---
title: "Bulk delete table rows"
method: POST
path: "/tables/{table_id}/rows/delete/"
tags: ["table-rows"]
---

# Bulk delete table rows

`POST /tables/{table_id}/rows/delete/`

## Request

- Base URL: `https://api.getoneprofile.ai` (the document declares no server; this is the origin it was published from)
- URL: `https://api.getoneprofile.ai/tables/{table_id}/rows/delete/`
- Auth: one of:
  - API key in header `X-API-Key`
  - HTTP bearer

Delete up to 1000 rows by record ID, or (with `all: true`) every row the listing matches, optionally narrowed by `where`/`row_status` (the rows query's own predicate vocabulary). Record IDs that do not exist are simply absent from the result; a predicate delete reports only the count. Row deletion has no recompute effect, so no run is requested.

## Path parameters

- `table_id` string, uuid, required

## Request body

- TableRowsDeleteSchema: Request body for the bulk row delete: two scopes, one body. Send ``record_ids`` to delete a checkbox selection. Send ``all: true`` to delete every row the current listing matches, optionally narrowed by ``where``/``row_status``, exactly the predicate vocabulary of the rows query, so "delete everything matching my filters" and "show me everything matching my filters" can never disagree about what matches. The explicit flag exists so an accidentally empty body deletes nothing.
  - `all` boolean: Delete every matching row rather than a named set. Required (and only honoured) when record_ids is absent.
  - `record_ids` string[], nullable: Record IDs of the rows to delete.
  - `row_status` 'errored' | 'fully_computed': A row-level state, derived from its cells rather than stored. ``ERRORED`` is "any cell of this row failed to compute"; ``FULLY_COMPUTED`` is its exact complement, so the two partition the table. Serves the built-in smart views, which are questions about computation rather than about values. The ordinary filter vocabulary cannot express either.
  - `where` TableRowsWhereSchemaInput: The whole predicate for a rows query. ``items`` mixes conditions with groups under one joiner, so ``A and (B or C)`` is an AND over ``[A, group(OR, [B, C])]``. Send no ``where`` at all rather than an empty one, so "no filter" stays a single state. The cap is on LEAVES, not on items: eight conditions cost eight correlated EXISTS clauses whether they are grouped or not, and it is the clause count that the query planner pays for.
    - `items` TableRowWhereItemSchemaInput[], required: Conditions and groups.
      - union
        - TableRowFilterSchemaInput: One predicate a row must satisfy. Which operators apply depends on the column's type: a range comparison on a checkbox is rejected rather than silently matching nothing. The valueless operators (``is_empty``, ``is_not_empty``, ``is_true``, ``is_false``) must not carry a value.
          - `column_id` union, required: A column ID, or `__any_column__` to ask about every cell in the row at once. `__any_column__` takes only `has_error` and `has_no_error`.
            - string, uuid
            - '__any_column__' | '__record_id__': The filter targets that name the whole ROW rather than one column. ``ANY_COLUMN`` asks a question of every cell in the row at once: "does any column have an error?" is the table-wide half of the per-column ``has_error``. It stands in the ``column_id`` slot of an ordinary condition (the same trick the system sort keys use), so a row-wide question travels through every surface that already carries a predicate tree (a saved view, a run's scope, an export, a count) with nothing to add to any of them. The dunder spelling keeps it apart from any UUID.
          - `operator` 'eq' | 'neq' | 'gt' | 'gte' | 'lt' | 'lte' | 'contains' | 'not_contains' | 'contains_any_of' | 'not_contains_any_of' | 'starts_with' | 'is_true' | 'is_false' | 'is_empty' | 'is_not_empty' | 'has_error' | 'has_no_error' | 'has_results' | 'has_no_results' | 'has_not_run' | 'is_stale' | 'is_not_stale' | 'run_condition_not_met' | 'run_stopped', required: A row filter predicate. Which operators are legal depends on the column's data type; the service validates the pairing and rejects a mismatch, so an operator here is a vocabulary entry rather than a promise it applies everywhere. Two families live here. The VALUE operators ask about what a cell holds and are keyed off the column's storage slot. The STATE operators ask about what the last run DID to the cell (errored, skipped, never ran, went stale) and are keyed off the column's kind instead: they read the compute metadata that only a computed column ever writes, and mean nothing on a hand-typed one.
          - `type` 'condition'
          - `value` union
            - string
            - integer
            - number
            - boolean
            - string, date-time
            - string, date
            - CellValueInput[]
            - object
        - TableRowFilterGroupSchemaInput: A parenthesised run of conditions, joined among themselves. Holds conditions and never groups: one level of nesting is expressible and a second is not, structurally. That covers ``A and (B or C)`` (what a filter panel offers) while keeping the SQL a flat walk.
          - `conditions` TableRowFilterSchemaInput[], required: The conditions inside the parentheses.
            - `column_id` union, required: A column ID, or `__any_column__` to ask about every cell in the row at once. `__any_column__` takes only `has_error` and `has_no_error`.
              - …
            - `operator` 'eq' | 'neq' | 'gt' | 'gte' | 'lt' | 'lte' | 'contains' | 'not_contains' | 'contains_any_of' | 'not_contains_any_of' | 'starts_with' | 'is_true' | 'is_false' | 'is_empty' | 'is_not_empty' | 'has_error' | 'has_no_error' | 'has_results' | 'has_no_results' | 'has_not_run' | 'is_stale' | 'is_not_stale' | 'run_condition_not_met' | 'run_stopped', required: A row filter predicate. Which operators are legal depends on the column's data type; the service validates the pairing and rejects a mismatch, so an operator here is a vocabulary entry rather than a promise it applies everywhere. Two families live here. The VALUE operators ask about what a cell holds and are keyed off the column's storage slot. The STATE operators ask about what the last run DID to the cell (errored, skipped, never ran, went stale) and are keyed off the column's kind instead: they read the compute metadata that only a computed column ever writes, and mean nothing on a hand-typed one.
            - `type` 'condition'
            - `value` union
              - …
          - `joiner` 'and' | 'or': How the members of one filter level combine.
          - `type` 'group'
    - `joiner` 'and' | 'or': How the members of one filter level combine.

## Response `200`

Successful Response

- TableRowsDeletedResponseSchema: Result of a bulk row delete; missing record IDs are simply absent. A predicate (`all: true`) delete reports only the count. Echoing ten thousand UUIDs would say nothing the count does not.
  - `count` integer, required
  - `deleted_record_ids` string[]

## Other responses

- `401`: Unauthorized
- `403`: Forbidden
- `404`: Table not found
- `422`: Invalid request made to the API.
- `500`: Internal server error.
- `502`: Bad gateway error.
- `503`: Service unavailable.

---

[API](https://skmtc.dev/getoneprofile/apis/api-reference-oneprofile.md) · [All operations](https://skmtc.dev/getoneprofile/apis/api-reference-oneprofile/llms.txt) · [OpenAPI document](https://skmtc.dev/getoneprofile/apis/api-reference-oneprofile/revisions/4a6bd71e3f02?raw)