# 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
```json
{
"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
```json
{
"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": "alice@example.com"
}
]
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.
---
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