Record ID

Adds a sequential Record ID column to the dataset, similar to Alteryx Record ID Tool. Supports grouping, sorting, and formatted incremental IDs.

Input

Accepts a DataFrame as input.

Output

Returns a DataFrame with a new Record ID column added.

Type

transform

Class

fire.nodes.etl.NodeGenerateRecordID

Fields

Name

Title

Description

outputCol

Output Column Name

Name of the column that will contain generated Record IDs.

startValue

Starting Value

The first value in the sequence. Default is 1.

dataType

Data Type

Select the data type of the Record ID column.

size

Size (String Only)

Applicable only when Data Type is String. Pads the value with leading zeros to match this length.

position

Column Position

Position of the new column in the output DataFrame.

generationScope

Record ID Generation Scope

Generate IDs across the entire dataset or restart numbering within each group.

groupByCols

Group By Columns

Columns to group by when generating Record IDs within groups.

orderByCols

Order Rows Within Each Group

Optional columns to define the order of rows within each group before numbering. Supports ascending or descending order.

Details

Record ID Node Details

The Record ID Node generates a unique sequential identifier for each row in a DataFrame, similar to the Alteryx Record ID Tool. It supports flexible configuration, including grouping, sorting, and formatting, making it useful for adding row indexes or uniquely identifying records across or within groups.

General:

Output Column Name:

Specifies the name of the new column that will contain generated Record IDs. Example: “RecordID” or “RowNumber”.

Starting Value:

Specifies the initial number in the sequence. Default is 1. Example: Setting 1000 will start IDs from 1000.

Data Type:

Determines the data type of the Record ID column. Options include:

  • Int32: Generates 32-bit integer IDs.

  • Int64: Generates 64-bit integer IDs.

  • String: Generates IDs as strings (supports zero-padding when “Size” is specified).

Size (String Only):

Specifies the number of characters for each Record ID when Data Type is set to “String”. IDs are padded with leading zeros to match this length. Example: Size=6 produces IDs like “000001”, “000002”, etc.

Column Position:

Defines where to insert the Record ID column in the output DataFrame. Options:

  • first: Adds Record ID as the first column.

  • last: Adds Record ID as the last column (default).

Record ID Generation Scope:

Specifies how Record IDs are generated across the dataset:

  • entire_table: IDs are assigned sequentially across all rows.

  • within_group: IDs restart from the start value within each group defined by Group By Columns.

Group By Columns:

Specifies one or more columns used to group rows. Record IDs reset for each group. Example: “Department” groups data by department before generating IDs.

Order Rows Within Each Group:

Specifies columns used to order rows before assigning IDs. This supports ascending or descending order notation, e.g., “date asc, amount desc”.

Output:

The node returns a DataFrame identical to the input but with an additional column containing generated Record IDs. The output respects ordering and grouping configurations.

Examples

Example: Record ID Node

Example 1: Sequential IDs for Entire Dataset

Input:

| Name  | Department |
|--------|-------------|
| John  | HR          |
| Mary  | IT          |
| Steve | HR          |

Configuration:

  • Output Column Name: RecordID

  • Starting Value: 1

  • Data Type: Int64

  • Column Position: last

  • Generation Scope: entire_table

Output:

| Name  | Department | RecordID |
|--------|-------------|----------|
| John  | HR          | 1        |
| Mary  | IT          | 2        |
| Steve | HR          | 3        |

---

Example 2: Group-wise Record ID Generation

Input:

| Name  | Department |
|--------|-------------|
| John  | HR          |
| Mary  | HR          |
| Steve | IT          |
| Alice | IT          |

Configuration:

  • Output Column Name: EmpID

  • Starting Value: 1

  • Data Type: Int32

  • Generation Scope: within_group

  • Group By Columns: Department

Output:

| Name  | Department | EmpID |
|--------|-------------|-------|
| John  | HR          | 1     |
| Mary  | HR          | 2     |
| Steve | IT          | 1     |
| Alice | IT          | 2     |

---

Example 3: String-formatted Record IDs with Zero Padding

Input:

| Product | Price |
|----------|--------|
| Pen      | 10     |
| Pencil   | 5      |
| Eraser   | 3      |

Configuration:

  • Output Column Name: RowID

  • Starting Value: 1

  • Data Type: String

  • Size: 4

  • Column Position: first

Output:

| RowID | Product | Price |
|--------|----------|--------|
| 0001  | Pen      | 10     |
| 0002  | Pencil   | 5      |
| 0003  | Eraser   | 3      |

---

Example 4: Ordered Record ID Generation Within Groups

Input:

| Dept | Salary | Name |
|-------|---------|------|
| HR    | 50000   | John |
| HR    | 60000   | Mary |
| IT    | 80000   | Alice |
| IT    | 75000   | Bob |

Configuration:

  • Output Column Name: RankID

  • Starting Value: 1

  • Data Type: Int64

  • Generation Scope: within_group

  • Group By Columns: Dept

  • Order Rows Within Each Group: Salary desc

Output:

| Dept | Salary | Name | RankID |
|-------|---------|------|--------|
| HR    | 60000   | Mary | 1      |
| HR    | 50000   | John | 2      |
| IT    | 80000   | Alice | 1     |
| IT    | 75000   | Bob   | 2     |