Update Data

Action ID: update_dataset_rows

Description

Update rows in a dataset that match filter conditions.

Input Parameters

Name	Type	Required	Default	Description
dataset	dropdown	✓	-	The dataset to update rows in.
conditions	array	-	[]	Filter conditions to select rows to update (empty = update all rows).
logic	dropdown	-	and	How to combine filter conditions (AND/OR). Options: and, or
updates	object	✓	-	Column name to new value mappings (e.g., {'status': 'completed', 'priority': 'high'}).

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": "Update Dataset Rows node input.",
  "properties": {
    "dataset": {
      "description": "The dataset to update rows in.",
      "title": "Dataset",
      "type": "string"
    },
    "conditions": {
      "default": [],
      "description": "Filter conditions to select rows to update (empty = update 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"
    },
    "updates": {
      "description": "Column name to new value mappings (e.g., {'status': 'completed', 'priority': 'high'}).",
      "title": "Column Updates",
      "type": "object"
    }
  },
  "required": ["dataset", "updates"],
  "title": "UpdateDatasetRowsNodeInput",
  "type": "object"
}

Output Parameters

Name	Type	Description
updated_count	integer	Number of rows that were updated.
success	boolean	Whether the update operation succeeded.

View JSON Schema

{
  "description": "Update Dataset Rows node output.",
  "properties": {
    "updated_count": {
      "description": "Number of rows that were updated.",
      "title": "Updated Count",
      "type": "integer"
    },
    "success": {
      "description": "Whether the update operation succeeded.",
      "title": "Success",
      "type": "boolean"
    }
  },
  "required": ["updated_count", "success"],
  "title": "UpdateDatasetRowsNodeOutput",
  "type": "object"
}

How It Works

This node updates rows in a dataset based on filter conditions. It first identifies rows that match the specified conditions using the chosen logic operator (AND/OR), then applies the updates defined in the updates object. The node validates the dataset ID format (26-character ULID) before execution. All matching rows have their specified columns updated with new values, and the node returns the count of updated rows along with a success status.

Usage Examples

Example 1: Update User Status

Input:

dataset: "01K8ZM9T72FNBZAGA629KZFXR5"
conditions: [
  {
    "column": "email",
    "operator": "equals",
    "value": "[email protected]"
  }
]
logic: "and"
updates: {
  "status": "verified",
  "verified_at": "2024-01-15T10:30:00Z"
}

Output:

updated_count: 1
success: true

Example 2: Bulk Status Update

Input:

dataset: "01K8ZM9T72FNBZAGA629KZFXR5"
conditions: [
  {
    "column": "status",
    "operator": "equals",
    "value": "pending"
  },
  {
    "column": "created_date",
    "operator": "less_than",
    "value": "2024-01-01"
  }
]
logic: "and"
updates: {
  "status": "expired",
  "expiry_reason": "automatic_timeout"
}

Output:

updated_count: 156
success: true

Example 3: Update Multiple Departments

Input:

dataset: "01K8ZM9T72FNBZAGA629KZFXR5"
conditions: [
  {
    "column": "department",
    "operator": "in",
    "value": "sales,marketing"
  }
]
logic: "and"
updates: {
  "division": "Revenue Operations",
  "budget_category": "customer_acquisition"
}

Output:

updated_count: 47
success: true

Example 4: Update with OR Logic

Input:

dataset: "01K8ZM9T72FNBZAGA629KZFXR5"
conditions: [
  {
    "column": "priority",
    "operator": "equals",
    "value": "urgent"
  },
  {
    "column": "age_days",
    "operator": "greater_than",
    "value": "30"
  }
]
logic: "or"
updates: {
  "review_required": true,
  "escalation_level": "high"
}

Output:

updated_count: 89
success: true

Example 5: Increment Counter

Input:

dataset: "01K8ZM9T72FNBZAGA629KZFXR5"
conditions: [
  {
    "column": "product_id",
    "operator": "equals",
    "value": "PROD-1234"
  }
]
logic: "and"
updates: {
  "view_count": 125,
  "last_viewed": "2024-01-15T14:22:00Z"
}

Output:

updated_count: 1
success: true

Example 6: Update All Rows

Input:

dataset: "01K8ZM9T72FNBZAGA629KZFXR5"
conditions: []
updates: {
  "migration_status": "v2_completed",
  "migrated_at": "2024-01-15T00:00:00Z"
}

Output:

updated_count: 2450
success: true

Common Use Cases

• Status Updates: Change record status based on conditions (e.g., pending → completed)

• Bulk Operations: Update multiple records matching criteria in a single operation

• Data Enrichment: Add or update fields with new information or calculated values

• Workflow State Management: Update processing states as workflows progress

• Time-Based Updates: Modify records that have aged beyond certain thresholds

• Quality Control: Flag or update records that meet quality criteria

• User Management: Update user attributes, permissions, or settings in bulk

• Inventory Updates: Modify product information, stock levels, or pricing

• Data Normalization: Standardize or clean up data values across multiple rows

• Feature Flags: Enable or disable features for specific user segments

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
Updates Required	Updates parameter is missing or empty	Provide at least one column-value pair in updates
Invalid Column	Column in updates doesn't exist	Verify column names match the dataset schema
Invalid Condition Column	Column in condition doesn't exist	Check condition column names against dataset schema
Invalid Operator	Operator not in allowed list	Use: equals, not_equals, contains, greater_than, less_than, in
Invalid Data Type	Update value type doesn't match column	Ensure values match the expected data types
Update Failed	Server error during update	Retry the operation or check server logs
Permission Denied	Insufficient permissions	Verify you have update permissions on the dataset

Notes

• Conditional Updates: Use conditions array to target specific rows. Empty conditions will update ALL rows in the dataset.

• Validation: The node validates that the dataset ID is a valid 26-character ULID format.

• Updates Object: The updates parameter is a JSON object where keys are column names and values are the new values to set.

• Multiple Columns: You can update multiple columns in a single operation by including multiple key-value pairs in the updates object.

• Logic Operators: Use "and" to narrow the selection (all conditions must match) or "or" to broaden it (any condition matches).

• Atomic Operation: The update is typically atomic - either all matching rows are updated or the operation fails.

• Success Verification: Always check the success field to confirm the operation completed without errors.

• Update Count: The updated_count shows how many rows were modified. Zero count means no rows matched the conditions.

• Data Types: Ensure update values match the expected data types for each column (string, number, boolean, object, array).

• Performance: Updating large numbers of rows may take time. Consider breaking very large updates into batches.

• Preview First: Use the Query Data node to preview which rows will be affected before executing the update.

• No Undo: Updates are permanent. Consider backing up critical data before bulk updates.

• Dynamic Dropdown: The dataset field dynamically lists available datasets in your workspace.