable per query?
2. Should dimension/measure selection reuse the current Chart view controls, or should live mode expose a simplified picker?
3. Which aggregations are first scope: count only, count/sum/avg, or broader numeric aggregations?
4. How should high-cardinality dimensions work: top-K with Other, hard cap, or explicit user choice?
5. Should a STREAM LIMIT query use the live chart path while running and then settle into a normal finite chart when complete?

Non-goals

• Enabling ClickHouse streaming settings on behalf of the user.
• Server-side incremental aggregation.
• A new charting dependency unless Chart.js proves insufficient.

Inizia leggendo src/core/stream.js, src/core/chart-data.js, src/ui/results.js e src/styles.css per comprendere i percorsi esistenti di streaming e dei grafici. Risolvi le domande elencate relative a finestra, aggregazione, controlli del grafico e alta cardinalità prima di procedere con l’implementazione. Il lavoro è completo quando sono presenti un’aggregazione vincolata, aggiornamenti del grafico raggruppati in-place, uno stato esplicito della finestra, il supporto per il movimento ridotto e la copertura con unit test delle decisioni di aggregazione ed eviction.