Delete Data

Action ID: delete_dataset_rows

Description

Delete rows from a dataset that match filter conditions.

Input Parameters

Name	Type	Required	Default	Description
dataset	dropdown	✓	-	The dataset to delete rows from.
conditions	array	-	[]	Filter conditions to select rows to delete (empty = delete all rows).
logic	dropdown	-	and	How to combine filter conditions (AND/OR). Options: and, or

Filter Condition Structure

Each condition in the conditions array contains:

Field	Type	Description
column	string	Column name to filter by
operator	dropdown	Comparison operator: equals, not_equals, contains, greater_than, less_than, in
value	string	Value to compare against. For 'in' operator, provide comma-separated values or JSON array

View JSON Schema

{
  "description": "Delete Dataset Rows node input.",
  "properties": {
    "dataset": {
      "description": "The dataset to delete rows from.",
      "title": "Dataset",
      "type": "string"
    },
    "conditions": {
      "default": [],
      "description": "Filter conditions to select rows to delete (empty = delete all rows).",
      "items": {
        "properties": {
          "column": {
            "description": "Column name to filter by",
            "title": "Column",
            "type": "string"
          },
          "operator": {
            "default": "equals",
            "description": "Comparison operator",
            "enum": ["equals", "not_equals", "contains", "greater_than", "less_than", "in"],
            "title": "Operator",
            "type": "string"
          },
          "value": {
            "description": "Value to compare against. For 'in' operator, provide comma-separated values or JSON array",
            "title": "Value",
            "type": "string"
          }
        },
        "required": ["column", "value"],
        "type": "object"
      },
      "title": "Filter Conditions",
      "type": "array"
    },
    "logic": {
      "default": "and",
      "description": "How to combine filter conditions (AND/OR).",
      "enum": ["and", "or"],
      "title": "Logic Operator",
      "type": "string"
    }
  },
  "required": ["dataset"],
  "title": "DeleteDatasetRowsNodeInput",
  "type": "object"
}

Output Parameters

Name	Type	Description
deleted_count	integer	Number of rows that were deleted.

View JSON Schema

{
  "description": "Delete Dataset Rows node output.",
  "properties": {
    "deleted_count": {
      "description": "Number of rows that were deleted.",
      "title": "Deleted Count",
      "type": "integer"
    }
  },
  "required": ["deleted_count"],
  "title": "DeleteDatasetRowsNodeOutput",
  "type": "object"
}

How It Works

This node deletes rows from a dataset based on filter conditions. It evaluates each row against the specified conditions using the chosen logic operator (AND/OR). Matching rows are permanently removed from the dataset. The node validates the dataset ID format (26-character ULID) before execution and returns the count of deleted rows. If no conditions are provided, all rows in the dataset will be deleted.

Usage Examples

Example 1: Delete Inactive Users

Input:

dataset: "01K8ZM9T72FNBZAGA629KZFXR5"
conditions: [
  {
    "column": "status",
    "operator": "equals",
    "value": "inactive"
  }
]
logic: "and"

Output:

deleted_count: 47

Example 2: Delete Old Records

Input:

dataset: "01K8ZM9T72FNBZAGA629KZFXR5"
conditions: [
  {
    "column": "created_date",
    "operator": "less_than",
    "value": "2023-01-01"
  }
]
logic: "and"

Output:

deleted_count: 1250

Example 3: Delete Multiple Status Types

Input:

dataset: "01K8ZM9T72FNBZAGA629KZFXR5"
conditions: [
  {
    "column": "status",
    "operator": "in",
    "value": "deleted,archived,expired"
  }
]
logic: "and"

Output:

deleted_count: 328

Example 4: Delete with Multiple Conditions (AND)

Input:

dataset: "01K8ZM9T72FNBZAGA629KZFXR5"
conditions: [
  {
    "column": "status",
    "operator": "equals",
    "value": "pending"
  },
  {
    "column": "age_days",
    "operator": "greater_than",
    "value": "90"
  }
]
logic: "and"

Output:

deleted_count: 156

Example 5: Delete with OR Logic

Input:

dataset: "01K8ZM9T72FNBZAGA629KZFXR5"
conditions: [
  {
    "column": "email",
    "operator": "contains",
    "value": "spam"
  },
  {
    "column": "valid",
    "operator": "equals",
    "value": "false"
  }
]
logic: "or"

Output:

deleted_count: 89

Common Use Cases

• Data Cleanup: Remove outdated, invalid, or obsolete records from datasets

• User Management: Delete inactive or suspended user accounts

• Compliance: Remove data past retention period for GDPR/privacy compliance

• Quality Control: Delete records that failed validation or processing

• Batch Operations: Remove multiple records matching specific criteria in one operation

• Status-Based Deletion: Clean up records in terminal states like "completed", "failed", or "cancelled"

• Archive Management: Remove archived records from active datasets

• Test Data Cleanup: Clear test or demo data from production datasets

Error Handling

Error Type	Cause	Solution
Dataset Not Found	Dataset ID doesn't exist	Verify the dataset ID is correct and the dataset exists
Invalid Dataset ID	Dataset ID format is incorrect	Ensure dataset ID is a 26-character ULID
Dataset ID Required	Dataset parameter is empty	Provide a valid dataset ID
Invalid Column	Column name doesn't exist in dataset	Check the dataset schema for available column names
Invalid Operator	Operator not in allowed list	Use: equals, not_equals, contains, greater_than, less_than, in
Malformed Condition	Missing required fields	Ensure each condition has column, operator, and value
Permission Denied	Insufficient permissions	Verify you have delete permissions on the dataset
Delete Failed	Server error during deletion	Retry the operation or check server logs

Notes

• Permanent Operation: Deleted rows cannot be recovered. Use with caution and consider backing up data first.

• Empty Conditions Warning: If conditions array is empty, ALL rows in the dataset will be deleted. Double-check before execution.

• Validation: The node validates dataset ID format (must be 26-character ULID) before attempting deletion.

• Atomic Operation: The delete operation is atomic, ensuring data consistency.

• Performance: Deleting large numbers of rows may take time. Monitor the deleted_count in the output.

• Filter First: Consider using the Query Data node to preview matching rows before deletion.

• Audit Trail: Keep logs of delete operations for compliance and troubleshooting purposes.

• Conditional Logic: Use AND logic to narrow down deletion criteria, OR logic to broaden the scope.

• Batch Processing: For very large deletions, consider breaking them into smaller batches to avoid timeout issues.

• Dependencies: Ensure no other workflows or processes depend on the rows being deleted.

• Cascade Effects: Be aware of any relationships or references that might be affected by row deletion.