Altinity · BorisTyshkevich · Jun 24, 2026 · Jun 24, 2026 · Jun 24, 2026
diff --git a/README.md b/README.md
@@ -12,6 +12,14 @@ dependency is **Chart.js** (the chart result view), inlined into that one file.
 Refactored from a single-file SPA into a fully modular, test-first codebase
 held at **100% test coverage**.
 
+## Demo & examples
+
+Try it live on the Antalya demo cluster: **https://antalya.demo.altinity.cloud/sql**.
+The [**ontime chart demo**](docs/ONTIME-CHART-DEMO.md) is a ready-made library of 10
+queries (load [`examples/ontime-charts.json`](examples/ontime-charts.json) via
+**File ▾ → Replace**) that walks through every chart type and feature against the public
+`ontime` flight dataset.
+
 ## How it works
 
 ```

diff --git a/docs/ONTIME-CHART-DEMO.md b/docs/ONTIME-CHART-DEMO.md
@@ -0,0 +1,65 @@
+# Chart demo — the `ontime` flight dataset
+
+A ready-made **Library** of 10 analytical queries that show off every chart type and
+feature in the Altinity SQL Browser, running against the public US flight-history
+dataset (`ontime`, ~230M rows, 1987–2025) on the Antalya demo cluster.
+
+- **Live demo:** **https://antalya.demo.altinity.cloud/sql**
+- **The library file:** [`examples/ontime-charts.json`](../examples/ontime-charts.json)
+  ([raw download](https://raw.githubusercontent.com/Altinity/altinity-sql-browser/main/examples/ontime-charts.json))
+- **Reproduce it:** [`examples/build-ontime-charts.mjs`](../examples/build-ontime-charts.mjs)
+  regenerates the JSON (it derives each chart's schema key live with
+  `clickhouse-client --connection antalya`).
+
+## Load it (≈30 seconds)
+
+1. Open **https://antalya.demo.altinity.cloud/sql** and sign in (**Continue with Google**,
+   or use the credentials box).
+2. Download [`ontime-charts.json`](https://raw.githubusercontent.com/Altinity/altinity-sql-browser/main/examples/ontime-charts.json)
+   (right-click → Save link as…).
+3. In the header, click **File ▾ → Replace…** and pick the file. The library is renamed
+   **ontime-charts** and fills with 10 saved queries (confirm the replace if you already
+   had queries saved).
+4. Click any query in the **Library** panel — it runs and opens straight into its chart.
+   Switch **Table / JSON / Chart** at the top of the results, or change the **Type / X / Y /
+   Series** dropdowns to re-encode any chart live.
+
+## What each query demonstrates
+
+| # | Query | Chart | Feature |
+|---|-------|-------|---------|
+| 1 | Busiest origin airports — 2023 | Bar (horizontal) | categorical axis; joined to `dim_airports` for readable names; hover any bar (long or short) for its exact value |
+| 2 | Flights by month — 2023 | Column | numeric `month` auto-detected as an ordinal axis; K/M-humanised value ticks |
+| 3 | Daily flights — 2023 | Line | `Date` axis auto-detected as a time series (~365 points) |
+| 4 | Daily on-time rate — 2023 | Area | filled time series (a percentage measure) |
+| 5 | Cancellation reasons — 2023 | Pie | share of a small category set, with a legend |
+| 6 | Monthly flights by carrier — 2023 | Grouped bars | a **Series** column (carrier) splits each month into per-carrier bars |
+| 7 | Average delay breakdown by carrier — 2023 | Multi-measure columns | four measures plotted together (“All measures”) |
+| 8 | Daily flights since 2022 | Line | result exceeds the chart cap → a **“first 500 of 1.5K rows”** note (the table keeps them all) |
+| 9 | Flights by day of week — 2023 | Column | ordinal `dayofweek` axis |
+| 10 | Worst average departure delay by airport — 2023 | Bar (horizontal) | a non-count measure (avg minutes), joined for names |
+
+Each saved query stores its chart configuration, so it reopens exactly as designed. (Charts
+plot the first 500 rows; the full result is always available in the Table view.)
+
+## Direct links
+
+Every query is also reachable as a single shareable link — open one and the SQL **and** its
+chart configuration are pre-loaded; press **Run**, then the **Chart** tab. (Inside the app,
+the **Share** button copies the same kind of link for whatever you're looking at.)
+
+- **Bar** — [Busiest origin airports — 2023](https://antalya.demo.altinity.cloud/sql#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)
+- **Column** — [Flights by month — 2023](https://antalya.demo.altinity.cloud/sql#eyJfX2FzYiI6MSwic3FsIjoiU0VMRUNUIE1vbnRoIEFTIG1vbnRoLCBjb3VudCgpIEFTIGZsaWdodHNcbkZST00gb250aW1lLmZhY3Rfb250aW1lXG5XSEVSRSBZZWFyID0gMjAyM1xuR1JPVVAgQlkgbW9udGhcbk9SREVSIEJZIG1vbnRoIiwiY2hhcnQiOnsiY2ZnIjp7InR5cGUiOiJiYXIiLCJ4IjowLCJ5IjpbMV0sInNlcmllcyI6bnVsbH0sImtleSI6Im1vbnRoOlVJbnQ4fGZsaWdodHM6VUludDY0In19)
+- **Line** — [Daily flights — 2023](https://antalya.demo.altinity.cloud/sql#eyJfX2FzYiI6MSwic3FsIjoiU0VMRUNUIEZsaWdodERhdGUgQVMgZGF0ZSwgY291bnQoKSBBUyBmbGlnaHRzXG5GUk9NIG9udGltZS5mYWN0X29udGltZVxuV0hFUkUgWWVhciA9IDIwMjNcbkdST1VQIEJZIGRhdGVcbk9SREVSIEJZIGRhdGUiLCJjaGFydCI6eyJjZmciOnsidHlwZSI6ImxpbmUiLCJ4IjowLCJ5IjpbMV0sInNlcmllcyI6bnVsbH0sImtleSI6ImRhdGU6RGF0ZXxmbGlnaHRzOlVJbnQ2NCJ9fQ==)
+- **Area** — [Daily on-time rate — 2023](https://antalya.demo.altinity.cloud/sql#eyJfX2FzYiI6MSwic3FsIjoiU0VMRUNUXG4gICAgRmxpZ2h0RGF0ZSBBUyBkYXRlLFxuICAgIHJvdW5kKDEwMCAqIGNvdW50SWYoQXJyRGVsMTUgPSAwKSAvIGNvdW50KCksIDEpIEFTIG9uX3RpbWVfcGN0XG5GUk9NIG9udGltZS5mYWN0X29udGltZVxuV0hFUkUgWWVhciA9IDIwMjNcbkdST1VQIEJZIGRhdGVcbk9SREVSIEJZIGRhdGUiLCJjaGFydCI6eyJjZmciOnsidHlwZSI6ImFyZWEiLCJ4IjowLCJ5IjpbMV0sInNlcmllcyI6bnVsbH0sImtleSI6ImRhdGU6RGF0ZXxvbl90aW1lX3BjdDpGbG9hdDY0In19)
+- **Pie** — [Cancellation reasons — 2023](https://antalya.demo.altinity.cloud/sql#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)
+- **Grouped columns** — [Monthly flights by carrier — 2023](https://antalya.demo.altinity.cloud/sql#eyJfX2FzYiI6MSwic3FsIjoiU0VMRUNUXG4gICAgTW9udGggQVMgbW9udGgsXG4gICAgQ2FycmllciBBUyBjYXJyaWVyLFxuICAgIGNvdW50KCkgQVMgZmxpZ2h0c1xuRlJPTSBvbnRpbWUuZmFjdF9vbnRpbWVcbldIRVJFIFllYXIgPSAyMDIzIEFORCBDYXJyaWVyIElOICgnV04nLCAnQUEnLCAnREwnLCAnVUEnKVxuR1JPVVAgQlkgbW9udGgsIGNhcnJpZXJcbk9SREVSIEJZIG1vbnRoLCBjYXJyaWVyIiwiY2hhcnQiOnsiY2ZnIjp7InR5cGUiOiJiYXIiLCJ4IjowLCJ5IjpbMl0sInNlcmllcyI6MX0sImtleSI6Im1vbnRoOlVJbnQ4fGNhcnJpZXI6TG93Q2FyZGluYWxpdHkoU3RyaW5nKXxmbGlnaHRzOlVJbnQ2NCJ9fQ==)
+- **Multi-measure columns** — [Average delay breakdown by carrier — 2023](https://antalya.demo.altinity.cloud/sql#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)
+- **Line (capped)** — [Daily flights since 2022](https://antalya.demo.altinity.cloud/sql#eyJfX2FzYiI6MSwic3FsIjoiU0VMRUNUIEZsaWdodERhdGUgQVMgZGF0ZSwgY291bnQoKSBBUyBmbGlnaHRzXG5GUk9NIG9udGltZS5mYWN0X29udGltZVxuV0hFUkUgRmxpZ2h0RGF0ZSA+PSAnMjAyMi0wMS0wMSdcbkdST1VQIEJZIGRhdGVcbk9SREVSIEJZIGRhdGUiLCJjaGFydCI6eyJjZmciOnsidHlwZSI6ImxpbmUiLCJ4IjowLCJ5IjpbMV0sInNlcmllcyI6bnVsbH0sImtleSI6ImRhdGU6RGF0ZXxmbGlnaHRzOlVJbnQ2NCJ9fQ==)
+- **Column** — [Flights by day of week — 2023](https://antalya.demo.altinity.cloud/sql#eyJfX2FzYiI6MSwic3FsIjoiU0VMRUNUIERheU9mV2VlayBBUyBkYXlvZndlZWssIGNvdW50KCkgQVMgZmxpZ2h0c1xuRlJPTSBvbnRpbWUuZmFjdF9vbnRpbWVcbldIRVJFIFllYXIgPSAyMDIzXG5HUk9VUCBCWSBkYXlvZndlZWtcbk9SREVSIEJZIGRheW9md2VlayIsImNoYXJ0Ijp7ImNmZyI6eyJ0eXBlIjoiYmFyIiwieCI6MCwieSI6WzFdLCJzZXJpZXMiOm51bGx9LCJrZXkiOiJkYXlvZndlZWs6VUludDh8ZmxpZ2h0czpVSW50NjQifX0=)
+- **Bar** — [Worst average departure delay by airport — 2023](https://antalya.demo.altinity.cloud/sql#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)
+
+## Tables used
+
+- `ontime.fact_ontime` — one row per US domestic flight (dates, carrier, origin/dest, delays, cancellations, …).
+- `ontime.dim_airports` — airport reference data; joined on `AirportCode = OriginCode AND IsLatest = 1` for human-readable airport names.
diff --git a/examples/build-ontime-charts.mjs b/examples/build-ontime-charts.mjs
@@ -0,0 +1,198 @@
+// Generator for examples/ontime-charts.json — a saved-queries "Library" file for
+// the Altinity SQL Browser that demonstrates every chart feature against the
+// public `ontime` flights dataset on the antalya cluster.
+//
+// Why a generator: the browser only restores a saved chart config when the
+// entry's `chart.key` exactly equals schemaKey(resultColumns) = "name:type|…"
+// (see src/ui/results.js chartCfgFor / src/core/chart-data.js schemaKey).
+// Hand-writing those type strings is error-prone, so we derive each key live
+// from `DESCRIBE (<query>)` against the real cluster.
+//
+// Run:  node examples/build-ontime-charts.mjs   (needs `clickhouse-client --connection antalya`)
+// Out:  examples/ontime-charts.json
+
+import { execFileSync } from 'node:child_process';
+import { writeFileSync } from 'node:fs';
+import { fileURLToPath } from 'node:url';
+import { dirname, resolve } from 'node:path';
+
+const here = dirname(fileURLToPath(import.meta.url));
+const CONNECTION = 'antalya';
+
+// Each spec: a query + the chart we want it to open with. `cfg` matches the
+// app's shape { type, x, y:[...], series }; x/series are column indices, y a
+// list of measure-column indices. `view:'chart'` makes a click open the chart.
+const SPECS = [
+  {
+    name: 'Busiest origin airports — 2023',
+    description: 'Top 15 departure airports by flight count (joined to dim_airports for readable names). Horizontal Bar — hover any bar, long or short, to read its exact value.',
+    cfg: { type: 'hbar', x: 0, y: [1], series: null },
+    sql: `SELECT
+    a.DisplayAirportName AS airport,
+    count() AS flights
+FROM ontime.fact_ontime AS f
+INNER JOIN ontime.dim_airports AS a
+    ON a.AirportCode = f.OriginCode AND a.IsLatest = 1
+WHERE f.Year = 2023
+GROUP BY airport
+ORDER BY flights DESC
+LIMIT 15`,
+  },
+  {
+    name: 'Flights by month — 2023',
+    description: 'Monthly US flight volume. A numeric column named "month" is detected as an ordinal axis → vertical Column chart, with K/M-humanised value ticks.',
+    cfg: { type: 'bar', x: 0, y: [1], series: null },
+    sql: `SELECT Month AS month, count() AS flights
+FROM ontime.fact_ontime
+WHERE Year = 2023
+GROUP BY month
+ORDER BY month`,
+  },
+  {
+    name: 'Daily flights — 2023',
+    description: 'One point per day across 2023 (~365 rows). A Date X axis is auto-detected as a time series → Line chart.',
+    cfg: { type: 'line', x: 0, y: [1], series: null },
+    sql: `SELECT FlightDate AS date, count() AS flights
+FROM ontime.fact_ontime
+WHERE Year = 2023
+GROUP BY date
+ORDER BY date`,
+  },
+  {
+    name: 'Daily on-time rate — 2023',
+    description: 'Share of flights arriving on time (< 15 min late) per day, as a percentage. Rendered as a filled Area chart.',
+    cfg: { type: 'area', x: 0, y: [1], series: null },
+    sql: `SELECT
+    FlightDate AS date,
+    round(100 * countIf(ArrDel15 = 0) / count(), 1) AS on_time_pct
+FROM ontime.fact_ontime
+WHERE Year = 2023
+GROUP BY date
+ORDER BY date`,
+  },
+  {
+    name: 'Cancellation reasons — 2023',
+    description: 'Why flights were cancelled in 2023 (carrier / weather / national air system / security). A small categorical breakdown → Pie chart with a legend.',
+    cfg: { type: 'pie', x: 0, y: [1], series: null },
+    sql: `SELECT
+    multiIf(CancellationCode = 'A', 'Carrier',
+            CancellationCode = 'B', 'Weather',
+            CancellationCode = 'C', 'National Air System',
+            CancellationCode = 'D', 'Security', 'Other') AS reason,
+    count() AS cancellations
+FROM ontime.fact_ontime
+WHERE Year = 2023 AND Cancelled = 1
+GROUP BY reason
+ORDER BY cancellations DESC`,
+  },
+  {
+    name: 'Monthly flights by carrier — 2023',
+    description: 'Flights per month split across four major carriers (WN, AA, DL, UA). The "carrier" column is used as the Series, producing grouped bars with a per-carrier legend.',
+    cfg: { type: 'bar', x: 0, y: [2], series: 1 },
+    sql: `SELECT
+    Month AS month,
+    Carrier AS carrier,
+    count() AS flights
+FROM ontime.fact_ontime
+WHERE Year = 2023 AND Carrier IN ('WN', 'AA', 'DL', 'UA')
+GROUP BY month, carrier
+ORDER BY month, carrier`,
+  },
+  {
+    name: 'Average delay breakdown by carrier — 2023',
+    description: 'Mean minutes of each delay cause (carrier, weather, NAS, late aircraft) for delayed flights, per carrier. Four measures plotted at once ("All measures") as grouped columns.',
+    cfg: { type: 'bar', x: 0, y: [1, 2, 3, 4], series: null },
+    sql: `SELECT
+    Carrier AS carrier,
+    round(avg(CarrierDelay), 1) AS carrier_delay,
+    round(avg(WeatherDelay), 1) AS weather_delay,
+    round(avg(NASDelay), 1) AS nas_delay,
+    round(avg(LateAircraftDelay), 1) AS late_aircraft_delay
+FROM ontime.fact_ontime
+WHERE Year = 2023 AND ArrDel15 = 1
+GROUP BY carrier
+ORDER BY carrier_delay DESC
+LIMIT 12`,
+  },
+  {
+    name: 'Daily flights since 2022',
+    description: 'Every day from 2022 onward (~1,460 points). The chart plots the first 500 and shows a "first 500 of N rows" note — the table view still has them all.',
+    cfg: { type: 'line', x: 0, y: [1], series: null },
+    sql: `SELECT FlightDate AS date, count() AS flights
+FROM ontime.fact_ontime
+WHERE FlightDate >= '2022-01-01'
+GROUP BY date
+ORDER BY date`,
+  },
+  {
+    name: 'Flights by day of week — 2023',
+    description: 'Volume by day of week (1 = Monday … 7 = Sunday). "dayofweek" is recognised as an ordinal axis → Column chart.',
+    cfg: { type: 'bar', x: 0, y: [1], series: null },
+    sql: `SELECT DayOfWeek AS dayofweek, count() AS flights
+FROM ontime.fact_ontime
+WHERE Year = 2023
+GROUP BY dayofweek
+ORDER BY dayofweek`,
+  },
+  {
+    name: 'Worst average departure delay by airport — 2023',
+    description: 'Airports with the highest mean departure delay (minutes) among those with ≥ 10,000 departures in 2023. Horizontal Bar of a non-count measure, joined for names.',
+    cfg: { type: 'hbar', x: 0, y: [1], series: null },
+    sql: `SELECT
+    a.DisplayAirportName AS airport,
+    round(avg(f.DepDelayMinutes), 1) AS avg_dep_delay
+FROM ontime.fact_ontime AS f
+INNER JOIN ontime.dim_airports AS a
+    ON a.AirportCode = f.OriginCode AND a.IsLatest = 1
+WHERE f.Year = 2023
+GROUP BY airport
+HAVING count() >= 10000
+ORDER BY avg_dep_delay DESC
+LIMIT 15`,
+  },
+];
+
+const ch = (query) =>
+  execFileSync('clickhouse-client', ['--connection', CONNECTION, '--query', query], {
+    encoding: 'utf8',
+    maxBuffer: 64 * 1024 * 1024,
+  });
+
+// schemaKey == columns.map(c => c.name + ':' + c.type).join('|'), derived from
+// DESCRIBE so it matches exactly what the app receives at run time.
+function schemaKey(sql) {
+  const out = ch(`DESCRIBE (${sql})`);
+  return out
+    .split('\n')
+    .filter((l) => l.trim())
+    .map((l) => { const [name, type] = l.split('\t'); return `${name}:${type}`; })
+    .join('|');
+}
+
+const resultRows = (sql) => Number(ch(`SELECT count() FROM (${sql})`).trim());
+
+const queries = SPECS.map((s, i) => {
+  const key = schemaKey(s.sql);
+  const rows = resultRows(s.sql);
+  console.log(`#${i + 1} ${s.cfg.type.padEnd(4)} rows=${String(rows).padStart(5)}  key=${key}`);
+  return {
+    id: 's' + (i + 1),
+    name: s.name,
+    sql: s.sql,
+    favorite: false,
+    description: s.description,
+    chart: { cfg: s.cfg, key },
+    view: 'chart',
+  };
+});
+
+const doc = {
+  format: 'altinity-sql-browser/saved-queries',
+  version: 1,
+  exportedAt: new Date().toISOString(),
+  queries,
+};
+
+const outPath = resolve(here, 'ontime-charts.json');
+writeFileSync(outPath, JSON.stringify(doc, null, 2) + '\n');
+console.log(`\nwrote ${outPath} (${queries.length} queries)`);