tabular-query

The tabular-query transform loads one tabular parent output into an in-memory SQLite table, runs one or more SELECT queries from configuration, and returns a JSON object of result tables under tables.

When to use it

Use tabular-query when you need SQL filtering, grouping, or joins-on-one-table over CSV/TSV without exporting data outside HEAT. Downstream nodes such as tabular-to-dataservice can consume tables when inputFormat is json.

Limitations

Single tabular input only (one loaded table; no automatic multi-file joins).
The full file is held in memory: suited to small and medium tables, not multi-GB raw logs.
Each query must be a single SELECT; all CSV columns are stored as TEXT (use CAST in SQL for numeric logic).
Optional captured_at / elapsed_ms helpers require timeColumn, timeColumnIsMs: true, and a valid baseCapturedAt.

Configuration (summary)

Property	Required	Description
`queries`	yes	Map of name → SQL string; each result becomes `tables[name]`
`inputFormat`	no	`auto`, `csv`, or `tsv` (default `auto`)
`tableName`	no	SQLite table alias (default `t`)
`timeColumn`	no	Column for elapsed ms or ISO8601
`timeColumnIsMs`	no	Default `true`
`baseCapturedAt`	no	ISO8601 base when `timeColumnIsMs` is true

Example


{
  "inputFormat": "auto",
  "tableName": "t",
  "queries": {
    "fast_rows": "SELECT time_ms, speed_mps FROM t WHERE CAST(speed_mps AS REAL) > 10.0 ORDER BY CAST(time_ms AS INTEGER)"
  }
}

Output shape


{
  "tables": {
    "fast_rows": {
      "columns": ["time_ms", "speed_mps"],
      "rows": [{ "time_ms": "1000", "speed_mps": "12.5" }]
    }
  }
}